This study proposes a novel hybrid simulation technique for analyzing structural deformation and stress using light detection and ranging (LiDAR)-scanned point cloud data (PCD) and polynomial regression processing. The method estimates the edge and corner points of the deformed structure from the PCD. It transforms into a Dirichlet boundary condition for the numerical simulation using the particle difference method (PDM), which utilizes nodes only based on the strong formulation, and it is advantageous for handling essential boundaries and nodal rearrangement, including node generation and deletion between analysis steps. Unlike previous studies, which relied on digital images with attached targets, this research uses PCD acquired through LiDAR scanning during the loading process without any target. Essential boundary condition implementation naturally builds a boundary value problem for the PDM simulation. The developed hybrid simulation technique was validated through an elastic beam problem and a three-point bending test on a rubber beam. The results were compared with those of ANSYS analysis, showing that the technique accurately approximates the deformed edge shape leading to accurate stress calculations. The accuracy improved when using a linear strain model and increasing the number of PDM model nodes. Additionally, the error that occurred during PCD processing and edge point extraction was affected by the order of polynomial regression equation. The simulation technique offers advantages in cases where linking numerical analysis with digital images is challenging and when direct mechanical gauge measurement is difficult. In addition, it has potential applications in structural health monitoring and smart construction involving machine leading techniques. Evaluating the usability of a target structure and verifying whether it meets the required structural performance necessitates obtaining kinematic variables such as deformation, strain, and stress. Typically, these variables are measured using contact sensors or non-contact measurement equipment that tracks installed targets ,2. ContaAlthough images can provide perspective data of a subject, they require additional processing to be used as coordinate or location information in planes or spaces. In contrast, LiDAR can provide point cloud data (PCD), a group of three-dimensional points representing the surface of a reflecting object, by measuring the time of flight of laser pulses and instantaneous scanning angles. Ongoing efforts are being made to apply this data for monitoring and analyzing target objects. For example, a method was proposed to compare PCD from different time points to identify damaged areas based on an initial reference and update the analysis model . AdditioHowever, although effective in capturing overall trends, the displacement tracking method can result in significant errors when reproducing overall kinematic variables based on the random distribution of points. Alternatively, some methods use polynomial regression to estimate overall displacement and strain by approximating the targets’ surface or cross-sectional shape in the PCD. Cabaleiro et al.  proposedJo et al.  and Jo eIn the past, research has primarily utilized methods that combine the finite element method (FEM) with non-contact sensors such as digital cameras or LiDAR to reproduce kinematic variables at desired locations from displacement information acquired directly or indirectly at arbitrary positions ,15,16. GTo avoid the difficulties associated with using meshes in numerical analysis using PCD, techniques such as the boundary element method (BEM)  or the eTo overcome the limitations of numerical analysis using PCD with meshes, a meshless technique that enables fast differentiation calculations and is not constrained by mesh or grid construction is necessary for calculating kinematic variables. The particle difference method (PDM) is a meshless technique that uses strong form or collocation-based rapid derivative approximations to quickly calculate displacement, strain, or stress without the meshes, grids, or integration cell. Since the modeling is performed using only nodes, any changes in the geometric shape of the target structure over time do not pose additional difficulties ,23,24,25Park et al.  investigThis study presents a novel method for determining kinematic variables across the entire area of a loaded structure by utilizing pre- and post-deformation PCD obtained through LiDAR scanning of a target-free structure and applying it to a strong form-based meshless method. This method estimates structural deformation by analyzing the PCD, extracting edge shape changes, and assigning essential boundary conditions to the numerical model boundary for the target structure. The governing differential equations of solid mechanics are given as a form of boundary value problem (BVP). A Dirichlet boundary problem is mainly considered here since the captured deformation is assigned as the essential boundary condition. The shape of the deformed structure is expressed through LiDAR PCD, edge extraction, and polynomial regression, and the Dirichlet boundary problem solving is conducted consecutively. Edge extraction was performed using eigenvalues for k-nearest neighbors in the acquired PCD. Since the PDM is the strong form-based meshless technique, creating a numerical model and handling the essential boundary condition is straightforward once the boundary displacements are determined by analyzing the PCD, allowing for rapid stress calculation in the target structure.In addition, the PDM has no issues even when the number of nodes increases owing to changes in node positions or changes in the analysis steps. Instead, we conducted an error analysis to validate the proposed method by applying it to a problem with a known analytic solution. Moreover, we demonstrated the method’s robustness by performing various simulations of a three-point bending test for a rubber beam and suggested several approaches to ensure accurate simulation results.The PDM is a meshless method that uses a strong formulation to analyze the stress of the target structure using nodes only. Governing equations are discretized directly at each node to obtain the numerical solution. The derivative approximation required for this process is obtained by constructing a Taylor series expansion for the nodes with the influence domain, utilizing the moving least squares method. Shape functions and their derivatives are naturally obtained while constructing the derivative approximation, and the degree of approximation depends on the degree of the applied polynomial basis vector. Unlike the FDM’s grid or FEM’s mesh, or weak formulation meshless methods using integration cells, the PDM does not encounter constraints associated with fixed meshes or grids. Instead, it can calculate kinematic variables without difficulty, even when node placement or the number of nodes changes during the analysis steps. In contrast, edge extraction results may present challenges in fixed model structures or methods with weak formulations, mainly when node placement is irregular.When using the strong formulation to formalize solid mechanics problems, the governing equation can be discretized using displacements, and Navier’s equation is typically derived, which requires second-order derivatives of displacements . Howeverx concerning the reference point y, based on the mth polynomial vector, can be represented as follows:mth order polynomial vector, and n represents the space dimension. Moreover, N nodes included in the influence domain of the reference point y, the relationship between the approximation of Equation (1) and the nodal solution r represents the radius of the influence domain that determines how many nodes are included in the approximation calculation. By minimizing Equation (2), y in x by the moving process, mth order according to the definition of the Taylor polynomial, the following equation, combining the shape function and the nodal solution, is obtained in matrix form:I at the x point, respectively. For example, the displacement value in the i direction at the x point can be calculated as mth order are computed concurrently. A detailed derivation for the PDM based on the first-order differential equation used in this paper can be found in Yoon et al. [A Taylor polynomial at an arbitrary position n et al. .Using LiDAR to acquire the shape information of a structure during a load test, regression analysis, and interpolation can be applied to the obtained PCD to determine the essential boundary conditions for the boundary value problem. Then, numerical analysis can be performed with the PDM to obtain kinematic variables such as displacement and strain. Solid mechanics problems are generally boundary value problems (BVPs), which allow for calculating kinematic variables within a problem domain for given boundary values using a PDM that discretizes the governing equation directly at the node level. The displacement values for boundary nodes, which are already known from the deformed shape information obtained from the PCD, are used to compute the internal nodes’ displacement, strain, and stress. As shown in N neighboring nodes within the influence domain, it is discretized as follows:When the deformations in the structure are small, and the problem is static, the equilibrium equation in the interior domain can be expressed as follows:This study uses the PDM to formulate solid mechanics problems by assembling the equilibrium equations for internal nodes and the essential boundary condition equations for nodes on the essential boundary to create the total system of equations. One advantage of the PDM is that it does not require distinguishing between the internal and boundary nodes when assembling the system of equations. Assembling the nodes in order produces the same result, simplifying the process of constructing the total system. Unlike in the FEM, the essential boundary terms can be treated without moving it to the right-hand side of the total system of equations, allowing for inverse matrix calculation while keeping the value in the total system. As shown in Equation (14), the essential boundary value at the base is located on the right-hand side of the total system of equations, eliminating the need to modify the system. However, when using numerical techniques based on weak formulation, additional calculations are required to handle essential boundary values.N, and M. The distinction between internal nodes and boundary nodes in Equation (14) is for the sake of expression convenience, but the same result can be obtained by simply assembling the nodes in order, irrespective of the governing equation type. In this study, the nodes were assembled according to their numbering.In this paper, the displacement values obtained from the transformed boundary coordinate values of the deformed shape are used as input for the BVP to perform numerical analysis, resulting in a pure essential boundary condition problem. Thus, the total system of equations, composed of the equilibrium equations for internal nodes and essential boundary condition equations for boundary nodes, is as follows:PCD consist of arbitrary points located on the surface of a specific object. To analyze a BVP, it is necessary to impose essential boundary conditions at the boundary of the PDM model. Typically, numerical and structural analyses are performed by directly applying displacement boundary conditions and loads. However, in this study, the deformed shape of the structure is considered to contain information on the kinematic variables of the interior nodes. Therefore, we validate a methodology that applies the deformed shape, tracked by LiDAR, as a boundary condition to the PDM model. In this section, before verifying the hybrid simulation technique, the accuracy of converting the deformation shape of the structure into the PDM essential boundary conditions is validated using a 2D elastic beam problem for which an analytical solution is known, as shown in ex denotes the exact solution, and PDM refers to the results calculated using the PDM. x and y-directional displacements of the PDM nodes plotted as surface plots, respectively.Before validating the methodology of extracting essential boundary conditions from structural deformation shapes, we conducted an accuracy analysis of the numerical solution obtained using the PDM for the essential BVP. In fact, the deformation shapes or essential boundary conditions for the four edges of the cantilever beam can be obtained from Equations (15) and (16). These boundary conditions assigned essential boundary values to the PDM boundary nodes. The analysis was performed to evaluate the error, and the relative sup-norm error in displacement was calculated as follows:Ω refers to the problem domain, Meanwhile, the PDM analysis results can also be evaluated using an This section presents a method for constructing a function formula to assign essential boundary values to the PDM boundary nodes. The essential boundary values are obtained from the deformation shape of the structural boundary extracted from PCD. A technique that utilizes the eigenvalues of k-nearest neighborhood points  was applA method for assigning displacement boundaries to boundary nodes of a PDM model is presented in Owing to the nature of LiDAR, the PCD acquired from the same target surface at different times will inevitably have a different number of points. Even if the number of points is the same, these values are 3D coordinates of randomly reflected laser points, so comparing the relationship between points before and after deformation with the initial PCD is impossible. Therefore, to determine deformation history or displacement at a specific location, fixed targets that can be verified at specific locations  or specix is the independent variable, y is the dependent variable, n is the highest degree of the regression equation, x and y coordinates, as observed in Equations (15) and (16). However, choosing a single independent variable with a dominant influence is more efficient. Therefore, this section constructs the polynomial regression equation concerning the more dominant coordinate axis.Below is the general form of the one-dimensional polynomial regression equation applied to track the shape changes of the deformed structural boundary.R2) of the regression equation [x or y coordinate is selected as the independent variable in the Cartesian coordinate system of the edge points. For instance, in R2 value does not significantly differ based on the choice of independent and dependent variables. However, to represent deformation shapes such as the left side of the beam (line 2), selecting the y coordinate as the independent variable yields a higher R2 than selecting the x coordinate, necessitating a third-degree or higher regression equation. This is because the y coordinates’ distribution of edge points is constant from −6 to 6, while the x coordinates’ distribution varies between positive and negative values. In this case, the analytic solution for displacement xu and yu is expressed as a combination of x and y up to the second-degree terms (including cross terms). However, since the function that can describe the deformation shape of an actual structure from PCD is unknown, it is necessary to idealize the deformation through appropriate assumptions. Thus, this section defines the cantilever beam deformation as a one-dimensional polynomial with a single independent variable to simplify the equation. For convenience, the independent variable of the polynomial regression equation is set to the coordinates along the edge-axis parallel to the edge direction.The coefficient of determination (equation  may varyR2 and to balance precision and efficiency when assuming and calculating an appropriate polynomial. Once the regression equation is calculated, it contains information about the coordinates of the nodes after deformation, and by knowing the coordinates of the nodes before deformation, it is straightforward to calculate the displacement boundary condition.Edge points extracted from PCD obtained through LiDAR scans are complex and unevenly distributed, and most contain outliers. Therefore, it is crucial to avoid overfitting when choosing a regression equation with a high x coordinates of the four corner points can be obtained from the intersection points of the regression equations with the same independent variables for the four edges. The y coordinates can then be obtained by substituting these values into the regression equations, determining the two-dimensional coordinates of the corner points after deformation. These values are then used as initial values for determining the essential boundary values of the PDM boundary nodes between the corner points in Among the PDM boundary nodes, corner points of a structure can be determined from the intersection points of regression equations without any additional assumptions. For example, in the case of a rectangle such as that in x and y coordinates of the exact solution were used in the regression process, most R2 values being close to 1. However, the lower R2 value for the x independent variable regression equation for line 2 compared to others is due to the trend mentioned above for line 2. In such cases, R2 can act as an error indicator when determining corner point displacements derived from the intersection finding process of other regression equations.x-axis, the independent variable coordinates n. The independent variables indicating the coordinates after deformation along the edge-axis at the ith edge node are denoted by In this study, the independent variable of the regression equation was set as the edge-axis coordinates. Therefore, if the edge-axis displacement of the PDM boundary node can be determined, the edge-axis coordinates after the deformation of the PDM boundary node can also be determined. These coordinates are used as the independent variable values in the regression equation, and the coordinates in the direction perpendicular to the edge-axis are the dependent variable values calculated through the regression equation. In other words, by using the estimated edge-axis displacement for the PDM boundary nodes, the coordinates of all the PDM boundary nodes after deformation can be determined; displacement of the PDM boundary nodes can be derived by subtracting the coordinates of the PDM boundary nodes after deformation from the coordinates. When the edge-axis is the a, b, and c are coefficients of the quadratic polynomial x-axis and the spacing of edge-axis nodes in the PDM model is uniform, the nodal displacement kth node can be given as the cumulative sum of strains, which is given as follows:The edge-axis displacement can be represented as a quadratic function of The deformation pattern in nth node, In This section presents the results of applying the essential boundary condition values to the PDM model. The boundary condition values were determined based on the pre- and post-deformation boundary shapes of the structures in x-directional displacement calculated by the PDM analysis for different displacement capturing methods. The results that were calculated assuming linear strain were similar to the exact solution, while those calculated using the uniform strain model displayed some differences. These differences arose because the edge-axis displacements determined by the two methods differed. However, as observed in y-directional displacement distributions were similar regardless of the degree of the regression function. However, variations in the values of the dependent variables due to differences in the independent variables resulted in variations in the values of the kinematic variables.x-directional displacement was fixed. However, as the polynomial regression equations accurately approximated the deformed shape, the absolute value of the resulting error was not significant, and its impact was limited to the nodes near Corner Point 1 and Corner Point 3, as shown in The rubber beam was subjected to displacement control, and LiDAR scans were performed at temporary stops every 2 mm stroke or deflection up to 20 mm and every 5 mm unit up to 40 mm. The edge points obtained after the processing were still represented in 3D coordinates. Therefore, to obtain a 2D PCD, the 3D edge points were projected onto the analysis plane, as shown in This section compares three different analysis methods to evaluate the accuracy of the displacement capturing method for boundary condition assignment based on the polynomial regression equation. The first analysis was performed using ANSYS under the same conditions as the previous physical experiment. The second analysis involved applying the essential boundary condition, determined through the displacement capturing method based on the regression equation from the edge points extracted from the deformed shape of the ANSYS analysis result and the PDM BVP analysis. The third analysis used the PDM BVP analysis, the essential boundary condition determined in x-directional displacement and y-directional displacement obtained by analyzing the three-point bending test of the rubber beam using the ANSYS program, i.e., the first analysis method. The analysis was performed where the displacement at the top center of the structure was 2 mm, corresponding to the moment of obtaining the PCD.x-directional displacement presented in The As shown in y-directional displacement shown in This study proposes a method for connecting the edge-axis coordinates of PDM boundary nodes and the independent variables of the regression equation. Therefore, the dependent variable, the Taking certain precautions when using PCD to simulate actual tests is essential. First, special attention should be paid when estimating boundary shapes and filtering outliers of the estimated boundary shapes. This is particularly important in situations where a slight change in the independent variable leads to a significant change in the dependent variable when modeling the deformed boundary shape using a polynomial regression equation. The steep gradient of the regression function can cause overfitting or sensitivity to outliers. It is also crucial to ensure that the regression function adequately approximates the deformed shape to maintain accuracy in tracking corner point displacements and overall boundary point displacement tracking. Moreover, it is necessary to verify the accuracy of PCD tracking for deformations in the linear-elastic region, and additional verification is required for large deformations or nonlinearity. By following these precautions, PCD can be a valuable tool for simulating actual tests and estimating boundary shapes with high accuracy. results a and PCD results b. The evThe hybrid simulation technique presented in this study involved using LiDAR to scan the structure at rest and then extracting essential boundary conditions from the PCD using polynomial regression equations and edge-axis displacement capturing methods to quantify deformation. These extracted boundary conditions were then applied to the PDM for BVP analysis. However, errors can arise from LiDAR scanning, regression analysis, and PDM analysis.The convergence rate of FEM has been extensively researched . SimilarMoreover, when applying essential boundary conditions determined from PCD to the PDM, linear and uniform strain models showed a tendency for errors to increase as the degree of the regression equation increased, as seen in This study proposed a new hybrid simulation technique for structural analysis of a loaded structure by capturing the change in boundary shape of the structure before and after deformation, using PCD obtained with LiDAR and polynomial regression processing. The method effectively estimates the edge and corner points of the deformed structure from the PCD and transforms it into a Dirichlet boundary for the PDM analysis. Unlike previous studies that used displacement information from digital images of deformed structures with attached targets to calculate kinematic variables , the appAlthough applying essential and natural boundary conditions when conducting a numerical analysis is typical practice, this study solely constructed the Dirichlet boundary problem with essential boundary conditions, as the entire boundary deformation was extracted as an essential boundary value from the PCD. To analyze the problem, the meshless PDM, based on the strong form, was employed, which is advantageous for handling essential boundaries, has no mesh constraints, and allows for changes in node rearrangement during stress analysis.The cantilever beam problem was analyzed to verify the developed hybrid simulation technique’s robustness. The boundary was defined using the analytic solution, and the BVP was solved using the PDM to obtain a numerical solution. The results confirmed that the kinematic variables acting on the target structure could be accurately calculated if the deformed shape was correctly converted to the essential boundary condition. Furthermore, the deformed shape could be estimated using linear and uniform strain models when applying polynomial regression for essential boundary condition assignment. The analysis of the errors in the developed technique confirmed that the error decreases as the number of nodes of the PDM model increases. Moreover, if the edge, initially straight, becomes curved after deformation, applying a linear strain model rather than a uniform strain model can enhance the accuracy of the kinematic variable evaluations.The developed hybrid simulation technique, which included processing PCD acquired through LiDAR scanning and applying polynomial regression, was verified by simulating a three-point bending test on a rubber beam. The results from this technique were compared with those obtained from ANSYS analysis under the same experimental conditions. In order to analyze the error factors during the PCD processing, we utilized the edge point coordinates estimated from the deformed shape derived from ANSYS analysis, and these were then compared with the results from the two cases mentioned earlier. In extracting the deformed shape from the PCD, polynomial regression equations ranging from quadratic to quintic were applied. The comparison of the calculation results for kinematic variables in various cases showed that the error converged as the number of analysis nodes increased. Moreover, when other conditions were the same, it was found that modeling the displacement of the edge-axis with the linear strain model resulted in less error compared to applying the uniform strain model. Additionally, it was confirmed that the errors occurring during the process of PCD acquisition for the deformed shape and the extraction of edge points from the obtained PCD affected the order of the applied polynomial regression equation. As a result, it was confirmed that the boundary displacement shape of the structure before and after deformation using PCD could be approximated with sufficient accuracy to be used as essential boundary conditions in BVP analysis.The hybrid simulation technique developed in this study, which combines LiDAR scanning and numerical analysis methods, offers significant benefits in cases where it is challenging to apply techniques that link numerical analysis with acquired and processed digital images by installing targets on test specimens. It is also advantageous when it is difficult to directly track displacement with measuring equipment. Furthermore, it can be utilized in structural health evaluation and maintenance, as it does not need target installation or displacement tracking sensors. The developed technique can be applied as long as the boundary shape of the target structure can be estimated, making it applicable even when images captured before and after deformation are available. However, the accuracy of the initially acquired PCD and the estimated deformed shape will significantly affect the overall accuracy of the hybrid simulation. Further research is necessary to improve the developed technique, such as diversifying strain modeling methods and enhancing regression analysis accuracy for boundary shape estimation.


From organoids to organ-on-a-chips, 3D models are considered the cutting-edge technology in cell culture. Cell choice is a big parameter to take into consideration when planning an in vitro model and cells capable of mimicking both healthy and diseased tissue, such as induced pluripotent stem cells (iPSC), are recognized as good candidates. Hence, we present a critical review of the latest models used to study neurodegenerative disease, how these models have evolved introducing microfluidics platforms, 3D cell cultures, and the use of induced pluripotent cells to better mimic the neural tissue environment in pathological conditions.Many neurodegenerative diseases are identified but their causes and cure are far from being well-known. The problem resides in the complexity of the neural tissue and its location which hinders its easy evaluation. Although necessary in the drug discovery process,  Neurodegenerative diseases are defined as the progressive loss of neuronal cells and the synapses formed between them, mainly due to the accumulation of proteins such as amyloid beta, tau, or alpha-synuclein . One perin vitro models capable of recreating the physiological characteristics necessary to test the efficacy and safety of a new drug  and microfluidic as the most versatile engineering process to mimic human tissue . Neurons, despite being the more popular cells, are not the most abundant in the brain, a position occupied by the glia cells family  arise as a promising cell type to develop more reliable neurological m layers . In thism layers . This nom layers .Cultivating diverse cell types not only enhances the intricacy of the model but also fosters the proliferation of these cell types, as their behavior is influenced by interactions with other cell types reminiscent of those found in native tissues. . There ain-vitro model for brain and neurodegenerative diseases because it includes mechanic and chemical cues that influence cell behavior. ECM takes around 20% of the space in the brain.  It is responsible for the low Young’s modulus (around 1 kPa) of this tissue. The glycosaminoglycans (proteoglycans and HA) contribute to the gel-like consistency and viscoelastic properties of this matrix while hydrating it. The low content of elastic fibers and collagen, when compared to other tissues of the body, also contributes to the low stiffness of this tissue . When bbstrates . This dibstrates .Bioactive cues, such as growth factors or peptides naturally present in the brain ECM, are necessary to mimic the real environment . Accordiin vivo conditions  stemness was assessed. Remarkably, it was dependent on the degradability and not the stiffness of the substrate  with polycaprolactone (PCL) nanofibers and evaluate their influence on neurite outgrowth of human iPSC. The results showed that this conjugation of synthetic materials increased the cells’ lifespan up to 2.3-fold and the neurite elongation by 3.8-fold. The migration of the cells was also increased when compared to PCL alone  and Dental LT Clear (DClear) as suitable biomaterials using a two-photon polymerization technique to create a scaffold for neural culture. This scaffold was biocompatible with the network development of these cells, facilitating observation using imaging techniques. The results showed a scaffold that supported the growth, differentiation, and alignment of the cells while being capable of assessing the individual cells optically  (PLGA). Results showed reduced cell proliferation and significant acceleration in neuronal differentiation when compared to 2D cultures. In addition, the 3D culture demonstrated higher levels of pathogenic amyloid-beta 42 and phospho-tau in cultures where neurons carried familial AD mutations when compared to heathy neurons  and alginate-polyvinyl alcohol (AL-PVA). The results proved that the scaffolds were tunable, with a similar stiffness to brain tissue. The presence of HA enhanced the neuronal growth of iPSC-derived neurons . The model was capable of replicating midbrain-type dopamine neuron degeneration, aggregation of pathologic α-synuclein, mitochondrial and lysosomal dysfunctions, and increase of intrinsic neuronal firing frequency .These organoids are used in several types of studies such as fetal development, cell-cell interactions, and/or drug screening . Bagley igration . Raja etmalities . Alternaed to AD . Wulansaet al. embedded endothelial cells from patients on 34-day older iPSC organoids with Matrigel®. These organoids showed robust vascularization ether during the 3–5 weeks of in vitro culture and 2 weeks after transplantation on mice  corticostriatal network. They use the chambers and channels to create a presynaptic, synaptic, and postsynaptic region. Results showed dysfunctions and hypersynchrony on HD devices , enabling the study of the axonal growth, signal propagation, and cell-cell interaction . These t devices . Li et a devices . The aut devices . Roach echannels . The chichannels . Sala-Jainjuries . Motor ninjuries . Anotherinjuries . Habertsartments . In thisartments .et al. used 3 channels where brain endothelial cells were cultured on the middle one for blood-brain barrier modeling . A possin vivo. Vatine et al. used a BBB-on-a-chip with co-culture of neural and endothelial cells, and perfusion which enhanced the performance of the barrier, exhibiting physiologically relevant TEER values  with four different cell types  for bioink(s) with different types of cells. The structural material of the device can be cured in advance, so cells are not harmed during the curing process. A more accurate and controlled positioning of the cells within the model would be beneficial, especially when taking into consideration the different placement of neural cells within the brain. Considering the brain has zones with different percentages of various cell types and that most models do not use multi-cell type culture and the spatial distribution is aleatory, bioprinting arises as a promising tool to help build an accurate brain model, with co-culture but also with the disposition of cells corresponding to the different brain regions. Johnson ic model . Yi et aplatform . Kajtez jections ..We can find some examples of organoids’ growth inside microfluidic devices to improve their perfusion and vascularization procedures. The incorporation of better characterization techniques or sensors is also being studied and integrated into recent models. Combining bioprinting with the microfabrication process of organs-on-chips can be a way to improve the mechanical properties as well as the porosity, microstructure, and even the polymerization mechanisms of the material chosen, for example, a hydrogel. This combination allows control over the composition of the device to better mimic the tissue. It is also possible to introduce growth factors, cytokines, or other molecules in the bioink and place them directly where it is desired. Printing cells into these devices would decrease the time and complexity of cell seeding in a chip. The possibility of creating a vascular network is another attractive option .in vitro modeling and their combination with 3D models is on the edge of the research.Neurodegenerative diseases affect a high percentage of elderly people around the world and due to the increasing in the age of the population, the number is estimated to advance rapidly. When animal models and traditional 2D culture are not enough to answer how to treat these conditions, more elaborate models are needed, recognizing the importance of tissue architecture and the use of more specific cells. It is critical to correctly mimic the communication that undergoes between all the different cell types present in the brain, not only in the case of a healthy situation but also in the case of a disease. To do so, induced pluripotent stem cells arise as an accurate choice since they are obtained from the patient and can create all the cell types from the three germ layers. This type of cell is then considered the future of de novo living tissue constructs which can be made to change in various mechanical or physio-spatial aspects when subjected to predetermined stimuli or trigger sources. 4D bioprinting techniques can be used to place both living cells and growth factors in highly ordered, biomimetic motifs which can undergo physiologically relevant transformations to accurately simulate developmental processes, such as tissue stretching, compression, or shifting of the biomaterial’s modulus. The fourth dimension in “4D bioprinting” refers to the time element on which one or more of a 3D printed object’s physical attributes are functionally dependent (However, neurodegenerative cell culture models still face some challenges. The time of culture is one of them when considering iPSC. The model must guarantee cell survival/viability and function for as long as the experiment is intended. Most times the model looks very promising but then the cells are only alive for a few hours or days which does not allow the mimicking of long-term diseases or their effects . Other cependent . New tecin vitro model for neurodegenerative disease needs still to be found but combined efforts of all these years have shown that we are going in the right direction and that there is still hope to find a more accurate model to better understand these diseases, how and when they appear, how they evolve and maybe, help in finding a better drug to treat them. Learning from every downside of the existing models is crucial to tune the right direction. The perfect scenario is one where we can combine all their advantages to include the self-organization of the cells occurring in organoids with the precise control over cell seeding and scaffolds that bioprinting allows or the compartmentalization combined with the possibility of recreating mechanical forces that microfluidic devices possess.The perfect


Environmental wind tunnels for high-speed trains play a significant role in their development. The cooling system of the wind tunnel poses a challenge as it requires lower temperatures and a higher cooling capacity during operation. The conventional approach to wind tunnel refrigeration uses evaporative cooling, which is less efficient at low temperatures and comes with environmental and safety risks. In this study, we propose an innovative air compression refrigeration method based on the Brayton cycle. This method converts high-pressure air into low-temperature air at atmospheric pressure for wind tunnel refrigeration. The new cooling system has reduced energy usage by 3.72 MW, leading to a 13.15% improvement. The return cooler of the system is modeled using the effective number of heat transfer units and the mean temperature difference design method. Additionally, the turbine within the system is analyzed using one-dimensional flow characteristic analysis and the principle of similarity. This method has been validated by comparing it to other published papers. Subsequently, we perform a thorough sensitivity analysis on the key design parameters of the system. We observe that with a sufficient heat transfer area of the recooler, the cooling efficiency of the system exhibits a gradual decline from 64% to 60% as the mass flow rate of the system rises. For a fixed turbine, the cooling efficiency of the system rises from 20% to 62% and subsequently declines to 37%, with an increase in the mass flow rate. As a result, we conclude that the design parameters of the turbine have a more significant influence on the cooling efficiency of the system than the recooler. Our study will establish a foundation for selecting parameters to optimize the refrigeration system in the future. Rail transport has become an indispensable part of transportation systems worldwide due to its speed and stability. In recent years, high-speed railway technology has matured and developed, elevating the importance of high-speed rail, particularly in countries like China, Japan, and Germany, where it was introduced earlier. However, the progress of technologies related to high-speed trains has been impeded by the lack of experimental facilities, specifically those that simulate the extreme temperature conditions experienced by these trains.Currently, only two laboratories, namely the Arsenal International Vehicle Test Station in Austria and the Kawasaki Heavy Industries Vehicle Environment Room in Japan, can conduct low-temperature experiments on trains. However, these facilities are unable to fully replicate the external conditions encountered by high-speed trains during operation, often restricting low-temperature experiments to natural outdoor conditions. This limitation significantly hampers the advancement of high-speed train technologies. Consequently, the need arises to establish a full-size high-speed railway train environmental wind tunnel to enhance the efficiency and capabilities of high-speed train experiments.An environmental wind tunnel for high-speed trains must accurately simulate various train operating conditions under natural circumstances. It should have the ability to replicate different temperatures and wind speeds. Given that the operation of the wind tunnel itself generates a substantial amount of heat, controlling its dissipation becomes crucial when simulating a high-temperature environment for experimentation. On the other hand, creating a low-temperature environment is challenging, especially when simulating the conditions of high-speed trains running at extremely low temperatures. In such cases, not only must the temperature within the experimental section of the wind tunnel be reduced to below −70 °C, but efficient heat management during operation is also essential. Therefore, selecting an appropriate wind tunnel cooling system becomes a vital aspect of designing and constructing an environmental wind tunnel for high-speed trains.Most existing environmental wind tunnels for high-speed trains utilize evaporative cooling. However, evaporative cooling has certain drawbacks, especially at low temperatures, where its efficiency is reduced. In some cases, auxiliary cooling methods, like liquid nitrogen are necessary to achieve lower ambient temperatures. Additionally, the evaporative refrigeration cycle requires the use of refrigerants, which not only pose safety and environmental pollution risks, but also increase equipment maintenance costs. On the other hand, the air compression–refrigeration cycle offers high reliability and ease of maintenance. In this experiment, normal dry air is used as the refrigerant, eliminating any possibility of environmental contamination. The application of air compression refrigeration has been increasingly used in ultra-low-temperature refrigeration and cooling, as well as in air conditioning, aircraft, automotive, and various other fields .3/h air compression system and explored methods to enhance its power [The air refrigeration system used in this study was designed by Normalair Garrett Limited, a British company. They have developed a high-speed railway train air conditioning system that employs a semi-open, two-stage compressed, and boosted air refrigeration system . A.J. Whts power . Recent In recent years, S. K. Park et al.  conducteRecoolers and water coolers are typically employed in air compression refrigeration systems to cool the high-temperature gas. Due to recirculation without any mass phase changes and the air’s low constant-pressure specific heat capacity, compact heat exchangers are preferred. Plate heat exchangers find broad application across multiple domains and systems owing to their compact structure; high heat transfer efficiency; flexible combination of heat transfer surfaces; and ease of installation, disassembly, and cleaning ,20,21. TThis paper aims to introduce a novel refrigeration system, namely the air compression refrigeration system, which differs from the conventional evaporative refrigeration system used in wind tunnels. The system pressurizes dry air into high-temperature, high-pressure air and utilizes the Brayton cycle to convert it into atmospheric low-pressure gas suitable for cooling the system. Subsequently, the low-temperature gas is directly discharged into the wind tunnel, where it mixes with the original gas to achieve cooling. In previous simulations of air compression refrigeration systems, studies on turbines failed to include the analysis of turbines with different sizes, resulting in difficulties in optimizing the system selection and elevating the probability of suboptimal optimization. To tackle this dilemma, we aim to utilize one-dimensional flow modeling and similarity principles in our study. We will establish a dynamic model of the constituents dependent on earlier research. Using this, a model of the air compression refrigeration system will be created. Through sensitivity analysis of the components, we identify the factors with the greatest influence on air compression refrigeration system efficiency, thereby paving the way for further research.Prior to diving into the design intricacies of the high-speed train environmental wind tunnel, it is crucial to contemplate the cooling requirements of the wind tunnel. The essential aspects can be understood from In the aerodynamic wind tunnel for high-speed trains, an air compression refrigeration system is employed, as shown in The air compression refrigeration system typically employs centrifugal compressors or screw compressors, both driven by electric motors or used in conjunction with turbines to enhance air pressure. The compression ratio, a vital parameter of the compressor, can be expressed as follows:The previous studies and calculations are based on conventional models of refrigeration systems. Whilst these models can determine the refrigeration performance of the system, they are not appropriate for a thorough examination of the system as the use of classical models is restricted.To examine the system comprehensively, it is necessary to model each individual component in detail. In an air compression refrigeration cycle, the primary components include a water cooler, recooler, compressor, and expander. A turbine is commonly employed as an expander in refrigeration systems. As the working process of the expander is analogous to that of the compressor, we concentrate on modeling the former to preclude redundancy. Water cooler and recooler are both types of heat exchangers. However, the primary purpose of the water cooler is to decrease the temperature of high-pressure and high-temperature gas, thereby maintaining system stability, and the heat exchange of the water cooler should be sufficient. In contrast, the design of the recooler is instrumental in enhancing refrigeration system efficiency. Thus, we modeled the recooler heat exchanger for analysis.The heat exchanger stands as the primary locus of thermal exchange within the ambient wind tunnel of a high-speed locomotive, epitomizing its indispensability in modeling the air cooling system of the evaporative cooling mechanism. This article endeavors to precisely simulate the heat transfer dynamics of the heat exchanger by meticulously selecting the optimum number of effective heat transfer units while also delineating the resistive characteristics inherent in a plate heat exchanger.Assuming a scenario wherein the fluid flows with impeccable conformity at the entrance of each flow channel within the heat exchanger, we further posit a symmetrical distribution of fluid temperature across the cross-sectional plane of every flow channel, disregarding the longitudinal conduction of heat within the interstitial bulkhead. Built upon this fundamental assumption, the heat transfer and resistance traits of each distinct set of flow channels in the heat exchanger remain uniform, permitting us to reduce the multidimensional flow within each channel to a unidirectional paradigm. In light of these considerations, we choose one of the aforementioned groups of flow channels for our comprehensive analysis.NTU) characterizes the heat transfer capacity of the heat exchanger and can be determined by the following equation:UA expresses the heat transfer per unit of average temperature difference within the heat exchanger. Based on the process of heat transfer within the heat exchanger, the calculation formula is as follows:R1 and R2 represent the plate fouling factor.Based on the law of conservation of energy, separate equations for the conservation of energy are established for the wall, hot fluid, and cold fluid, as follows:The formula for the heat transfer film coefficient can be written asThe resistance of a fluid in a heat exchanger is generally calculated using Euler’s number.The turbine expander is an air compression refrigeration system that utilizes the output of cold air and shaft work components. By introducing compressed air through the inlet into the turbine for expansion, both external work is performed and the air temperature decreases as the pressure decreases. The gas undergoes an approximately isentropic adiabatic expansion process in the turbine, with the expansion ratio Assuming that gas leakage occurring in the compressor is small and negligible, meaning that the mass flow rate of the imported and exported gas is conserved, the power required by the compressor can be calculated using the following equation:The efficiency of a turbine expander generally consists of three components: peripheral loss, friction loss, and leakage loss. The overall efficiency of the expander can be expressed ass method . At the The flow process of air from the compression refrigeration cycle into the working fluid in the turbine is illustrated in c0 represents the ideal expansion velocity of the work mass in a centripetal turbine in m/s, Δh represents the total enthalpy change during the operation of the expander.ax) and the degree of reaction (Ω) are two crucial design parameters that significantly impact turbine efficiency. The speed ratio refers to the previously mentioned parameter. Furthermore, there exists a limiting relationship between various turbine parameters, such as the speed ratio and the degree of inversion [For ease of characterization, the magnitude of the system’s velocity triangle is commonly expressed through the relative velocity:nversion .By utilizing these design parameters, it becomes possible to determine additional design parameters in the expander system, such as the impeller inlet cross-sectional area and the rotor inlet diameter. The rotor inlet diameter can be expressed asIn the determination of the relevant parameters, it is necessary to consider other design factors, such as the number of blades on the wind turbine. When optimizing these parameters, aiming for the simultaneous maximum peripheral efficiency of all parameters may lead to aerodynamic or structural design issues. Therefore, it is crucial to analyze and establish specific design parameters in advance. These include the static blade outlet Mach number, dynamic blade inlet impulse angle, and turbine rotational speed. By doing so, other design aspects can be appropriately constrained, ensuring the final selection of an aerodynamically, structurally, and reasonably sound scheme. This consideration guarantees the rationality of the chosen scheme in terms of aerodynamics, strength, and structure.ax) and the reaction degree (Ω) of the centripetal turbine are the two design parameters that exert the greatest influence on the turbine’s efficiency, with the speed ratio mentioned previously. Furthermore, there exists a certain limiting relationship between each parameter of the turbine, such as the speed ratio and the degree of inversion (Ω) [The literature reveals that the speed ratio (sion (Ω) ,29. The Similarity theory, initially applied in geometry, refers to situations where two figures possess similar geometric shapes but differ in geometric dimensions. In the context of turbines, similarity theory is primarily employed in two aspects. Firstly, during the design of a new turbine, the performance curve of the new machine is deduced from an existing turbine model. Secondly, when testing the machine, certain test conditions such as inlet conditions, mass, and speed might not align with the design conditions. In such cases, the aerodynamic performance of the machine must be correctly converted from the test conditions to the design conditions. The application of similarity principles relies on satisfying four similarity conditions: the deterministic criterion for characterizing viscous effects; the deterministic similarity criterion for characterizing compressibility; the deterministic similarity criterion for non-constant flow; meeting geometrical similarity;similarity of inlet velocity graphs; equality of the Mach number and Reynolds number;and equality of the gas constant entropy exponent k.To compare the size scale between different turbines, we introduce the gauge coefficients, which is defined as follows:k) is also equal for two similar turbines, it follows thatIn the case of turbines, the constant entropy energy head of the system is related by the following equation:A one-dimensional analysis model and a turbine similarity principle code have been developed in-house by the authors using Simulink in MATLAB 2018b. The calculation procedure is illustrated in Xia  conducteThis section comprises two main parts. Firstly, we conduct a thermodynamic analysis of the environmental wind tunnel refrigeration system designed for high-speed trains, which utilizes an air compression refrigeration system to ascertain its thermodynamic performance, and we compare our design to a conventional evaporative refrigeration system. Secondly, a sensitivity analysis was conducted for the key parameters in the refrigeration system using the established model. The main methodology involved determining the impact of different parameters on system performance via simulation numerical modeling.7 W. This is because during the actual working process, a certain amount of air needs to be extracted from the wind tunnel for the air compression refrigeration cycle. As a result, the airflow in the second half of the wind tunnel decreases since the real air flow becomes smaller. Due to the constant air density, this reduction in airflow leads to a decrease in the air velocity within the wind tunnel, thereby reducing flow losses. Consequently, the actual amount of cooling required to achieve the same experimental conditions (100 m/s wind speed and −70 °C) in the experimental section is lower compared to evaporative cooling.The impact of the air cooling cycle on the cooling efficiency of air compression refrigeration systems was studied under the assumption of demand conditions that are identical to those of evaporative cooling. In the experimental section of the train, the temperature was maintained at −70 °C, and the wind speed was set at 100 m/s. Previous field simulations indicated that the required cooling capacity for the application of air compression refrigeration is approximately 1.8 × 105 Pa was selected. Other main equipment parameters include an inlet compression ratio of 2.5, a cooling water temperature of 40 °C for the water cooler, and a source gas temperature of −40 °C for the return cooler. The return cooler is connected to the ambient wind tunnel and possesses a sufficiently large heat transfer area to ensure the adequate cooling of the compressed air. This process allows the final compressed air to exit the return cooler at the same temperature as the gas drawn from the wind tunnel.In addition to accounting for the cooling requirements of the wind tunnel, the quality of the external air also impacts the efficiency of the cooling cycle. Air compression cooling necessitates the compression of external air to obtain a cooling source. For the analysis, dry air with a temperature of 25 °C and a pressure of 1.0 × 10The mechanical energy generated by the turbine expander, through the work of air expansion, is fully utilized in the turbo compressor air compression system. The pressure–enthalpy and temperature–entropy diagrams for the system are shown in The operating conditions of the different components in the different air cooling systems were also obtained in At this stage, the air compression system energy flow diagram is shown in To compare the cooling effect of the air compression system, a conventional cascade refrigerating system was introduced, and the system schematic is shown in 7 W to achieve the same experimental conditions in the wind tunnel section.Assuming that the temperature in the experimental section of the train is still −70 °C and the wind speed is 100 m/s, the power loss of the system is reduced due to the air extraction required for air compression cooling. Additionally, the resistance in the wind tunnel increases as a result of the additional heat exchange network. Therefore, under the same conditions in the experimental section, the evaporative cooling cycle method needs to provide more cooling capacity in order to achieve the desired experimental environment. Simulation calculations indicate that the refrigeration system needs to deliver a cooling capacity of approximately 2.0 × 10With an external ambient temperature of 25 °C, an evaporator temperature of −70 °C, a condensing temperature of 40 °C, and an evaporator–condenser temperature difference of 5 °C, the midpoint temperature of the evaporative refrigeration cycle was determined to be −23 °C. By observing the characteristics, R23 was selected as the refrigerant for the low-temperature part of the refrigeration system, and R404A was chosen for the high-temperature part, allowing for the design of the experimental state at each point. Calculations are carried out using Solkane, and the completion of pressure enthalpy and temperature entropy diagrams are shown on The operating conditions of the various components in different evaporative refrigeration systems were obtained, as shown in At this point, an external energy input of approximately 32.1 MW is required to supply the refrigeration system (assuming a cooling efficiency of 4.5 for the water-cooled tower), resulting in an approximate system cooling efficiency COP of 0.624.The comparison reveals that, although the cooling efficiency of air compression refrigeration is not significantly different from evaporative refrigeration, the air compression system has a lesser impact on the cooling section of the wind tunnel. Consequently, it requires less energy for the refrigeration system under the same experimental conditions. Specifically, the air compression system necessitates approximately 3.72 MW less energy, resulting in an improvement of approximately 13.15% over the efficiency of the evaporative refrigeration system.A sensitivity analysis is conducted for the design parameters of the aforementioned components, encompassing two main areas. The first area involves studying the influence of various parameters of the refrigeration equipment on the system under specific external conditions. The second area explores the impact of different external conditions on the cooling sensitivity of the system once the refrigeration equipment has been selected.Heat exchanger:The initial step is to assess the effect of different design parameters on the heat transfer capacity of the system’s return cooler and the cooling efficiency of the air compression refrigeration system. For this particular case, a plate heat exchanger was chosen as the return cooler. Design parameters for the plate heat exchanger include total heat transfer area, single plate heat transfer area, plate configuration form, plate spacing, plate thickness, individual flow path cross-section, etc. While these design parameters are somewhat inter-related, the total heat transfer area of the heat exchanger exhibits the greatest relative impact on heat transfer performance. It serves as an essential indicator for evaluating the heat transfer capacity of the plate heat exchanger and can be derived from the product of the heat transfer area per single plate and the number of heat exchanger plates.Assuming that the other design parameters of the return cooler remain constant, and with inlet temperatures of 313 K and 228 K, respectively, for the hot and refrigeration sides of the return cooler, as well as inlet pressures of 350 Kpa and 110 Kpa, the sole parameter adjusted is the total heat transfer area of the heat exchanger . The relationship between the heat exchanger area and the outlet temperature of the return cooler is presented in Further analysis of the effect of changes in the heat transfer area of the return cooler on the cooling efficiency of the air compression refrigeration system assumes that the system is as described above, with unchanged parameters for the components. In this system, the compressor increases the atmospheric gas to 250 Kpa and subsequently recovers it to 110 Kpa through a turbine–compression system. The high-pressure gas then passes through a water cooler, reducing its temperature to 45 °C. A certain amount of gas is extracted through the wind tunnel and used as the cold source gas in the recooler, where the temperature of the cold source gas is −45 °C. It should be noted that if the system efficiency is below 0.2, the air compression refrigeration system will not function effectively. With variations in the heat exchanger area, the system experiences changes in heat transfer efficiency, as shown in However, once the heat transfer efficiency reaches its peak value, any further increase in heat transfer area results in a gradual decline in system cooling efficiency. Moreover, this decline is more pronounced with higher mass flow rates in the system. This phenomenon occurs because after reaching a certain limit of heat transfer efficiency, further increases in heat transfer area no longer reduce the outlet temperature of the system. Instead, they increase the resistance within the heat exchanger, leading to a drop in outlet pressure within the system. Consequently, the subsequent expander work is reduced, resulting in a decrease in air temperature and an increase in the refrigeration efficiency of the system.It can be concluded that for the air compression system, the heat transfer area of the return cooler should be appropriately increased to ensure a lower exit temperature. This is essential as reducing the exit temperature effectively improves the refrigeration efficiency of the system. Although excessive heat transfer area may have a certain impact on system efficiency, it is smaller compared to other factors. Therefore, during design selection, it is advisable to choose a heat exchanger with a larger heat transfer area as the system’s return cooler.During the operation of the environmental wind tunnel for high-speed trains, various operating conditions arise, necessitating the study of unified heat exchanger performance at different mass flow rates. Assuming the system’s design parameters remain constant, the hot side inlet temperature and pressure of the return cooler are 313 K and 350 Kpa, respectively, while the cooling side’s inlet temperature and pressure are 228 K and 110 Kpa, respectively. Only the heat exchanger’s mass flow rate is varied to observe how the outlet temperature and pressure change with different heat transfer areas. As depicted in Further analysis of the effect of using different heat transfer areas on the cooling efficiency of air compression refrigeration systems with variations in mass flow rates is presented. This study assumes that the remaining components of the system operate at constant efficiency. In this system, the compressor increases the atmospheric gas pressure to 250 KPa, and then the turbine–compressor component restores the system pressure to 110 Kpa. The high-pressure gas passes through a water cooler, reducing its temperature to 45 °C. A certain amount of gas is extracted through the wind tunnel and used as the source gas in the recooler, where the temperature of the source gas is −45 °C. It is assumed that when the system efficiency falls below 0.2, the air compression refrigeration system cannot function effectively.With changes in the heat exchanger area, the system’s heat transfer efficiency varies, as shown in While the cooling efficiency of the system is an important parameter, more attention is typically given to the cooling capacity of the air compression system. The cooling capacity is determined by the difference between the inlet and outlet temperatures and the mass flow rate of air in the system. If the mass flow rate of the system changes significantly while the two parameters of the system gas remain largely unchanged, the cooling capacity can be affected. In this study, it was observed that an increase in the mass flow rate of the system leads to an increase in the outlet temperature of the return cooler, subsequently raising the temperature of the system’s cold air. As a result, the cooling efficiency of the system decreases, directly affecting its cooling capacity. Moreover, when the mass flow rate exceeds a certain range, the refrigeration efficiency of the air compression system falls below 0.2, rendering the entire refrigeration system incapable of delivering a qualified cooling source for the high-speed train’s environmental wind tunnel. When the mass flow rate in the system is low, the cooling capacity increases proportionally with the mass flow rate, and the growth rate remains relatively consistent. The heat exchanger with a larger heat transfer area exhibits only slightly lower efficiency than the heat exchanger with a smaller heat transfer area. However, as the mass flow rate increases, it becomes evident that insufficient heat exchange area in the system causes the cooling capacity to decrease instead of increasing.From the above analysis, it can be concluded that the heat transfer area of the return cooler has a significant impact on both the cooling efficiency and cooling capacity of the system. However, this impact is mainly notable when the heat transfer area of the return cooler fails to meet the system’s mass flow rate requirements. At this point, an increase in the mass flow rate leads to a rapid decline in the system’s cooling efficiency, while the cooling capacity remains unchanged. Conversely, when the heat transfer area of the return cooler is sufficiently large, the system’s refrigeration efficiency only experiences a gradual decline. Therefore, during system design and selection, it is advisable to choose a heat exchanger with a slightly larger heat transfer area based on the system’s requirements. This decision facilitates the improvement of the system’s efficiency. Although increasing the heat transfer area may raise the construction cost to some extent, as indicated by the previous analysis, the overall impact on the system’s economy is insignificant compared to operational and maintenance costs. Hence, it can be assumed that when the heat transfer area of the recuperator in the system is adequately large, any subsequent changes in heat transfer area or mass flow rate will have minimal impact on the system’s economic performance. Consequently, the change in heat transfer area can be considered to have a low sensitivity.2.Turbine ExpanderThe sensitivity of turbine expander design parameters has been extensively studied in previous articles ,31. ThesIn our study, an expander with an applicable mass flow rate of approximately 360 kg/s and a design inversion ranging from 0.3 to 0.8 was selected. It should be noted that the original expander might not meet the requirements of the wind tunnel for high-speed train environments. Hence, based on similar principles, three times the amplification parameters were set and used to select the final compressor. We acknowledge that using multiple compressors in parallel could achieve the same effect, but we do not investigate this approach in this paper.2, with an outlet temperature consistently maintained at −40 °C. The outlet pressure follows the aforementioned model law. The expander’s efficiency under these conditions is illustrated in The changes in turbine performance, system performance, and cooling capacity at different air mass flow rates, while keeping the degree of reactivity constant, are further investigated. The remaining settings of the turbine model remain the same as before. The simulation results are shown in The performance of air compression refrigeration systems follows similar to turbine operation, with an initial increase in efficiency followed by a decrease. However, the rate of change in the system is much greater than the rate of change in the turbine itself, as explained in the previous section. Comparing In the daily selection process, it is necessary to match the equipment’s scale factor with the operating mass flow rate so that the turbine system can operate at the appropriate mass flow rate. An expander with a rated flow rate of 360 kg/s was selected as a benchmark. In order to study the impact of different magnitude coefficients on the expander’s performance, 330 kg/s and 390 kg/s expanders were built using similar principles for comparison. For the entire air compression refrigeration system, Regarding the relationship between the turbine’s gauge coefficients (rated flow) and the maximum cooling capacity of the system, This analysis demonstrates that the scale factor and degree of inversion of the turbine have a significant influence on the system’s operating efficiency and cooling capacity. These design parameters are highly sensitive in air compression refrigeration systems and should be considered as important optimization parameters in subsequent parameter optimization processes. Additionally, since the compressor and turbine belong to the same rotating machinery and operate based on similar principles, the design parameters of the compressor in the air compression refrigeration system are equally important. The compressor plays a crucial role in increasing the gas pressure and is considered to be no less significant than the turbine in the system. Thus, the design parameters of the compressor should be an important target for subsequent optimization.Based on these results, the design parameters identified as highly sensitive in air compression refrigeration systems include the inverse motion and magnitude coefficients of the compressor and turbine. Conversely, the system exhibits relatively low sensitivity parameters for the design parameters of the heat exchanger.Through the evaluation of two refrigeration cycles, it was found that the evaporative refrigeration cycle has a refrigeration efficiency of about 0.624 and requires approximately 32.1 MW of external energy for operation. On the other hand, the air compression refrigeration system has a COP refrigeration efficiency of about 0.636 and needs 28.3 MW of energy to function. The comparison confirms the air compression system’s superiority in energy efficiency, requiring 3.72 MW less energy from external sources compared to the evaporative refrigeration system. The air compression system consumes 3.72 MW less external energy to operate compared to the evaporative refrigeration system, with a 13.15% improvement in efficiency.A dynamic model of the heat exchanger was developed using the heat transfer effectiveness approach. This model enables the calculation of heat transfer efficiency based on outlet temperature, pressure, and the design dimensions and operating parameters of the heat exchanger. The modeled heat exchanger was employed as a return heat exchanger in the refrigeration system. The study revealed that an increase in the heat transfer area of the return cooler led to a decrease in outlet temperature while maintaining a constant mass flow rate. However, beyond a certain threshold, the temperature of the heat exchanger remained largely unchanged. Additionally, the outlet pressure of the recuperator gradually decreased as both the heat transfer area and mass flow rate of air passing through it increased.One-dimensional flow characteristics analysis and the similarity principle were applied in the modeling of rotating machinery, such as turbines and compressors. The operational efficiency of these machines was initially established by analyzing the one-dimensional flow of the model. This involved utilizing velocity triangle and energy loss models to study turbine operation under different dimensional and operational parameters. To ensure rational aerodynamic and structural changes during the system optimization process, the concept of dimensional coefficients was introduced based on the principle of similarity. The study of turbine parameters focused on the turbine reaction degree and magnitude coefficient as key design optimization indicators. It was found that optimal values for both the turbine reaction degree and magnitude coefficient existed under specific operating conditions.System sensitivity analysis was conducted for various design parameters. The influence of the heat transfer area of the return cooler on the cooling efficiency and capacity of the system was only significant when it failed to meet the heat transfer requirements of the system. Once the heat transfer area reached a sufficient size, further increases had a limited impact on the cooling efficiency. In the case of turbines and compressors, variations in the reactance and magnitude coefficients of the equipment had a substantial impact on the cooling efficiency and capacity of the system. Moreover, there were optimal reactance and magnitude coefficients for different cooling demands, making them highly sensitive design parameters that should be prioritized in subsequent optimization studies.This paper proposes a novel cooling solution for high-speed train wind tunnels as an alternative to previous methods. The proposed solution utilizes an air compression cycle based on the Brayton cycle. It generates dry air at ambient temperature and pressure, which is then injected directly into the wind tunnel to serve as a cooling source instead of employing traditional evaporative cooling methods. Due to the complexity of the new system, dynamic modeling of the refrigeration equipment and sensitivity analysis of key design parameters were conducted to facilitate subsequent equipment selection optimization. The key findings are as follows:


Intrusion detection systems (IDS) play a crucial role in securing networks and identifying malicious activity. This is a critical problem in cyber security. In recent years, metaheuristic optimization algorithms and deep learning techniques have been applied to IDS to improve their accuracy and efficiency. Generally, optimization algorithms can be used to boost the performance of IDS models. Deep learning methods, such as convolutional neural networks, have also been used to improve the ability of IDS to detect and classify intrusions. In this paper, we propose a new IDS model based on the combination of deep learning and optimization methods. First, a feature extraction method based on CNNs is developed. Then, a new feature selection method is used based on a modified version of Growth Optimizer (GO), called MGO. We use the Whale Optimization Algorithm (WOA) to boost the search process of the GO. Extensive evaluation and comparisons have been conducted to assess the quality of the suggested method using public datasets of cloud and Internet of Things (IoT) environments. The applied techniques have shown promising results in identifying previously unknown attacks with high accuracy rates. The MGO performed better than several previous methods in all experimental comparisons. The need to secure online data, information, and related systems has grown in importance with the development of information communication, particularly the Internet. Sensitive data is created, transported, stored, or updated worldwide daily in enormous quantities. Private emails, financial transactions, simple holiday photos, and military communications are all examples of sensitive information. Malicious parties have sought to steal, alter, or erase this information for a long time. Hackers and other hostile actors have developed, exploited, and enhanced various cyberattacks to accomplish these objectives . A paradIn recent years, a new direction was utilized for the IDS by employing the power of the metaheuristic optimization algorithms adopted in different and complex engineering and optimization problems, including IDS. For example, Alazab et al.  employedSuggest a different method for securing IoT by combining DL and feature selection techniques.Use a CNN model to analyze network traffic records and identify complex feature representations.Create a modified version of Growth Optimizer (GO) for improved intrusion detection in IoT environments. The modification uses the operators of the Whale Optimization Algorithm (WOA). The proposed method, called MGO, is employed to address the issue of discrete feature selection.Evaluate the performance of the MGO against established methods using four actual intrusion datasets.Following the successful applications of MH optimization algorithms in IDS, we propose an efficient feature selection technique called MGO. This method is developed based on two aspects; the first is to utilize the power of the Growth Optimizer (GO) in the exploration phase of the search process. The second aspect is to employ the integration between GO and WOA in the exploitation phase. The main objective of this study can be simplified as the following points:The paper is structured as follows: In this section, the Growth Optimizer (GO) simulates how people train and reflect as they progress in society. In the learning phase, the information is collected from the environment, whereas the reflection aims to examine the shortcomings and improve the learning method.X which stands for the solutions for the tested problem.r is the random value and the limits of the search domain of the problem are represented using U and L. N refers to the total number of solutions in X.In general, the GO starts by using Equation  to generX is divided into three parts according to the parameters named N), whereas the best solution is the leader of the upper level.Following , X is diBy confronting disparities between people, examining the causes of those differences, and learning from them, individuals can be greatly helped in their progress. The GO’s learning stage simulates four key gaps that are formulated:X and the growth of Following , the indAccording to the information collected from After that, the solution X using the fitness value.The quality of the updated version of X must identify all of their areas of weakness, make up for them, and retain their information. They ought to adopt the undesirable attributes of successful X while retaining their outstanding qualities. When the lesson of a specific aspect cannot be mended, the prior information should be abandoned and systematic learning should resume. Equations   X must iAfter the complete reflection stage, Equation  is also a denotes a parameter that decreases from 2 to 0 during the process of updating the solution, formulated as The WOA  draws inEquation :(10)Disia in Equation X and andXb utquations  21]; th; thXb utanced as:X(t+1)=XbEquation  [21]:.i, whereas n and d are sample and feature numbers, respectively. The normalized version of The developed MGO starts by preparing the IoT dataset by normalizing it. This is performed using the min−max technique that applied to the IoT data DS , which iConvolutional neural networks (CNNs) are widely used in computer vision as they are robust feature extraction modules, especially when employing pre-trained models alongside transfer learning methods. Meanwhile, CNN is also used in applications where the data are one-dimensional such as in natural language processing. Our study aims to train a DL model that benefits from the big data generated from IoT devices to perform network intrusion detection and reduce processing complexity and inference time. Thus, this section proposes a light CNN model to automatically learn helpful patterns/representations rather than relying on the raw data collected from experimental and real network intrusion detection experiments. In addition, we extract the learned features for further processing (feature selection) to improve the overall framework performance (detection accuracy) and reduce the dimensionality space of the represented feature to accelerate the inference process.X, where each row is a one-dimensional raw sample representing a network traffic record which includes several network attributes (columns) related to the possible attack class, such as flags related to the IP address, TCP flags, destination, source information, type of service, communication protocols, and protection protocols. The CNN architecture, as shown in k, activation function, and pooling operation. Each ConvBlock uses a kernel of size t where i is the input channel. A non-linear activation function name rectified linear unit (ReLU) after each convolution operation is followed by a max-pooling with size two to output the final feature maps.The proposed CNN architecture receives a set of samples, We developed an alternative FS approach based on a modified version of GO algorithm using WOA as given in X is built as given in Equation Xi=The next step is to generate the Boolean version of Thereafter, we determined the best solution quations –15). Pr. PrXb anThen, the stop condition is checked and in case they are met, the update process is stopped. Otherwise, we repeat it again. After that, N, (2) the dimension of features D, and (3) the number of iterations The time complexity of the developed MGO as FS method depends on some factors such as (1) the size of population The section uses a set of experimental series to assess the developed IoT security based on a modified version of the GO algorithm using WOA. These experimental series are implemented using a set of real-world IoT datasets.The effectiveness of the suggested technique and all comparing methodologies is evaluated using several indicators.Average accuracy  and the training was performed using Nvidia GTX 1080.In our experiments, we trained the CNN model on each dataset record for 100 epochs with early stopping and Adam with a 0.005 learning rate to update the network parameters. A batch of size 2024 is used to iterate over the data samples. In addition, batch normalization and dropout with a 0.38 ratio were used as regularization techniques to prevent overfitting, increase generalization, and accelerate the training. The network hyper-parameters were selected based on several experiments with different setups where the best hyper-parameters were used in our experiments that maximize the detection accuracy. The CNN was developed using Pytorch framework . However, it falls behind the RSA regarding performance on the BIoT dataset. Furthermore, MGO excels in detecting attack types using testing samples on all four datasets compared to other methods.In addition, the accuracy value of RSA is better than other methods, followed by MFO which allocates the third rank overall to the other methods during the training stage, whereas the Accuracy of RSA based on the testing set is the best after the developed MGO algorithm. Based on Precision, F1-Measure, and Recall, the FFA, GWO, and MFO is the best algorithm that allocates the second rank among the tested algorithms within the testing phase.To further analyze the results, we applied the Friedman test to determine if there was a significant difference between the different methods. The Friedman test gave us the mean rank for each method, as seen in This section compares the results of the developed MGO with other techniques as given in From the previous results, it is clear that the developed method has a high potential to improve the prediction of attacks in IoT environments. However, the method has some limitations, such as being time-consuming due to the model learning process. These limitations can be addressed by using transfer learning techniques. In addition, the MGO still requires handling the imbalanced datasets in IoT and this can be handled by using the mechanism mentioned in .Our study investigated the development of a two-phase framework to improve the detection accuracy over existing intrusion detection systems (IDS). In addition, the developed framework integrates a deep learning (DL) model and swarm intelligence (SI) technique to combine both techniques’ advantages and facilitate the deployment of the framework in the Internet of Things (IoT) system. We implemented a convolutional neural network architecture as a core feature extraction module to learn and extract new feature representation from the raw input data (network traffic records). In addition, we proposed a novel feature selection (FS) approach based on a modified variant of the Growth Optimizer (GO) algorithm to reduce the extracted feature representation space, speed up the inference, and improve the overall framework performance on IDS. The proposed FS method relies on applying the GWO to boost the search process of the traditional GO algorithm. Thus, the results show that the suggested method performed best compared to several optimization techniques using different evaluation indicators with several public IDS datasets. In future work, the developed MGO can be extended and experimented with in different applications such as healthcare, human activity recognition, fake news detection, and others.