Simulation Modelling Practice and Theory最新文献

Cloud computing provides users and programs with scalable resources and on-demand services virtually in real time, making it a fundamental paradigm in modern computing. The concept for using remote computing resources is novel. Cloud computing relies on task scheduling to boost system performance, reduce execution time, and optimize resource use. Due to exponential task increase and problem complexity, the search space is huge. Optimization tasks like this are NP-hard. This work aims to find a near-optimal solution for a multi-objective task scheduling problem in the cloud while lowering search time. Using the Genetic Algorithm (GA) and Gravitational Search Algorithms (GSA) benefits while avoiding their drawbacks, we offer a standard cloud computing task scheduling method to improve system performance and optimize the Quality of service (QoS) parameters like energy, makespan, resource utilization and throughput. We use CloudSim to test standard functions, real-time, and synthetic workloads. The obtained results are compared to other similar, metaheuristic-based techniques that were evaluated under the same conditions. The designed technique outperforms Gravitational Search Algorithms (GSA), Ant Colony Optimization(ACO), and Particle Swarm optimization(PSO) in Degree Of Imbalance (12%), resource utilization (9%), Mean Response Time (7%) and energy consumption (6%).

The linear cutting process in rock poses challenges for verification in field experiments, laboratory investigations, or numerical simulations. This study aims to analyze the rock cutting process and disc cutter force estimation when using linear cutting mode. Three-dimensional numerical simulations using the explicit dynamic finite element method (LS-DYNA software) are conducted to characterize the cutting process. In this regard, two computational algorithms (Lagrangian and Smoothed Particle Hydrodynamics (SPH)) and two material models (Johnson-Holmquist Concrete (JHC) and Riedel-Hiermaier-Thoma (RHT)) are compared, with SPH and RHT identified as more suitable for rock cutting simulation. The results of comparative analyses show that the Lagrangian computational algorithm is highly dependent on the erosion value, hence this method is not suitable for the simulation of the rock-cutting process. Comparing to the RHT material constitutive model, the Johnson-Holmquist model does not well model the post-failure softening strain behavior, which leads to a reduction in the width of the failure area. The comparative analyses also show that the normal and rolling forces predicted by the JHC model are well over 30% higher than the actual experimental results, while the RHT model shows a good agreement between the predictions and the actual results. Overall, the RHT material model with the use of the SPH computational algorithm shows a very good combination in rock cutting process simulation.

The pervasive uncertainties in multiple port equipment scheduling frequently result in container handling delays or ineffective plans. To address the complexities and uncertainties of port multiple equipment integrated scheduling problem, this paper introduces a Digital Twin-driven (DT-driven) proactive-reactive scheduling framework for the first time. This framework is designed to promptly respond to uncertainties in the scheduling process and provide a transparent visualization of operational information. It specifically tackles the integrated scheduling problem of port quay cranes, Intelligent Guided Vehicles (IGVs), and yard cranes, considering uncertainties such as fluctuations in operating time, equipment failures, and IGV route conflicts. By developing a virtual container port simulation, which features a U-shaped port layout and double-cycling mode drawn from real-world scenarios, the paper evaluates the proposed framework's effectiveness. The experimental results demonstrate that the digital twin framework method significantly improves efficiency and conserves energy. Additionally, in large-scale conditions, the makespan difference between the DT-driven approach and the non-DT-driven approach is as much as 19.56 %. In terms of energy consumption savings, the DT-driven approach's scheduling plan can save 3.67 % of energy consumption under large-scale conditions. Moreover, as the fluctuation index increases, the energy consumption savings become even more significant. This paper also discusses the potential implications of adopting this framework for port companies, highlighting its benefits in enhancing operational and energy efficiency and its incorporation into port management systems. The sensitivity analysis can offer guidance to port companies on optimal equipment allocation strategies.

This paper introduces Indoor Navigation Framework for Fire Evacuation Dynamics (INFED), a novel indoor navigation framework that combines dynamic fire constraints and path congestion management. INFED considers the three-dimensional (3D) attributes of both the agents (speed, volume, location, count) and the environment (3D volume, congestion, corridor height, and corridor length) to estimate navigation routes that avoid fire-affected evacuation paths. It achieves this by integrating various proposed algorithms as modules: Environment Establisher, Fired/Safe Node Identifier, Pre-processor, Weighted Graph Generator, and Path Generator. The 3D features of the agent and environment are used to effectively estimate the capacity of the corridors in an indoor environment for the estimation of path congestion. The path congestion so computed is used during evacuation to identify the safest and congestion-free path. We discuss the performance of INFED by implementing it on various realistic scenarios in a commercial floor setup. We found that the incorporation of safety constraints results in longer evacuation routes, ranging from a 6% increase under mild fire and congestion conditions to a 40% increase under severe fire and congestion conditions. In the event of a worst-case scenario where fire-free paths are scarce, INFED utilizes congestion to reduce agent speed along the recommended evacuation route. This mechanism is activated when congestion surpasses a threshold of 0.3. The system can be used by stakeholders to test various evacuation hypotheses, which can lead to better preparedness and rescue operations, ultimately saving lives in the event of a fire.

Existing fluid simulation techniques mainly process single-phase fluids, and they have difficulties in accurately simulating and visualizing multiphase fluid dynamics. This paper proposes a new method for the real-time rendering of multiphase fluid simulations, which uses smoothed particle hydrodynamics in screen space. Meanwhile, the method employs phase fraction textures to differentiate various materials in multiphase fluid simulations, thereby portraying mixing and separation effects more realistically. Besides, efficient texture computation allows it to be integrated seamlessly into real-time simulation rendering workflows. Extensive testing confirms the effectiveness of the proposed method in rendering multiphase fluid behaviors with high visual fidelity and demonstrates its capability to process frames within 0.01 s, even in cases with up to 300,000 particles. This study enhances the fluid dynamics simulation field and provides a more accurate and efficient method for visualizing complex multiphase fluids in simulations.

The evolution of simulation and implementation of P systems has been intense since the theoretical model of computation was created. In the field of software simulation of P systems, the proposals made so far have taken advantage mainly of the parallelism of GPUs, but not of the parallelism of existing multi-core processors. This paper proposes a new model for simulating P systems using a multi-threaded approach in a multi-core processor. This simulation approach establishes a new paradigm that is entirely in line with the philosophy of P-systems: since objects must react in parallel, asynchronously and autonomously with other objects, simulation using multiple synchronized threads completely mimics the behavior of objects within a membrane. This proposal has been implemented and tested using a simulator programmed in C#, and its correct operation has been tested for confluent and non-confluent systems. The experimental results confirm that the simulator scales well with the number of hardware threads of a multiprocessor. The obtained results show that the new model is correct and that it can be extended to other more complex types of P systems, in order to discover which are the limit of this multi-threaded approach when running it in multi-core processors.

Additive manufacturing processes, including selective laser sintering (SLS) and selective laser melting (SLM), are rapidly developing industrial fields that require scientific support. Although SLS and SLM are very similar, the level of modeling and simulation of SLM is much higher than that of SLS. This results in the number of publications before 2024 according to Web of Science with SLM simulation approximately five times more than with SLS. To test the possibility of adequate SLS simulations, a platform based on the lattice Boltzmann method (LBM), previously developed and applied to model the SLM process, was used. In addition, the possibility of modeling similar processes (SLM, SLS, and SLS/SLM) using the same modeling tool on the same modeling platform is important. The objective of this paper is to present a model of the SLS process and confirmation of the possibility of using LBM for simulation of the SLS process. A simulation of SLS and SLM with the use of LBM, and qualitative comparison of the results of these simulation for bioactive glass 45S5 is the basis of the methodology used for the research. The simulation presented in this study confirmed the possibility of simulating SLM, SLS processes using common principles, approaches, and models. The results of SLS process simulations can be treated as qualitative and require further verification, whereas SLM simulations have been previously verified. The application of the lattice Boltzmann method, which is a computational fluid dynamics (CFD) method, opens the possibility of using almost every CFD method for the simulation of several kinds of SLS, and can accelerate research in this field.

Healthcare systems face critical issues worldwide such as data breaches, lack of interoperability and inefficiencies in patient data management. These challenges hinder the quality of care and patient outcomes. The increasing adoption of Electric Vehicles (EVs) in Smart Healthcare Systems (SHSs) has brought about new security and privacy challenges. EVs, including electric ambulances, rely on communication networks to exchange critical information and perform energy trading. However, the open nature of these networks makes them vulnerable to various attacks, such as false information dissemination and collusion attacks. In the recent years, Blockchain (BC) technology has emerged as a transformative solution for various industries, including healthcare. The integration of BC in healthcare systems offers enhanced security, transparency and efficiency in managing patient data and other critical information. The paper introduces a data-oriented trust paradigm that is facilitated by revocation transparency. In order to enable the present EVs operating in a SHS to realize their full potential, the model aims to successfully manage security, privacy, storage and other issues. The electric ambulance, an integral part of an SHS, is a special type of EV, which is considered in the study. The proposed approach employs the Password Authenticated Key Exchange by Juggling (J-PAKE) mechanism to provide mutual authentication across distinct entities inside a SHS. Moreover, the Real-time Message Content Validation (RMCV) approach precludes collusion attacks by performing a message credibility check. Moving ahead, anonymization of reputation data is performed via K-anonymity algorithm. Restrictions on the identification of the consistent patterns seen in the reputation data serve to avoid privacy leaks. Additionally, a Proof of Revocation (PoR) technique helps to provide revocation transparency. The Inter Planetary File System (IPFS), a decentralized storage system, houses the vehicle data in order to lessen the BC storage problem. Hashes of the data recorded in IPFS are also uploaded to the immutable BC ledger to prevent disputes. Moreover, IPFS and Cuckoo Filters (CFs) are used to enhance the efficiency of the system. In terms of execution time, data size and storage overhead, the performance evaluation is carried out to assess the proposed model’s efficiency. The simulation results show the execution time for a vast number of messages to be less than 0.6 s. Moreover, K-anonymity ensures storage overhead reduction of almost 35%–40%. Finally, Oyente is used to identify bugs in the smart contract. Overall, it is determined that the proposed approach is effective in establishing mutual authentication, revocation transparency and trust.

Human-initiated disruptions such as cyberattacks on connected vehicles have the potential to cause cascading failures in transport systems, leading to systemic risks. ‘ISO/SAE 21434:2021 Road vehicles - Cybersecurity engineering’ is the current standard for risk management of road vehicles. However, the threat analysis and risk assessment framework given in the standard focuses on asset-level analysis and assessment. Hence, this study develops a novel simulation-based framework to perform threat analysis and risk assessment on connected vehicles from a transport network perspective. The proposed framework is developed based on the ISO/SAE 21434 threat analysis and risk assessment methodology. We demonstrate the applicability and usefulness of the framework through a remote attack via the cellular network on the in-vehicle communication bus system of a connected vehicle to show the potential impacts on the transport network. Based on the findings of our case studies, we exemplify how cyberattacks on individual system components of a connected vehicle have the potential to cause systemic failures.

The rise of shared bicycles has increased the demand for group riding, integrating bicycles into social groups. Additionally, retrograde riding, where cyclists travel against the designated direction, is a common behavior observed in bicycle flows. The interaction and self-organization phenomenon of group and retrograde behaviors are complex, significantly impacting traffic efficiency. This paper develops a two-dimensional Extended Moore Neighborhood and constructs state-updating rules for regular riding, group riding and retrograde riding. Each rule comprises a psychological decision layer and a physical execution layer, forming a cellular automaton model for group and retrograde bicycles. Field experiments are conducted to calibrate the model parameters and verify the behavioral characteristics. Finally, we execute numerical simulations at a signalized intersection to explore the coupling effects of group and retrograde behaviors on self-organization within the bicycle flow and the traffic capacity. The results indicate that group behavior increases queue length while reducing start wave speed and expansion degree. Retrograde behavior intensifies the negative effects on bicycle flow. These findings provide insights for managing both forward and retrograde bicycle flows.