Simulation Modelling Practice and Theory最新文献

{"title":"Numerical modelling of drop mass shape influence on energy absorption of welded wire mesh in dynamic impact conditions","authors":"Ceren Karatas Batan ,&nbsp;Selahattin Akdag ,&nbsp;Chengguo Zhang ,&nbsp;Joung Oh ,&nbsp;Serkan Saydam","doi":"10.1016/j.simpat.2025.103207","DOIUrl":"10.1016/j.simpat.2025.103207","url":null,"abstract":"<div><div>Underground mines rely on a ground support system (i.e. reinforcement and surface support elements such as welded wire mesh) to control rock deformation and maintain excavation stability under dynamic loading conditions. Designing an effective ground support system requires a detailed understanding of the mechanical behaviour of these support components and their response to impact scenarios. This study investigates the influence of drop mass geometry on the deformation and failure mechanisms of welded wire mesh utilising a 3D finite element analysis (FEA) based on geometries used in laboratory testing. Five drop mass configurations, prism, spherical, cylindrical, ETAG 027, and irregular, were evaluated under the same energy input to explore their effects on mesh behaviour. Although the developed dynamic testing setup offers valuable insights into mesh performance, the lack of standardised drop mass shapes remains a significant challenge, as it causes inconsistencies in test results and complicates data comparison across different studies or reliable experiment replication. The FEA model was developed and calibrated using experimental data. The results demonstrated that the drop mass shape strongly affects load distribution, displacement patterns and the extent of damage in the mesh. The prism shape, used for calibration, provided a good match with the laboratory result. Cylindrical geometries demonstrated more favourable energy dissipation, absorbing 5.69 kJ, whereas the irregular and spherical shapes exhibited lower energy absorption, 2.83 kJ and 2.55 kJ, respectively, due to the concentrated nature of the initial impact load being distributed over a smaller contact area. The ETAG 027 geometry produced a balanced response, with a peak displacement of approximately 152.77 mm and an energy absorption of 3.06 kJ, accompanied by moderately distributed plastic deformation. This study can support the development of more reliable testing procedures and energy-based design approaches for support systems in dynamic underground environments.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"145 ","pages":"Article 103207"},"PeriodicalIF":3.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118471","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}

{"title":"Optimization of start-up strategies of gas injection compressor in underground gas storage using deep reinforcement learning","authors":"Xingyu Zhao ,&nbsp;Changhe Li ,&nbsp;Pengzhi Lu ,&nbsp;Wei Li ,&nbsp;Weiwei Qiu ,&nbsp;Wuchang Wang ,&nbsp;Yuxing Li","doi":"10.1016/j.simpat.2025.103204","DOIUrl":"10.1016/j.simpat.2025.103204","url":null,"abstract":"<div><div>Injection compressors, as the core equipment in the gas injection process of underground gas storage (UGS) facilities, play a vital role in ensuring the safe and efficient operation of UGS systems. However, traditional optimization methods often struggle to adapt dynamically under complex operating conditions and may lead to excessive energy consumption. To address these challenges, this study proposes a deep reinforcement learning (DRL)-based approach to optimize compressor start-up strategies. First, a high-fidelity hybrid simulation model is developed by integrating thermodynamic equations of reciprocating compressors with a residual correction network based on a multilayer perceptron, forming a Mechanism-Data fusion Model framework. This model achieves prediction errors of &lt;5 % for power and &lt;3 % for discharge flow rate. Based on the accurate simulation model, an optimization framework is constructed using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. Within this framework, continuous control variables—such as the number of operating compressors, inlet throttling levels, and relative clearance volume adjustments—are mapped to the action space of the reinforcement learning agent. A multi-objective reward function is designed to incorporate penalties for gas injection deviations, the number of active compressors, inlet pressure constraints, and clearance volume limits. By introducing delayed updates to the target network and applying an adaptive noise clipping mechanism, the proposed strategy ensures optimal parameter control across the entire gas injection cycle while satisfying operational and safety requirements. Experimental results demonstrate that the proposed method reduces compressor energy consumption by 5.18 %, offering a precise, adaptive, and intelligent decision-making solution for dynamic optimization of UGS compressor operations.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"145 ","pages":"Article 103204"},"PeriodicalIF":3.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010686","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}

{"title":"Multi-objectives optimization of evacuation path based on improved matrix translation model and exit balance algorithm","authors":"Chun Sheng ,&nbsp;Qize He ,&nbsp;Liping Yu ,&nbsp;Jiacheng Wang ,&nbsp;Haoming Xie ,&nbsp;Zhiming Fang ,&nbsp;Zhongyi Huang","doi":"10.1016/j.simpat.2025.103201","DOIUrl":"10.1016/j.simpat.2025.103201","url":null,"abstract":"<div><div>Emergency evacuation planning requires balancing multiple objectives like minimizing time, avoiding hazards, and ensuring fairness. Traditional methods struggle to strike a balance between macroscopic efficiency and microscopic realism. This study proposes a new multi-objective optimization framework based on an improved Matrix Translation Model (MTM) and Exit Balance Algorithm (EBA): the improved MTM efficiently simulates the evacuation process and obtains individual objectives, thereby deriving group evacuation time objective <span><math><msub><mi>f</mi><mi>t</mi></msub></math></span>, detour objective <span><math><msub><mi>f</mi><mi>d</mi></msub></math></span>; crowding objective <span><math><msub><mi>f</mi><mi>c</mi></msub></math></span>, injury objective <span><math><msub><mi>f</mi><mi>i</mi></msub></math></span> and fatality objective <span><math><msub><mi>f</mi><mi>f</mi></msub></math></span>. <span><math><msub><mi>f</mi><mi>d</mi></msub></math></span>, <span><math><msub><mi>f</mi><mi>c</mi></msub></math></span>, <span><math><msub><mi>f</mi><mi>i</mi></msub></math></span> and <span><math><msub><mi>f</mi><mi>f</mi></msub></math></span> are converted into penalty terms for <span><math><msub><mi>f</mi><mi>t</mi></msub></math></span>, and the improved EBA algorithm balances evacuation times across different exits to solve the multi-objective problem. This framework ensures precise statistical analysis of individual evacuation parameters while guaranteeing that each iteration moves closer to the optimal solution, enabling rapid convergence. Optimization results from a scenario with 2 floors, 42 rooms, and 1688 evacuees demonstrate that the algorithm can complete the simulation within 8–15 s, and the evacuation time reduced by 16 % while controlling detour and crowding duration in the scenario without fire, and the cumulative injury probability cut by 42 % in the fire scenario. This work bridges macroscopic efficiency and microscopic realism, offering a practical solution for dynamic evacuation planning.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"145 ","pages":"Article 103201"},"PeriodicalIF":3.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144907974","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}

{"title":"Optimization of passenger evacuation path in flood scenarios considering companion behaviors","authors":"Xiaoxia Yang ,&nbsp;Jiahui Wan ,&nbsp;Haojie Zhu ,&nbsp;Chuan-Zhi (Thomas) Xie ,&nbsp;Botao Zhang","doi":"10.1016/j.simpat.2025.103212","DOIUrl":"10.1016/j.simpat.2025.103212","url":null,"abstract":"<div><div>The optimization of emergency evacuation paths for passengers in underground rail transit hubs has become a critical challenge in urban flood prevention and disaster mitigation systems. Most previous evacuation path optimization methods assume passengers move independently as individuals without considering socially connected groups traveling together. To address this, this paper proposes a novel passenger evacuation path optimization method considering companion behavior during subway station flooding incidents, and develops an innovative ETACO algorithm to solve the path optimization model. Taking an actual subway station as a case study, a station simulation system constructed using PathFinder is employed to simulate passenger evacuation processes, demonstrating the effectiveness of the proposed path optimization scheme. An improved entropy weight method is introduced to conduct a multidimensional evaluation of evacuation performance. The results indicate that: (1) Companion behavior significantly inhibits evacuation efficiency, with higher proportions of grouped evacuees leading to increased evacuation time and reduced average movement speed; (2) The proposed ETACO dynamic optimization strategy remarkably enhances the convergence performance of the path optimization model solution, achieving a 16% improvement in average optimal objective improvement rate compared to conventional ACO, while generating more efficient path optimization strategies; (3) Increasing numbers of interrupted road sections progressively slow down passenger evacuation; (4) Evacuation effectiveness evaluation further verifies the enhancement of station safety performance through the path optimization strategy. This research provides a more realistic solution for evacuation path optimization considering companion behavior in complex flood scenarios.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"145 ","pages":"Article 103212"},"PeriodicalIF":3.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266850","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}

{"title":"A digital twin-based platform for testing and optimization of path planning algorithms","authors":"Guanglie Wang ,&nbsp;Zhijia Zhang ,&nbsp;Aleksandra Nazarova","doi":"10.1016/j.simpat.2025.103206","DOIUrl":"10.1016/j.simpat.2025.103206","url":null,"abstract":"<div><div>With the rapid development of autonomous driving technology, there is an increasing demand for safety, reliability, and optimization efficiency in path planning algorithms. However, traditional physical testing is often costly, time-consuming, and subject to environmental uncertainties, making it difficult to efficiently verify and optimize these algorithms. To address this issue, this paper proposes a high-fidelity digital twin-based platform for testing and optimizing path planning algorithms. By constructing a simulation environment that mirrors the physical world, the platform minimizes the gap between simulation and real-world scenarios, enhancing the safety and stability of path planning. The platform integrates global path planning using the A* algorithm, local path planning with the Timed Elastic Band method, and optimization using Bézier curves to improve the smoothness, feasibility, and safety of the path. Additionally, it incorporates the vehicle’s physical characteristics — such as velocity, steering angle, and drive mode — into the parameter optimization process, ensuring consistency between the simulation and the real-world environment. Experiments were conducted by deploying identical path planning algorithms in both the simulation and the physical environments. The results demonstrate that algorithms optimized through the digital twin platform can be reliably transferred to real-world scenarios, improving obstacle avoidance and overall path planning safety. The planned paths in the physical environment closely matched those in simulation, confirming the effectiveness of the digital twin approach for path planning testing and optimization. This research provides new insights into environmental adaptability, safety assurance, and engineering deployment of path planning in autonomous driving.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"145 ","pages":"Article 103206"},"PeriodicalIF":3.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145105825","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}

{"title":"Guest Editors’ Introduction on Cloud and Fog Computing for Real-Time IoT Applications","authors":"Helen D. Karatza","doi":"10.1016/j.simpat.2025.103196","DOIUrl":"10.1016/j.simpat.2025.103196","url":null,"abstract":"","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"144 ","pages":"Article 103196"},"PeriodicalIF":3.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925657","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}

{"title":"Simulation model and performance evaluation of automated valet parking technologies in parking lots","authors":"Ning Ma ,&nbsp;Angjun Tang ,&nbsp;Jingxin Hai ,&nbsp;Fang Yuan","doi":"10.1016/j.simpat.2025.103175","DOIUrl":"10.1016/j.simpat.2025.103175","url":null,"abstract":"<div><div>Autonomous valet parking (AVP) is widely employed among parking lots and city logistics worldwide, expanding the applications of autonomous driving technologies. Auto companies are promoting three technology roadmaps to improve the implementation of AVP: AVP with autonomous driving system (AVP-ADS), AVP with Intelligent Infrastructure Systems (AVP-IS), and AVP with cooperative vehicle infrastructure systems (AVP-CVIS). Specifically, AVP-ADS can further be divided into LIDAR solution (AVP-ADS-LIDAR) and visual solution (AVP-ADS-VISUAL). This paper presents a simulation model to evaluate and compare the performances of AVP-CVIS, AVP-IS, AVP-ADS-LIDAR, AVP-ADS-VISUAL and Manual Parking (MP) in the real parking lot. The vehicle parking system is modeled as a discrete event simulation model, in which the controller module simulates driving behavior and defines the control logics of these three parking solutions. Extensive experiments are conducted and metrics are presented to evaluate the performance of these AVP technical solutions. The results indicate that AVP-CVIS exhibits more efficient parking performance. Management insights are provided to facilitate a more effective implementation of AVP.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"144 ","pages":"Article 103175"},"PeriodicalIF":3.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144571579","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}

{"title":"CoFANN: A collaborative framework for accelerating DNN inference in drone-based agricultural monitoring systems","authors":"Nhu-Y Tran-Van,&nbsp;Kim-Hung Le","doi":"10.1016/j.simpat.2025.103176","DOIUrl":"10.1016/j.simpat.2025.103176","url":null,"abstract":"<div><div>Plant leaf diseases pose a major threat to global agricultural productivity, causing substantial crop losses annually. While drone-based monitoring systems equipped with deep neural networks (DNNs) offer a promising solution for large-scale disease detection, their deployment is hindered by the computational limitations of IoT devices and the latency issues associated with cloud and edge computing. Existing collaborative inference approaches aim to mitigate end-to-end latency by offloading computation across devices. However, these methods often compromise model accuracy and add computing latency in generating inference strategies. To address these challenges, we present CoFANN, a novel collaborative framework to accelerate DNN inference in dynamic IoT environments. Our framework includes two key advances: a differentiable strategy search space with a gradient-based optimization algorithm for efficiently identify optimal partitioning strategies, and an adaptive model partitioning algorithm that effectively divides and allocates DNN components across computing devices based on their capabilities and network conditions. Experimental results in the plant disease dataset demonstrate that CoFANN reduces the total inference latency by up to 70% compared to device-only and 50% compared to edge-only approaches under varying network conditions, while maintaining comparable accuracy from 93.7% to 95.8%.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"144 ","pages":"Article 103176"},"PeriodicalIF":3.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144611623","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}

{"title":"Preference-based D2D offloading in IoT-edge-cloud continuum","authors":"Haripriya Chaturvedi,&nbsp;Gaurav Baranwal","doi":"10.1016/j.simpat.2025.103188","DOIUrl":"10.1016/j.simpat.2025.103188","url":null,"abstract":"<div><div>Computation tasks are offloaded to the edge server from the Internet of Things (IoT) and mobile devices due to their restricted computation capacity and battery life. However, the fixed capacity of edge servers makes serving multiple IoT and mobile devices challenging for edge servers. The collaborative architecture, i.e. Device-to-Device (D2D)-edge computing, plays a great role in solving this resource gap problem between the IoT and mobile devices and edge servers. The architecture allows the offloading from IoT and mobile devices with restricted resources to IoT and mobile devices with resource-rich resources instead of only edge servers. This collaborative architecture also provides solutions to other edge server-related problems like workload, congestion, bandwidth consumption, and energy consumption along with the utilization of idle resources of nearby IoT and mobile devices. This paper proposes a preference-based D2D offloading model in IoT-Edge-Cloud Continuum. The proposed business model introduces the concept of surcharges to increase the utilization of computing resources. Compared to state-of-the-art work, the model generates more social welfare, increases the number of allocations, and increases participants' utility. With the help of simulation work, we have shown that the work is effective and outperforms.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"144 ","pages":"Article 103188"},"PeriodicalIF":3.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724918","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}

{"title":"MBMRF: A modified bidirectional IPv6 multicast protocol with mixed upward and downward forwarding for TSCH-enabled WSANs","authors":"Eden Teshome Hunde ,&nbsp;Shereen Ismail","doi":"10.1016/j.simpat.2025.103172","DOIUrl":"10.1016/j.simpat.2025.103172","url":null,"abstract":"<div><div>Wireless Sensor and Actuator Networks (WSANs) consist of numerous embedded devices that collaborate to perform complex tasks, surpassing the capabilities of traditional wired networks. This collaboration is efficiently enabled through multicast protocols. While multicast protocols offer significant advantages for WSANs, many fail to meet certain performance requirements. To address these challenges, we propose the Modified Bidirectional Multicast RPL Forwarding (MBMRF) protocol.</div><div>This study tackles limitations in existing Internet Protocol version 6 (IPv6) multicast protocols, including the Routing Protocol for Low Power and Lossy Networks (RPL) and Bidirectional Multicast RPL Forwarding (BMRF). The proposed MBMRF protocol introduces a novel mixed upward and downward multicast packet forwarding mechanism optimized for multi-channel Time Slotted Channel Hopping (TSCH) networks. Furthermore, to ensure sufficient timeslot allocation for scheduling mixed up-and-down packet transmissions, the protocol incorporates a modified version of the Orchestra scheduling algorithm.</div><div>The proposed MBMRF protocol was implemented and simulated on Zolertia (Z1) motes using the Contiki operating system and evaluated against existing IPv6 multicast protocols, including Stateless Multicast RPL Forwarding (SMRF), Enhanced Stateless Multicast RPL Forwarding (ESMRF), and BMRF. Results show that MBMRF significantly reduces buffer overflow and network-wide energy consumption compared to SMRF, ESMRF, and BMRF, with only a marginal increase in memory usage.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"144 ","pages":"Article 103172"},"PeriodicalIF":3.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144482452","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}