Isfahan University of TechnologyJournal of Computational Methods in Engineering2228-769844220251122Determination of convective velocity of eddies in turbulent channel flows using acoustic wave propagationDetermination of convective velocity of eddies in turbulent channel flows using acoustic wave propagation117366310.47176/jcme.44.2.1053FAMahdiRasmiDepartment of Mechanical Engineering, Sharif University of Technology, Tehran, IranMehrdadTaghizade ManzariDepartment of Mechanical Engineering, Sharif University of Technology, Tehran, Iran.0000-0003-0297-7053Journal Article20250609In recent years, the use of turbulent flow noise in internal flows, as a non-invasive method for measuring fluid flow velocity, has increasingly attracted the attention of engineers. The main challenge in this method is establishing a meaningful relationship between the characteristics of turbulent flow and the sound signals received on the wall. This paper investigates turbulent flow in a straight rectangular channel to explain the connection between turbulent flow structures and the acoustic signals recorded on the channel walls. The friction Reynolds number of the flow is 395, and the flow analysis is carried out using the Large Eddy Simulation (LES) method for a three-dimensional channel. After solving the flow field and obtaining the incompressible sources, the acoustic field is also derived using a hybrid acoustic model. Analyses showed that, at low Mach numbers, incompressible pressure fluctuations, known as pseudo-sound, dominate the sound spectrum. These fluctuations originate from the local convection of turbulent vortices on the channel walls and leave significant effects on the wall surface. These effects are measurable, and by tracking them along the channel walls, it is possible to determine the convection velocities of flow structures at different length scales. For large, energy-dominant structures (integral length scales), the convection velocities range between 0.6 to 0.8 times the mean channel velocity. It was also found that, with increasing the mean flow velocity, the accuracy of the velocity measurement obtained from this method is improvedIn recent years, the use of turbulent flow noise in internal flows, as a non-invasive method for measuring fluid flow velocity, has increasingly attracted the attention of engineers. The main challenge in this method is establishing a meaningful relationship between the characteristics of turbulent flow and the sound signals received on the wall. This paper investigates turbulent flow in a straight rectangular channel to explain the connection between turbulent flow structures and the acoustic signals recorded on the channel walls. The friction Reynolds number of the flow is 395, and the flow analysis is carried out using the Large Eddy Simulation (LES) method for a three-dimensional channel. After solving the flow field and obtaining the incompressible sources, the acoustic field is also derived using a hybrid acoustic model. Analyses showed that, at low Mach numbers, incompressible pressure fluctuations, known as pseudo-sound, dominate the sound spectrum. These fluctuations originate from the local convection of turbulent vortices on the channel walls and leave significant effects on the wall surface. These effects are measurable, and by tracking them along the channel walls, it is possible to determine the convection velocities of flow structures at different length scales. For large, energy-dominant structures (integral length scales), the convection velocities range between 0.6 to 0.8 times the mean channel velocity. It was also found that, with increasing the mean flow velocity, the accuracy of the velocity measurement obtained from this method is improvedhttps://jcme.iut.ac.ir/article_3663_ddf9029977a61241841edeae15e9b53f.pdfIsfahan University of TechnologyJournal of Computational Methods in Engineering2228-769844220251122Numerical Implementation of a Hyperelastic-Viscoplastic Constitutive Model to Simulate the Mechanical Behaviour of Two Segmented Thermoplastic Elastomer PolymersNumerical Implementation of a Hyperelastic-Viscoplastic Constitutive Model to Simulate the Mechanical Behaviour of Two Segmented Thermoplastic Elastomer Polymers1947366410.47176/jcme.44.2.1054FAEhsanAhmadiDepartment of Mechanical Engineering, Isfahan University of Technology, Isfahan, IranMohammad RezaForouanDepartment of Mechanical Engineering, Isfahan University of Technology, Isfahan, IranPeimanMosaddeghDepartment of Mechanical Engineering, Isfahan University of Technology, Isfahan , IranManizhehAghaeiDepartment of Mechanical Engineering, Isfahan university of Technology, Isfahan, IranJournal Article20250619Thermoplastic polyurethane (TPU) elastomers are widely used in industries such as automotive and medical due to their unique mechanical properties. However, their complex deformation behavior, resulting from the interaction between soft amorphous and hard crystalline phases, necessitates accurate numerical modeling for reliable prediction under various loading conditions. This study aims to characterize the deformation behavior of TPU through experimental testing and the implementation of a suitable constitutive model. A phenomenological material framework was developed and implemented in the ABAQUS/Explicit finite element software via a user-defined VUMAT subroutine. The model consists of an equilibrium hyperelastic component representing the soft phase, based on the Arruda-Boyce eight-chain model, and a elastic-viscoplastic- component for the hard phase. The latter is formulated using a linear elastic spring, a nonlinear viscous damper based on a modified Ree-Eyring model, and a frictional element. The model parameters were calibrated using a series of uniaxial compression tests under monotonic and cyclic loading at various strain rates at room temperature. The results showed that, as the fraction of the hard component increased, TPU exhibits stronger plastic behavior, with more energy dissipation and less shape recovery. Moreover, when subjected to a strain of -1.0 after each loading-unloading cycle, it exhibits a residual strain that is not fully recovered even several weeks after the end of the test. Furthermore, the model demonstrates the capability to simulate TPU response under other loading scenarios such as tension and stress relaxation. This work offers physical insight into the deformation mechanisms of TPU and provides a practical modeling tool for its complex elastomeric-plastic behavior.Thermoplastic polyurethane (TPU) elastomers are widely used in industries such as automotive and medical due to their unique mechanical properties. However, their complex deformation behavior, resulting from the interaction between soft amorphous and hard crystalline phases, necessitates accurate numerical modeling for reliable prediction under various loading conditions. This study aims to characterize the deformation behavior of TPU through experimental testing and the implementation of a suitable constitutive model. A phenomenological material framework was developed and implemented in the ABAQUS/Explicit finite element software via a user-defined VUMAT subroutine. The model consists of an equilibrium hyperelastic component representing the soft phase, based on the Arruda-Boyce eight-chain model, and a elastic-viscoplastic- component for the hard phase. The latter is formulated using a linear elastic spring, a nonlinear viscous damper based on a modified Ree-Eyring model, and a frictional element. The model parameters were calibrated using a series of uniaxial compression tests under monotonic and cyclic loading at various strain rates at room temperature. The results showed that, as the fraction of the hard component increased, TPU exhibits stronger plastic behavior, with more energy dissipation and less shape recovery. Moreover, when subjected to a strain of -1.0 after each loading-unloading cycle, it exhibits a residual strain that is not fully recovered even several weeks after the end of the test. Furthermore, the model demonstrates the capability to simulate TPU response under other loading scenarios such as tension and stress relaxation. This work offers physical insight into the deformation mechanisms of TPU and provides a practical modeling tool for its complex elastomeric-plastic behavior.https://jcme.iut.ac.ir/article_3664_b67fb3360ae5597d85a005153451dd4e.pdfIsfahan University of TechnologyJournal of Computational Methods in Engineering2228-769844220251122Evaluation of Signal Processing and Deep Learning Methods for Inter-Beat Interval Extraction from Ballistocardiography SignalsEvaluation of Signal Processing and Deep Learning Methods for Inter-Beat Interval Extraction from Ballistocardiography Signals4961367810.47176/jcme.44.2.1056FARoyaTabashiri EsfahaniDepartment of Mechanical Engineering, Isfahan University of Technology, Isfahan 84156-83111, IranAliLoghmaniDepartment of Mechanical Engineering, Isfahan University of Technology, Isfahan, IranAmirAkhavanDepartment of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, IranAmirtahaTaebiDepartment of Bioengineering, Lehigh University, Bethlehem, PA 18015, USAJournal Article20250708Cardiovascular diseases remain the leading cause of mortality worldwide, highlighting the critical need for continuous and non-invasive monitoring of cardiac function to enable early detection and effective management. Ballistocardiography (BCG), which captures the mechanical forces associated with cardiac activity, holds great promise for unobtrusive heart monitoring in daily-life settings without requiring direct electrode contact. However, the inherent complexity and high susceptibility to noise in BCG signals make the accurate extraction of key cardiac parameters—particularly inter-beat intervals (IBIs)—a challenging task. This study presents a comprehensive evaluation of five distinct signal processing and deep learning approaches for IBI estimation from BCG signals, validated against synchronized electrocardiogram (ECG) recordings. In contrast to the previous works, we employ a publicly available dataset distinct from those commonly used, enabling a broader assessment of method generalizability—particularly for the CLIE algorithm. The evaluated methods include: Continuous Local Interval Estimator (CLIE), CLIE with adaptive windowing, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory (BiLSTM) network. For the deep learning methods (MLP and CNN), we propose novel network architectures specifically tailored to the characteristics of BCG signals, leading to improved performance compared to conventional designs. Furthermore, our BiLSTM-based method not only incorporates testing on a dataset different from that of previous reference studies, but also focuses on the accurate prediction of R-peak locations in the BCG signal, from which IBIs are subsequently derived. Evaluation based on Mean Absolute Error (MAE), 95th percentile error, and correlation coefficient shows that the CLIE method achieved the best overall IBI estimation accuracy, with an MAE of 28.7 milliseconds and the highest correlation coefficient (0.77). The BiLSTM method, while having a slightly higher MAE (40.1 milliseconds), demonstrated superior robustness to outliers by achieving the lowest 95th percentile error (9.5%). The MLP and CNN methods showed moderate performance, and the adaptive windowing variant of CLIE performed the worst. These findings demonstrate that accurate IBI extraction from BCG signals is feasible, and that both the CLIE and BiLSTM approaches are promising candidates for implementation in intelligent, home-based cardiac monitoring systems—offering, respectively, high accuracy and strong resilience to large errors.Cardiovascular diseases remain the leading cause of mortality worldwide, highlighting the critical need for continuous and non-invasive monitoring of cardiac function to enable early detection and effective management. Ballistocardiography (BCG), which captures the mechanical forces associated with cardiac activity, holds great promise for unobtrusive heart monitoring in daily-life settings without requiring direct electrode contact. However, the inherent complexity and high susceptibility to noise in BCG signals make the accurate extraction of key cardiac parameters—particularly inter-beat intervals (IBIs)—a challenging task. This study presents a comprehensive evaluation of five distinct signal processing and deep learning approaches for IBI estimation from BCG signals, validated against synchronized electrocardiogram (ECG) recordings. In contrast to the previous works, we employ a publicly available dataset distinct from those commonly used, enabling a broader assessment of method generalizability—particularly for the CLIE algorithm. The evaluated methods include: Continuous Local Interval Estimator (CLIE), CLIE with adaptive windowing, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory (BiLSTM) network. For the deep learning methods (MLP and CNN), we propose novel network architectures specifically tailored to the characteristics of BCG signals, leading to improved performance compared to conventional designs. Furthermore, our BiLSTM-based method not only incorporates testing on a dataset different from that of previous reference studies, but also focuses on the accurate prediction of R-peak locations in the BCG signal, from which IBIs are subsequently derived. Evaluation based on Mean Absolute Error (MAE), 95th percentile error, and correlation coefficient shows that the CLIE method achieved the best overall IBI estimation accuracy, with an MAE of 28.7 milliseconds and the highest correlation coefficient (0.77). The BiLSTM method, while having a slightly higher MAE (40.1 milliseconds), demonstrated superior robustness to outliers by achieving the lowest 95th percentile error (9.5%). The MLP and CNN methods showed moderate performance, and the adaptive windowing variant of CLIE performed the worst. These findings demonstrate that accurate IBI extraction from BCG signals is feasible, and that both the CLIE and BiLSTM approaches are promising candidates for implementation in intelligent, home-based cardiac monitoring systems—offering, respectively, high accuracy and strong resilience to large errors.https://jcme.iut.ac.ir/article_3678_2151b4c76b4dcb048d06a5c32942b6f6.pdfIsfahan University of TechnologyJournal of Computational Methods in Engineering2228-769844220251122Evaluation of the Creep Behavior of HP35Nb Heat Resistant Steel Using Finite Element MethodEvaluation of the Creep Behavior of HP35Nb Heat Resistant Steel Using Finite Element Method6378367910.47176/jcme.44.2.1059FAAboozarTaherizadehDepartment of Materials Engineering. Isfahan University of Technology, Isfahan, 84156-83111, IranYasaminMohammadiDepartment of Materials Engineering. Isfahan University of Technology, Isfahan, 84156-83111, IranJournal Article20250713Prediction of creep behavior in heat-resistant alloys, especially under elevated stress and temperature conditions, is crucial for the design and optimization of industrial components. Significant risks and costs often arise from sudden damage and failure due to creep. Experimental tests for evaluation of creep behavior are time-consuming and expensive; thus, developing accurate numerical models for creep prediction is essential. The power law model and theta projection model are known as two widely-used methods. This study investigated the creep behavior of HP35Nb alloy under varying stress conditions and elevated temperatures through simulations using both power law and theta projection models. A comparison of the numerical results with experimental data was conducted to assess the accuracy of each model in predicting creep behavior. The findings showed that the theta projection model, due to its ability to represent all three stages of creep, aligned more closely with the experimental data and was identified as a suitable choice for predicting the long-term creep. This model was able to estimate rupture time with minimal discrepancy compared to the experimental results, and displayed consistent performance under both high and low stresses. In contrast, the power law model demonstrated high accuracy during the initial and secondary stages of creep, but its predicted strains were lower than the experimental values in the tertiary stage. Moreover, at elevated stresses, the power law model exhibited significant deviation from the experimental data, while its predictions were more accurate at lower stresses.Prediction of creep behavior in heat-resistant alloys, especially under elevated stress and temperature conditions, is crucial for the design and optimization of industrial components. Significant risks and costs often arise from sudden damage and failure due to creep. Experimental tests for evaluation of creep behavior are time-consuming and expensive; thus, developing accurate numerical models for creep prediction is essential. The power law model and theta projection model are known as two widely-used methods. This study investigated the creep behavior of HP35Nb alloy under varying stress conditions and elevated temperatures through simulations using both power law and theta projection models. A comparison of the numerical results with experimental data was conducted to assess the accuracy of each model in predicting creep behavior. The findings showed that the theta projection model, due to its ability to represent all three stages of creep, aligned more closely with the experimental data and was identified as a suitable choice for predicting the long-term creep. This model was able to estimate rupture time with minimal discrepancy compared to the experimental results, and displayed consistent performance under both high and low stresses. In contrast, the power law model demonstrated high accuracy during the initial and secondary stages of creep, but its predicted strains were lower than the experimental values in the tertiary stage. Moreover, at elevated stresses, the power law model exhibited significant deviation from the experimental data, while its predictions were more accurate at lower stresses.https://jcme.iut.ac.ir/article_3679_74791edf1f8e8b8289a5067737630874.pdfIsfahan University of TechnologyJournal of Computational Methods in Engineering2228-769844220251122Impact of Calcified Plaque Material Properties on TAVI Performance: A Finite Element AnalysisImpact of Calcified Plaque Material Properties on TAVI Performance: A Finite Element Analysis79104367710.47176/jcme.44.2.1055FAMohammadAsadiDepartment of Mechanical Engineering, Isfahan University of Technology, Isfahan, P. O. Box: 8415683111, IranMehdiSalmani TehraniDepartment of Mechanical Engineering, Isfahan University of Technology, Isfahan, P. O. Box: 8415683111, IranZahraMatin Ghahfarokhi-Department of Mechanical Engineering, Jundi-Shapur University of Technology, Dezful, Iran.
- Department of Mechanical Engineering, Qom University of Technology, Qom, IranJournal Article20250701Transcatheter aortic valve implantation (TAVI) has revolutionized the treatment of aortic stenosis, offering a minimally invasive alternative to traditional open-heart surgery. Despite its advantages, TAVI procedures are still associated with substantial complications, including embolism, paravalvular leak, aortic root rupture, and prosthesis migration. To enhance procedural safety and efficacy, advanced computational simulations are increasingly being employed as powerful tools to aid clinicians in pre-operative planning and mitigate potential risks. In this paper, a patient-specific approach was utilized to reconstruct a high-fidelity 3D model of a patient's heart from CT scan images using Mimics software. To achieve this, three distinct finite element simulations were performed to model the TAVI implantation process under various conditions; a healthy valve without calcification, and valves with calcified plaques exhibiting two different mechanical properties. The simulation results demonstrated that the presence and specific mechanical characteristics of calcified plaques within the native aortic valve profoundly impact the stress distribution and structural deformations of both the host cardiac tissue and the prosthetic valve. Specifically, calcified lesions significantly altered the biomechanical environment, leading to localized stress concentrations and altered leaflet coaptation. This research underscores the critical importance of accurately incorporating the mechanical properties of calcified plaques into computational models for precise prediction of implanted prosthetic valve behavior and optimization of TAVI outcomes. These findings contribute valuable insights for personalized procedural planning and the development of next-generation TAVI devices.Transcatheter aortic valve implantation (TAVI) has revolutionized the treatment of aortic stenosis, offering a minimally invasive alternative to traditional open-heart surgery. Despite its advantages, TAVI procedures are still associated with substantial complications, including embolism, paravalvular leak, aortic root rupture, and prosthesis migration. To enhance procedural safety and efficacy, advanced computational simulations are increasingly being employed as powerful tools to aid clinicians in pre-operative planning and mitigate potential risks. In this paper, a patient-specific approach was utilized to reconstruct a high-fidelity 3D model of a patient's heart from CT scan images using Mimics software. To achieve this, three distinct finite element simulations were performed to model the TAVI implantation process under various conditions; a healthy valve without calcification, and valves with calcified plaques exhibiting two different mechanical properties. The simulation results demonstrated that the presence and specific mechanical characteristics of calcified plaques within the native aortic valve profoundly impact the stress distribution and structural deformations of both the host cardiac tissue and the prosthetic valve. Specifically, calcified lesions significantly altered the biomechanical environment, leading to localized stress concentrations and altered leaflet coaptation. This research underscores the critical importance of accurately incorporating the mechanical properties of calcified plaques into computational models for precise prediction of implanted prosthetic valve behavior and optimization of TAVI outcomes. These findings contribute valuable insights for personalized procedural planning and the development of next-generation TAVI devices.https://jcme.iut.ac.ir/article_3677_71d7232b9fed020ca23729017873089e.pdfIsfahan University of TechnologyJournal of Computational Methods in Engineering2228-769844220251122Simulation of Tumor Growth and Division Under Chemical Driving Forces Using the Phase Field MethodSimulation of Tumor Growth and Division Under Chemical Driving Forces Using the Phase Field Method105126371910.47176/jcme.44.2.1058FAPeymanNaderiDepartment of Mechanical Engineering, Isfahan University of Technology, Isfahan, IranMehdiJavanbakhtDepartment of Mechanical Engineering, Isfahan University of Technology, Isfahan, IranAhmadrezaPishevarDepartment of Mechanical Engineering, Isfahan University of Technology, Isfahan, IranJournal Article20250712In this paper, growth and division of tumors are investigated using the phase field modeling. By considering the environment as fluid, conditions such as chemotaxis and haptotaxis processes are used to study the growth and division of tumors. In this model, growth pressure, velocity field and different concentrations coupled with the phase field equation are used to simulate the membrane of the tumor, separating it from the extracellular matrix (ECM). Also, processes such as chemotaxis and haptotaxis, different meshes, initial tumor ovalities, interface thicknesses and surface tensions are used to model the tumor evolution. The obtained results show that in tumors with higher initial ovality, the evolution accelerates but the morphology remains unchanged. Using this model, a membrane thickness range is found, out of which the growth is unphysically suppressed. Large and small surface tension coefficient suppresses the growth and leads to the interface widening, respectively. The physical range of the surface tension coefficient is also found, below which the growth is suppressed and above which interface widening occurs. The rate of tumor growth increases by adding the haptotaxis and in particular, chemotaxis. The former results in tumor dividing while the latter causes the tumor branching. Higher taxis coefficient results in higher branching rate. Chemotaxis shows a larger effect on the tumor morphology and kinetics than the haptotaxis. Combining both mechanisms leads to simultaneous tumor division and branching. The obtained results help for a better understanding of the key parameters in tumor growth and division.In this paper, growth and division of tumors are investigated using the phase field modeling. By considering the environment as fluid, conditions such as chemotaxis and haptotaxis processes are used to study the growth and division of tumors. In this model, growth pressure, velocity field and different concentrations coupled with the phase field equation are used to simulate the membrane of the tumor, separating it from the extracellular matrix (ECM). Also, processes such as chemotaxis and haptotaxis, different meshes, initial tumor ovalities, interface thicknesses and surface tensions are used to model the tumor evolution. The obtained results show that in tumors with higher initial ovality, the evolution accelerates but the morphology remains unchanged. Using this model, a membrane thickness range is found, out of which the growth is unphysically suppressed. Large and small surface tension coefficient suppresses the growth and leads to the interface widening, respectively. The physical range of the surface tension coefficient is also found, below which the growth is suppressed and above which interface widening occurs. The rate of tumor growth increases by adding the haptotaxis and in particular, chemotaxis. The former results in tumor dividing while the latter causes the tumor branching. Higher taxis coefficient results in higher branching rate. Chemotaxis shows a larger effect on the tumor morphology and kinetics than the haptotaxis. Combining both mechanisms leads to simultaneous tumor division and branching. The obtained results help for a better understanding of the key parameters in tumor growth and division.https://jcme.iut.ac.ir/article_3719_9e740b84bb48a64dde25061566299467.pdfIsfahan University of TechnologyJournal of Computational Methods in Engineering2228-769844220251122Robust Optimization Model for Project Scheduling Problem with Resource ConstraintsRobust Optimization Model for Project Scheduling Problem with Resource Constraints127149372010.47176/jcme.44.2.1060FANasimNahavandiIndustrial Engineering, Tarbiat Modares University, Tehran, Iran0000-0002-1445-6557MozhdehMoradiIndustrial Engineering, Tarbiat Modares University, Tehran, IranJournal Article20250727Accurate management of projects is necessary to keep companies competitive. The resource-constrained project scheduling problem (RCPSP) includes activities that must be planned according to priority and resource constraints, and minimize the project completion time. This has become a well-known standard problem in the field of project planning, and various formats of the initial RCPSP have been developed. On the other hand, due to the inherent uncertainty of the project environment, which causes uncertainty in the time and resource parameters, examining such issues considering this uncertainty at the same time as the complexity of the issue is very necessary. The purpose of this research is to provide an approach for the allocation of limited resources in the multi-project scheduling problem, with the uncertainty of the duration of the project activities. Also, in the space of this problem, the selection of the supplier of resources has been considered as the objectives of the problem. After introducing the stable model based on the scenario, the answers were analyzed using the meta-heuristic algorithm of multi-objective genetic, and the results show that with an increase in the cost of providing resources, the cost of the whole project increases, but this increase is more in the fourth scenario. This increase is not only in the cost, but also leads to an increase in the project time. On the other hand, the risk of supplier failure can affect the cost and time of the project. As the risk of supplier failure increases, the cost increases and the highest increase occurs in scenario 1, while in scenario 3, the lowest cost increase occurs due to the risk of supplier failure.Accurate management of projects is necessary to keep companies competitive. The resource-constrained project scheduling problem (RCPSP) includes activities that must be planned according to priority and resource constraints, and minimize the project completion time. This has become a well-known standard problem in the field of project planning, and various formats of the initial RCPSP have been developed. On the other hand, due to the inherent uncertainty of the project environment, which causes uncertainty in the time and resource parameters, examining such issues considering this uncertainty at the same time as the complexity of the issue is very necessary. The purpose of this research is to provide an approach for the allocation of limited resources in the multi-project scheduling problem, with the uncertainty of the duration of the project activities. Also, in the space of this problem, the selection of the supplier of resources has been considered as the objectives of the problem. After introducing the stable model based on the scenario, the answers were analyzed using the meta-heuristic algorithm of multi-objective genetic, and the results show that with an increase in the cost of providing resources, the cost of the whole project increases, but this increase is more in the fourth scenario. This increase is not only in the cost, but also leads to an increase in the project time. On the other hand, the risk of supplier failure can affect the cost and time of the project. As the risk of supplier failure increases, the cost increases and the highest increase occurs in scenario 1, while in scenario 3, the lowest cost increase occurs due to the risk of supplier failure.https://jcme.iut.ac.ir/article_3720_532b81fa223a1b1ec74139a5b8151d12.pdfIsfahan University of TechnologyJournal of Computational Methods in Engineering2228-769844220251122On the Use of Error Weight Function in the Numerical Solution of Nonlocal Problems Using the Finite Element MethodOn the Use of Error Weight Function in the Numerical Solution of Nonlocal Problems Using the Finite Element Method151165372110.47176/jcme.44.2.1062FAAliGeramiDepartment of Mechanical Engineering, Isfahan University of TechnologyMohammadSilaniDepartment of Mechanical Engineering, Isfahan University of TechnologyMahdiJavanbakhtDepartment of Mechanical Engineering, Isfahan University of TechnologyJournal Article20250830Various functions can serve as weight functions in the numerical solution of nonlocal problems, including the error function. The value of the error function at a given point depends on the nonlocal parameter and the distance from the target point. To reduce computational costs in numerical procedures, interactions are often neglected between the points with a distance above a specific radius (interaction radius). Consequently, a relationship between the radius and the nonlocal parameter in the error function is established to determine the optimal interaction radius. Due to the widespread utilization of the finite element method in the solution of non-local problems, and considering application of the integral value of the weight function in two-phase kernels, the effect of element size on the calculation of the integral value is investigated. The findings indicate that, due to discretization, the error in calculating the integral value of the weight function increases as the ratio of the element size to the nonlocal parameter grows. This can result in an integral value exceeding 1, thereby violating the normalization condition of the weight function.Various functions can serve as weight functions in the numerical solution of nonlocal problems, including the error function. The value of the error function at a given point depends on the nonlocal parameter and the distance from the target point. To reduce computational costs in numerical procedures, interactions are often neglected between the points with a distance above a specific radius (interaction radius). Consequently, a relationship between the radius and the nonlocal parameter in the error function is established to determine the optimal interaction radius. Due to the widespread utilization of the finite element method in the solution of non-local problems, and considering application of the integral value of the weight function in two-phase kernels, the effect of element size on the calculation of the integral value is investigated. The findings indicate that, due to discretization, the error in calculating the integral value of the weight function increases as the ratio of the element size to the nonlocal parameter grows. This can result in an integral value exceeding 1, thereby violating the normalization condition of the weight function.https://jcme.iut.ac.ir/article_3721_9e406957d45fcb6c6f38c2ada7bace91.pdfIsfahan University of TechnologyJournal of Computational Methods in Engineering2228-769844220251122Modeling of Transient Nonlinear Heat Conduction with Temperature-Dependent Material PropertiesModeling of Transient Nonlinear Heat Conduction with Temperature-Dependent Material Properties167188372210.47176/jcme.44.2.1066FAAminMoosaieDepartment of Mechanical Engineering, Isfahan University of Technology0000-0002-4976-914XJournal Article20251012In recent years, growing attention has been directed toward developing more realistic physical models for simulating complex and technically significant problems. This approach often leads to the formulation of nonlinear, multidimensional, and highly intricate models, whose analysis and solution require advanced numerical and analytical techniques. One of the most notable examples in this context is the transient and nonlinear heat conduction problem with temperature-dependent material properties. This paper presents, analyzes, and evaluates a comprehensive set of possible mathematical models for simulating this phenomenon, some of which are introduced here for the first time. Special emphasis is placed on the application of the Kirchhoff integral transform, typically used in steady-state nonlinear problems, and its precise implementation under transient conditions is thoroughly investigated. Based on this transform, both linear and nonlinear models are derived and compared. The linear models are particularly valuable due to their ability to yield accurate analytical solutions, facilitate inverse heat conduction analyses, enable temperature field control strategies, and offer clearer physical interpretations. Additionally, three new nonlinear models are proposed, two of which demonstrate very high accuracy, while the third, despite its lower precision, provides superior computational simplicity and faster performance. The findings of this study can serve as a foundation for developing more advanced thermal models and optimizing the design of engineering heat transfer systemsIn recent years, growing attention has been directed toward developing more realistic physical models for simulating complex and technically significant problems. This approach often leads to the formulation of nonlinear, multidimensional, and highly intricate models, whose analysis and solution require advanced numerical and analytical techniques. One of the most notable examples in this context is the transient and nonlinear heat conduction problem with temperature-dependent material properties. This paper presents, analyzes, and evaluates a comprehensive set of possible mathematical models for simulating this phenomenon, some of which are introduced here for the first time. Special emphasis is placed on the application of the Kirchhoff integral transform, typically used in steady-state nonlinear problems, and its precise implementation under transient conditions is thoroughly investigated. Based on this transform, both linear and nonlinear models are derived and compared. The linear models are particularly valuable due to their ability to yield accurate analytical solutions, facilitate inverse heat conduction analyses, enable temperature field control strategies, and offer clearer physical interpretations. Additionally, three new nonlinear models are proposed, two of which demonstrate very high accuracy, while the third, despite its lower precision, provides superior computational simplicity and faster performance. The findings of this study can serve as a foundation for developing more advanced thermal models and optimizing the design of engineering heat transfer systemshttps://jcme.iut.ac.ir/article_3722_56e6a93212e4482d99c84a639d254b67.pdfIsfahan University of TechnologyJournal of Computational Methods in Engineering2228-769844220251122Parametric Study of the Thrust Augmentor of a Pulsejet Engine in a Low-Mach-Number Compressible Flow Regime Using Unsteady Numerical SimulationParametric Study of the Thrust Augmentor of a Pulsejet Engine in a Low-Mach-Number Compressible Flow Regime Using Unsteady Numerical Simulation189224372310.47176/jcme.44.2.1064FAYousofRiasatfardFaculty of Mechanical Engineering, Isfahan University of TechnologyMahdiNili AhmadabadiFaculty of Mechanical Engineering, Isfahan University of TechnologyFarhadGhadakFaculty of Aerospace Engineering, Imam Hossein Comprehensive UniversityJournal Article20250915The pulsejet engine has always attracted the attention of researchers in the field of air-breathing propulsion due to its simple structure, low weight, and inexpensive manufacturing process. In this study, a parametric optimization was performed on an augmentor installed at the exhaust of a pulsejet engine. To this end, a transient axisymmetric numerical simulation of a valved pulsejet engine under flight conditions was carried out using ANSYS Fluent. The k–ω SST turbulence model was employed to capture the flow behavior, and the combustion process was modeled as a volumetric energy source. A suitable downstream section of the engine was selected to accurately compute the net thrust using the momentum equation. Simulations were conducted over multiple consecutive cycles until the flow and performance parameters exhibited harmonic oscillations. The thrust was then time-averaged over the final three cycles. Subsequently, assuming a cylindrical augmentor and parameterizing its geometry, the effects of three key parameters including the augmentor’s distance from the engine exit, its diameter and its length, on the thrust augmentation were investigated. Finally, a detailed aerodynamic analysis of the engine, with and without the augmentor, was presented. The results demonstrated that the optimized augmentor, by entraining ambient air and enhancing the exhaust momentum, increased the net thrust by approximately 170% compared to the configuration without an augmentor.The pulsejet engine has always attracted the attention of researchers in the field of air-breathing propulsion due to its simple structure, low weight, and inexpensive manufacturing process. In this study, a parametric optimization was performed on an augmentor installed at the exhaust of a pulsejet engine. To this end, a transient axisymmetric numerical simulation of a valved pulsejet engine under flight conditions was carried out using ANSYS Fluent. The k–ω SST turbulence model was employed to capture the flow behavior, and the combustion process was modeled as a volumetric energy source. A suitable downstream section of the engine was selected to accurately compute the net thrust using the momentum equation. Simulations were conducted over multiple consecutive cycles until the flow and performance parameters exhibited harmonic oscillations. The thrust was then time-averaged over the final three cycles. Subsequently, assuming a cylindrical augmentor and parameterizing its geometry, the effects of three key parameters including the augmentor’s distance from the engine exit, its diameter and its length, on the thrust augmentation were investigated. Finally, a detailed aerodynamic analysis of the engine, with and without the augmentor, was presented. The results demonstrated that the optimized augmentor, by entraining ambient air and enhancing the exhaust momentum, increased the net thrust by approximately 170% compared to the configuration without an augmentor.https://jcme.iut.ac.ir/article_3723_afa299a4d1d8c52e75dd8a24c3ce534f.pdf