Simulation Modelling Practice and Theory最新文献

With the rapid development of intelligent manufacturing, the application of automated guided vehicles (AGVs) in intelligent warehousing systems has become increasingly common. Efficiently planning the conflict-free paths of multiple AGVs while minimizing the total task completion time is crucial for evaluating the performance of the system. Distinguishing itself from recent approaches where conflict avoidance strategy and path planning algorithm are executed independently or separately, this paper proposes an improved conflict-free A* algorithm by integrating the conflict avoidance strategy into the initial path planning process. Based on the heuristic A* algorithm, we use the instruction time consumption as the key evaluation indicator of the cost function and add the turning consumption in the future path cost evaluation. Moreover, the expansion mode of child nodes is optimized where a five-element search set containing the “zero movement” is proposed to implement a proactive pause-wait strategy. Then the prediction rules are designed to add constraints to three types of instructions based on the timeline map, guiding the heuristic planning to search for conflict-free child nodes. Extensive simulations show that the coordination planning based on the improved conflict-free A* algorithm not only effectively achieves advanced conflict avoidance at the algorithmic level, but also exhibits lower computational complexity and higher task completion efficiency compared to other coordination planning methods.

In the realm of e-commerce, the growing complexity of dynamic workloads and resource management poses a substantial challenge for platforms aiming to optimize user experiences and operational efficiency. To address this issue, the PredictOptiCloud framework is introduced, offering a solution that combines sophisticated methodologies with comprehensive performance analysis. The framework encompasses a domain-specific approach that extracts and processes historical workload data, utilizing Domain-specific Hierarchical Attention Bi LSTM networks. This enables PredictOptiCloud to effectively predict and manage both stable and dynamic workloads. Furthermore, it employs the Spider Wolf Optimization (SWO) for load balancing and offloading decisions, optimizing resource allocation and enhancing user experiences. The performance analysis of PredictOptiCloud involves a multifaceted evaluation, with key metrics including response time, throughput, resource utilization rate, cost-efficiency, conversion rate, rate of successful task offloading, precision, accuracy, task volume, and churn rate. By meticulously assessing these metrics, PredictOptiCloud demonstrates its prowess in not only predicting and managing workloads but also in optimizing user satisfaction, operational efficiency, and cost-effectiveness, ultimately positioning itself as an invaluable asset for e-commerce platforms striving for excellence in an ever-evolving landscape.

The complexity of the underground environment in coal mines often leads to varying load conditions during the operation of the scraper conveyor, which can affect the lifespan of its components and result in unnecessary energy consumption. A test platform for the scraper conveyor was constructed based on the similarity theory to measure torque, speed, chain tension, and scraper acceleration during transportation. A DEM-MBD model of the scraper conveyor was developed and validated through transport tests and similarity theories to analyze the rigid-discrete coupling effect under different chain speed-load conditions. The results revealed a stratification phenomenon and a Brazilian fruit effect in the movement of coal. The average velocity of the upper and lower coal layers gradually increased during the transportation, while the difference between them gradually decreased. As the load increased, the stacking density and height of coal between scrapers also increased, leading to a higher force exerted on the scraper and chain. As the chain speed increased, the stacking density and height of coal between scrapers decreased, along with a decrease in the force applied to the scraper and chain. The formation of three-body wear necessitates a specific positional condition. When the scraper (chain)- coal-deck plate (chute liner) forms a particle stagnation state, severe wear occurs on the parts. This study provides a foundation for analyzing the transport mechanism of scraper conveyor from the particle perspective, offers a simulation reference for analyzing the mechanical and tribological characteristics of the line pan and scraper chain, and serves as a guideline for the future development of transportation state monitoring and the optimization and enhancement of components under different working conditions.

Parameter calibration is a critical step in accurately modeling using the discrete element method (DEM), but the time-consuming and complex calibration process limits the practical utilization of DEM. Herein, a catch-up penalty algorithm was proposed to simultaneously adjust multiple micro parameters of the flat-joint model through iterations. The effect of micro parameters on macro parameters was investigated by conducting 64 sets of orthogonal tests in PFC3D and analyzing the results by ANOVA. Regression analysis was used to establish the preliminary formulas for directly obtaining initial values of micro parameters and the trend equations for deriving iterative formulas. Based on the preliminary and iterative formulas, the calibration process for the algorithm was proposed, in which the micro parameters of each iteration can be calculated, thereby reducing researchers' dependence on the experience. The calibration capability of the algorithm was verified on four types of rocks, and the results showed that the average calibration error between the simulation results and the target values was reduced to within 5 % after six iterations, proving the reliability and applicability of the algorithm.

Natural disasters drastically impact the society, causing emotional disorders as well as serious accidents that can lead to death. These kinds of disasters cause serious damage in computer and communications systems, due to the complete or partial destruction of the infrastructure, causing software applications that actually run on those infrastructures to crash. Additionally, these software applications have to provide a stable service to a large number of users and support unpredictable peaks of workloads. In this work, we propose a methodology to predict the performance of software applications designed for emergency situations when a natural disaster strikes. The applications are deployed on a distributed platform formed of commodity hardware usually available from universities, using container technology and container orchestration. We also present a specification language to formalize the definition and interaction between the components, services and the computing resources used to deploy the applications. Our proposal allows to predict computing performance based on the modeling and simulation of the different components deployed on a distributed computing platform combined with machine learning techniques. We evaluate our proposal under different scenarios, and we compare the results obtained by our proposal and by actual implementations of two applications deployed in a distributed computing infrastructure. Results show that our proposal can predict the performance of the applications with an error between 2% and 7%.

The unclear understanding of right-turning vehicle behavior at signalized intersections complicates the interaction with pedestrians. Current micro-dynamic modeling research falls short of effectively simulating this complexity. Specifically, the existing models fail to adequately capture the three states that right-turning vehicles may undergo: car-following, free right-turn, and avoidance of conflicting pedestrians. Moreover, pedestrian behavior is typically influenced by encountering conflicting vehicles and surrounding pedestrians, as well as traffic signals. To simulate these behaviors, the right-turning and yielding intelligent driver model (RTYIDM), the modified social force model (MSFM) considering green light pressure, and the yielding decision model between pedestrians and vehicles have been established. Model calibration is performed using detailed behavioral data collected and extracted from field observations. Furthermore, a microsimulation platform with 3D visualization and playback features has been developed to facilitate testing and demonstration. Model validation is performed by comparing it with actual trajectories in three representative scenarios of pedestrian crossing with conflict between pedestrians and vehicles. Meanwhile, the calibrated model's ability to predict pedestrian-interaction events and estimate vehicle yielding rates is also assessed. The well-established simulation performance of the proposed model makes it a useful tool for evaluating existing traffic operations.

Titanium alloys, including Ti6Al4V, are considered hard to cut materials due to their low thermal conductivity, low elastic modules and high chemical reactivity. This leads to high cutting forces and high surface roughness. Thermal assisted machining is used to improve the machinability of Ti6Al4V. To improve the performance of thermal assisted machining, this study investigates how are the cutting force, cutting zones temperatures, chip morphology, shear plane angle and strain rate are affected by the cutting speed and the heating element characteristics during thermally assisted machining of Ti6Al4V. A 2D numerical model simulating orthogonal cutting process was created using ABAQUS/Explicit software. In this model, Johnson Cook constitutive model was used to describe the material behavior during cutting process. Also, Johnson Cook damage model was used to simulate chip separation mechanism. After the verification of the model by comparison with results found in the literature, a number of simulations were run at different levels of four factors: cutting speed (40, 60, 80, 100, 120 and 140 m/min), heat source temperature (200, 400 and 600 °C), heating source distance from the cutting tool (0.3, 0.6 and 0.9 mm) and heating source size/diameter (0.6, 0.8 and 1 mm). Taguchi L18 orthogonal mixed level design was used to plan for simulation runs using Minitab software. ANOVA analysis was used to investigate the significance of the four factors. The response table of means and the main effect of means are used to compare between the four factors and find their ranking. Based on 95% confidence Interval (CI), the results show that cutting speed has a significant effect on cutting force, strain rate, chip compression ratio, cutting tool nose temperature, cutting tool and chip temperature in the secondary deformation zone, average chip thickness at peaks and average chip thickness at valleys and average pitch. This conclusion is based on the P-values which are << 0.05 and the contribution which reaches 99.01%. Similarly, based on P-values (< 0.05) and contributions (up to 12.16%), the heating source temperature has a significant effect on average chip thickness at valleys, chip compression ratio and strain rate. The cutting speed has Rank 1 among the four factors affecting cutting force, cutting zones temperatures, chip morphology, shear plane angel and stain rate. The effect of instantaneous heating directly before cutting process is negligible compared to the effect of plastic deformation and fracture mechanism in the cutting zone.

High-density built-up areas in cities often enlist the underground realm to provide solution space for transport, shopping and other purposes. The special location, layout, and accessibility of underground structures often generate unique and acute safety-risk concerns. They are inadequately understood and managed and cannot be tackled appropriately by conventional risk assessment and abatement methods. This study focused on evacuating underground commercial streets (UCS) with a heavy concentration of people in Fuzhou city in China. Despite the widespread use of building information modeling (BIM) in construction, it has rarely been applied to studies of underground shopping streets. This study adopted BIM technology as the core method, in conjunction with PyroSim fire and Pathfinder evacuation simulation software. Different fire scenarios in four fire protection zones and the most unfavorable fire sources were set in the model. Based on a calculated number of persons at the start of a fire, different movement paths, stair configuration and exit width were simulated. The choice of escape routes, congestion locations, and slack time windows were identified by the graphical images of the simulation programs. Required safe egress time was compared with available safe egress time, and the number of successful evacuees was reckoned. The effects of three escape-stair forms on evacuee utilization and evacuation rates were evaluated. Their evacuation efficiency was ranked: crossed stair > straight stair > parallel-double stair. The simulation results can optimize building layout design and improve understanding of evacuation-efficiency factors. The findings can contribute to reducing casualties and property losses and improving UCS's fire safety management.

With the emergence of new applications driven by the popularization of mobile devices, the next generation of mobile networks faces challenges to meet different requirements. Virtual Network Functions (VNFs) have been deployed to minimize operational costs and make network management more flexible. In this sense, strategies for VNF placement can impact different metrics of interest. Invoking and visiting VNFs in a specific execution order may be required for different use cases, resulting in a complete network service called Service Function Chain (SFC). The SFC placement problem is to define a feasible path in the physical infrastructure whose nodes and edges meet the computational and bandwidth requirements for the VNFs and virtual links, respectively. It has already been proved that this process is NP-hard and it is difficult to find an optimal solution to this problem. Therefore, in this paper, we propose the use of meta-heuristics to solve the SFC placement problem in cellular networks. We consider a triathlon competition leading to different mobility patterns. We collected real data about the competitors to simulate their movements through the scenario as well as the measured signal quality of the network. We formulate the SFC placement problem as a multi-objective problem where we try to minimize the placement cost and the total SFC delay. To solve the problem, we propose the use of two algorithms, NSGA-II and GDE3, which compare two different greedy approaches that prioritize the different optimization metrics considered in this work. Our results show that the meta-heuristics provide better results for each of the metrics. For all competition stages, GDE3 presented a slightly lower placement costs than NSGA-II, while NSGA-II had a lower delay in some scenarios.