Dry-type power transformers play a critical role in the power system. Detecting various overheating faults in the running state of the power transformer is necessary to avoid the collapse of the power system. In this paper, we propose a novel deep variational autoencoder-based anomaly detection method to recognize the overheating position in the operation of the dry-type transformer. Firstly, the thermal images of the transformer are acquired by the thermal camera and collected for training and testing datasets. Next, the variational autoencoder-based generative adversarial networks are trained to generate the normal images with different running conditions from heavy to light loading. Through the pixel-wise cosine difference between original and reconstructed images, the residual images with faulty features are obtained. Finally, we evaluate the trained model and anomaly detection method on normal and abnormal testing images to demonstrate the effeteness and performance of the proposed work. The results show that our method effectively improves the anomaly accuracy, AUROC, F1-scores and average precision, which is more effective than other anomaly detection methods. The proposed method is simple, lightweight and has less storage size. It reveals great advantages for practical applications. Power transformers play a critical role in the power system. A dry-type transformer refers to the electric transformer in which the core and winding are cast together with epoxy resin, instead of being immersed in oil. The dry-type power transformer is usually installed in indoor applications due to the advantages of a lower maintenance workload, high operating efficiency, compact size and low noise. However, in view of the rapid growth of power demand, the stability of the dry-type transformer is gradually becoming a noteworthy problem, which directly affects the reliability of the entire power grid ,2,3.There are different ways of insulating and dissipating heat between dry-type and oil-immersed transformers. Oil-immersed transformers are insulated by insulating oil, and the heat generated by the coil is transferred to the radiator of the transformer via the insulating oil inside the transformer for heat dissipation. Dry-type transformers are generally insulated with resin, cooled by natural air or by fans. Compared to the liquid-filled transformer, one of the disadvantages of dry-type is that, due to lack of oil for insulation or cooling, they require sufficient cooling capacity . There aGenerally, all kinds of transformer failures have a huge impact on the power grid. So far, many monitoring techniques and detection methods have been proposed to diagnose the faults for the transformer ,11,12,13Infrared thermography (IRT) is the technology that converts invisible heat energy into visible thermal images. The advantages of IRT are that its characteristics are non-invasive and non-contact, as well as its mobility . SeveralAnomaly detection is a common application of machine learning algorithms, which mainly finds out the unexpected or anomalous data or patterns in various unsupervised learning problems. Anomaly detection methods usually use available normal data to extract, characterize and model the patterns, and then develop reasonable anomaly detectors to discover new or abnormal patterns in the newly observed data ,20,21. DIn the past few decades, work has been carried out on image anomaly detection techniques, and we focus on several methods related to the proposed method. Bergmann et al.  propose We propose an unsupervised anomaly detection method system based on IRT image recognition. Without a large number of labeled defect images, the system can monitor the operation of the transformer online and find the outlier situation early.Compared with other existing methods, the proposed method leads to significantly finer reconstructed images and achieves good results in terms of anomaly accuracy, AUROC, F1-scores and average precision.The proposed method also has the advantages of fewer weights and less storage space. In addition, ablation studies are conducted to verify the effectiveness of the combination of the loss function.In this paper, we develop a novel deep variational autoencoder-based anomaly detection technique for cast-resin power transformers. First, we employed an IRT image-captured system to monitor the operation of the power transformer and handle training and testing data preparation. Then, we trained a variational autoencoder-based generative adversarial network on only defect-free images with different running conditions from heavy to light loading, as discussed in The rest of this paper is organized as follows. The autoencoder (AE) performs efficient feature extraction and feature representation on high-dimensional data based on an unsupervised learning method. The AE framework contains two networks: encoding process ocess DE . The basAlthough AE requires that the original image and the reconstructed image be as similar as possible, this process cannot control the distribution of the latent space. The high degree of freedom of the autoencoder makes the encoding and decoding not lose information, which will lead to serious overfitting.ith input image and Kingma and Welling proposed the variational autoencoder (VAE)  to contrThe reconstruction loss function The Kullback–Liebler divergence loss The model structure of the generative adversarial network (GAN) is also an unsupervised learning method, which learns by making two neural networks confront each other. The GAN consists of a generative network G and a discriminative network D. The G tries to generate an image, while the D network decides whether the generated image is real or fake. D and G play the following two-player minimax game with value function . The losGANomaly was presented by Akcay et al. , which iWasserstein GAN (WGAN) presented by Arjovsky et al.  improvesThe advantage of using Wasserstein distance as a loss function is that even if there is no intersection between Inspired by GANomaly and WGAN, the proposed variational autoencoder generator (VAG) model applied in the full-time online fault detecting in this paper takes the reconstruction error, Kullback–Liebler divergence, the adversarial feature error and Wasserstein distance as the loss function with the corresponding weighting parameters to adjust each loss.The proposed framework, as seen in The dataset is collected by capturing images of the dry-type transformer in a substation by using the fixed thermal imaging cameras as shown on the left in The daily average load rate is calculated based on the size of the load and the length of usage time. The loading change rate of the transformer is 20–65%. The average loading rate is 34.2%, and the daily average temperature of the ambient temperature is 30 °C–35 °C. The monitoring system captures the running normal IRT images of the transformer under different load conditions from light to heavy with a thermal camera and sends these images to the remote server through the internet. Each IRT image is normalized and converted into the 120 × 160 × 3 grayscale image, which is regarded as the input dimension of the proposed model. The voltage level of the observed transformer is 24 kV. The maximum ambient temperature, temperature-rise limitation and maximum permissible temperature are 40 °C, 100 K and 155 °C, respectively. The standard capacity, primary voltage and second voltage are 1000 KVA, 24 kV and 380 V, respectively.The training is divided into two steps as shown in The VAGWDD model parameters are initialized. ion (13) .(15)Ld_The generator loss function s to VAE  and GANos to VAE  architecThe loss combination of image reconstruction and Kullback–Liebler divergence, In this paper, we use L1 loss formulation as the image reconstruction loss The Kullback–Liebler divergence To identify the WDD image feature loss The Wasserstein loss  to compute the error between the features of the generated image and of the original image. The smaller the error, the closer the features are. Immediately after the fourth layer, the global average pooling is performed to converge the dimension to 128. Finally, the single fully connected layer, which enables the sigmoid function, is used to identify the real image from the generated image.In the first stage of training, the VAG image reconstruction model is trained to learn and extract features from 300 normal state samples. The first steps involve obtaining a batch of 32 original images, and then the discriminator is trained. After that, the discriminator parameters are frozen, and then the generator is trained. The generator loss function in Equation (15) has weights of To assess the quality of the reconstructed image, several methods of the image quality assessment (IQA) for evaluation were used. The most common evaluation methods were PSNR  and SSIM  . PSNR isTo evaluate the effectiveness and performance of our proposed work and other methods, we take the binary classification accuracy, the area under the receiver operating characteristic curve (ROC-AUC), F1-score and average precision (AP) for one  vs. anomaly-free as our evaluation metrics.We define true positive  is one of the most important measurements for evaluating the detection ability of binary detection. The receiver operating characteristics (ROC) is a probability curve and AUROC represents the degree or measure of separability. The ROC curve is plotted with TPR against the FPR, where TPR is on the The AP is a measurement for summarizing the precision-recall curve into a single value representing the weighted mean of precisions achieved at each threshold. The AP is calculated by using a loop that goes through all precisions and recalls. The difference between the current and next recalls is computed and then multiplied by the current precision.In this work, we respectively show the results calculated based on four different evaluation indicators and compare them with other recent related methods to assess the achievement of the proposed method. Label F0 means the normal testing images. Labels from F1 to F8 represent the abnormal testing images with different fault locations. L2  in the FAs summarized in L2  with a 1We further compute the F1 Score to evaluate the anomaly detection performance of the model. As depicted in Average Precision (AP) points out the performance of the model, which can correctly distinguish all positive examples without unexpectedly labeling other negative examples as positive. AP is estimated for the area under the curve that measures the balance between precision and recall at different decision thresholds. As shown in In order to further compare the amount of memory required of the deep learning model during the testing process, the number of parameters and size for each model evaluated in this paper is given in Distance metrics are a very critical approach for unsupervised anomaly detection. These distance metrics are generally used to calculate the distance error between images to measure the similarity ,44. Whenp = 2. It is the natural distance in a geometric interpretation.The Euclidean distance (Euclidean): the L2-norm of the difference, a special case of the Minkowski distance with The squared Euclidean distance (Sqeuclidean): like the Euclidean distance metric but does not take the square root. As a result, classification with the Euclidean squared distance metric is faster than classification with the regular Euclidean distance.p = 1 and equivalent to the sum of absolute difference.The Manhattan distance (Manhattan): the L1-norm of the difference, a special case of the Minkowski distance with Here we demonstrate the utility of the additional components we used to stabilize the adversarial training VAGWDD model by systematically removing each component in turn. It is crucial for us to perform the ablation experiment to display the different combinations of loss functions on the CT-IRT datasets. All proposed loss functions we designed in this paper, shown in We notice that FID scores obtained for the proposed model is lower than in other cases. In general, lower scores mean the model regenerated images with higher quality and close to the original ones. The AUROC and AP of case 1 are respectively recoded as 0.842 and 0.964. When the loss function of the discriminator is added by binary cross-entropy . The proposed method also has a smaller number of weight parameters and less storage space. Our next aim is to optimize the proposed algorithms to improve the amount of the parameters and ensure real-time performance in fault detection accuracy. Apart from that, we will explore more effective ways to develop unknown fault detection in unsupervised or semi-supervised learning.


CBD is a promising candidate for treatment of many diseases and plays a major role in the growing trend to produce high-end drugs from natural, renewable resources. In the present work, we demonstrate a way to incorporate the anti-inflammatory drug CBD into smart microgel particles. The copolymer microgels that we chose as carrier systems exhibit a volume phase transition temperature of 39  Individual concepts of drug delivery and targeting mechanisms play a key role in future developments in pharmacy, with many new drugs emerging in the pharmaceutical market in the last few decades. Acrylamide-based responsive microgels have been discussed as potential candidates for this in several publications, including two recent reviews ,2. GenerN-isopropylacrylamide) (PNIPAM) and poly(N-isopropylmethacrylamide) (PNIPMAM). At a certain temperature, the so-called volume phase transition temperature (VPTT), the particles undergo a drastic decrease in size. In contrast to linear PNIPAM chains, which precipitate above the LCST of ca. 32 One of the most remarkable properties of microgels is their ability to respond to different external stimuli, e.g., temperature, pH-value and ionic strength. Our study focuses on the thermoresponsive materials poly. The main issue regarding the use of ethanol as a solvent for the microgel particles is their loss of thermosensitivity compared to microgels in water. Thus, it is important to ensure that the loading of microgels with CBD in ethanol is successful and no loss of CBD occurs. As a further improvement of the presented approach, the microgels loaded in ethanol are to be subjected to a solvent exchange to water, and once more, the loading with CBD and its release are investigated subsequently to the solvent exchange.The study is employing different spectroscopic methods, in particular, UV/Vis spectroscopy to determine the amount of CBD inside the microgel network and transferred via temperature dependent release. Moreover, the influence of CBD loading into the microgel particles on their structure is investigated via atomic force microscopy (AFM). The carrier particles are tailored by the copolymerization of NIPAM and NIPMAM to exhibit a VPTT slightly above the body temperature of about 36 ents see .After verifying the purity of the used CBD sample see , we perfThe main difference between the two absorbance spectra of CBD and the copolymer microgel is the characteristic absorbance band at 278 nm. In contrast to the wavelength dependent increase of the microgel absorbance, which is only caused by the light scattering from the particles and does not contain any molecular absorbance, a clear electron excitation band can be observed for CBD. Moreover, the observed absorption band in the spectrum can be assigned to the resorcinol substructure of CBD see , which hDue to the characteristic and very distinct absorption maximum of CBD at 278 nm, it will be possible to quantify the amount of CBD, that is released by the microgel particles using the same method. Therefore, we measured the absorbance spectra of CBD at various concentrations in EtOH and scrutinized the molecular extinction coefficient The microgel particles obtained by a surfactant assisted precipitation polymerization were characterized in detail by means of atomic force microscopy (AFM), performed on adsorbed microgels in the completely dried state. The results of the AFM measurements of the particles are presented in In the AFM image, we observe the microgel particles with a circular cross section adsorbed onto a silicon wafer surface. To ensure the successful sample preparation on the surface, it was coated with a thin layer of polyethyleneimine (PEI) prior to the deposition of the microgel particles . Due to Subsequently, we performed temperature-dependent photon correlation spectroscopy (PCS) measurements to characterize the copolymer microgels in the dispersed state. Additionally, we investigated the particles with angle-dependent PCS measurements at two temperatures in the completely swollen (20 The swelling curve of the microgels (A) depicts the typical behaviour for most copolymer microgels of PNIPAM and PNIPMAM, which has been described by Keerl et al. . The VPTal scale . NeverthWe expected some difficulties in the loading and release process inside the microgel particles in water, because CBD is an extremely hydrophobic substance with almost zero solubility in water. Therefore, we prepared an alternative loading route, employing ethanol as a solvent for the microgel particles. To ensure complete solvent exchange, we performed numerous consecutive centrifugation, redispersion cycles in ethanol until no water was left in the system. The main problem we anticipate here is that pure ethanol is a good solvent for PNIPAM chains, regardless of the temperature at most concentrations . Of courIt is clearly observable in the swelling curve A, that tSubsequently, we performed some temperature-dependent UV/Vis measurements of the copolymer microgel particles in EtOH to investigate if the swelling that we observe in the measurements could be comparable with effects observed recently by von Klitzing et al. . While oAs a result of the measurements, we observe a constant decrease in transmittance, which usually occurs in correlation with swelling of the microgel particles upon temperature increase and has not been observed before in comparable systems dispersed in pure ethanol. It is important to note that the observed effect for the decrease in transmission is rather small (4%), but it is consistent throughout the measurement and was also observed in several repeated measurements.As a summary of the previous section, it can be stated that the copolymer microgel synthesis was successful and we obtained particles exhibiting the desired VPTT and size. By anticipating potential issues with the CBD loading and release in water, we developed an alternative route which includes the dispersion of the copolymer particles in ethanol. As expected, PCS measurements show the non-existence of LCST behavior in EtOH. Instead, we observe a continuous swelling of the particles upon temperature increase. During the investigation of this size increase, we considered the possibility of a rather complex swelling behaviour, which has been discussed in literature recently for microgels in solvent mixtures. However, we have not been able to reveal the complete picture behind the mentioned effect yet, but it could be a hint that the temperature-dependent CBD uptake and release will potentially be possible with our alternative method, if we are not able to use the microgel system in water. Additionally, the microgels can be loaded in ethanol and subsequently transferred back to water, which allows temperature-triggered release.Firstly, we performed loading experiments with CBD in the aqueous microgel suspension by trying to dissolve the CBD inside the microgel particles in water. Right from the beginning, it became obvious that the enormous hydrophobicity of CBD does not allow the usage of this approach. Even after centrifugation, the substance floats on top of the water surface, without dissolving at all  and N-isopropylmethacrylamide  were recrystallized from n-hexane . N,N′-methylenebisacrylamide , sodium dodecyl sulfate  and ammonium peroxodisulfate  were purchased from Sigma-Aldrich  and used without further purification. Cannabidiol (CBD) was obtained as a sample from Cannovum AG  and used as obtained.All microgels were synthesized via a surfactant-assisted precipitation polymerization, first established by Pelton and Chibante . BrieflyThe incorporation of CBD in EtOH at 20 ient see .The same sample preparation was used to study the release of CBD in EtOH. The prepared samples with a total volume of 1 mL were centrifuged for 15 min . The supernatant was completely removed and replaced by the same amount of EtOH and centrifuged again for 15 min  at 40 In addition, the release of CBD in ethanol at 40 To investigate the microgels loaded with CBD in an aqueous solution and after the release of CBD, the microgels were loaded with the appropriate CBD concentrations in EtOH as described above. Afterwards, the solvent was completely removed and the microgels loaded with CBD were redispersed in the same amount of water and transferred to Hellma cuvettes . The cuvettes were placed in an Ulbricht sphere . The measurements were performed at room temperature using a UV/Vis spectrometer . The released amount of CBD in aqueous solution in the supernatant was investigated similarly to the release in EtOH.Nano-ESI mass spectra were recorded using an Esquire 3000 ion trap mass spectrometer  equipped with a nano-ESI source. The sample was dissolved in EtOH and introduced by static nano-ESI using in-house pulled glass emitters. Nitrogen served both as nebulizer gas and dry gas. Nitrogen was generated by a Bruker nitrogen generator NGM 11. Helium served as cooling gas for the ion trap and collision gas for MSn experiments. The mass axis was externally calibrated with ESI-L Tuning Mix  as calibration standard. MestReNova was used for processing the spectra.For the determination of the particle size of the microgels, PCS measurements on highly diluted samples in ethanol or purified water were performed. Angle-dependent PCS measurements were done using a 3D-LS Spectrometer Pro  equipped with a HeNe LASER  from 40To study the temperature-dependent phase behaviour and to obtain the volume phase transition temperature (VPTT) of the microgels, PCS measurements at a fixed angle of 45q as magnitude of the scattering vector:n the refractive index of the solvent and T, the Bolzmann constant The obtained auto-correlation functions were analysed using the method of cumulants  to obtaiAtomic force microscopy was used to investigate the microgel particles for structural changes depending on the used solvent (water or ethanol) and due to the incorporation of CBD into the microgel network. All measurements were performed with a DI Nanoscope IIIa  mounted on a Zeiss Axiovert 135 inverted microscope  in semi-contact mode using Budget Sensors  Al-Reflex Tap300Al-G cantilevers with a tip radius of <10 nm, a resonance frequency of about 300 kHz and a spring constant of 40 N/m. For the sample preparation, a silicon wafer  was cleaned with ethanol (HPLC grade) and subsequently in a plasma cleaner . Afterwards, the cleaned wafer was spin-coated with GWYDDION .Turbidity measurements on microgels in EtOH were performed using an Agilent 8453 UV/Vis spectrometer  equipped with a diode-array detector in the range from 20 To study the time-dependent change in viscosity of pure microgels, microgels loaded with CBD, and microgels after the release of CBD rheological measurements were performed using a Physica MCR 101 rheometer  equipped with a cone plate shear cell. For the measurements 15 In this work, we used copolymer microgels for loading and release experiments of the hydrophobic drug CBD. Since loading of the microgels with CBD in water was not possible due to the hydrophobicity of the drug, we successfully developed an alternative incorporation route by redispersing the copolymer microgel particles in ethanol at 20 Nevertheless, copolymer microgel systems have proven to be an extremely interesting carrier system for CBD and showed outstanding uptake when they were used in an appropriate solvent. In the end, it might be beneficial that CBD as a rather new drug does not have a perfectly developed carrier system so far, which opens up an interesting development opportunity for microgel carriers.


With the inclusion of tactile Internet (TI) in the industrial sector, we are at the doorstep of the tactile Industrial Internet of Things (IIoT). This provides the ability for the human operator to control and manipulate remote industrial environments in real-time. The TI use cases in IIoT demand a communication network, including ultra-low latency, ultra-high reliability, availability, and security. Additionally, the lack of the tactile IIoT testbed has made it more severe to investigate and improve the quality of services (QoS) for tactile IIoT applications. In this work, we propose a virtual testbed called IoTactileSim, that offers implementation, investigation, and management for QoS provisioning in tactile IIoT services. IoTactileSim utilizes a network emulator Mininet and robotic simulator CoppeliaSim to perform real-time haptic teleoperations in virtual and physical environments. It provides the real-time monitoring of the implemented technology parametric values, network impairments , and data flow between operator (master domain) and teleoperator (slave domain). Finally, we investigate the results of two tactile IIoT environments to prove the potential of the proposed IoTactileSim testbed. The rapid development of communication technologies from First-Generation 1G) to Sixth-Generation (6G) has gained enormous attention due to its emerging services like human-to-human H2H), machine-to-machine (M2M), and human-to-machine (H2M) communication. These emerging services are induced by drivers like mobile Internet, Internet of Things (IoT), and tactile Internet (TI). The IoT envisions to fill the gap between the cyber and physical world [G to SixtH, machinSeveral international standard organizations, such as the international telecommunication union, the Third-Generation Partnership Project (3GPP), and the Institute of Electrical and Electronics Engineering (IEEE), are working to enable the existing and develop new network architectures to carry haptic data over the communication in real-time. The TI standard working group IEEE P1819.1 has already initiated and defined reference architecture, technical functions, and the definition of the TI . MoreoveMoreover, these requirements become more critical for loss-intolerant and delay-sensitive TI industrial and medical applications. For example, remote industrial management and the automation of industrial robots (sensitivity of control circuits) demand latency between 0.25–10 ms with a packet loss of ≤10−9 . Therefo10−9 ,12. Tabl10−9 ). The reAn in-depth discussion on conventional and emerging industrial is presented in , where tOne of the effective ways to investigate the tactile IIoT application requirements, performance, and testing the new solutions to ensure QoS and QoE, is to set up a virtual testbed similar to the real network. The testbed must allow us to utilize and maintain hardware and software virtually on a standard computer without purchasing them. In the literature, several recent articles have proposed testbeds to overcome the above-mentioned challenges. The work in  proposedIn , a frameThe study in  presentsWe presented the details of TI in the context of various industrial environments and discussed some emerging applications of the tactile IIoT.A hybrid virtual testbed, IoTactileSim, is proposed by combining a network emulator and an industrial robotic simulator to simulate tactile IIoT applications and investigate their performance.We designed the IoTactileSim by adopting a hierarchical approach, where the network is divided into two parts; a core and an edge layer. The core layer consists of SDN routers to perform intelligent routing, while the edge layer performs as an intelligent support engine for tactile IIoT services.The proposed IoTactileSim identifies the challenges imposed by the tactile IIoT and their strict QoS/QoE requirements. Moreover, it focuses on investigating the communication network parameters  and other configurations corresponding to the identified requirements.We conduct two different experiments in the tactile IIoT environment to evaluate the performance and present the potential of the proposed IoTactileSim testbed.The primary contributions of this work are summarized below as:As illustrated in In this section, we describe the proposed IoTactileSim testbed to support a broad range of tactile IIoT services. At first, we will present the network emulator to mimic the real-world communication network, followed by a detailed discussion on the industrial robotic simulator. Finally, we present the topologic view of the proposed simulator, along with the basic parametric settings.The structure of the proposed testbed IoTactileSim as depicted in As can be seen in http://mininet.org/, accessed on 10 November 2021). An overview of the basic architecture of the Mininet with open-source virtual switches Open vSwitch (OVS) and SDN standard protocol OpenFlow is depicted in IoTactileSim allows users to integrate several tactile I/O modules with the Mininet environment.IoTactileSim enables users to implement each network module on a separate physical device.IoTactileSim also has an embedded tactile support engine which is not present in Mininet. This support engine can be modified by the user based on their use cases.The proposed IoTactileSim enables the evaluation of large custom topologies with actual application traffic traces by deploying them into the physical network. It also enables the utilization of emerging technologies such as SDN, NFV, and MEC. In the SDN framework, control planes are separated from the forward plane in the network. The emulator is written in python language and freely available at the Mininet official website  . The reaIn the proposed IoTactileSim, we utilized the python remote API client to interact with the smart industrial application in the CoppeliaSim environment through socket communication. The control code of the developed IIoT applications is executed in the same computing machine where the network emulator was employed. The CoppeliaSim simulator is connected with a network to represent network-slave interaction that was designed utilizing Mininet. Additionally, the network is linked with the master domain and makes the master-network relationship. The overview of the connection between master, network, and slave domain using Mininet and CoppeliaSim in a single computing machine  is illustrated in This section focuses on the architecture of the proposed IoTactileSim testbed as depicted in Initialization: In the initialization module, the simulator reads the parametric configuration files. It sets packet size, packet rate, Internet Protocol (IP) suite, IP address, link bandwidth, and link latencies. This module facilitates the users to provide the parameter settings as per their experiment need. If the user does not provide the parametric settings, then it automatically uses the default values of the parameters, as defined in Start Network Emulator: In order to design a real-world network, a Mininet emulator creates a custom topology with five OVSs and three hosts. The hosts act as a standard computing machine and are responsible for the master domain, slave domain, and tactile support engine. The OVSs are connected with a single SDN controller. The SDN controller decides to handle the data plane and allows the network operator to control and manage the whole network via API provided by the Mininet. Therefore, we utilized the Mininet Python API in IoTactileSim, so that the users can change the network settings as per their experiment demands to evaluate their newly developed approaches. The users can change the parametric values from the configuration files as discussed earlier in the initialization step.Simulation Cycle: After creating the network topology with three hosts that work as a master, slave, and tactile support engine, the simulator enters into the simulation cycle. In the simulation cycle, the actual experiments are performed as per defined conditions by the users through parametric settings or default values. The network host that acts as the master side, connects to the haptic device that the human operator uses to send the control signals to the salve side manipulator. The other host acting as a slave, connects to the CoppeliaSim simulator for executing the control commands in the designed virtual/physical tactile IIoT applications. After conducting a desired control experiment, the haptic feedback is sent to the human teleoperator at the master domain. This loop runs until the simulator reaches the defined threshold values like 1000 packets, etc. Finally, at the end of the simulation cycle, the simulator stores the experimental results into a file to investigate various network impairments.https://github.com/zubair1811/IoTactileSimV1.git, accessed on 30 November 2021) to the interested researchers to conduct extensive experiments to evaluate their suggested approaches.Performance Analysis: This module receives the stored experimental data file and compiles result graphs to understand the strengths and weaknesses of the conducted experiments from the QoE and QoS perspectives. After representing the experimental result in the visual form, it stores these results, and enables the users to perform new experiments or to exit the simulator. The list of the default parameter values and settings used in the proposed IoTactileSim are summarized in In this section, we demonstrate the effectiveness of the proposed IoTactileSim with two different tactile industrial scenarios using the simulation environment and parameters setting defined in Scenario I: Teleoperation with 3 Degree-of-Freedom (3DoF)Scenario II: Haptic-driven remote operationsMoreover, these scenarios can be classified into two categories, like offline and real-time applications. On the one hand, offline (teleoperation with 3DoF) experimentation means that we already have a static dataset of some real-world teleoperation applications and utilize previously collected data for analytical analysis. On the other hand, real-time or online (haptic-driven remote operations) experiments indicate that interaction data between operator and teleoperator are collected in real-time to make a suitable decision to ensure stability and transparency. The real-time scenario is complex compared to offline because it deals with more data under time constraints. Most of the existing studies on industrial testbeds just utilized the offline methodology, while the proposed IoTactileSim considered both scenarios. The discussion considering offline and real-time scenarios is presented in detail in the following subsections.Scenario I considers the offline experimentation, where publicly available 3DoF haptic traces in  on teleoWe adopt the same method in our experiments; after reading the sensors data, the system makes packets of the haptic traces as following:I is depicted in The interface of the IoTactileSim during communication between master and slave domain is depicted in The performance analysis for a scenario I in terms of round trip delay is presented in Contrary to In this section, we will present the real-time control of the teleoperator in the virtual environment to mimic the real-world tactile industrial remote operations. Similar to the scenario I, II also consists of the master, slave, and network domain where virtual teleoperator developed in CoppeliaSim acts as salve domain. In the master domain, physical haptic devices  are used to interact with the virtual environment, as illustrated in To observe the effect of the number of data packets on latency for scenario II, the simulation results are summarized in In To elaborate this packet latency convergence in a better way, In summary, the packet latency convergence analysis in In our previous work , we analhttps://wiki.dfrobot.com/, accessed on 30 November 2021 ). The proposed IoTactileSim connects the LLHOG and bionic robot hand to develop a closed control loop. The ITE is also integrated with the proposed IoTactileSim to monitor the network dynamics and guarantee the QoS and QoE requirements for tactile IIoT applications.In future work, more practical challenges regarding tactile IIoT in the real-world scenario need to be considered. As discussed above, providing required QoS and QoE in the real-time complex industrial application is more challenging than simulation analysis. Therefore, we indented to test the proposed IoTactileSim in real-time physical IIoT scenarios and demonstrate the real-world experiment design overview in In this paper, we proposed a virtual testbed termed as IoTactileSim to investigate and provision QoS and QoE strict requirements for tactile industrial IoT applications. The proposed IoTactileSim is equipped with a network emulator Mininet and an industrial simulator CoppeliaSim to mimic the real-world communication network and industrial IoT environment. It provides the users to evaluate the efficacy of their designed strategies under possible settings, including advanced core network technologies , edge intelligence, and application-agnostic parameters  for improving QoS and QoE. The proposed IoTactileSim is investigated for two different industrial use case scenarios with haptic data traces and real-time remote interaction. The simulation results indicate that the IoTactileSim is able to handle real-time data traffic then offline scenario by providing communication latency ranges from 6 to 2.2∼2.0 ms, and from 5.8 to 2.1 ms for 10 to 10,000 data packets, respectively. Moreover, the experimentation analysis indicates that the IoTactileSim allows the user to investigate network impairments  and can support complex tactile industrial environments with a higher number of data packets. In the early future, we plan to extend the IoTactileSim with network coding and machine learning approaches like federated reinforcement learning at the tactile support engine to integrate it with the 6G network infrastructure.


Silicone printing can enable a lot more accessibility and customizability towards utilizing silicone in different applications, including medicine for its biocompatibility. However, challenges existed for printing in specific geometries due to the lack of guidelines and studies on the mechanical properties. To support the understanding of printing three-dimensional silicone structure having different infill patterns and gel-like material, this paper conducted a parametric study for the specimens printed using a Bowden-type silicone printer and measurements of the tensile properties. Four printing parameters of print speed, infill density, flow rate, and infill pattern, are categorized following the Taguchi L9 method, and arranged into the four-parameter-three-level orthogonal array. The signal-to-noise (S/N) ratio was calculated based on the principle of the-larger-the-better, and analysis of variance (ANOVA) was also obtained. Tensile performance was further discussed with the characterization of internal structure, using the cross-sections of the printed specimens. It was found that the change of flow rate is the most significant to the tensile stress; and for the tensile strain, infill pattern was found to be the most significant parameter. The Line infill pattern consistently presented the highest tensile stress. Agglomeration can be seen inside the printed structure, hence optimal printing parameters play an important role for complicated geometry, while ensuring the flow rate and infill density do not exceed a reasonable value. This study would serve as the guideline for printing three-dimensional silicone structures. Additive manufacturing technology, so-called 3D printing, has been on the rise in the recent years. In particular, 3D printing has seen commercial success in the realm of hobbyists and industrial prototyping fields, even reaching the heights of aerospace industries . The usuOne outcome of this is that Silicone Printing has started to have a presence in the commercial market, such as the S052 from SanDraw . This prIt is then important to note the strategies used in silicone printing research to have an idea on how to tackle these challenges. The study in  makes usAnother more applied example of using silicone printing would be the soft robotic fabrication study by , where tOne such challenge is the setting of parameters to achieve said additive manufacturing for this experimental material and configuration. For the regular 3D printing of PLA, studies that involve parameter setting is shown in , where tAn application of silicone printing into fabricating a meniscus implant is shown in . Here, tExperimental design of optimization is an additional method to obtain optimal processing parameters, and the Taguchi method is one of the most efficient methods. This method is used in work about additive manufacturing such as ,3,19,20,Relating to the output parameters, other than , tensileIn terms of tensile properties, they are the main criteria in characterizing silicone structures for various applications, with flexibility being its defining characteristic. They are then used for characterization in research not limited to the Taguchi method. One example related to characterizing printed silicones is in , which mThis paper aims to do a parametric study for the tensile properties of silicone rubber specimens printed using a Bowden-type silicone printer. The printing parameters selected in this study are the print speed, infill density, flow rate, and infill pattern. The levels of these parameters are arranged in an orthogonal array based on Taguchi L9 method. The tensile tests are conducted by following the standard of ASTM D412. The results show the tensile properties, and analysis of the signal-to-noise ratios and ANOVA significance with the parameter effects. In this study, a silicone printer SanDraw S052  is introduced for investigation. It uses two customized horizontally attached plastic barrels that hold the components of the silicone mixture  simultaneously. Its mechanism is similar to Bowden style 3D printers where the silicone barrels are connected by a tube to the nozzle. For the movement of the nozzle, two stepper motors have threads attached to them, making the X and Y axes of the printer, have a pulley system style of operations. The z-axis has two stepper motors, and these are attached to threaded rods that are screwed into the bed. The home positions of the x and y axes are indicated by limit switches. Calibration of the z-axis makes use of a sensor component that is adjusted manually using micrometer knob. There is a glass plate attached onto the bed of the silicone printer for the post baking process. These act similar to a pallet system to enable the ease of handling workpieces, for putting them onto the oven for curing, for storage, and for executing printing processes while the curing operations are being done. Extrusion of the silicone components in the barrels is achieved through the threaded rod attached to the stepper motors. These rods push the airtight lid of the horizontally attached barrels, which in turn exerts pressure onto the silicone components, making them flow to the tubes and then onto the nozzle. Melding of the two components of silicone is achieved through the mixing blades embedded into the tube connected to the nozzle. This nozzle has an inner diameter of 0.4 mm, similar to the inner diameter of a regular 3D printer. A schematic diagram of printing mechanism of S052 and an example of printing operation can be seen in The software  used by the silicone printer utilizes the same principles and parameters that can be seen in 3D slicer software such as Cura  . The genThe used silicone material Sil50  is a two-part liquid silicone rubber (LSR) and is colored opaque white. It gets its name from its hardness of shore A 50. It has a tensile strength of 1750 psi or 12.07 MPa and a tear strength of 40 kgf/cm . These pFor this study, the Taguchi method of experimental design is chosen for its robustness and efficiency ,29. SpecThe following parameters in the FAMufacture software are selected due to their large contribution toward the success of printing the 3D structure . PrintinIn addition to the specimens printed using the parameters of the orthogonal array, specimens printed at 100% infill and molded specimens are also made to have a baseline comparison. The 100% infill specimen is printed at a 60 mm/s speed and at a 100% flow rate. The parameters of print speed and flow rate are both optimized after doing the orthogonal array experiments, where its selection is based upon which would let it retain the specimen dumbbell shape without much deviation. The molded specimen is fabricated through creating a 3D printed mold for the shape of the dumbbell specimen using a Fused Deposition Modeling (FDM) printer and then pouring the extruded silicone in the said mold. The extruded silicone is then spread onto the mold.The constant parameters in the experiments include the nozzle diameter of 0.4 mm, shell thickness of 0.8 mm, top and bottom thickness of 0.6 mm, and infill overlap of 15%. The function of retraction is not applicable during the printing since the gel-like silicone material is difficult to withdraw. These are printed at room temperature, ranging around 21 to 23 degrees Celsius and in a humidity of around 30–50%.The specimens used for this study follow the standard in ASTM D412 for type-D dumbbell shape specimens with a thickness of 3 mm . The priThe tests are performed using the Instron 5865 tensile testing machine  with a 5 kN Load Cell. Accordance to the standard of ASTM D412, the tests are done at a rate of 500 mm/min . 0 is the original length of the Type D specimen based on ASTM D412 dimensions. On the other hand, engineering stress is calculated by0 the cross-sectional area of the Type-D specimen based on ASTM D412. Considering the tensile behavior of hyperelastic materials, such as silicone rubber, the area of cross section deforms relatively more during the process of elongation. Thus, the typical assumption of the cross-sectional area not changing during the deformation process is not applicable. These engineering stress-strain properties are then used to calculate true stress-strain properties, based on the instantaneous cross-section area and length [The output data are processed through the software OriginLab , including calculation and statistical analysis. It is recorded until the specimens all break at the maximum force. Tensile tests output force and displacement data. The displacement data are then used to calculate engineering strain denoted by the equationd length . The truThe maximum values of tensile stress and tensile strain are compiled for each of the specimens. These data are then arranged according to the input printing parameter and their levels. After compilation of the maximum output values of tensile stress, and tensile strain for each specimen in the orthogonal array, the Signal-to-Noise (S/N) Ratio is calculated. This is done using the larger the better formula:r levels ,29. For The corresponding S/N ratio per parameter level is then sorted and averaged. This is done by grouping first the combinations based on one parameter. An example would be grouping engineering stress results by print speed: the combinations with 10 mm/s print speed are combined and the average of their S/N ratio in engineering stress is obtained. Similarly, the combinations with 35 mm/s and the 60 mm/s print speed would be averaged. This same operation for all parameter levels would be done for infill density, flow rate, and infill pattern. This process is then repeated for a different tensile property, until all parameter levels have an S/N ratio for each tensile property.The range of these S/N ratios are then computed to determine which parameter has the largest effect on each output. In addition, one-way analysis of variance (ANOVA) is done to further explore the differences between output data by printing parameters. This is done by grouping the results into different parameter levels as their factors for the one-way ANOVA. The groupings are done individually per printing parameter in order to determine how much this individual parameter affects the output tensile property.Characterization on both external appearance and internal structure of printed specimens are conducted. There are two main purposes, including to measure the geometric errors of the print and to clarify the effect of the printer parameters to the tensile properties. After the specimens have undergone the tensile tests, images of the external appearance are taken using a camera Panasonic Lumix . A pre-preparation of the internal structure characterization is done by cutting a thin cross-sectional area from the middle of the broken specimens. These cross-sectional images are then captured to be analyzed using an Olympus SZX7 Microscope . In order to quantify characterization, the cross-sectional images are then segmented using MATLAB . The segmentation is done to determine the total cross-section area in terms of square micrometers. After segmentation, the total area of the cross-section is then calculated. The segmented image is then inverted to get the area of the voids. These are then used to calculate the segmented area ratio:It is then compared to the ratio of the cross-section that is based on the toolpath slicer results. This is used in order to compensate for the variances in measuring and scaling the actual area of the silicone due to the splattered cross-section. The segmented ratio error calculated would be in the equationThis is done for all the combinations of parameters, with five specimens each. This error output would also be processed through the Taguchi analysis of mean response and S/N ratio, using the smaller-the-better formula. This is denoted byAll averaged maximum values of these tensile properties are compiled in Regarding the engineering tensile strength, p-values lower than 0.05 are statistically significant. It can be seen that the variable of flow rate has a relative small p-value of 7.13 × 10−12 and 3.13 × 10−11 in terms of engineering stress and true stress, respectively. This means that flow rate is the most significant among all the variables at the 0.05 alpha level in terms of engineering and true stress.The external appearance of the silicone-printed specimens grouped by different configurations can be seen in In In the results, the staircase effect is mainly observed at the sides. Regarding the internal structures, For a quantitative study of the geometric errors in printing, an example cross-section image and its resulting segmentation can be seen in It can be observed in As stated in the results of tensile stress, the flow rate has shown the highest rank in terms of S/N ratio and is the one with the most significance in ANOVA. The reason is that in having higher flow rate, more material would thus be deposited onto the cross-sectional area of the dumbbell specimens. A larger cross-sectional area would thus mean that the dumbbell specimen could withstand more forces, as these forces are distributed in a larger area. The other reason that flow rate would have a higher contribution than infill density would be that the structure is small enough that the outer shells accommodate a larger portion of the cross-sectional area. Structures with larger cross-sectional area would then be more dependent on the infill density in terms of tensile stress as compared to the flow rate. The optimal parameters found for engineering and true stresses are 60 mm/s print speed, 53% infill density, 100% flow rate, and line infill pattern.Regarding the behavior of tensile strain, the studied parameters have very close values in terms of S/N ratios, reflecting that the performance depends on the printed structure as a whole rather than relying on one specific parameter. The infill density and flow rate has consistently shown a large range in the S/N ratios for the two tensile properties. Thus, these two would be the first parameters that need consideration in order to get the higher tensile properties when designing. For a maximum tensile strain, the optimal printing parameters are the 53% infill density with a 75% flow rate. On the other hand, an optimal printing parameter for the maximum tensile stress is achieved with a 90% infill density with a 100% flow rate. For both tensile properties, the most optimal condition of print speed is 60 mm/s, and for the infill pattern it is the Line pattern.Although the tensile strength seems highly correlated to the density of specimens, this study demonstrates that the result of a solid specimen density does not always present the highest engineering tensile strength and the longest strain. In In addition, comparing the experimental results of the tensile stress to the specification given by the company supplier, it is shown that the lowest value of true stress (13.18 MPa) in this study exceeds the specification of 12.07 MPa. With the assistance of parametric study, applying the optimal parameter configuration in printing has the potential to improve tensile performance.The remaining strings on the external appearance can be seen in all the printed specimens in this study. This is due to the mechanism of a Bowden-type silicone printer but lacking retraction due to the difficulty in controlling the flow of gel-like materials. Such a silicone printing requires post processing for the string removal after the specimens are cured. This issue of clean appearance does not affect the tensile properties, but it may cause other phenomena such as holes, voids, and oozes.Having a very low printing speed on the other hand accentuates the extreme levels of the flow rates. In Results of external structures with lower flow rates show poor formation of top surfaces. Lower print speeds accentuate the extreme values in flow rate and exhibits phenomena of oozing in addition to the poor formation of the top surface. Internal characterization of cross-sections results shows infill densities of 53% and 90%, and exhibits solid cross-sections due to the pre-cured silicone’s liquid behavior. Low flow rate combinations exhibited poor layer fusion. Line infill patterns shows its consistency and advantage in the design of its patterns to exhibit high values and influence in the tensile properties. Quantitative area segmentation results show that print speeds and flow rate have the most contribution to this output, with slower print speeds showing less error, as well as the 75% flow rate.The parameter effects on the internal structure can be referred to the results of the cross-section images in Infill pattern seems to be not the only parameter that affects the amount of void. For example, the resulting cross-sections look mostly solid in It is difficult to distinguish the effect of flow rate on the amount of voids. An increase from 50% to 75% of flow rate shows a decrease in segment ratio error value, but the increase from 75% to 100% flow rate shows an increase instead. In silicone printing, the resulting staircase effect on the appearance seems correlated with lower flow rates. When increasing the flow rate, influence of the staircase effect on the resulting print decreases via the observation from the microstructure of the cross-sectional images. The layers in the cross-section of the printed specimens look fused in appearance. However, the tensile tests of ASTM D412 is uniaxial, along the length of the dumbbell specimen. The tensile strength influenced by the staircase effect when printing on the other orientation can be discussed in a future study.One study that also made use of tensile testing to discuss silicone printing results would be , where tAlthough the focus of the study in  is to crSimilar trends in parameter studies can be observed in Taguchi studies in ,3,23, buThis paper presents the relationship of different parameter levels toward the tensile properties of silicone dumbbell specimens printed using a Bowden-type silicone printer. The experimental investigations are based on the Taguchi method. Images characterization of external appearance and internal structure of printing provide the evidence of parameter effects. The results suggest that for the engineering and true tensile stress, the changes in flow rate have the most significant influence from both mean response and S/N ratio graphs, followed by changes in infill density. For the engineering and true strain, the contributions of the printing parameters have more equal ranges, with the infill pattern the one with the highest influence. The above trends of the parametric studies can serve as the guideline for printing three-dimensional silicone structures. The contribution of this study can increase the feasibility of printing biocompatible and multifunctional silicone material and further widen the accessibility and customizability of silicone for medical applications.


Gaussian mixture probability hypothesis density (GM-PHD) filtering based on random finite set (RFS) is an effective method to deal with multi-target tracking (MTT). However, the traditional GM-PHD filter cannot form a continuous track in the tracking process, and it is easy to produce a large number of redundant invalid likelihood functions in a dense clutter environment, which reduces the computational efficiency and affects the update result of target probability hypothesis density, resulting in excessive tracking error. Therefore, based on the GM-PHD filter framework, the target state space is extended to a higher dimension. By adding a label set, each Gaussian component is assigned a label, and the label is merged in the pruning and merging step to increase the merging threshold to reduce the Gaussian component generated by dense clutter update, which reduces the computation in the next prediction and update. After pruning and merging, the Gaussian components are further clustered and optimized by threshold separation clustering, thus as to improve the tracking performance of the filter and finally realizing the accurate formation of multi-target tracks in a dense clutter environment. Simulation results show that the proposed algorithm can form a continuous and reliable track in dense clutter environment and has good tracking performance and computational efficiency. Multi-target tracking (MTT) is a process of assigning the measured values to the targets, filtering them at the same time, and managing the tracks of multiple targets according to the time step ,2. RFS pTraditional tracking algorithms, such as probabilistic data association (PDA) and multiple hypothesis tracking (MHT) ,11,12,13PHD filter has a rigorous mathematical theoretical basis, which can realize joint detection and tracking of targets. The structure is complete and clear, and the amount of calculation is small. PHD filter cannot identify the tracks of different targets in the tracking process. With the increase of targets or the approach of distances, the wrong judgment of tracks leads to the inability of the GM-PHD filter to track the targets that need attention. Therefore, for a PHD filter, the accurate distinction of tracks is the key to ensuring tracking performance ,23,24.In MTT, it is the premise of establishing the track on determining the identity of the target. The data association algorithm  proposedIn the background of dense clutter, the PHD filter is difficult to form a continuous and stable track and has a large tracking error. In the framework of GM-PHD filtering, this paper introduces the Gaussian component label set into the pruning and merging step and proposes a threshold separation clustering algorithm considering velocity and position information to extract the target state. This method not only outputs the target track accurately but also improves the tracking accuracy and reduces the computational complexity. The main contributions of this paper are as follows: (1) the merging and pruning step is improved, the Gaussian components with the same label are merged, and the continuous missing detection parameters are used for moderate track preservation. (2) A method of clustering Gaussian components by threshold separation is proposed. After pruning and merging, Gaussian components are clustered by velocity threshold and Euclidean distance. The filtering performance of the improved algorithm has been verified by simulation scenes.The following chapters are arranged as follows: n-th target state and the m-th measurement at time k, respectively. k, respectively, and they change with time and the environment. Assuming that the prior probability of multi-target approximately obeys Poisson distribution, according to the RFS statistics theory, the PHD recurrence equation is [k on k − 1, k, Considering the following MTT scenario, the target state set is ation is (1)Dk|k−ectively , ps,k|k−GM-PHD filter represents the prior PHD (1) and the posterior PHD (2) of multi-target as Gaussian mixture formation, and its iterative recursion can be expressed as a prediction update structure similar to Kalman filter (KF), and the algorithm is processed in a multi-target space.k − 1 can be expressed as a Gaussian mixture, and the equation is as follows:k − 1. In general, the number of i-th Gaussian component, i-th Gaussian component. Then time k multi-target prediction PHD and time k posterior multi-target PHD can be expressed as Gaussian mixture form:j-th component in the update intensity function, I is the identity matrix.Assume that the multi-target posterior PHD at time The expected number of targets k.Among them, Therefore, the terms required to represent In Reference , a trackStep 1: initialize the Gaussian mixture posterior PHD at i-th Gaussian component, and the mean is Assign a unique label to each Gaussian component:Step 2: Predict according to Equation (4), the label set The surviving Gaussian component still retains the original label, and the newborn or spawned Gaussian component assigns a newborn label, then the predicted label set is defined as:Among them, Step 3: Updating is performed according to Equation (7), and all Gaussian terms obtained after updating are assigned the same labels as their previously associated prediction terms.Step 4: Pruning and mergingk; Set a truncation threshold A and B.A:Gaussian components weighted greater than the truncation threshold are put into set A: B asThen find out the index value of Gaussian term with the largest weight in set If the two Gaussian components satisfy Equation (18), they are combined into one term. The combined component parameters are obtained by weighted summation:Step 5: Multi-target state extractionBesides extracting the target state, the set composed of Gaussian component labels is also extractedStep 6: Track extractionAt each moment, if there is a Gaussian component or its spawned Gaussian term In a dense clutter environment, due to the existence of a large number of clutters, a large number of Gaussian components with unknown sources will appear in the pruning and merging step. If not properly processed, the computational complexity of the label GM-PHD filter will be greatly increased in the subsequent prediction and update steps. The existence of dense clutter will also affect the final state extraction of the filter and then directly affect the formation of correct multi-target tracks. It is necessary to consider improving the pruning and merging method, and on this basis, optimize the state extraction process of the algorithm, thus that the output track is correct and stable, and at the same time improve the efficiency of the algorithm.As mentioned earlier, GM-PHD filter uses the Gaussian mixture distribution to model multi-target to update the posterior strength of multi-target. However, such as other PHD filters, this filter does not consider the time sequence correspondence of Gaussian components in the tracking process. Thus, it is impossible to distinguish targets. Label GM-PHD filtering method solves this problem, which uses pruning and merging process to exclude unreal targets and ensure the formation of tracks. However, the pruning and merging method does not consider the label set of Gaussian components. When the target was occluded, or the distance was close, the Gaussian components corresponding to the real target were pruned or merged, which significantly degrades the tracking performance of the algorithm. The main reason why targets cannot be tracked when they are close to each other is that the label of the Gaussian component is ignored. If the merging threshold The combined component parameters were obtained by Equations (19)–(21). In order to reduce the operation time of each step, reference  proposes1. For 2. Set the set 3. AFind the index value of the maximum weight value in set The proposed algorithm flow is as follows:Obtain the set 4. Weighted summation to obtain Gaussian component parameters5. Put component parameter Based on the improve pruning and merging methods, GM-PHD extracts Gaussian components with weight greater than 0.5 after pruning and merging as output and retains the remaining Gaussian components to continue iterative filtering. Due to the persistent interference of strong clutter in a dense clutter environment, it is easy to produce wrong Gaussian terms with large weights. If they are extracted and regarded as newborn targets, it will lead to high tracking errors.It can be seen that finding the real target in a dense clutter environment and effectively separating the real target from the wrong Gaussian term are the key to solving the problem. Reference  proposedB was found, and the weight value was placed in the set N represents the number in set B after pruning and merging.Firstly, the index value of the Gaussian component with the largest weight in set B were calculated.The velocity difference and the corresponding Euclidean distance between the Gaussian component with the largest weight and the Gaussian component with the same label in set Finally, all Gaussian components satisfying the given velocity threshold k, and In the Gaussian component set If there is a track in the set If Gaussian component Finally, multi-target state estimation can be obtained:Aiming at the problem of poor tracking performance and low computational efficiency of traditional label GM-PHD in dense clutter environment, a robust label GM-PHD algorithm was proposed. The overall steps were as follows:Step 1: Initialize the parametersWhen Each Gaussian component is recursive by the Bayesian criterion.Step 2: Predict stepThe Gaussian component is predicted according to Equation (4), and then newborn labels are assigned to the newborn and spawned Gaussian components, and the newborn label set and the spawned label set are added to the predicted label set:Step 3: Update the stepsk, and then all updated Gaussian terms are assigned the same label as their previously associated prediction terms.According to Equation (7), the prediction state is updated using the measurements at time Step 4: Pruning and mergingA and B.A state-updated Gaussian mixture PHD A, and then the Gaussian term with the largest weight A was found, and the Gaussian term was used for screening and merging:The Gaussian component satisfying the truncation threshold was found and put into the set The combined component parameters were obtained by weighted summation:A that occurs in both Set A and Set B was deleted until Set A was an empty set.The Gaussian component of Set Step 5: Threshold separation clusteringA velocity threshold and an Euclidean distance threshold were set, and Gaussian components satisfying Equations (43) and (44) and having the same label were clustered, and then Gaussian components satisfying conditions were put into a set Step 6: Tracking association to form trackIf there is a track with a label If there is a track in the set If Gaussian component In order to verify the effectiveness of the proposed algorithm in MTT scenarios, the improved label GM-PHD filter was compared with GM-PHD filter, label GM-PHD filter, and Gaussian mixture cardinalized probability hypothesis density (GM-CPHD)  filter. The tracking scene was set to multiple targets in four possible locations or spawned from other targets, and the observation area was The state vector of the target consists of position and velocity components: T is 1 s, the total tracking time was 100 s, and the noisy process is The sampling interval Among them, The measurement vector is position information: In this MTT scene, clutter rate In the process of pruning and merging the traditional GM-PHD filter and the label GM-PHD filter, all Gaussian components were pruned and merged. The algorithm proposed in this paper only processes the Gaussian components with the same label. It will undoubtedly greatly reduce the amount of calculation of the Gaussian component and avoid the problem of exponential increase in the amount of calculation. Moreover, the algorithm proposed in this paper also includes a threshold separation clustering method, which also processes Gaussian components with the same label. It can be seen in the following simulation that processing the Gaussian components with the same label will reduce the computational complexity very well.In this MTT scene, clutter rate In this MTT scene, the survival probability In this MTT scene, the survival probability Simulations of the above four scenarios show that the improved label GM-PHD filtering algorithm has good tracking performance in dense clutter and low detection probability environment. It also has good stability in the environment where clutter rate and detection probability change.This algorithm is based on GM-PHD and focuses on multi-target tracking in a dense clutter environment, and the motion of the target is also linear. In the next work, we can study non-linear high-maneuvering multi-targets and Multi-sensor observation scenes, using SMC algorithm for non-linear moving targets. By considering the tracking problems in the case of model uncertainty, the algorithms ,17,18 caAiming at the problem of low accuracy and low computational efficiency of MTT in dense clutter environments, this paper proposes an efficient and accurate robust label GM-PHD filtering algorithm. Based on the traditional label GM-PHD, this algorithm improves the pruning and merging step to merge Gaussian components with the same label, which greatly reduces the computational burden and error merging in a dense clutter environment. According to velocity threshold and Euclidean distance, the newborn state extraction method can effectively reduce the occurrence of false tracks and improve the performance of MTT in dense clutter environments. The proposed algorithm can be further improved to adapt to MTT in an unknown parameter environment.


Cold rolling is widely employed in the manufacturing industry for the production of metal plates. In the cold rolling process, the thickness reduction of the metal plate under the recrystallization temperature generates severe anisotropy; this influences the subsequent forming processes. Therefore, the generation and prediction of metal plate anisotropy during cold rolling is a highly interesting research topic involving upstream studies of sheet metal forming. In this study, using the finite element method with zooming analysis, we established an efficient elastic–plastic analysis method to predict the metal plate texture after cold rolling. This method for cold rolling texture prediction was confirmed by comparing the experimental and simulation results of cold rolling for an S45C plate with a body-centered cubic lattice. Further, the numerical analysis method proposed in this study can contribute to the study of anisotropy as an alternative to experimental approaches. Cold rolling is a widely used process in the manufacturing industry for metal plate production . In thisHowever, there is still room to improve the accuracy and efficiency of texture prediction. For instance, Das  adopted In this study, the stress and strain distributions of the cold-rolled plate were calculated at the macro scale first, then at the micro scale to simulate the cold-rolled texture using the previously obtained strain distribution as an input. In this way, the texture calculation was only performed in the magnified area, thereby improving the calculation efficiency. The predicted rolling texture can reflect the anisotropy of a cold-rolled plate and clarify the generation of anisotropy caused by the cold rolling process. Moreover, if the predicted rolling texture is sufficiently accurate, this method can be used in the study of metal plate anisotropy as an alternative to experimental methods such as electron backscatter diffraction (EBSD) and X-ray diffraction.In this section, the FE modeling of the rolls and cold-rolled plate, modeling of the normal and tangent contact behaviors between the rolls and cold-rolled plate, and co-rotational formulation for the FE analysis are introduced in detail.For the FE modeling of the cold rolling process, the rigid analysis body was used to model the rolls and the first-order brick element with eight nodes ), associated flow rule (Equation (11)), and Kim–Tuan isotropic hardening rule (Equation (12)) for an accurate description of the flow stress for large deformation . The reak, k, The Taylor model  was adopBased on the minimum slip principle , the gralgorithm  under thV is the volume of the cold-rolled plate, In this study, the cold rolling process was analyzed as a static problem; thus, the static implicit method was applied to the FE analysis of cold rolling using the co-rotational formulation mentioned in The element tangent stiffness equation is used to assemble the corresponding global tangent stiffness equations The global tangent stiffness equations are stored using the skyline method and the modified Cholesky method is adopted to solve the equations because of its compatibility with the skyline method . When soTo validate the proposed elastic–plastic analysis framework under the co-rotational formulation, we conducted a cold rolling experiment with a 28.33% thickness reduction for an S45C plate  with an 84 mm width and 6 mm thickness. The FE analysis and texture prediction for the cold rolling process were then conducted for comparison using the analysis method mentioned in The FE modeling parameters were matched with the experimental cold rolling conditions, while the detailed FE analysis conditions for the S45C plate are listed in The distributions of the rolling pressure and rolling friction in the contact area calculated by the FE analysis are shown in vergaard . The expFor the texture prediction, because of the BCC lattice structure of the cold-rolled S45C plate, 48 slip systems  and (28) to predict the cold rolling texture.2 in the central layer of the center part of the cold-rolled plate. Comparing this with the texture prediction, we calculated the same number of random original lattice orientations using the Taylor model and the nodal plastic strain increment obtained using FE analysis. The predicted texture is shown in We proposed an elastic–plastic analysis framework, which incorporated a macro-scale FE analysis, using a phenomenological constitutive model for stress and strain calculations and a micro-scale zooming analysis using a crystal plasticity constitutive model for lattice orientation calculation. The proposed framework was applied to the elastic–plastic FE analysis of cold rolling using the static implicit method under a co-rotational formulation to predict the local cold rolling texture.The cold rolling texture between the predicted results and EBSD measurements on the S45C plate was in good agreement, thereby validating the proposed elastic–plastic analysis framework. Therefore, the proposed method qualifies as an effective numerical method for future studies on anisotropy generation and prediction related to the cold rolling process. It is also a potential alternative to experimental measurements of cold rolling texture. Further improvement of the current analysis framework may include adding feedback from the micro scale to the macro scale  for multi-scale analysis, which will be the focus of our future studies.


Computational Fluid Dynamics is a fundamental tool to simulate the flow field and the multi-physics nature of the phenomena involved in gas turbine combustors, supporting their design since the very preliminary phases. Standard steady state RANS turbulence models provide a reasonable prediction, despite some well-known limitations in reproducing the turbulent mixing in highly unsteady flows. Their affordable cost is ideal in the preliminary design steps, whereas, in the detailed phase of the design process, turbulence scale-resolving methods (such as LES or similar approaches) can be preferred to significantly improve the accuracy. Despite that, in dealing with multi-physics and multi-scale problems, as for Conjugate Heat Transfer (CHT) in presence of radiation, transient approaches are not always affordable and appropriate numerical treatments are necessary to properly account for the huge range of characteristics scales in space and time that occur when turbulence is resolved and heat conduction is simulated contextually. The present work describes an innovative methodology to perform CHT simulations accounting for multi-physics and multi-scale problems. Such methodology, named U-THERM3D, is applied for the metal temperature prediction of an annular aeroengine lean burn combustor. The theoretical formulations of the tool are described, together with its numerical implementation in the commercial CFD code ANSYS Fluent. The proposed approach is based on a time de-synchronization of the involved time dependent physics permitting to significantly speed up the calculation with respect to fully coupled strategy, preserving at the same time the effect of unsteady heat transfer on the final time averaged predicted metal temperature. The results of some preliminary assessment tests of its consistency and accuracy are reported before showing its exploitation on the real combustor. The results are compared against steady-state calculations and experimental data obtained by full annular tests at real scale conditions. The work confirms the importance of high-fidelity CFD approaches for the aerothermal prediction of liner metal temperature. The mid-long range transportation is dominated by aviation, which is increasing year after year the number of passenger, thanks to the improvements in safety and the cost reduction of air travel. Recent ICAO forecasts , releaseIn this context, the research efforts are devoted to improve the engine performance, mainly by increasing the Turbine Inlet Temperature (TIT) and the Overall Pressure Ratio (OPR). This trend greatly affects the hot gas path parts in the turbine and combustion chamber, which are subjected to stronger thermal stress that can undermine their integrity. As far as the combustor is concerned, the aforementioned development trend leads to higher coolant and flame temperature, reducing the cooling potential and increasing both convective and radiative heat loads. As a consequence, the thermal management of liners becomes more and more challenging.The cooling system is strongly stressed, regardless of whether the combustor is based on a rich-burn or a lean-burn architecture. The requirements for controlling turbine inlet temperature profile, emissions, and metal temperature generate an intense competition for the management of the air flow available . The resMore and more aeroengine manufacturers are working to overcome the limitations of standard Rich-Quench-Lean (RQL) technology, which is approaching the limit as emissions reduction capability, limiting the chance to fulfill future standards for NOre peaks .In such conditions, the more effective cooling schemes are required for the thermal management of the liners. The open literature on combustor cooling reports a wide range of solutions, such as double-wall schemes, tiles, matrix cooling, and transpiration cooling . During O(1 s) against O(1 ms), respectively) makes fully-coupled methods infeasible in an unsteady framework. Therefore, to effectively handle this problem and reduce computational costs, a loosely coupling is preferred, where segregated simulation processes are adopted for each involved physics  exchanging only the relevant quantities among domains at a given frequency. The open literature reports several approaches suitable for the analysis of long transients [Considering the issues represented by cost and complexity of experimental campaigns at high temperature and pressure conditions, Computational Fluid Dynamics (CFD) has been adopted in the last few decades as reference tool to analyze the complex phenomena occurring in aeroengine combustors. Despite that, the complicated interactions among combustion, turbulence, radiation, and conduction heat transfer make the prediction of metal temperature a very challenging task, demanding for an exploitation of multi-physics method. RANS approaches for turbulence modeling, which represents the most common tool in the industrial framework for preliminary design steps, do not show the required fidelity to permit adequate accurate predictions. Indeed, turbulence has huge effects in swirling reacting flows on both chemistry, spray dynamics, and wall heat transfer. It is, therefore, recommended to adopt Scale Resolving models (SRS) to accurately address the aerothermal field in place of RANS formulations ,17,18. Wansients ,20 or quansients ,22,23 meThe current works aims at reporting the development and benchmarking of the multi-physics tool named U-THERM3D, which allows the transient high-fidelity prediction of metal temperature in gas turbine combustors using the commercial package ANSYS Fluent adopting Scale-Resolving turbulence modeling. At this purpose, the test case considered is represented by the annular combustor designed by Avio Aero in the context of the European project LEMCOTEC, based on the lean burn injection system PERM . The full scale tests performed at the Central Institute of Aviation Motors (CIAM) provided fundamental validation results at different operating conditions in terms of outlet temperature maps, pollutant emissions, and wall temperature. Steady-state CFD analyses were carried out on the very same annular combustor with the THERM3D tool in Reference  to prediThe article is outlined as follows: The first part is focused to the description of the theoretical formulation behind U-THERM3D and its implementation in ANSYS Fluent. Then, the methodology is tested in terms of computational efficiency and verified on simplified test cases. Subsequently, the Avio Aero combustor is presented, along with the numerical modeling required and the most relevant results obtained.Exploiting a past experience in the modeling of steady Conjugate Heat Transfer in the context of ANSYS CFX , a new The founding concept of U-THERM3D procedure is a desynchronization of time advancements in the calculation of the involved phenomena, i.e., conduction within the solid, convection between fluid and solid, as well as radiation in the gas and its interaction with the solid. It worth emphasizing that the major benefits of SRSs lie in the convection phenomenon, which is driven by turbulence and can include several sub-phenomena, such as, for example, combustion and spray evolution, largely affected by mixing. Each involved physics is solved in separated processes which are coupled with a parallel strategy. Following Reference , at the h is a coupling relaxation parameter, and The user coding capabilities of ANSYS Fluent are used to customize the solver and develop the methodology. In particular, some User Defined Functions (UDFs) in C/C++ language were prepared to manage solvers synchronization and data exchange at interface patches, and this last step was also supported by dedicated scripts in the Ansys Fluent Scheme language. Indeed, as depicted in eference . Howevereference , when theference  and is aEven if, in the present work, the above described methodology is applied for the multi-physics investigation of a lean-burn combustor, the basic architecture of the tool, being developed as a framework where different solvers can be coupled, could be potentially used for other applications modifying few code lines. Indeed, compared against the available capabilities of ANSYS fluent, U-THERM3D is proposed as a general customizable framework to include your own physics solved in a dedicated instance. For example, in the developed approach for CHT problems on combustor liners, the additional modelings that actually are not possible with the standard solver are the decoupled solution of radiative problem using a mesh that is coarser than fluid domain one. To enforce this last sentence, The present approach shows the best performances in massively parallel computing. For this reason it is suitable in the prediction of the complex thermal interactions within gas turbine combustors. Nonetheless, a good balancing in terms of CPU is required between the involved solvers to minimize the queue time and, therefore, the waste of computational resources. This goal is pursued with a synchronization in CPU time in place of physical time.P, a fully coupled approach uses all the available resources to solve the complete set of equations. On the other hand, in a loose parallel coupling, a portion of the total processors is devoted to solve only the equations modeling a certain phenomenon. In a convective-conduction problem, for instance, hypothesizing a perfect scaling for both the solvers, load balancing requires that the processors Given a certain number of processors uch that :(3)PfPf+x” solver on a single grid element exploiting a single processor, whereas f), radiation (r), and conduction (s), a strongly coupled approach would require an execution time A deeper analysis to quantify the advantages in terms of saved computational resources can be performed for the present methodology, keeping the hypothesis of ideal scaling. In the following, P demanded to the “x” solver, or Moving to the U-THERM3D approach, the parallel scheme provides a different form of the execution time. Defining the solution time of the different solvers as follows:ΔTf=ΔτfΔt derived:ΔTuth=maxIt is worth remembering that the present analysis was performed with the main hypothesis of ideal communication between the solvers, corresponding to Equation . HoweverAn assessment was performed on the backward-facing step, depicted in The case study investigated in this work is a straight-through annular combustor developed and tested in the research project LEMCOTEC . The comThe LEMCOTEC combustor is based on an overall lean, swirl-stabilized, spray flame. The flame is realized by lean direct injection burner called PERM . The PERM concept is designed to work at intermediate OPR (between 20 and 35) and is based on two co-rotating radial swirlers encompassing a pre-filming atomizer (representing the main fuel injection device) and a pressure atomizer located at nozzle axis, which is adopted for main flame piloting to improve stability at low power conditions see . A variaA complete full annular test campaign was carried out at the CIAM center  to validate combustor up to full load conditions. In this numerical study, the Approach and max Take-Off conditions are considered for modeling validation; in The metal temperatures of combustor liners were measured by several thermocouples located on the cold side of both inner and outer liners. The thermocouples were located on different sectors varying angular position: for the sake of simplicity during comparison with numerical results, thermocouples were grouped and referred to the same ideal sector of the combustor. The temperature pattern at combustor outlet and the exhaust emissions were measured, thanks to a rotating probe located at combustor exit see . The proAll the simulations discussed in this work were carried out with the commercial package ANSYS Fluent version 17.1. In the following sections, detailed descriptions of the adopted numerical models are reported.C being model constants, S is the strain rate tensor. The quantity L in Equation  formulation, where the capability to solve turbulent scales is obtained by locally reducing eddy viscosity, thanks to a source term cale LvK :10)QSASQSASQSAS Z and the progress variable Following a series of previous successful validation studies carried out by the authors in the recent past , theapproach . To accovariable . For theegration:ψ˜=∫∫ψ with the Discrete Ordinate (DO) model . In thiseference .As widely discussed in As a result of preliminary RANS simulations, a fluid time step of A sketch of the simulated computational domain, together with the applied boundary conditions, is depicted in The potential of Scale-resolving applications is widely appreciated, where several phenomena are strongly coupled each other and strictly related to turbulence. In the present work, this condition can be found in the flametube that shows turbulence, combustion, and spray evolution. Moreover, the unsteadiness of the hot swirling flow has an indirect effect on the wall heat fluxes: this is a key point for the final target of obtaining a high-fidelity prediction of the liner thermal load. Hence, to speed-up the flow field simulation without an appreciable lack of accuracy, a domain including only the flametube and an upstream plenum was taken into account. An investigation of the whole system, including hot and cold sides, is illustrated in Reference  using a The solid is solved using a different domain corresponding to the inner and outer liners. In this work, radiation through the wall thickness is not included because of the radiative properties of the liner, that can be considered opaque and, for this reason, absorbs and reflects the beams at the fluid-solid interface obstructing the transmission.k-Ad-hoc computational grids were generated in ANSYS Meshing for the three domains, with respect to the mesh requirements of each simulation. As the sensitivity on the mesh parameters is a time-consuming task in a multi-physics problem, being characterized by several meshes and unsteady simulations, the size of the grids were a trade-off between computational effort and accuracy. In particular, a computational grid built by 8.7 M tetrahedral elements was exploited for the flow field solution and is shown in Concerning solid and radiation grids, these were the same exploited in Reference . In the Following the experience gained in References ,25, the As the focus of the present work is on the estimation of the benefits of scale resolving approaches on the CHT problem applied to aeronautical gas turbine combustors, having neglected the solution of the cold side is justified. In the multi-perforation holes, instead, where the turbulence scale would be extremely small making the SAS solution infeasible, the U-THERM3D methodology is capable of exploiting a correlative approach to keep a good accuracy. As a result, heat transfer coefficients and reference temperatures coming from the THERM3D steady analysis on the full single sector geometry performed in Reference  were setLiner was treated as a metal alloy with temperature-dependent conductivity and heat capacity. A similar dependency by the temperature was set also in the fluid, where, in addition, the effect of a change in the gas composition caused by the combustion phenomenon was included trough a polynomial-dependency by progress variable and mixture fraction. Properties taken from  were useIn this section, the results obtained for the two test points will be presented and discussed, providing a comparison against the THERM3D results . The resThe influence of the double swirler concept adopted in the PERM injection system was extensively discussed in previous works . Its geoThe core region of the combustion chamber seems not affected at Approach by the effects of the coupling with the radiative and thermal calculations if compared to the adiabatic simulation already reported in Reference . The higThe temperature distribution is also analyzed on the Plane 40 so as to provide more evidences about the reliability and robustness of the numerical methodology employed. The results are reported in The fluctuating and averaged parts of the energy source term produced by radiation are reported in Moving the focus on the inner and outer liners, it is possible to demonstrate how solving part of turbulence spectrum affects the predicted convection and liner temperature distributions, when an unsteady coupling is adopted. Temperature maps on the gas side of the liners for THERM3D  (left) aA quantitative comparison with experimental data is carried out by considering temperature measured on the cold side of the liners by thermocouples. The temperature distribution along the centerline for both inner and outer liners is reported in Further similar comparisons can be carried out by looking at the measured data along tangential directions, as highlighted in Increasing values of Similarly to what was done for the Approach condition, the temperature fields analyzed on the Plane 40 are reported in The more severe conditions within the flametube cause a significant increase in the aerothermal loads on the liners compared to the Approach condition. This is clearly illustrated by the metal temperature distributions reported in the solution of part of the turbulence spectrum offered by SAS smooths the temperature gradients ;with the resolved turbulent mixing more hot gases are transported into the outer recirculation zones, producing an increase in metal temperature of the liner in the proximity of the slot exit; andslot and film cooling flows are interacting more uniformly with the swirling flow in the spanwise direction, almost eliminating the cold coolant streaks reproduced by THERM3D.U-THERM3D predicts a considerably higher metal temperature compared to the values provided by the corresponding THERM3D simulation. A consistent increase in temperature can be observed almost everywhere. Peak values up to Focusing on the relative temperature difference between unsteady and steady simulations, the downstream portion of the Outer Liner shows an opposite trend compared to the previous condition. At Approach, this region appears heated by the hot gases which, however, have exchanged a significant amount of heat with the primary zone of the liner and, for this reason, have lost part of their temperature. The SAS calculation further increases such heat transfer in the primary zone, thus resulting in even colder gases and ultimately fairly lower metal temperature. On the contrary, at Take-Off the flame is shifted more downstream and has, indeed, more heating potential, with hot radiating gas pockets convected to the last portion of the liner.Similar to The different heat transfer modes  have different contributions not only when different operating conditions are considered but also on the different regions of the liner . Obtaining a deeper insight about the role played by the two mechanisms can help the designers in better understanding the metal temperature trends. At this purpose, the energy budget for Inner Liner and Outer Liner were calculated and are reported in Overall, the heat load is more than doubled as a consequence of the different operating condition, even closer to a three-fold increase on the Outer Liner. As explained, the different distribution of radiative heat flux between the two liners can be addressed by the shaping of the annular geometry, that generates a higher viewing factor from the Inner surface and the flame to the Outer surface, while the Outer surface interacts with the Inner part with a lower viewing factor. As a consequence, the Inner Liner fully irradiates to the opposite surface that, contrarily, partly self irradiates. Non-uniformities in liner-to-liner radiation are even more significant at Take-Off, when solid temperatures are higher. This outcome is also confirmed by the relative contributions of radiation and convection which are, respectively, Conjugate heat transfer is a challenging problem, made even more complex in gas turbine combustors by multi-physics interactions between fluid dynamics phenomena, heat conduction and radiation. Experimental campaigns on real aeroengine burners cannot provide a deep insight into the involved phenomena. The more and more widespread use of massively parallel resources is pushing CFD to support the detailed design of modern combustors. Among all the requirements involved in their design, the management of metal temperature is one of the most relevant.This article reports the main outcomes of a modeling activity finalized at investigating the thermal loads on lean burn combustor cooled by multiperforated liners. A desynchronized loosely-coupled approach, called U-THERM3D, was developed to reduce the computational effort of the computational intensive multi-physics problem. The tool was applied to a full-annular test case from a EU-funded research program. The work represents the continuation of previous preliminary tasks focused on steady and unsteady contexts. The Approach and Take-Off operating conditions of the aeroengine were considered. The results of the present methodology were compared with the predictions obtained with the steady multi-physics tool THERM3D , as wellDespite the greater computational cost (roughly one order of magnitude higher than the steady version), the new tool returns an improvement of the accuracy in the prediction of metal temperature, as confirmed by the comparison with experimental data at Approach condition, with the overall prediction of higher metal temperatures and reduced associated gradients. As a consequence to a more accurate and physical prediction of the unsteady interaction between the swirling annular jet and combustor liners, common to both investigated operating conditions, higher temperature levels are computed in the first half of the liner with the SAS approach. As a matter of fact, the hot gases entrainment in the corner recirculation regions, the related increase in the wall heat flux, and the unsteady wash-out of film cooling is better reproduced by the Scale-Resolving Simulation approach. A heat load analysis reveals opposite trends for radiative heat transfer in the two operating conditions when the unsteady coupling is considered in place of the steady one. At the Approach condition, maximum gas temperature is not altered with respect to the THERM3D calculation and, contemporary to the higher liner temperature, it justifies a reduction of radiation. On the contrary, at Take-Off the radiative transfer slightly increases due to a larger flame volume in the reaction zone. Not far from what observed with the steady THERM3D solution, the ratio between convection and radiation is roughly