Pub Date : 2025-03-03DOI: 10.1016/j.simpat.2025.103095
Paweł Marcin Kozyra
Both the system reliability and the resilience evaluation of multi-state flow networks (MFNs) play a crucial role in designing and analyzing these networks. The system reliability at level is the probability of successfully transmitting at least units of flow. In turn, system resilience allows us to analyze the ability of systems to withstand and bounce back from disruptive events. The paper presents a new simulation approach based on minimal cuts (MCs) and parallel computations to compute the system reliability for all possible non-integer levels. An extension with a time attribute is also considered to investigate the reliability degradation with time. Moreover, it also introduces a novel heuristic that for a given integer and a state vector , finds an MC for which the capacity under the system state is the smallest among MCs containing some of coordinates of with the smallest capacities. It is also shown how this approach can be used to compute the network resilience at a given time and the system integrated resilience metric is introduced. Numerical experiments are conducted to demonstrate the efficiency and advantages of the presented algorithm.
{"title":"A simulation approach with heuristic rules for reliability estimation of two-terminal multi-state networks based on minimal cuts and parallel computations","authors":"Paweł Marcin Kozyra","doi":"10.1016/j.simpat.2025.103095","DOIUrl":"10.1016/j.simpat.2025.103095","url":null,"abstract":"<div><div>Both the system reliability and the resilience evaluation of multi-state flow networks (MFNs) play a crucial role in designing and analyzing these networks. The system reliability at level <span><math><mi>d</mi></math></span> is the probability of successfully transmitting at least <span><math><mi>d</mi></math></span> units of flow. In turn, system resilience allows us to analyze the ability of systems to withstand and bounce back from disruptive events. The paper presents a new simulation approach based on minimal cuts (MCs) and parallel computations to compute the system reliability for all possible non-integer levels. An extension with a time attribute is also considered to investigate the reliability degradation with time. Moreover, it also introduces a novel heuristic that for a given integer <span><math><mi>K</mi></math></span> and a state vector <span><math><mi>x</mi></math></span>, finds an MC for which the capacity under the system state <span><math><mi>x</mi></math></span> is the smallest among MCs containing some of <span><math><mi>K</mi></math></span> coordinates of <span><math><mi>x</mi></math></span> with the smallest capacities. It is also shown how this approach can be used to compute the network resilience at a given time and the system integrated resilience metric is introduced. Numerical experiments are conducted to demonstrate the efficiency and advantages of the presented algorithm.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"141 ","pages":"Article 103095"},"PeriodicalIF":3.5,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143550234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-27DOI: 10.1016/j.simpat.2025.103097
Panagiotis Karmiris-Obratański
Electric Discharge Machining (EDM) is a non-conventional process well-suited for machining hard-to-machine materials, offering high dimensional accuracy and an acceptable surface finish where traditional methods fall short. This study investigates the machining of Hastelloy C-276 using a composite copper-tungsten electrode through a combined experimental approach and a multiphysics thermo-fluid FEM model to simulate crater formation. The model incorporates a Gaussian heat source, energy absorption coefficients, and molten material flow under plasma pressure gradients, considering latent heat, mushy zone viscosity, and temperature-dependent thermophysical properties. Results indicate that optimizing plasma flushing efficiency (∼30 %) at low current and pulse-on time (9 A, 50 µs) enhances material removal while minimizing white layer formation. Higher pulse-on times lead to increased white layer thickness, stabilizing at 25 [A] and 200 [µs]. Surface roughness rises by 33.3 % at 9 [A] and up to 40 % at 25 [A] as pulse duration extends from 50 to 200 µs, highlighting the influence of increased energy input. The model accurately predicts material removal rates and white layer thicknesses, with deviations of 1–5 % from experimental results. These findings provide insights for optimizing EDM parameters to balance material removal efficiency, surface integrity, and process stability.
{"title":"Multiphysics thermo-fluid modeling and experimental validation of crater formation and rim development in EDM of inconel C-276","authors":"Panagiotis Karmiris-Obratański","doi":"10.1016/j.simpat.2025.103097","DOIUrl":"10.1016/j.simpat.2025.103097","url":null,"abstract":"<div><div>Electric Discharge Machining (EDM) is a non-conventional process well-suited for machining hard-to-machine materials, offering high dimensional accuracy and an acceptable surface finish where traditional methods fall short. This study investigates the machining of Hastelloy C-276 using a composite copper-tungsten electrode through a combined experimental approach and a multiphysics thermo-fluid FEM model to simulate crater formation. The model incorporates a Gaussian heat source, energy absorption coefficients, and molten material flow under plasma pressure gradients, considering latent heat, mushy zone viscosity, and temperature-dependent thermophysical properties. Results indicate that optimizing plasma flushing efficiency (∼30 %) at low current and pulse-on time (9 A, 50 µs) enhances material removal while minimizing white layer formation. Higher pulse-on times lead to increased white layer thickness, stabilizing at 25 [A] and 200 [µs]. Surface roughness rises by 33.3 % at 9 [A] and up to 40 % at 25 [A] as pulse duration extends from 50 to 200 µs, highlighting the influence of increased energy input. The model accurately predicts material removal rates and white layer thicknesses, with deviations of 1–5 % from experimental results. These findings provide insights for optimizing EDM parameters to balance material removal efficiency, surface integrity, and process stability.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"141 ","pages":"Article 103097"},"PeriodicalIF":3.5,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143520234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-20DOI: 10.1016/j.simpat.2025.103093
Sihem Tlili , Sami Mnasri , Thierry Val
Due to their robustness and resource limitations, IoT objects pose several multi-objective optimization challenges, making routing in mesh IoT networks a critical issue. Meanwhile, meta-heuristic and multi-objective optimization approaches provide promising results. This paper proposes a novel geographic, hybrid and multi-objective routing method for IoT mesh networks. Routing is formulated as an optimization problem with multiple objective functions. The proposed Improved Multi-Objective Gray Wolf Optimizer (IMOGWO) meta-heuristic is applied between communicating objects in a distributed manner to solve and optimize routing decisions. The work presents the first application and evaluation of IMOGWO to a real-world problem, specifically routing in IoT mesh networks. Combined IoT simulations (using both real and simulated nodes) are performed to evaluate the introduced approach and show its effectiveness in comparison to other existing routing methods, including MOGWO-based routing, an AcNSGA-III-based QoS routing and a BFOA-based geographic routing algorithm. Results indicate that the proposed approach enhances network performance. Particularly, IMOGWO increases the stability period by 9.30% compared to MOGWO, 20.51% compared to AcNSGA-III and 38.24% compared to BFOA. In addition, it ensures a better packet delivery ratio (92.2%). Furthermore, it maintains a lower average transmission latency (1.93s) than AcNSGA-III and BFOA. These improvements, validated through inferential statistical tests, demonstrate that IMOGWO optimizes routing for IoT mesh networks effectively.
{"title":"Edge-computing-enabled hybrid and multi-objective geographic routing for mesh IoT networks: An IMOGWO-based approach","authors":"Sihem Tlili , Sami Mnasri , Thierry Val","doi":"10.1016/j.simpat.2025.103093","DOIUrl":"10.1016/j.simpat.2025.103093","url":null,"abstract":"<div><div>Due to their robustness and resource limitations, IoT objects pose several multi-objective optimization challenges, making routing in mesh IoT networks a critical issue. Meanwhile, meta-heuristic and multi-objective optimization approaches provide promising results. This paper proposes a novel geographic, hybrid and multi-objective routing method for IoT mesh networks. Routing is formulated as an optimization problem with multiple objective functions. The proposed Improved Multi-Objective Gray Wolf Optimizer (IMOGWO) meta-heuristic is applied between communicating objects in a distributed manner to solve and optimize routing decisions. The work presents the first application and evaluation of IMOGWO to a real-world problem, specifically routing in IoT mesh networks. Combined IoT simulations (using both real and simulated nodes) are performed to evaluate the introduced approach and show its effectiveness in comparison to other existing routing methods, including MOGWO-based routing, an AcNSGA-III-based QoS routing and a BFOA-based geographic routing algorithm. Results indicate that the proposed approach enhances network performance. Particularly, IMOGWO increases the stability period by 9.30% compared to MOGWO, 20.51% compared to AcNSGA-III and 38.24% compared to BFOA. In addition, it ensures a better packet delivery ratio (92.2%). Furthermore, it maintains a lower average transmission latency (1.93s) than AcNSGA-III and BFOA. These improvements, validated through inferential statistical tests, demonstrate that IMOGWO optimizes routing for IoT mesh networks effectively.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"140 ","pages":"Article 103093"},"PeriodicalIF":3.5,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143463467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-19DOI: 10.1016/j.simpat.2025.103092
Sylvain Chabanet, Hind Bril El-Haouzi, Philippe Thomas
Many challenges have been raised in the scientific literature regarding the development of digital twins that can predict future states of production processes from data streams. This study is concerned with the coordination of several of their submodels to balance precision with computational requirements. A method to use stream-based active learning sampling strategies to couple two such models is proposed. Both models perform the same prediction task but have different advantages and disadvantages. The first is a simulation model that is supposed to have a high fidelity level, but to be slow. The second is a machine learning model, which is fast but less accurate and requires many labeled examples to be trained on, which may require a lot of time and effort to gather. The objective is to leverage confidence measures in the predictions of the machine learning model. These measures are used to couple the two models and take advantage of their respective strengths. In particular, the aim is to reduce the digital twin’s average prediction error while operating under limited computational capacity. Moreover, an application within the sawmill industry and numerical experiments are presented.
{"title":"Active learning confidence measures for coupling strategies in digital twins integrating simulation and data-driven submodels","authors":"Sylvain Chabanet, Hind Bril El-Haouzi, Philippe Thomas","doi":"10.1016/j.simpat.2025.103092","DOIUrl":"10.1016/j.simpat.2025.103092","url":null,"abstract":"<div><div>Many challenges have been raised in the scientific literature regarding the development of digital twins that can predict future states of production processes from data streams. This study is concerned with the coordination of several of their submodels to balance precision with computational requirements. A method to use stream-based active learning sampling strategies to couple two such models is proposed. Both models perform the same prediction task but have different advantages and disadvantages. The first is a simulation model that is supposed to have a high fidelity level, but to be slow. The second is a machine learning model, which is fast but less accurate and requires many labeled examples to be trained on, which may require a lot of time and effort to gather. The objective is to leverage confidence measures in the predictions of the machine learning model. These measures are used to couple the two models and take advantage of their respective strengths. In particular, the aim is to reduce the digital twin’s average prediction error while operating under limited computational capacity. Moreover, an application within the sawmill industry and numerical experiments are presented.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"140 ","pages":"Article 103092"},"PeriodicalIF":3.5,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-19DOI: 10.1016/j.simpat.2025.103089
Alishba Tahir, Rafia Mumtaz, Muhammad Saqib Irshad
Connected and Autonomous Vehicles (CAVs) are transforming transportation. The paper describes a new method of fog simulation applied to LiDAR data for self-driving cars with a focus on enhancing 3D object detection in low visibility conditions. As opposed to the previously used methods, synthetic fog augmentation is combined with deep learning models and it is proven that the proposed method is superior to the previous methods when it comes to object detection accuracy in various fog levels. Another challenge that has been discussed in the study to ensure the reliability of autonomous navigation is the question of how the fog and the LiDAR point cloud should be modeled which eventually helps in improving the decision-making safety and operation. Fog can drastically reduce visibility and safety, making it crucial to test LiDAR-based perception algorithms for CAVs under such conditions. These simulations aim to ensure CAVs can navigate safely and efficiently through fog. However, challenges like sensor calibration and data integration need to be addressed. Despite these hurdles, the research foresees a future where CAVs, equipped with advanced LiDAR-based perception algorithms and fog-handling capabilities, enhance safety and efficiency in transportation. Notably, using synthetic fog augmentation improved detection by 5.27% for cars and 8.11% for cyclists. Furthermore, the study showcases improvements of 4.76%, 2.92%, and 3% in Mean Average Precision (mAP) across the distinct object categories of easy, moderate, and hard difficulty levels, respectively.
{"title":"3D vision object detection for autonomous driving in fog using LiDaR","authors":"Alishba Tahir, Rafia Mumtaz, Muhammad Saqib Irshad","doi":"10.1016/j.simpat.2025.103089","DOIUrl":"10.1016/j.simpat.2025.103089","url":null,"abstract":"<div><div>Connected and Autonomous Vehicles (CAVs) are transforming transportation. The paper describes a new method of fog simulation applied to LiDAR data for self-driving cars with a focus on enhancing 3D object detection in low visibility conditions. As opposed to the previously used methods, synthetic fog augmentation is combined with deep learning models and it is proven that the proposed method is superior to the previous methods when it comes to object detection accuracy in various fog levels. Another challenge that has been discussed in the study to ensure the reliability of autonomous navigation is the question of how the fog and the LiDAR point cloud should be modeled which eventually helps in improving the decision-making safety and operation. Fog can drastically reduce visibility and safety, making it crucial to test LiDAR-based perception algorithms for CAVs under such conditions. These simulations aim to ensure CAVs can navigate safely and efficiently through fog. However, challenges like sensor calibration and data integration need to be addressed. Despite these hurdles, the research foresees a future where CAVs, equipped with advanced LiDAR-based perception algorithms and fog-handling capabilities, enhance safety and efficiency in transportation. Notably, using synthetic fog augmentation improved detection by 5.27% for cars and 8.11% for cyclists. Furthermore, the study showcases improvements of 4.76%, 2.92%, and 3% in Mean Average Precision (mAP) across the distinct object categories of easy, moderate, and hard difficulty levels, respectively.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"140 ","pages":"Article 103089"},"PeriodicalIF":3.5,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With rapid development of Internet of Vehicles (IoVs), various computation-intensive vehicular applications impose great challenges on the limited computing resources of vehicles. To improve the user experience of vehicular applications, the emerging vehicular edge computing (VEC) offloads tasks to roadside edge servers. However, competition over communication and computing resources is inevitable among vehicles. How to make optimal task offloading decisions for vehicles, so as to reduce delay, balance server load and save energy, is worth researching in-depth. In this paper, first, a vehicle-to-vehicle (V2V) communication path acquisition algorithm is designed, and a task caching mechanism introduced which cache some completed applications and related codes on the edge server. Then, a vehicular networking model with joint task caching mechanism for edge–cloud collaboration is proposed. To obtain the near-optimal solutions to this problem, we design a multi-objective evolutionary algorithm based joint task caching and edge–cloud computing decision algorithm (JTCEC-MOEA/D) to maximize the utilities of vehicles. Finally, the proposed algorithm is evaluated by the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method. The simulation results show that the proposed algorithm can make optimal task offloading-making for vehicles.
{"title":"Computation offloading and task caching in the cloud–edge collaborative IoVs: A multi-objective evolutionary algorithm","authors":"Zi-xin Chai , Zheng-yi Chai , Junjun Ren , Dong Yuan","doi":"10.1016/j.simpat.2025.103087","DOIUrl":"10.1016/j.simpat.2025.103087","url":null,"abstract":"<div><div>With rapid development of Internet of Vehicles (IoVs), various computation-intensive vehicular applications impose great challenges on the limited computing resources of vehicles. To improve the user experience of vehicular applications, the emerging vehicular edge computing (VEC) offloads tasks to roadside edge servers. However, competition over communication and computing resources is inevitable among vehicles. How to make optimal task offloading decisions for vehicles, so as to reduce delay, balance server load and save energy, is worth researching in-depth. In this paper, first, a vehicle-to-vehicle (V2V) communication path acquisition algorithm is designed, and a task caching mechanism introduced which cache some completed applications and related codes on the edge server. Then, a vehicular networking model with joint task caching mechanism for edge–cloud collaboration is proposed. To obtain the near-optimal solutions to this problem, we design a multi-objective evolutionary algorithm based joint task caching and edge–cloud computing decision algorithm (JTCEC-MOEA/D) to maximize the utilities of vehicles. Finally, the proposed algorithm is evaluated by the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method. The simulation results show that the proposed algorithm can make optimal task offloading-making for vehicles.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"141 ","pages":"Article 103087"},"PeriodicalIF":3.5,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143486607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-15DOI: 10.1016/j.simpat.2025.103090
Mustafa Daraghmeh , Yaser Jararweh , Anjali Agarwal
Serverless computing offers scalability and cost-efficiency, but balancing performance and cost remains challenging, particularly in scaling decisions that can lead to cold starts or resource misallocation. This research is motivated by the need to minimize the impact of cold starts and optimize resource utilization in serverless applications by developing intelligent, data-driven scaling decisions. We delve into using machine learning and feature engineering to model and simulate predictions for optimal scaling decisions for Azure Function Apps (AFA). Our focus lies in predicting the ideal timing for provisioning or de-provisioning the Function App’s environment. Using historical invocation data, we applied a sliding window to transform the time-series data into patterns categorized as load or unload classes, considering various target periods. To identify the most effective model, we compared the performance of various baseline models with and without calibration (isotonic and sigmoid) to enhance precision. In addition, we assess multiple feature extraction methods in invocation patterns and explore the use of Principal Component Analysis (PCA) for dimensionality reduction to reduce computation costs. Using the best-identified configurations, we model and simulate the class patterns over time to compare the actual classes with the predicted ones, focusing on memory usage and the costs associated with cold starts. The proposed model is thoroughly evaluated using various metrics under different setups, revealing notable improvements in scaling decisions achieved by applying calibration and feature engineering methods. These findings demonstrate the potential of machine learning for intelligent, data-driven scaling decisions in serverless computing, offering valuable insights for cloud providers to optimize resource allocation and for developers to build more efficient and responsive serverless applications. Specifically, the proposed method can be integrated into serverless platforms to automatically adjust resource provisioning based on predicted workload demands, reducing cold start latency and improving cost-effectiveness.
{"title":"Leveraging machine learning and feature engineering for optimal data-driven scaling decision in serverless computing","authors":"Mustafa Daraghmeh , Yaser Jararweh , Anjali Agarwal","doi":"10.1016/j.simpat.2025.103090","DOIUrl":"10.1016/j.simpat.2025.103090","url":null,"abstract":"<div><div>Serverless computing offers scalability and cost-efficiency, but balancing performance and cost remains challenging, particularly in scaling decisions that can lead to cold starts or resource misallocation. This research is motivated by the need to minimize the impact of cold starts and optimize resource utilization in serverless applications by developing intelligent, data-driven scaling decisions. We delve into using machine learning and feature engineering to model and simulate predictions for optimal scaling decisions for Azure Function Apps (AFA). Our focus lies in predicting the ideal timing for provisioning or de-provisioning the Function App’s environment. Using historical invocation data, we applied a sliding window to transform the time-series data into patterns categorized as load or unload classes, considering various target periods. To identify the most effective model, we compared the performance of various baseline models with and without calibration (isotonic and sigmoid) to enhance precision. In addition, we assess multiple feature extraction methods in invocation patterns and explore the use of Principal Component Analysis (PCA) for dimensionality reduction to reduce computation costs. Using the best-identified configurations, we model and simulate the class patterns over time to compare the actual classes with the predicted ones, focusing on memory usage and the costs associated with cold starts. The proposed model is thoroughly evaluated using various metrics under different setups, revealing notable improvements in scaling decisions achieved by applying calibration and feature engineering methods. These findings demonstrate the potential of machine learning for intelligent, data-driven scaling decisions in serverless computing, offering valuable insights for cloud providers to optimize resource allocation and for developers to build more efficient and responsive serverless applications. Specifically, the proposed method can be integrated into serverless platforms to automatically adjust resource provisioning based on predicted workload demands, reducing cold start latency and improving cost-effectiveness.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"140 ","pages":"Article 103090"},"PeriodicalIF":3.5,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-15DOI: 10.1016/j.simpat.2025.103094
J. Mohanraj, Jambeswar Sahu
The demand of additive manufacturing (AM) processes has increased in the industry due to its realistic printing of complex geometry parts. The process involves with continuous melting of powder and rapid solidification. The heating and cooling attributes to the formation of residual stress which leads to the distortion in the AM part. The prediction of distortion and residual stress before printing could minimize the rejection of parts due to dimensional variation. In the present research work, an attempt was made to simulate LPBF AM process using MSC Simufact software for Ti-6Al-4V material. The simulation results are compared with the existing literature. The simulation parameters are optimized to minimize the deviation between experimental and simulation results. The inherent strain value, voxel size and other simulation parameters are utilized to predict the distortion and residual stress of a micro-tensile specimen. The distortion and residual are predicted in different orientations (0°, 30°, 45°, 60° and 90°) and at position of the base plate. It is observed that voxel size (accumulation of physical layers) has a significant effect on the prediction accuracy. The specimen placed near the power collector bin and gas inlet side shows minimum residual stress. The residual stress in the gauge section of 45° orientation is minimal compared to other-oriented specimens. The limited distortion is noticed for the 0° orientation specimen as the height of the sample is minimal.
{"title":"Numerical prediction of residual stress and distortion for laser powder bed fusion (LPBF) AM process of Ti-6Al-4V","authors":"J. Mohanraj, Jambeswar Sahu","doi":"10.1016/j.simpat.2025.103094","DOIUrl":"10.1016/j.simpat.2025.103094","url":null,"abstract":"<div><div>The demand of additive manufacturing (AM) processes has increased in the industry due to its realistic printing of complex geometry parts. The process involves with continuous melting of powder and rapid solidification. The heating and cooling attributes to the formation of residual stress which leads to the distortion in the AM part. The prediction of distortion and residual stress before printing could minimize the rejection of parts due to dimensional variation. In the present research work, an attempt was made to simulate LPBF AM process using MSC Simufact software for Ti-6Al-4V material. The simulation results are compared with the existing literature. The simulation parameters are optimized to minimize the deviation between experimental and simulation results. The inherent strain value, voxel size and other simulation parameters are utilized to predict the distortion and residual stress of a micro-tensile specimen. The distortion and residual are predicted in different orientations (0°, 30°, 45°, 60° and 90°) and at position of the base plate. It is observed that voxel size (accumulation of physical layers) has a significant effect on the prediction accuracy. The specimen placed near the power collector bin and gas inlet side shows minimum residual stress. The residual stress in the gauge section of 45° orientation is minimal compared to other-oriented specimens. The limited distortion is noticed for the 0° orientation specimen as the height of the sample is minimal.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"140 ","pages":"Article 103094"},"PeriodicalIF":3.5,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-15DOI: 10.1016/j.simpat.2025.103091
Odyssefs Diamantopoulos Pantaleon, Aisha B Rahman, Eirini Eleni Tsiropoulou
Edge computing has emerged as a transformative technology in public safety and has the potential to support the rapid data processing and real-time decision-making during critical events. This paper introduces the BRAVE framework, a cutting-edge solution where the UAVs act as Mobile Edge Computing (MEC) servers, addressing users’ computational demands across disaster-stricken areas. An accurate UAV energy consumption model is introduced, including the UAV’s travel, processing, and hover energy. BRAVE accounts for both the users’ Quality of Service (QoS) requirements, such as latency and energy constraints, and UAV energy limitations in order to determine the UAVs’ optimal path planning. The BRAVE framework consists of a two-level decision-making mechanism: a submodular game-based model ensuring the users’ optimal data offloading strategies, with provable Pure Nash Equilibrium properties, and a reinforcement learning-driven UAV path planning mechanism maximizing the data collection efficiency. Furthermore, the framework extends to collaborative multi-agent reinforcement learning (BRAVE-MARL), enabling the UAVs’ coordination for enhanced service delivery. Extensive experiments validate the BRAVE framework’s adaptability and effectiveness and provide tailored solutions for diverse public safety scenarios.
{"title":"BRAVE: Benefit-aware data offloading in UAV edge computing using multi-agent reinforcement learning","authors":"Odyssefs Diamantopoulos Pantaleon, Aisha B Rahman, Eirini Eleni Tsiropoulou","doi":"10.1016/j.simpat.2025.103091","DOIUrl":"10.1016/j.simpat.2025.103091","url":null,"abstract":"<div><div>Edge computing has emerged as a transformative technology in public safety and has the potential to support the rapid data processing and real-time decision-making during critical events. This paper introduces the BRAVE framework, a cutting-edge solution where the UAVs act as Mobile Edge Computing (MEC) servers, addressing users’ computational demands across disaster-stricken areas. An accurate UAV energy consumption model is introduced, including the UAV’s travel, processing, and hover energy. BRAVE accounts for both the users’ Quality of Service (QoS) requirements, such as latency and energy constraints, and UAV energy limitations in order to determine the UAVs’ optimal path planning. The BRAVE framework consists of a two-level decision-making mechanism: a submodular game-based model ensuring the users’ optimal data offloading strategies, with provable Pure Nash Equilibrium properties, and a reinforcement learning-driven UAV path planning mechanism maximizing the data collection efficiency. Furthermore, the framework extends to collaborative multi-agent reinforcement learning (BRAVE-MARL), enabling the UAVs’ coordination for enhanced service delivery. Extensive experiments validate the BRAVE framework’s adaptability and effectiveness and provide tailored solutions for diverse public safety scenarios.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"140 ","pages":"Article 103091"},"PeriodicalIF":3.5,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-10DOI: 10.1016/j.simpat.2025.103088
Felipe de Souza , Omer Verbas , Joshua Auld , Chris M.J. Tampère
Macroscopic traffic flow is a common choice for large-scale traffic simulations. These models do not provide individual-specific metrics as outputs. However, this treatment is necessary in agent-based-models, as in, for example, assigning routes based on personal characteristics. In this paper, we propose an extension of the link-transmission-model, an efficient and yet accurate discretization of the Lighthill–Whitham–Richards (LWR) model, which allow vehicles to be tracked individually while keeping the main features of the underlying model. The extension comprises modifying the link and node models to ensure that the flow between links is always at discrete levels. Therefore, every unit of flow is associated with one individual vehicle moving from its current to its next link. An upper bound of the discretization error is provided. We show that the proposed model resembles its continuous counterpart on lane drop, merge, and diverge cases. In addition, we apply the model into three different networks to validate its applicability in large networks. Finally, we also confirm the parameter transferability between continuous and discrete models and that both can well reproduce field data.
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