An ever-growing population together with globally depleting water resources pose immense stresses for water supply systems. Desalination technologies can reduce these stresses by generating fresh water from saline water sources. Reverse osmosis (RO), as the industry leading desalination technology, typically involves a complex network of membrane modules that separate unwanted particles from water. The optimal design and operation of these complex RO systems can be computationally expensive. In this work, we present a modeling and optimization strategy for addressing the optimal operation of an industrial-scale RO plant. We employ a feed-forward artificial neural network (ANN) surrogate modeling representation with rectified linear units as activation functions to capture the membrane behavior accurately. Several ANN set-ups and surrogate models are presented and evaluated, based on collected data from the H Due to a worldwide growing population the demand for water is ever-increasing, leading to global water scarcity that is not only driven by water quantity, but also by water quality issues . The watSuch stresses imply that conventional water sources are no longer sufficient to meet human water demands, especially in water-scarce regions . Desalinsh water . Neverthsh water ,14.The operation and design of brackish water reverse osmosis (BWRO) desalination plants has already been studied in the literature . Ruiz-GaGenerally, the optimization of RO systems requires modeling the mass transfer of the employed membrane modules , which mANNs have already been applied to RO membranes and processes to analyze their respective performance ,33,34,35Furthermore, ANNs can be used for the operation optimization of membrane-based desalination systems, enabling effective decision making and better design ,44. MadaTo generate sustainable solutions for RO systems it is vital to consider the system implications to resources other than water, including energy and food, referred to as food-energy-water nexus (FEWN) ,50,51,52In this work, we employ a feed-forward ANN to approximate the energy consumption of an industrial-scale RO plant. We use hourly measured industrial-scale RO plant data, representing one and a half years of plant operation, to train and analyze several ANN set-ups and other surrogate models to comprehensively describe the complete plant behavior. A multi-objective optimization study is employed, where sustainability considerations are systematically taken into account to evaluate the trade-off between permeate production and energy consumption.The remainder of this paper is structured as follows: The next section introduces the RO desalination plant under investigation, describes the obtained data and states the problem definition. In The San Antonio Water System (SAWS) H 1300mgL . The inoMeasurements of all marked points for one and a half years in an hourly frequency were available (beginning of 2017 to mid 2018), resulting in 14,542 data points for each parameter. In January 2017, the HThe concentration of the permeate flows of each stage, the permeate flow of the primary RO train and the overall permeate concentration have been measured. The first and second stage consist of four parallel RO flows, whereas the third stage consists of two parallel flows. For each respective parallel RO flow separate pressure measurements are available. Taking into account the number of membranes, columns and pressure vessels incorporated in each stage, this results roughly in a “4-2-1” RO separation set-up. Only data regarding the feed flow of the system has been collected. It is important to note that the pressure measurement of the retentate in the third stage is taking place after the energy recovery device. Also, the retentate flow of the first stage is the feed flow of the second stage.  as such , thus thThus, in this work, we present a framework for the optimal operation of industrial-scale RO desalination plants, using plant data to derive optimized process parameter results, with the goal of minimizing the overall energy consumption of the RO system. To do so, the obtained data is analyzed to derive linear surrogate models for all possible process parameters. If the dependencies between parameters are non-linear, ANNs with rectified linear units (ReLU) as activation functions are introduced to capture the observed behavior adequately. Then, the derived surrogate models can be reformulated as one mixed-integer linear programming (MILP) problem to optimize the energy consumption of the plant. The optimization model can further be modified to obtain Pareto-optimal solutions regarding the energy consumption minimization and the output water maximization. This work further includes a comparison of various ANN set-ups with changing inputs and outputs for calculating the overall permeate concentration of the plant. Depending on the research task at hand, researchers can choose models most tailored to their application.Using the phenomenological transport equations for RO membranes to derive operational desalination plant parameters, results in a complex system of equations, which is computationally expensive to solve, especially within the context of optimization ,25. To oi and parallel flow j a linear correlation based on the respective feed pressure has been assumed, as summarized in Equation Pr,iThe results of the approximation are summarized in Regarding stage 3, it can be seen that even with a relatively good root mean square error of e system . Thereforessures . FurtherSince it may be desired to not only approximate the pressure difference across the ERD, but also the outlet pressure of the ERD, another ANN with ReLUs as activation functions is utilized to to so. The most advantageous result was obtained when the ANN was only trained with the data set of parallel flow one, resulting in To ensure that the RO plant operates adequately in terms of water consumption, the water recoveries have been estimated for each stage based on the actual pressure measured  assumed , as intrThis approach results in water recovery estimations based on the obtained data as specified in Since surrogate models for the retentate pressures and energy recovery have already been defined, the only variable left to compute is the overall permeate concentration. To do so, again an ANN with ReLUs as activation functions is employed. Further, instead of simply using defined inputs as the sole inputs of the ANN , with aIt is important to mention that also a multivariate linear regression was employed instead of using an ANN, for the same inputs and outputs as the selected surrogate, which resulted in less accurate approximations (R = 0.848), as can be seen in To calculate the overall RO plant energy consumption, the specific energy consumption (SEC) of a single pump, according to Equation , is applHere, e η2=0.8 . The remEquation :(6)SEC*=t and To incorporate the ANN to calculate the pressure difference across the ERD and the ANN to approximate the permeate concentration in the optimization model, both ANN models are reformulated as MILPs, as presented in ,70. Thert. Then, the water recoveries of each stage based on the presented linear pressure correlations  of the Next, the optimization model is used for two studies. First of all, the energy of the system is minimized for distinct water recovery and feed flow restrictions to enable a comparison of generated results with the actual energy consumption of the desalination plant. Then, the trade-off between energy minimization and water utilization in terms of the feed flow and the overall water recovery are visualized in a Pareto front. To do so, the mization .To evaluate the optimization model, the overall energy consumption of the RO plant for the year 2017 is compared to the energy optimization results. Therefore, the data set for the year 2017 for the feed flow and the estimated water recovery of the system are split into 12 sets according to the month of the year. Then, the monthly average of the feed flow and the overall water recovery are calculated. The obtained values are summarized in The results of this case study are presented in Originally, the obtained monthly energy consumptions were planned to be compared to literature values. However, the energy consumption of RO plants is reported in the literature normalized with the permeate output in After confirming the capabilities of the optimization model, the model was expanded to consider multiple objectives using multi-objective optimization. To do so, the constraints summarized in Equations (8) and (9) are updated to calculate the minimized energy for a set of water recoveries and feed flows. Compared to the previous study, where a pair of water recovery and feed flow were the inputs, an array of water recoveries and feed flows are used as inputs, performing the minimization at each feed flow for all specified water recoveries. The multi-objective optimization has only been performed in the RO plant’s relevant water recovery region of 40% to 85%. The obtained Pareto-optimal solutions are visualized in To further analyze the results, the specific energy consumption in As expected, the specific energy consumption increases with the water recovery of the system. Increasing the water recovery by a factor of 1.7 from 50% to 85% results in approximately doubling the specific energy consumption (factor of 2.07), from d 3kWhm3 . In addiWe presented a comprehensive methodology to utilize industrial scale RO desalination plant data for surrogate modeling and subsequent optimization. Linear surrogate models were developed for the retentate pressures of the parallel flows of the first and second stage. For the pressure difference across the ERD, as well as the output pressure of the ERD, ANNs with ReLUs were trained and reformulated as MILPs with the feed pressure of stage three as the sole input. To calculate the permeate concentration of the system several possible ANN formulations have been presented and compared in terms of their respective inputs and accuracy (R). Depending on the task at hand different surrogate models can be selected. For the subsequent optimization case studies the simplest ANN system resulted in sufficiently accurate results and was therefore reformulated as a MILP.The optimization model was first used for minimizing the energy consumption of the RO plant based on the monthly averaged feed flows and water recoveries of the year 2017. The derived energy consumption constituted at most 51% of the monthly overall RO desalination energy consumption and on average 40% throughout the year, underlining the major energy saving potential using the presented methodology. In fact, the derived energy consumption is on the same order of magnitude as the energy consumption of the high service pumps of the RO plant.Then, the model was used to derive a Pareto front to illustrate the trade-offs between minimizing the energy of the system as well as maximizing the feed flow and water recovery of the system. Here, the potential for indirect savings was uncovered by comparing the increase in energy consumption for the lowest and highest utilized feed flows. The results were further analyzed in terms of the specific energy consumption of the system, which ultimately showed the significant impact of changing water recoveries on the specific energy consumption.Overall, the presented methodology should be understood as a recipe for other researchers on how to move from obtained RO data sets to an optimization model for single and multi-objective optimization using linear data driven surrogate models. In subsequent work, the objective function can be re-evaluated by introducing a nonlinear function and compare obtained results to the linear case. Moreover, if other data points across the plant can be obtained, i.e., the permeate flows or water recoveries for each stage, the accuracy of the surrogates can be further validated and if necessary modified.


This study develops an engineering prediction model for stress relaxation of polymer composites, allowing the prediction of stress relaxation behaviour under a constant strain, over a range of temperatures. The model is based on the basic assumption that in the stress relaxation process the reversible strain is transformed to irreversible strain continuously. A strain-hardening model is proposed to incorporate nonlinear elastic behaviour, and a creep rate model is used to describe the irreversible deformation in the process. By using stress relaxation data at different temperatures, under different strains, the dependence on temperature and initial strain of the model parameters can be established. The effectiveness of the proposed model is verified and validated using three polymer composite materials. The performance of the model is compared with three commonly used stress relaxation models such as the parallel Maxwell and Prony series models. To ease the use of the proposed model in realistic structural problems, a user subroutine is developed, and the stress relaxation of a plate structure example is demonstrated. Stress relaxation is a common phenomenon in polymer composite materials and the rate of relaxation can be affected by time, temperature, initial strain, environment, and so on ,2,3,4. IIn the stress relaxation process, stress decreases under constant strain. The total strain The constant strain condition yields a zero variation of the total strain with respect to time, i.e., Using the linear viscoelastic assumption, the reversible and irreversible strain rate components in Equation (2) can be expressed as n series . The parn series  and the n series  in EquatWW model . It shouWW model ,13,14.(In Equation (3), variants  have beevariants , glass fvariants , siliconvariants , reclaimvariants , plain wvariants , open-cevariants , and so In the Maxwell model and its variants, the irreversible strain behaviour is described using a purely viscous damper. By modelling the irreversible strain rate component based on different irreversible deformation mechanisms, the corresponding stress relaxation equations can be obtained. In Ref. , the plaIn the aforementioned studies, the reversible strain component was usually treated as the linear elastic strain. However, laboratory data show that the nonlinear elastic behaviour is very common in many polymer composites ,29. The In view of this, the goal of this study is to develop a new stress relaxation model by incorporating the nonlinear elastic effect into the stress relaxation process. The novelty of the model is twofold. For one thing, it can reduce the potential linear approximation error in those commonly used models which are based on linear viscoelasticity. For another, the model can eliminate the need for tuning the number of Maxwell elements in existing parallel Maxwell and Prony series models to meet the required fitting accuracy. To this end, a strain-hardening model is adopted, allowing for taking the nonlinear effect into the reversible strain component. A general creep rate model is employed to describe creep deformation. By integration the two components and incorporating the temperature effect into the model parameters, a new stress relaxation prediction model at multiple temperatures is formulated.The remainder of this study is as follows. First, the detailed construction of the proposed stress relaxation model is presented. Next, the effectiveness of the proposed model is verified using testing data on polymer glass and is subsequently validated using testing data on FM-73 adhesive. The performance the proposed model is further compared with three commonly used stress relaxation models using testing data on HDPE. Following that, a user material subroutine of the proposed model is developed for the ANSYS finite element package, allowing for applying the proposed stress relaxation model to practical engineering problems with parts having irregular shapes. The stress relaxation of a plate-like structure is demonstrated.In this study, the total strain in the stress relaxation process of polymer composite material is assumed to be a superposition of the three main components,The nonlinear elastic effect is taken into account using the strain-hardening model and is detailed below.erred to . It can Based on the results shown in Knowing that For a small time, variation In continuous form, Equation (11) writesIt is noted that the actual form of A phenomenological full-stage creep rate model previously developed for polymer bonded composite material is adopted in this study . For the in Ref.  and is oSubstituting Equation (13) into Equation (12), the stress relaxation rate model is obtained as,Given the initial stress of In addition, the stress and hardening rate are non-negative in the physical process of stress relaxation, i.e.,The constraint in Equation (17) is incorporated into Equation (16) using the Heaviside function to obtain the final form of the stress relaxation equation asAs the strain-hardening rate is independent of creep or other deformation mechanisms, the model parameters in Equation (18) are estimated sequentially. First, the strain-hardening rate vs. stress results are acquired using stress–strain testing data, and the model parameters Testing data on three different polymer composites are used for the purpose of verification, validation, and performance comparisons of Equation (18). The sources of the data used are listed in It should be noted that the stress relaxation mechanisms of polymer composites can be divided into physical relaxation caused by macromolecular chain movement and chemical relaxation resulting from the breakdown of covalent bonds ,33. In hTo quantify the performance of each model, the residual sum of squares (RSS) error and the root mean square error (RMSE) defined below are used.Data acquired by a 3-point bending test on polymer glass material reported in Ref.  are usedAccording to the stress–strain testing data of polymer glass reported in Ref. , the str in Ref. . The varThe nonlinear least squares estimator is used to obtain the creep parameters for the six sets of creep testing data shown in It is shown by the results that the parameters −6, respectively.Substituting the temperature-dependent strain-hardening model Equation (23) and creep model Equation (25) into stress relaxation model Equation (18), an intermediate stress relaxation model is obtained as follows.Using the intermediate stress relaxation model Equation (26) to fit the stress relaxation data at different temperatures shown in The model prediction results using Equation (26) are compared with the stress relaxation testing data, as shown in The performance of the model is further validated using testing data of FM-73 adhesive. The creep data in Ref.  and streUsing the data in With Equation (28), the equation of Substituting Equation (29) into the stress relaxation model Equation (18) to haveThe fitting results and the actual testing data are compared in The testing data acquired at temperature 50 °C are used as independent validation data. The stress relaxation prediction is made using Equation (30) with To further investigate the performance of the proposed model, the model is compared with three commonly used stress relaxation models in Equation (32) in Ref. , and are in Ref. , as showThe stress–strain data in The fitting creep parameters are obtained using Equation (14) with the data presented in With the above strain-hardening equation and creep parameters, the final stress relaxation model is written as,The same data are used to obtain the model parameters of the parallel Maxwell, Prony series model with three Maxwell elements, and the KWW model, respectively. The model prediction results of those models and testing data are compared in RMSEs and RSSs of the proposed model and reference models under the two loading cases are compared in To ease the application of the proposed stress relaxation model to realistic structural problems, a user material subroutine for finite element analysis (FEA) package ANSYS is developed. Taking the FM-73 adhesive in the model validation section as an example, a sheet specimen of 180 × 11.5 × 1.8 mm (Ref. ) shown i−4 s. This step imposes the required initial strain to the specimen. Next, the resulting strain is kept constant in the time period of  to simulate the stress relaxation process of the material. During the stress relaxation step, a total number of 500 uniform sub-steps are prescribed, corresponding to a uniform time increment of Two loading steps are used to simulate the stress relaxation process of the specimen. First, during the displacement-controlled loading period, the bottom face is fixed in all directions and the initial displacement of the top face is linearly applied from 0 s to 1 × 10The stress relaxation process of the specimen at 30 °C under the constant strains of 0.8% and 1.4% are solved. The results of the equivalent stress contours at three different time instances are shown in The stress relaxation for the testing conditions shown in In this study, a general engineering prediction model of stress relaxation for polymer composites was developed. The model is based on the assumption that in the stress relaxation process, the reversible strain is transformed to irreversible strain continuously. A strain-hardening model is employed to incorporate nonlinear elastic deformation of the material, and a creep rate model is used to describe the irreversible strain. The dependence of hardening rate and creep model parameters on temperature was established using the response surface method, allowing for predicting stress relaxation prediction under a range of temperatures. The effectiveness of the developed model was verified using polymer glass data and was further validated using FM-73 adhesive data. Furthermore, the performance of the model was compared with three commonly used stress relaxation models using HDPE data. The user material subroutine of the proposed stress relaxation model was developed and verified for the structural applications.Compared with the commonly used models such as the parallel Maxwell and Prony series, the developed model has the following two distinct potential advantages. (1) The model eliminates the need for tuning the number of Maxwell elements to reach a required fitting accuracy, and (2) the model takes the nonlinear elastic effect into the stress relaxation process, which can reduce the possible linear approximation error in those commonly used models. Based on the current study, the following conclusions are drawn.(1)The proposed model provides an alternative to existing stress relaxation models and can account for the nonlinear elastic effect which is absent in the commonly used models based on linear viscoelasticity assumption.(2)The proposed model can be applied to general polymer composites. In this study, the effectiveness of the model is verified using three different polymer composite materials. Compared with commonly used models, the proposed model yields the smallest statistical error in terms of RMSE in the example case.It is worth mentioning that the proposed model was established with short-term stress relaxation largely attributed to the physical relaxation of polymer composites. For long-term stress relaxation behaviour due to the combination of physical relaxation and chemical relaxation, the applicability of the proposed model is unknown. In addition, the applicability of the developed to unconfined compression test data is unknown due to the current limited access to such testing data. This aspect will be investigated in the future.


The current system-on-chip (SoC)-based devices uses embedded memories of enormous size. Most of these systems’ area is dense with memories and promotes different types of faults appearance in memory. The memory faults become a severe issue when they affect the yield of the product. A memory-test and -repair scheme is an attractive solution to tackle this kind of problem. The built-in self-repair (BISR) scheme is a prominent method to handle this issue. The BISR scheme is widely used to repair the defective memories for an SoC-based system. It uses a built-in redundancy analysis (BIRA) circuit to allocate the redundancy when defects appear in the memory. The data are accessed from the redundancy allocation when the faulty memory is operative. Thus, this BIRA scheme affects the area overhead for the BISR circuit when it integrates to the SoC. The spare row and spare column–based BISR method is proposed to receive the optimal repair rate with a low area overhead. It tests the memories for almost all the fault types and repairs the memory by using spare rows and columns. The proposed BISR block’s performance was measured for the optimal repair rate and the area overhead. The area overhead, timing, and repair rate were compared with the other approaches. Furthermore, the study noticed that the repair rate and area overhead would increase by increasing the spare-row/column allocation. The recent SoC-based devices play a more important role as technology enhances day by day. These modern SoC designs are dense with memory, and the users need more promising features from their devices. A smooth-functioning memory-test algorithm and architecture are required to maintain the product’s reputation. Present system-on-chip (SoC) designs consist of embedded memory in a large portion. The embedded memory area in recent SoC-based devices is higher and is approximately equal to 95% of the total chip area ,2,3. DueFurthermore, the memories are more prone to faults than the actual logic, as memories do not consist of the logic elements such as flip-flops ,4, and tMemory test and repair are two separate processes involved in yield enhancement for any modern SoC design semiconductor memories. Memory built-in self-test (MBIST) is a verified and reliable method for testing embedded memory ,8,9,10, The failure information generated by the MBIST design during the testing process is available for the built-in self-repair (BISR) block after completing the test. The BISR circuitry uses this memory failure information to repair the defective memory cell. The BISR method is common and popular for memory yield improvement in SoC ,17,18,19The multiple memories, test, and repair scheme is proposed targeting the low area and time ,16. CompAmple research has been conducted for maintaining the low area and high repaired rate characteristics by the proposed methods of BISR, and it has achieved a suitable figure. However, still, it needs improvement, and it is possible to enhance this characteristic further. Therefore, a slight change in the technique to improve the test and repair method for the memory will help in increasing the product yield. This research tried to reduce the area overhead with a reasonable repaired rate and fault coverage by the proposed BIST and BISR. The contributions made by this research are listed below.Implementation of BIST for critical testing by using the proposed March algorithm to improve a product’s quality. This memory BIST finds the faults and types in the memory to improve the fault coverage.A time- and cost-effective BISR solution to repair the faulty cells of the memory under test (MUT). The BIST controller generates the fault cells’ information to use spare memory. The BISR block performed the replacement of the faulty cells by redundant blocks, which are fault-free.Thus, the BISR block allows defective cells to be replaced with the spare block without losing original data if memory contains critical information. Therefore, it ensures product reliability at a low cost.The proposed architecture of the built-in self-repair method is shown in The proposed memory BIST algorithm is used for fault testing. It diagnoses the memory for failures and preparing fail information for the repair block.To deliver high-grade SoC products, the SoC manufacturer needs high fault coverage. Therefore, most manufacturers try to implement a sophisticated memory BIST algorithm to reach reasonable percent fault coverage. Most memory BIST schemes are based on the marching algorithm to test the embedded memories and get significant fault coverage. Generally, a complex March algorithm can detect fault types, such as stuck-at fault, address decoder fault, transition faults, and some coupling faults. The current product’s increased chip density and technology result in new fault types in the SoC, such as stuck-open fault and neighborhood pattern sensitive faults (NPSFs). The designer needs to provide the algorithm carefully with these advancements in technology.This research proposed a novel memory BIST algorithm to test the various memories for multiple faults in an SoC-based device. The memory BIST algorithm is based on March elements and is used to test the memories by applying different patterns, as mentioned in the algorithm. The algorithm itself writes various patterns, such as 0s and 1s, to the different memory locations of memory under test (MUT). As per the element written in parenthesis, each operation is performed at every memory location, one after another. The algorithm is marching 0s and 1s patterns in ascending or descending order of address sequence for the regress testing of the memory. A comparison with the original pattern  occurs during every read operation. While comparing if mismatches arise, the result will record into the failure information register. The March-sift operations continue after displaying the results from the next consecutive memory location, and it repeats the same until it reaches the last location. The proposed March-sift algorithm is expressed below, in Equation Box (1), and the operations are mentioned in Memory BIST controller works on the patterns of a March-sift algorithm. The March-sift algorithm consists of eight elements to perform read–write operations on the selected memory under test (MUT). Each element of the algorithm is considered a separate state. The controller state machine is designed by considering eight states and two extra states to start the test operation and store fails memory status. The memory BIST controller state machine is shown in sift0: In the first step of test conduction, the writing operation performs at all the MUT locations. The address sequence is not essential in this state. It can be started in either direction, ascending or descending order.sift1: In this step, two operations performed one after another at each memory location under test. Before coming to this state, memory is full of zeros. The first read zero will perform and compared with the desired zero patterns. The write ones will follow after reading zero at each location in ascending order. During every read, a comparison takes place with the original data pattern. While comparing if a mismatch takes place, the result will display in the fail information state, and the state machine jumps to the next memory location and repeat the same operations. During the comparison pass, the state machine directly jumps to the next address location.sift2: At this step, three operations  are performed sequentially at each memory location (in descending order). Starting with the highest address, the first operation (r1) reads the memory location, where the expected value is one, and compares the reading value with the desired one pattern. If a mismatch occurs, the result will be stored in the status register. The second operation (w0) writes or fills the memory location with zero, followed by (r0). After completing the functions of sift2, the state machine repeats the same actions at the next address location and all remaining addresses until it reaches the lowest address of the memory locations under test.sift3: At this step, the two operations are sequentially performed throughout the memory in ascending address order. Starting with the zero address, the first operation (r0) followed by (w1) write 1s. During a read operation of a memory location, the reading data value will compare with the desired zero patterns. If a mismatch of any bit occurs, the state machine will record the result. After these operations, the state machine jumps to the next consecutive location and repeats the same procedures until it reaches the memory’s highest address location.sift4: At this step, the two operations performed sequentially throughout all memory locations under test (in descending order). Starting with the highest address, the first operation (r1) reads the memory location, where the expected value is one, and compares the reading value with the desired one pattern. If a mismatch occurs, the result will be recorded. The second operation (w0) writes or fills the memory location with zero. After these operations, the state machine jumps to the next memory location and repeats the same activities until it reaches the lowest address location of the memory under test.sift5: At this step, the operation is performed throughout all memory locations under test (in descending address ordering). Starting by the lowest address, the action (r0) reads the memory location, then writes 0s (w0), and reads 0s (r0) at the same location are performed. The reading value will be compared with the original zero patterns and recorded when a mismatch occurs every time. After reading and analyzing, the state machine jumps to the next memory location, repeats the same operations, and continues until it reaches the highest memory locations.sift6: Similarly, as the state sift5, all operations performed with a change in the elements w0 to w1 and r0 to r1.sift7: At this step, the operation performed throughout all memory locations under test (in ascending or descending address ordering) direction is not essential. Starting by the lowest address, the action (r1) reads the memory location, where the expected value is one. The reading value will be compared with the original 1s pattern and recorded when mismatch.Memory BIST operations start when the state machine gets the start signal to perform March-sift functions. The procedures mentioned in each March-sift element will execute sequentially one after another in each state machine’s states to test MUT. After completing the write 0s operations in sift0, the state machine jumps to the next state (sift1) and starts performing read 0s, followed by writing a 1s procedure at each memory location. While performing read operations, the read data will compare with the desired 1s pattern. If the comparison fails, the state machine jumps to the status state and displays the failure information into the status register for the BISR block. The memory tag, fail address, defective cell position, and defect count will be stored in the status register.The state machine displays the failure information, jumps back to the state from where it arrives, and starts performing the March-sift operation at the next consecutive address location. During testing, if the stop signal triggers, the state machine will jump to the status state and display the same failure information with the pass or fail indication. Similarly, suppose that the halt  is programmed. In that case, the state machine jumps to the status state on any fail condition, displays failure information, and waits for the join signal to continue its operations. In this way, the state machine performs all the procedures mentioned in the algorithm until the last address location, jumps to status state, and displays the testing result. If the fail count is nonzero, the testing result of memory fails; otherwise, it will pass. The completion signal will also be active if the test finishes.The memory defects are often increasing by increasing memory size and the high-end technology in recent memory-based devices. These increasing defects are tackled only by the BISR method as it is less expensive, fast, and has a high repairable rate than the other methods. The defective memory part will be replaced with the spare memory, which is not faulty. Therefore, fault repair method by BISR is treated as a reliable and cost-effective solution . The conThis research study proposed the fault repair approach by the built-in redundancy analysis (BIRA) method. The BIRA block consists of redundancy analysis (RA), Fault Table (FT), Buffer, and a redundancy signature register (SR). The faulty addresses are stored in the redundancy logic. The data will be accessed from the redundant locations if those locations are available in RL while memory is operated. The faulty addresses are programmed during memory BIST, and an overflow bit is used to indicate the overflow of the false addresses. This overflow bit indicates that there are more failing addresses than that of the repair cell. The BIRA block is shown in The redundancy analysis is simple and straightforward, and it selects minimum numbers of spare rows and spare columns to cover the faulty cells. It starts immediately after the BIST passes the status about the fail information. The RL block uses a fault table (FT) to store fault addresses. Based on the fault information, the fault address is stored in the fault table every time if it is not available previously. An overflow bit indicates to the BIST controller that the fault address table is packed, and no other space remains to store the new faulty address.The repair analysis (RA) is performed on the incorrect addresses available in the fault table. The minimum number of spare rows and spare columns are calculated to access the error-free contents from the spare redundancy in place of the memory’s defective cells. The RA coordinates with BIST to develop the repair strategy as per the available redundancy. The repair information is stored in the signature register (SR), whereas the false information is stored in the fault table. The buffer is used to store the fault addresses and the fault position. It helps when there is more than one faulty bit detected from a particular defective address. It contains the faulty bit address to provide a repair solution. The memory or fault is unrepairable only when FT cannot store the fault address or no redundant cell is available.The BISR flow is shown in The memory BIST block tests the MUT for faults. If fault detects, the BIST generates the failure information, and if no defects, the BIST stops.Once the BIST programs failure information, the repair process starts. The RA block reads the faulty addresses and compares them with the addresses previously available in the fault table. If an address is not previously known, it stores it into the fault table; otherwise, it ignores it.Depending on the erroneous information, the RA performs a repair strategy and calculates fault count in a faulty row and faulty column.The repair signature is prepared based on the repair strategy and will store in the signature register.For whichever is higher, the row-defect count or column-defect count, it will allocate first and repeat until it reaches one fault in a particular row or particular column.If the row fault count equals the column fault count, the spare row will assign.If there is only one faulty cell that remains, the spare row allocates.The repair solution is provided for the memory under test whether the fault is repairable or unrepairable. In the case of multiple memories, the same BISR block will share within the numerous memories by selecting one after another.The proposed repair algorithm workflow can understand by the example taken for a faulty memory shown in The proposed BISR block is implemented on the FPGA platform by using the Xilinx tool and on the ASIC platform, using Synopsys Design Compiler. The register transfer logic (RTL) is written by using Verilog HDL (hardware description language). BISR internally consists of test and repair blocks. The results are for the top-level block consisting of both the blocks (test and repair).The functional test has carried out on the Xilinx simulator. The proposed BISR is implemented using Verilog HDL. The test benches are written compute different faults and their types. The faults at various locations at different positions are injected, and fault types are calculated for negative testing. Some test scenarios are considered for detecting defects and their types by inserting defective bits at various locations in different positions. The simulation results for finding the faults, such as stuck-at-0, stuck-at-1, NPSFs, transition faults, address decoder faults, coupling faults, Write Destructive Faults (WDFs), Read Destructive Faults (RDFs), and Deceptive Read Destructive Faults (DRDFs), are tabulated in The simulation results of the proposed BISR block are shown in The results of the fault and their type detection are received and compared with the other existing methods. We used eight steps in the algorithm to conduct the test and to enhance the results. The proposed method is a short and efficient fault coverage method and finds more fault types and provides an optimal fault coverage than other existing algorithms. Most of the studies cover only SAF, TF, ADF, and some CFs. Although some faults still may exist in the memory. Therefore, we took a step to cover those faults by the presented method of the March sift algorithm. It covers SAF, TF, ADF, CFs, NPSFs, WDFs, RDFs, and DRDFs from memory under test to improve fault coverage. The fault coverage comparison with the proposed and other existing methods is given in The row and the column count for the detected faulty cell are shown in The synthesis process is completed by using the Xilinx synthesis tool and the Synopsys tool Design Compiler. The top-level module consists of BIST, BIRA, and memory wrappers. The synthesis report for the area, timing, and power are obtained.The package layout view of the Xilinx FPGA is shown in The Xilinx tool specification is given in The proposed BISR block is compared with the other existing approaches for the area used and is tabulated in The area overhead for the proposed BISR and the other existing approaches is determined. The area overhead is estimated as per the area report of the proposed block consisting of the slice registers count. The other existing methods for comparison are also implemented on the Xilinx platform, and from the obtained results, the area, the timing, and the area overhead percentage are determined.The results have also developed on the ASIC platform for the proposed and other BISR approaches in this research study. The BISR block is synthesized by Design Compiler with the specifications of 32 nm technology, using HVT slow library cells. The obtained result for hardware utilization in terms of cell count is tabulated in The synthesis process for the proposed BISR and the different existing approaches target area, timing, and power. The comparison with other approaches for area, timing, and power are tabulated in The test was conducted on the significant memories of 8 k, 16 k, 24 k, 32 k, 64 k, and 128 k sizes. The area overhead percentage was calculated, and the obtained results are tabulated in The obtained result of the proposed method is compared with the other approaches. Two groups of the memories are formed by combining 8 k and 16 k memory as a Group 1 and 32 k and 64 k memory as Group 2 for the experiment purpose. The comparative results for the proposed and other existing methods for these two groups for area overhead are tabulated in 2, respectively, whereas the areas of the BISR block for these groups of memories are 32,989 and 35,063 nm2 received, respectively. The area overhead percentage comparison is tabulated with other methods in The proposed BISR block with three rows and two-column redundancy is implemented, and experiments are conducted by considering two groups of memories. The Group 1 memory and Group 2 memory areas obtained are 942,542 and 2,004,586 nmThe comparative features of the other existing approaches and the proposed method are given in The repair rate is compared with the approaches discussed and the proposed method. It is concluded that, from the results and by the study of other existing BISR methods, the repair rate will increase by increasing the spare row and column count, but it results in the penalty of more area overhead. The estimated chart between repair-rate percentage and the spare-row/column matrix is plotted in the chart of Memory test and repair is a widely used method for enhancing the yield of SoC-based products. The process consists of two different step’s fault test and fault repair for any memory under test. Many researchers have presented these two steps separately as a new finding. However, we produced this technique both to test and repair the memories for SoC-based devices. The fault test has been covered in the research ,23,24,25As indicated, the proposed BISR scheme in this research targets both test and repair mechanisms. The repair process starts when the test controller provides false information to the BIRA block. The repair strategy is prepared as per the steps described in the repair flow, and the memory is repaired by allocating spare rows and columns. The research studies ,17,26,27The BISR method of a research study  is offerThe proposed BISR block in this research study aims to enhance the area overhead, timing, and repair rate. The other existing BISR approaches of the studies ,16,17,26The redundancy allocation method is used to repair the memories of the recent SoC-based devices. The spare row and spare column are used when the faults are existing in the memory under test. The BIST block tests the memories for faults and computing faults and their types from the embedded memories. Different types of defects are calculated, and the false information is prepared to provide to the BIRA block for the start of the repair process. The BIRA block repairs the memory by using the redundancy allocations method. The obtained results on the FPGA and ASIC platforms for the area, timing, and repair rate are discussed and compared with other approaches. It is noticed that the proposed method is a good choice to test and repair the memories for recent SoC-based devices. The only limitation is area overhead when integrating with the SoC to develop the current embedded system-based product. The presented BISR scheme can modify the next-generation systems to test and repair the memories of large sizes as the technology enhances.


Distributed optical fiber sensors are a promising technology for monitoring the structural health of large-scale structures. The fiber sensors are usually coated with nonfragile materials to protect the sensor and are bonded onto the structure using adhesive materials. However, local deformation of the relatively soft coating and adhesive layers hinders strain transfer from the base structure to the optical fiber sensor, which reduces and distorts its strain distribution. In this study, we analytically derive a strain transfer function in terms of strain periods, which enables us to understand how the strain reduces and is distorted in the optical fiber depending on the variation of the strain field. We also propose a method for back-calculating the base structure’s strain field using the reduced and distorted strain distribution in the optical fiber sensor. We numerically demonstrate the back-calculation of the base strain using a composite beam model with an open hole and an attached distributed optical fiber sensor. The new strain transfer function and the proposed back-calculation method can enhance the strain field estimation accuracy in using a distributed optical fiber sensor. This enables us to use a highly durable distributed optical fiber sensor with thick protective layers in precision measurement. The demands for structural health monitoring of large-scale structures keep increasing, and various technologies are developing, such as distributed sensors covering wide areas ,2,3,4, sMost distributed optical fiber sensors made of silica materials are fragile and are easy to break during handling, installation, and service. Thus, packaging or coating with nonfragile materials is commonly used to protect them ,33,34,35In contrast to the local sensors, distributed optical fiber sensors can sense varying strain fields in a wide area, for instance, varying strain fields around the impact damage of a composite structure . In the In this study, we adopt the spatial wavelength-dependent strain transfer approach  to consiThe remainder of the paper is structured as follows. First, in In this section, we analytically derive the strain transfer function for a distributed optical fiber sensor. Our analysis uses a simplified model consisting of an optical fiber sensor (OFS), a coating layer, an adhesive layer, and a base structure, as shown in Here we use the same type of model to obtain an analytical expression of strain transfer function under a periodic strain distribution. This function is then used for accurately back-calculating the strain distribution on the surface of a base structure.For the strain transfer function, we begin by deriving a differential equation for fiber strain  and (6) into Equation (1) and differentiate Equation (1) in terms of The modulus of fiber , which represents the strain transfer from the base structure to the fiber, becomes Equation (11) in the region where the boundary effect is negligible.This demonstrates that the strain transfer function depends on the spatial period ) included in the strain field. Here we propose a method for back-calculating the base strain field using the strain transfer function in Equation (11). Its procedure is shown in We use the finite element method to validate the strain transfer function derived from the analytical approach (Equation (11)) and to investigate the effects of plastic deformation, material properties, and the thickness of a coating and an adhesive layer on strain transfer from the base to the optical fiber. The same type of FE model has been used in the strain transfer analysis of a metal-coated optical fiber sensor under a constant strain field, and it is validated with experimental data in .The inset of D sets for the maximum strain becoming 0.356%. This corresponds to the maximum stress of 600 MPa in the base structure.To validate the strain transfer function, we suppress the torsion and bending deformation of the 3D FE model using boundary conditions. The nodes at the centerline of the base structure along z-direction are suppressed from moving x- and y- direction, while the nodes at the centerline of the fiber along z-direction are suppressed from moving x-direction  are analyzed, and their strain transmission to the fiber are compared in We analyze the strain transfer in a 70 mm length structure with free and z-symmetric boundary conditions, as shown in To investigate the effect of the spatial period of the base strain on strain transfer to the fiber, we analyze various strain fields over different periods from 2 mm to 20 mm. For efficient FE analysis, we use a single-period model with periodic boundary conditions at both ends of the structure. Five strain distributions a are appTo further investigate the effect of local plastic deformation of the coating layer on strain transfer, we compare three models with different coating thicknesses  as shown in We also investigate the effect of coating and adhesive thickness on the strain transmission ratio. In this section, we analyze strain distribution around an open hole on the composite plate and its transfers to the optical fiber bonded onto the surface see . We alsoWe can back-calculate the base strain distribution by restoring the reduced strain amplitude of each strain period in the distorted strain distribution (OFS strain) following the procedure in It is noted that here we use a simplified model for the validation of the spatial period dependent strain transfer function and the strain back-calculation method proposed. However, for practical applications of the proposed methods, one can use other OFS models ,39,40,43In this study, we analytically derive a strain transfer function in terms of a spatial period of the strain field for a distributed optical fiber sensor bonded onto a base structure. We numerically validate the analytical strain transfer function using the finite element method. The effects of various parameters on strain transfer are investigated, including the effects of local elastoplastic deformation, coating, and adhesive layer thickness, and the spatial period of the strain field. We find that local plastic deformation can induce effective stiffness variation and distort strain distribution in the optical fiber. We also find that strain transfer decreases as the strain period decreases, which is significant in a small period regime. This variation of the strain transmission depending on the strain period can substantially distort a strain field. Here we propose a way to back-calculating the base strain from the distorted strain distribution and numerically demonstrate that it properly restores the base strain.This back-calculation method with the strain transfer function enables us to enhance both the durability and accuracy of distributed optical fiber sensors. A distributed sensor with a thin protecting layer can have high accuracy but has low durability, while a sensor with a thick protecting layer can have high durability but has low accuracy. We can increase the accuracy of the durable sensor with a thick protecting layer using the proposed back-calculation method. This is useful for designing and determining installation parameters of the distributed optical fiber sensors for precision sensing under harsh environments such as structures exposed to severe vibration, external impacts, high temperature, or cryogenic temperature.Although this study focuses on the analytical and numerical investigation of the strain transfer function and strain back-calculation method, further studies can include investigation of the temperature effect on the strain transfer and experimental validation of the proposed method.


Sharing your possessions or donating them to others, as well as helping others, can be summarized under the term, “prosocial behavior”. Recently, researchers have become interested in whether, and in which situations, animals help and share. In this study, we trained carrion crows and azure-winged magpies—two bird species that have previously been found to donate food to their group members—to exchange objects, called “tokens”, with an experimenter for food. We then tested whether the birds would provide these tokens to their group members when they themselves did not have access to the experimenter, but their group members did. We found, however, that there were very few attempted and/or successful token transfers between the birds, suggesting that they were not prosocial in this situation. We argue that the carrion crows and azure-winged magpies might not have fully understood the value of the tokens, either as placeholders for food or as “currency” with which food could be obtained. This limited understanding might have prevented them from exhibiting prosocial behavior in the current study. Therefore, we advocate the use of simpler and more naturalistic paradigms to study prosocial behaviors, such as providing food or resources to others, in a broader range of species.To study the evolution of humans’ cooperative nature, researchers have recently sought comparisons with other species. Studies investigating corvids, for example, showed that carrion crows and azure-winged magpies delivered food to group members when tested in naturalistic or simple experimental paradigms. Here, we investigated whether we could replicate these positive findings when testing the same two species in a token transfer paradigm. After training the birds to exchange tokens with an experimenter for food rewards, we tested whether they would also transfer tokens to other birds, when they did not have the opportunity to exchange the tokens themselves. To control for the effects of motivation, and of social or stimulus enhancement, we tested each individual in three additional control conditions. We witnessed very few attempts and/or successful token transfers, and those few instances did not occur more frequently in the test condition than in the controls, which would suggest that the birds lack prosocial tendencies. Alternatively, we propose that this absence of prosociality may stem from the artificial nature and cognitive complexity of the token transfer task. Consequently, our findings highlight the strong impact of methodology on animals’ capability to exhibit prosocial tendencies and stress the importance of comparing multiple experimental paradigms. Prosocial behavior  has lonProviding food to others is often regarded as a relatively common form of prosocial behavior in non-human animals that can be easily observed in naturalistic or semi-naturalistic settings. In such studies, one individual typically receives one or multiple food item(s), and it is observed whether the individual provides some or all of this food to other group members . HoweverAnother common way to investigate prosocial behavior in non-human animals is through prosocial choice tasks. In these tasks, a donor animal and a recipient animal are placed in adjacent compartments. The donor is given a choice between two trays or containers with different reward combinations, one of which typically contains food rewards for both the donor and the recipient , while the other contains only a food reward for the donor  and not when there was nothing to gain for the donor  . EurasiaDespite the fact that tokens have been used successfully to test economic decision-making processes in corvids and parrots , Corvus corone) and azure-winged magpies (Cyanopica cyana). Cooperative breeding, including food provisioning for breeding females and offspring by kin and non-kin helpers, has been documented in both species in the wild [Therefore, in this study, we decided to use the simple token transfer paradigm that has already been used in common ravens, African grey parrots and blue-headed macaws ,54 to inthe wild ,32. Boththe wild ,55. Addithe wild . Consequ® . The crows had ad libitum access to water in each compartment of the aviary at all times.We tested seven captive carrion crows (3F/4M) and four captive azure-winged magpies (3F/1M). In terms of appearance, the crows were either carrion crows or hybrids of carrion and hooded crows, reflecting the hybridization belt in Europe. Both crow species have highly similar life histories and are often considered to be subspecies . All of ®, Versele-Laga; Deinze, Belgium) in each compartment of the aviary at all times.The four azure-winged magpies were siblings from the same nest, 4 years old at the time of testing, and hand-raised. The magpies were housed in a social group setting at the Animal Care Facility of the Department of Behavioral and Cognitive Biology, the University of Vienna, Vienna, Austria. The outdoor aviary (4.3 × 3 × 3m) was partially covered with a semi-transparent roof and consisted of two equally sized compartments that were separated by a central wire mesh partitioning. The two compartments could be closed off with two sliding doors made from wire mesh. The magpies were fed a diverse diet of different fruits, insects, seeds, meat, and egg. The experiment was conducted prior to the first feeding of the day with a high-quality food reward . The magpies had ad libitum access to water and pellets  within 10 min in a minimum of 4 training sessions  for a piece of food. We chose different tokens for each species due to the size difference between the two species. For the carrion crows, the tokens were green plastic bottle tops  and for the azure-winged magpies, they were white plastic rings . Exchanging was shaped using positive reinforcement with a food reward and verbal praise. First, we started to train the birds to exchange in a group setting in several different areas around the respective aviaries. Subsequently, they were trained to exchange when isolated from the group and The training and subsequent experiments with the carrion crows were conducted between September 2015 and September 2016 with Daisy, Paula, Peppi, Caruso, and Saul and between April 2017 and September 2017 with Walter and Willi. The individual training to criterion took 3–11 months. The training and subsequent experiments with the azure-winged magpies were conducted between July 2020 and October 2020. The individual training to criterion took 2 months. The birds were never trained to transfer tokens through the wire mesh partitioning between the two compartments that were used in the experimental setup . The two sessions were conducted on two different days. The sequence of conditions was semi-randomized, with the stipulation that it was different in each of the subject’s sessions. Each subject was tested once in the left compartment and once in the right compartment, and the first location was counterbalanced across birds.The azure-winged magpies started to lose motivation to participate in the experiment after the first session (see results section). Therefore, with the magpies, we added 3 days of token training and a second round of two test sessions for each subject. During the second round, we conducted the experimental sessions in the morning before the first feeding of the day and conducted additional individual training sessions every day in the afternoons.® HD, Canon Inc., Tokyo, Japan; Panasonic HC-X909, Panasonic Co., Osaka, Japan).All experiments were recorded simultaneously using two high-definition camcorders  spent within one body length distance of the wire mesh partitioning, where they could have received a token  did so within the first two minutes of the trial . The azure-winged magpie donors started to lose motivation to participate in the experiment after the first session: two birds exchanged no tokens for food rewards in the motivation control condition in the second session , and one bird exchanged only one token (Rey). After introducing additional training sessions, in the second round, the azure-winged magpie donors exchanged all available tokens in the motivation control conditions .n = 7), followed by the test (n = 5) and the social control (n = 1). Only 1 token was transferred by an azure-winged magpie donor (Poe). The bird transferred the token in the non-social control through the outer wire mesh of the aviary, near to where the exchange table was placed and opposite to the compartment containing the group members , followed by the test  and the social control (NRound1 = 1). Across the two species, there was a significant difference in the number of attempts between the three conditions, both when considering only the first round of the azure-winged magpies  and when considering the magpies’ attempts in both rounds . Importantly, however, there was no significant difference between the test and any of the control conditions.Over the course of the experiment, the carrion crow donors only transferred 2 tokens to the adjacent compartment  and when considering the magpies’ caches across both rounds . The azure-winged magpie donors never showed this behavior.Further, there was no significant difference in how many tokens were cached for both species combined between the three conditions, both when considering only the first round of the azure-winged magpies  or all other vocalizations  between these two conditions.Combining both species, there was no significant difference in how much time the recipients spent close to the wire mesh partitioning between the test and the social control, both when considering only the first round of the azure-winged magpies (Wilcoxon test: p = 0.001). This difference was a non-significant trend, when considering the azure-winged magpies’ behavior averaged from both rounds , and only three tokens were transferred successfully. All of the transfers occurred in the non-social control condition and were transferred either to an empty compartment or through the outer wire mesh of the aviary, where no potential recipients were present in both cases. This result contradicts our main hypothesis that the crows and magpies would behave prosocially towards their group members, as has been observed in the wild ,32 and dIt is, however, always difficult to interpret null results, because apart from an actual lack of a prosocial disposition, they may stem from a flaw in the experimental setup, its applicability to the specific species, and/or a lack of understanding of the procedure on the part of the subjects. In our case, for example, the possibility to transfer tokens through the wire mesh partitioning to the adjacent compartment where the potential recipients were located might not have been apparent to the donors due to a lack of experience. In order to get a spontaneous reaction in the test and to avoid shaping the birds’ behavior, we never let them transfer tokens through that particular wire mesh partitioning during training. In a similar experiment with capuchin monkeys, the subjects were trained to both receive and transfer tokens through the partitioning separating them from their recipients prior to the test . This miScrutinizing the pattern of attempted transfers, however, might help to resolve the donors’ motivations for transferring the tokens. There was a significant difference in the number of attempted transfers between the conditions, with the greatest number of attempts occurring in the non-social control condition. For both species, the attempted transfers in this condition occurred through the wire mesh that separated the donors from the area where the exchange table was placed, but no group members were present. For the azure-winged magpies, this meant attempting to transfer tokens outside of the aviary, rather than through the wire mesh partitioning to the other compartment, where potential recipients would have been present. In contrast, only one transfer attempt was made by each species in the social control condition, in which potential recipients would have been present but there was no exchange table. Therefore, it is likely that the pattern of attempted token transfers observed in the current study reflected the birds’ attempts to move the tokens closer to the out-of-reach exchange table, rather than their willingness to provide the tokens to their group members. Similar arguments have been made when analyzing the patterns of token transfers in capuchin monkeys  and blueThe results obtained in the current study seem to indicate that the birds did not fully understand the quasi-symbolic nature of the token as a placeholder for food. Instead, they may have acquired an association between the action of exchanging an item with the experimenter and the corresponding food reward during training. While the birds never received food for transferring objects other than the tokens in the course of the study, the birds sometimes pushed other objects through the wire mesh when no tokens were immediately available. Similar observations have been made in ravens  and capuAnother indication that the donors did not understand the value of the tokens is that they only rarely cached the tokens when they themselves did not immediately have the possibility to exchange them with the experimenter . In contrast, common ravens cached about half of the tokens when tested in a similar paradigm . The ravUsing a token transfer paradigm, we found no evidence of prosociality in carrion crows and azure-winged magpies. We propose that the absence of prosocial token transfers in the current study may have stemmed from the complexity of the token transfer task and the required quasi-symbolic representation of the tokens. The extensive training required in this task also makes this paradigm less generalizable across contexts . Consequently, using experimental paradigms that are more intuitive and put less cognitive load on the subject appear more promising for the investigation of prosocial behavior in corvids and other non-human animal species in general. Naturalistic food provisioning experiments, for example, allow animals to express their species-specific behavior, while well-thought-out control conditions can still help to probe the underlying motivations for providing the food  and can


This article describes the characteristics of one type of sealing system used in warm edge glazing units and analyses the possible causes of damage. Attention was focused on the performance of the dual seal, PIB/silicone system. This type of glazing is widely used for modern curtain walls and roofs of office buildings and shopping centres. Study was focused on PIB displacement defects, which affects both the appearance and thermal performance of the curtain wall system. Wide-ranging field surveys were conducted to examine the problems identified in some office buildings. The information gathered in this way was used to identify the critical areas and causes of seal displacement in the analysed insulating glass units (IGUs). Laboratory tests were conducted on PIB and silicone seals retrieved from the removed defective units. The properties of these materials were determined and used to evaluate the applied edge sealing system and build a representative numerical model. Due to the problems encountered in deriving accurate analytical formulas, finite element (FE) approximation was used as a problem solving tool. The generated FE model and strain analysis were the key parts to obtaining a true representation of the actual behaviour of IGUs subjected to various environmental loads, taking into account the influence of the air cavity. Results of computer simulations and laboratory tests were compared for model validation. The effect of changes in ambient pressure was examined, showing the development of tensile strains in the silicone and PIB, which can lead to debonding. The greatest principal strains occur at the silicone/butyl rubber interface and this location should be considered to be the most susceptible to failure. The observations are summarised in the final conclusions. Additionally, as field study showed, after ten years in service, the percentage of damaged units is considerable. More frequent IGUs inspection should cover both appearance and thermal imaging to detect unsealed panels. From the standpoint of both durability and appearance, dual silicone/PIB should be phased out in favour of modern seal systems. There is more and more room for the use of glass in the present day architecture. Owing to its properties, glass has become a vital part of large panel shopfronts, shopping malls, high-profile showrooms and, last but not least, residential buildings. Minimizing the consumption of energy has become a worldwide issue and an important goal in the design of buildings ,2. With Effective edge sealing is key to obtaining high quality insulating glass units . The traNowadays, glass manufacturers have at their choice a number of different sealing options . PolymerThese two main requirements can be satisfied most effectively by a dual-seal configuration . The firWhile the idea of edge sealing of glass units dates back to the 1940s, it took much time before such glass became commercially available. This technology gained momentum in the 1970s, when glass manufacturers focussed on improving the design of the edge sealing system and the quality assurance methods. Wolf .There is a wide variety of different tests for determining the durability and the expected lifetime of double-pane insulating glass. The history, scientific basis and application of internationally recognised durability tests for insulating glass are presented in .While the article considers the overall serviceability of the dual-seal system, particular attention is paid to the failures of the PIB primary seal. Polyisobutylene, a.k.a. butyl rubber, is widely used as a primary seal in the insulating glass edge sealing system. This material owes its popularity to easy machining, high yield and cost-effectiveness. There are many manufacturers of PIB, same as there are many manufactures of windows who use PIB as edge sealant in their end products.When it comes to composition, the primary sealant formulation typically includes polyisobutylene (PIB), reinforcing fillers, special additives and pigments. Polyisobutylene is chosen due to its excellent moisture and gas diffusion properties. The other ingredients are included in the formulation to enhance the performance properties of the sealant, such as UV and high temperature resistance, rheology and flow characteristics, sheet strength and adhesion to the surfaces of IGU components . These In some windows PIB can be seen in the vision area of the unit which is a matter of concern. Recently, the number of reported structural defects of insulated glass windows has been on the increase. We see an increasing number of claims made by building owners and developers based on visible displacement of PIB sealant from its original position on the edge of the unit into the cavity, where it disturbs the vision area. Tests are therefore necessary to determine whether the seal displacements are caused by material flaws or assembly faults. There are certain environmental conditions  which may preclude use of this type of insulating glass.In  Watson ePIB squeeze out, a.k.a. PIB creep see a–movemenPIB Migration see b–progresPIB pumping action–caused by pressure variation in the cavity. The pressure variation is caused by changes in environmental conditions . For example, the fill gas may become very hot in summer, increasing the pressure inside the cavity, a process aggravated by absorbing coating or tinted glass. The edge seals widen up (the edges of glass come apart) and the panes deflect come apart to accommodate the outward acting pressure. When the temperature drops the process is reversed. Such cycling produces the “pumping” effect, in which the seals become alternately stretched and compressed with the changes of weather conditions.The National Glass Association (NGA), together with the Insulating Glass Manufacturer’s Alliance (IGMA), recently published the guidelines for the use of PIB primary sealant . The guiThe first two of the above-mentioned problems are caused directly by the material flaws, and therefore will not be analysed directly in this article. Instead, the authors investigated the movements which were most likely caused by the pumping effect.The above-described “pumping” effect should be analysed as strains caused by mechanical effects. A dual-seal is composed of two parts that are subjected to the same amount of displacement, yet the induced stresses differ greatly due to very different values of stiffness of the two materials. This situation was comprehensively described in . It is nThe above-described problems appear to be aesthetic in nature, yet it needs to be verified if they can compromise the thermal performance of the window.PROBLEM-1. Over time, the primary seal material (PIB) is subjected to degradation, resulting in cracks which open the path for diffusion of fill gasses. With the diffusion rate of argon being three times higher than that of the air migrating in the opposite direction, the amount of gas filling the cavity will be generating suction applied to the glass panes. This is only one of the possible explanations and the problem needs to be thoroughly investigated.PROBLEM-2. After installation IGUs are exposed to a variety of environmental factors, such as wind loads, working loads, temperature and atmospheric pressure fluctuations, solar radiation, water vapour. Thermal loads and pressure differences  cause expansion or contraction of the respective IGU components, including fill-gas. In theory, the edge seal, owing to its elasticity, should partly accommodate the resulting stress. The stress is smaller the more compliant the edge sealing is, which by expansion or contraction (as the case may be) accommodates part of the load resulting from the pressure difference. This, however, happens at the expense of the seal strength and excessive strains may lead to irreversible displacements.This research was triggered by the authors’ awareness of problems in long-term use of the above-described IGU sealing systems. An example of such problems were considerable and frequent deformations of insulating glass seals on many windows a large office buildings in Warsaw after about 10 years of service. A visual examination was carried out and sealing defects were found in more than 30% of the total number of 4500 evaluated units.One of the removed (replaced) windows is shown in This problem occurred in many glazing units, irrespective of their shape or dimensions. It was estimated that 10 years after installation, problems of this kind occur in up to 30% curtain wall glazing units. Investigation of the causes and attempts to predict the development of such defects were necessitated by large number of glazing units with similar defects, the probably progressive nature of such defects and a high cost of replacement of these components.Besides adhesion between spacers and glass, gas tightness is also an issue of primary importance. A complete sealing failure was not found in a majority of the examined units (over 95%). Despite the inner seal deformation, some amount of noble gas (argon) remained inside the cavity. Infrared thermal imaging was employed to determine the thermal performance of the tested glazing units. A FLIR Systems AB infrared camera, model T1020 was used, with a pixel count of 1024 × 768 px and a sensitivity of 0.02 °C to 30 °C. The tests were carried out as recommended in ISO 6781:1983 “Thermal insulation–Qualitative detection of thermal irregularity in building components”. The test was done in mid-November at an outdoor temperature of −1 °C. In the tested building, the temperatures during the measurements were stable and slightly varied (depending on the location) from 22 to 24 °C. The difference between the external and internal temperatures was in excess of the ISO requirement.In most glazing areas, no significant temperature differences were observed, but on some portions a significant number of glass panes were found to deviate from the others in terms of surface temperature. In some units, a lower temperature was obtained in the middle, which indicates a concave deformation of glass panes, and most probably also sealing failure and diffusion of air in place of argon.The identified defects were compared with the thermal imaging results to reveal a strong correlation between low temperatures on the surfaces of the glazing units and the presence of defects . Pulling of warm edge seals into the cavity can compromise the thermal performance of the glazing unit. We can hypothesise that this process will continue and aggravate the problem.2d) at room temperature increases to almost 10 g/(m2d) at 80 °C ,T–temperature.nR = const). In an isothermal process the ideal gas law can be expressed by the equation pV = const., i.e., the energy stored in the gas is completely converted to mechanical energy. Since the fill gas is hermetically sealed, a volume change will change the pressure of the gas. The above-mentioned volume change results from the deformation of the loaded panes. Any change from the initial temperature and pressure levels will entail a volume change, i.e., bulging of the glass panes. The initial state of an IGU depends on the fill gas parameters during the IGU assembly process (both place and time are relevant). For the purposes of this research, the following initial fill gas parameters were assumed: pressure of p = 101,325 kPa and temperature of T = 20 °C.Because the cavity is hermetically sealed, the amount of fill gas remains constant throughout , beginning of the analysis and final phase. For the sake of simplicity, phases 1 and 2 are assumed to be identical . Any ambient pressure or temperature change during simulation, e.g., due to seasonal or daily variation of temperature, will change the gas condition according to the following ideal gas law equation:In view of the above, to accurately predict the mechanical response of IGU the model should treat it as a gas-filled structure. The interaction between the deformation of the structure and the pressure exerted by the contained gas is a primary difficulty that needs to be overcome. ABAQUS includes a feature to represent gas-filled cavities by a “fluid cavity interaction” and surface-based volume definition. The fluid behaviour is based on a pneumatic model , the fluid is compressible, and its volume is a function of pressure and temperature . This nuFinite element simulations were performed in ABAQUS/Standard , a commercial software package which is appropriate for finding numerical solutions to non-linear problems. A few three-dimensional models of different shapes were used to simulate the IGU behaviour. Examples of rectangular (617 mm × 1517 mm) and triangular (2193 mm × 2148 mm) shapes are shown in The typical FE model consisted of quadrilateral shell finite elements representing the glass pane (S8R element type from the ABAQUS library ). Glass For the frame discretization, solid brick elements C3D8RH were used, with a regular pattern and four layers of elements across the thickness. Hybrid elements were defined to represent nearly incompressible hyper-elastic materials. In addition, hybrid elements overcome the volume strain ‘locking’ problem. This mesh configuration was selected after a comprehensive convergence study, which showed that any further mesh refinement produced minimal variations in the stress distribution.Triangular shapes are more challenging to test experimentally, as they require defining a custom test set-up. On the other hand, for rectangular shapes a standard test set-up can be employed and interpretation of results is more intuitive. For this reason, a rectangular shape model was chosen for validation purposes.A typical analysis, carried out for each FE model, consisted of two steps with different types of loading: Step 1, in which a dead load was applied, and Step 2 with the load imposed by varying temperature or external pressure. The purpose of Step 2 was to consider atmospheric loads, i.e., changes in temperature and ambient pressure. When the outside pressure is higher than the pressure of the fill gas inside the unit the panes will recess inward forming a concave shape. In an opposite situation, the glass panes will bulge outward forming a convex shape. The impact of the pressure gradient on the strain increase rate was investigated using the relevant meteorological data for the area of Warsaw, Poland.Different boundary conditions and loading scheme were applied only in the validation test . An expeTwo main parameters were evaluated in the simulations: stress concentration in the seal and strain distribution in both panes. It is worth noting, at this point, that the main goal of these simulations was to obtain a better insight into IGU behaviour and to validate or reject some hypotheses. Therefore, the properties of the different materials and their potential inaccuracy do not compromise the validity of the subsequently derived conclusions.In order to evaluate the relative accuracy of representation of the main physical behaviour by the proposed finite element model, it was subjected to the model validation process. In this process the response predictions were compared with the experimental data. The load-displacement relationship obtained from the experimental tests was compared with that obtained from the calculations . The tesNext the units were analysed numerically, taking into account the combined effect of the applied dead load and ambient conditions. More attention was paid to the deformations of elastomer elements, as the primary objective was to determine the effect of loading on the behaviour of the spacer frame .The influence of additional environmental loads was assessed using the FE approach presented in Further FE incremental results are reported to assess the influence of negative/positive pressure gradients on the deformation magnitude. In the case of negative gradient the glass pane and the seal are subjected to suction. The focus was placed on identifying points of the greatest deformations to determine where the strain of glass may affect the seal. Contour maps of maximum principal strains in the top and bottom glass panels are presented in A similar analysis was performed for the positive pressure gradient, under which the panes bulge outward, because the ambient pressure is lower than the pressure of argon filling the cavity of the unit. The strain values were smaller , but the observations regarding the characteristic points on the IGU perimeter were similar. In both cases tensile strains of the butyl-rubber seal were concentrated on its interface, which when combined with a locally not sufficient bond between PIB and silicone can cause breaking of the seal and sucking it into the unit.The calculations carried out for triangular units gave the characteristic points of the greatest tensile strains on the unit perimeter. These points are the most probable locations of separation between the PIB seal and silicone, after which the PIB seal becomes displaced inward the cavity. Interestingly enough, these points are not located at the middle of the span, as one would expect, but at intermediate points, as shown in the drawings. These locations are supported by the results of the in situ inspection.The greatest principal strains occurred at the silicone/butyl rubber interface. This should be considered the place that is most susceptible to failure. When insufficient bond strength comes into play, the two materials will come apart at these points. The significance of this phenomenon was reported also in other studies, for example in .A constant pressure of argon was assumed. The effect of external factors was checked in two cases: with the outside pressure higher (negative gradient) and lower (positive gradient) than the pressure of the fill gas in the cavity. Negative gradient results in suction applied to the glass panes and to the seal. Positive gradient causes outward bulging of the glass panes. Both situations cause development of tensile strains in the silicone and butyl rubber, which can lead to debonding and displacement inward the cavity.With insufficient adhesion between the silicone and PIB sealants, repeated strain cycles due to changes in ambient pressure or temperature may lead to seal displacement. Therefore, frequent in-process testing should become a priority during IGU production. This is particularly important for units that will be exposed to high temperatures due to sunlight, i.e., facing south-west. The peel test can be carried out for this purpose, for example according to ASTM C794-10  as recomOver 95% of glazing units remained tight despite visible damage to the materials of the warm edge system layers. Thus, they still meet the conditions of safe use. As long as the glazing unit remains tight, the surface stresses induced in the outer glass pane due to the action of wind, snow and other factors are transferred to the inner pane by the fill gas. However, when the warm edge system becomes unsealed, the applied load must be carried solely by the outer pane. As a result, the unit loses its initial load capacity and must be replaced immediately.After ten years in service, the percentage of damaged units is considerable and thus warm edge glazing systems must be inspected for damage. The inspection should cover both the effect of damage on the appearance and loss of integrity of sealing and unsealed units should be replaced. The warm edge structure can be initially screened with thermal imaging technique to detect unsealed panels based on compromised thermal performance and inspected visually for the presence of condensate (or traces of condensation) on the inner surfaces of the glass panes.From the standpoint of both durability and appearance , dual silicone/PIB should be phased out in favour of by stiffer seals including aluminium or recently launched Super Spacer dual seal warm edge spacers . This tyThis article evaluates the mechanical performance of insulating glass units, a system used, for example, on the building facades. The evaluation included both in-situ tests and numerical analyses using Finite Element (FE) models. Refined FE models were developed for the purposes of this research, to obtain a true representation of the actual behaviour of IGUs subjected to various environmental loads, taking into account the influence of the air cavity. This allowed for considering all the relevant phenomena occurring in IGUs during service. The modelling methodology was verified and successfully validated by laboratory testing. The research and FE simulations discussed in this paper allow us to draw the following conclusions: