Simulation Modelling Practice and Theory最新文献

The real-world exponential increase in data traffic has brought attention to a new computing paradigm termed Fog Computing (FC), which is intended for task offloading in fault-free fog networks. It is a potential aid which that provides greater processing aids at lower costs and with greater availability, flexibility, and cost. The issue typically arises due to the high task count and impacts task offloading in fog scenarios. In order to address a problem that arises in the Fog-IoT network for providing dependable and error-free transmission, an appropriate technique is required. Based on fault minimization and cost optimisation, the novel FT mechanism is proposed in this research. First, proposed Priority based Task offloading with Fault Tolerance (PToFT) scheme is used to identify the faulty-FNs using FN's remaining residual energy. To find the neighbour candidate Fog access node for replacing the faulty-FNs, the Min-cost Neighbour Candidate Node Discovery based on replication and forwarding (MNCND-RaF) technique is proposed for effective task processing and also tracks the task information towards the new nodes. These proposed methods are simulated, evaluated, and compared with the current Fault Tolerance (FT) techniques. The results shows that the compared results of the proposed methods will outperforms with current approaches like Without FT, NFT-WOA, and DFTLA methods, as 42.3 %, 36.2 %, and 27.7 %, respectively. Additionally, it utilized 1.53 J of residual energy as compared with HBI-LB and 0.84 J without replicas.

This study presents a comprehensive framework for optimizing intelligent transport systems (ITS) by integrating advanced communication and information technologies into vehicles, roads, and infrastructure. The primary goal is to enhance transportation efficiency, safety, and environmental sustainability while improving overall mobility for people and goods. Leveraging contextual information, the framework offers personalized, proactive services such as real-time traffic updates, route recommendations, and parking availability. Additionally, it enhances safety and security by providing early hazard warnings and adapting to changing road conditions. Our proposed framework utilizes the enhanced coral reef optimization (ECRO) algorithm to efficiently group vehicles for energy-saving data collection, maximizing information gathering efficiency. Collected data is then transmitted to a central data gathering center via a sink node optimized through the modified pelican optimization (MPO) algorithm, considering various vehicle node design constraints. An incident detection module accurately classifies and detects road incidents, enabling timely emergency service requests and alternate route recommendations. To facilitate incident detection, we introduce the deep Rigdelet neural network (DRNN), a novel deep learning technique tailored for decision-making in incident classification. We validate our framework's performance through NS-2 simulations using the SUMO traffic generator, demonstrating its effectiveness in meeting quality of service (QoS) metrics. Through comparative analysis with existing frameworks, our proposed approach stands out for its superior performance and ability to optimize ITS operations.

An extended floor field cellular automata (FFCA) model considering the stampede accidents on inclined staircases is proposed to study shoving behavior and pedestrian dynamics. In this model, two stampede evolution pathways are investigated: Pedestrians falling after losing balance, and falling directly due to being crowded. The results show that this model could trigger some characteristics of real irrational evacuation processes, such as: (1) the mutual crowding and shoving among pedestrians; (2) the unbalance phenomenon on inclined staircases; (3) the effect of pedestrians falling like dominoes, which is consistent with the findings of most stampede investigations to some extent. The proposed model considers the impact of fallen pedestrians on the movement of ordinary pedestrians, which shows a reduction in the overall evacuation efficiency. Moreover, the steeper the slope, the greater the risk and severity of injuries during the crowded evacuation in this scenario. Additionally, falling phenomena of pedestrians show a certain lag related to the state of unbalance. Unbalanced pedestrians tend to appear from the rear to the front successively, and fallings often occur some time later the onset of unbalance, progressing from front to rear. This pattern reflects the “domino effect” among pedestrians. Lastly, unbalanced pedestrians constitute a significant portion of the total injured pedestrians. Considering the time delay of fallings after being unbalanced, the importance of early emergency response and intervention during crowded evacuations is emphasized. It is expected to provide some theoretical support for safety management.

As collaborative simulations gain prominence, there is a pressing need for methodologies that seamlessly integrate interoperability while safeguarding intellectual property. This paper presents a novel approach rooted in Model-Driven Architecture and combined with the High-Level Architecture standard. This approach expedites the simulation process by automating the generation of Federation Object Model files and federate codes. Distinctly, we produce two federates: one exclusively in Java and another integrating Java with the Discrete Event System Specification, addressing diverse simulation paradigms. Utilizing the Unified Modeling Language and the Business Process Model and Notation standards, we devise a systematic procedure for modeling business operations while maintaining confidentiality. While our automation framework is robust, certain intricacies of federate behavior necessitate manual adjustments to ensure secure data transmission and protection of proprietary knowledge. The efficacy of our approach, striking a balance between interoperability and confidentiality in High-Level Architecture-based simulations, is demonstrated through a comprehensive experiment.

The inevitable wear and degradation of disc cutters during the rock-crushing process significantly impacts the efficacy, timeline, and cost-effectiveness of tunnel construction. Optimizing cutter arrangements and adjusting suitable excavation parameters are crucial to reducing cutter wear in Tunnel Boring Machine (TBM) operations. This study probes the interaction between disc cutters and rock, employing an enhanced Bonding model to more accurately depict the failure behavior of rock specimens. Numerical simulations of the rock-breaking process using two disc cutters were conducted, focusing on highly influential excavation parameters—penetration depth (3, 5, 7, 9 mm) and cutter arrangements—tip width (14, 17, 20, 23 mm) and cutter spacing (50, 65, 80, 95, 110 mm). These simulations analyzed the impact of various factors on cutter force, wear, specific energy of rock breaking, and crushing unit rock cutter wear. The results show that increased penetration depth leads to higher cutter force and wear, with specific energy and unit wear remaining low when penetration is less than 5 mm. A larger cutter tip width incurs higher forces and wear of the first cutter, but when the tip width exceeds 20 mm, the force and wear of the second cutter will be reduced. Optimal specific energy for rock breaking and unit wear of rock volume were identified within a cutter spacing range of 80 to 95 mm. These findings can facilitate the analysis of how excavation parameters and cutter arrangements affect wear behavior, offering superior construction recommendations.

During the numerical simulation of blasting in jointed rock masses, the accuracy of joint geometric parameters is one of the key factors affecting the numerical results. To facilitate the numerical simulation, most of the previous studies on blasting in jointed rock masses were conducted on regular jointed rocks, which is not conducive to fully revealing the dynamic responses and blast-induced damage characteristics of jointed rock mass. In this study, scanline sampling and borehole sampling were employed to obtain the surface and internal joint structures of the rock bench. To represent the joint geometry, a reconstruction technique for three-dimensional (3D) jointed rock masses in LS-DYNA was proposed utilizing MATLAB code. In the process, the elements on joint surfaces were identified and assigned mechanical parameters of joints to construct the 3D jointed rock model, where the geometrical properties of generated joints obey the statistical distribution obtained from the scanline survey. Taking an open-pit limestone mine as an example, a statistical analysis of the 3D distribution of joints was carried out and used to construct a 3D jointed rock numerical model for bench blasting. Comparisons between the bench slope extracted from the numerical model and the actual joint trace mapping from a rock exposure are performed, and the similarity between the two contour plots of joint orientations reaches 91.6 %. For comparison tests, the bench blasting was simulated by an intact rock model and the jointed rock model. The results indicate that the dynamic responses and blast-induced damage characteristics of jointed rocks are significantly affected by the geometry of joints. Compared with the intact rock model, the presence of joints causes stress concentration and local strengthening of rock damage between adjacent joints, which results in a 30.5 % increase in the damage volume. Furthermore, a field blasting test was conducted to analyze the accuracy of the jointed rock model. The results show that the fragment size distributions obtained from the jointed rock numerical model and the filed test are generally consistent, and the error between them in the proportion of rock fragments with a size of 0 ∼ 100 mm is only 12.8 %. These findings indicate that the proposed reconstruction method of the jointed rock model is considerably robust for characterizing the joint geometry of in situ rock masses and simulating the bench blasting in jointed rock masses.

The long-term freeze-thaw effect leads to the development of rock fractures and strength degradation in cold region engineering construction, which poses a serious challenge to the stability of the project. In this paper, the microscopic model of sandstone freeze-thaw cycles was established using a particle flow code (PFC2D). Through numerical simulation, the variation law of mechanical properties of rock mass with micro-cracks is systematically studied from the aspects of displacement, crack development, strain and strength. The results show that: (i) The freeze-thaw loading displacement is concentrated on both sides of the initial micro-cracks, and the crack development is not controlled by it. When the number of freeze-thaw cycles is greater than 17, the displacement and crack development are significant. The crack increases with the increase of the crack distance ratio. (ii) When the number of freeze-thaw cycles is less than 15, the compressive crack develops at the end of the initial micro-crack, and the development is controlled by it. When the number of freeze-thaw cycles is greater than 21, the crack increases sharply, and the development is no longer controlled by the initial micro-cracks. When the number of freeze-thaw cycles is less than 15, the damage strain shows minimal variation but decreases sharply with the increase of freeze-thaw cycles. (iii) Under each crack distance ratio, the strength changes in three stages: when the number of freeze-thaw cycles is less than 15, the strength changes little, and the strength decreases sharply with the increase of freeze-thaw cycles. With the increase of the crack distance ratio, it increases first and then decreases. And it is more significant when the number of freeze-thaw cycles is greater than 17. The research results can provide theoretical support for the influence and evaluation of rock freeze-thaw action in cold regions.

In the context of distributed driving six-wheel steering (DD-6WS) commercial vehicles, the integration of auxiliary steering systems and direct yaw moment control (DYC) is critical for improving maneuverability and stability while driving. However, the nonlinear dynamic characteristics of vehicles under high-speed conditions make it difficult to fully exploit the benefits of multi-subsystem functionality. To address this issue, a sub-regional linearization (SRL) theory is proposed that uses nonlinear tire dynamic data to accurately capture the dynamics of commercial vehicle models. Additionally, a nonlinear stability criterion (the Lyapunov exponent) and a Mixed-Logic Dynamic (MLD) approach are used to create an intervention mechanism for multiple subsystems. Furthermore, hysteresis control is incorporated to mitigate frequent subsystem interventions caused by minor fluctuations in state variables. The results of simulations across various speed ranges using the HYSDEL toolbox and MATLAB-Trucksim platform demonstrate that using SRL models significantly improves the lateral control stability of commercial vehicles at high speeds while effectively reducing the frequency of triggers for auxiliary systems through the successful implementation of MLD control at high or low speeds. An orderly and precise triggering logic solves challenges caused by coupling and conflicts in vehicle redundant control.

Software-Defined Networking (SDN) technology has emerged as a promising solution to guarantee high reliability in the Industrial Internet of Things (IIoT) ecosystem. Through SDN, both fault tolerance-based Route Protection (RP) and fault tolerance-based Route Restoration (RR) are available to provide traffic rerouting when a network failure occurs in IIoT. RR redefines routes dynamically based on the current network status. However, it increases significantly the recovery time, which is not suitable for the Quality-of-Service (QoS) requirements of IIoT. In contrary, RP ensures fast failover, but it cannot be updated when the network status changes until the timeout interval expires. To deal with these issues, we propose a Dynamic Route Protection (DRP) mechanism that recalculates and reinstalls new optimal link-disjoint routes in accordance with the change rather than awaiting the controller to retransmit new flow instructions. Moreover, DRP responds speedily to forward the data packets from the secondary route to the main route if it is repaired. To recover rapidly when the connection failure affects both a link on the main route and the link on the secondary route simultaneously, the DRP mechanism utilizes the strategy of caching the third route in the controller memory using the Dynamic Hash Table (DHT) structure. DRP considers the heterogeneous traffic flows such as either delay-sensitive or both delay-sensitive and loss-sensitive. Again, this paper introduces a candidate fault tolerance architecture for Software-Defined IIoT (SDIIoT) that decouples IIoT networks into three functional layers. The results from the simulation network and the experimental hardware testbed illustrated that the DRP mechanism outperforms the FT-RP, RR, LFR, Pro-VLAN, and SDNRMbw mechanisms by minimizing the failure recovery time, end-to-end delay, packet violation rate, packet loss rate, and the time required to reuse the main route when it is repaired, while maximizing the packet delivery ratio.

Real-time e-business applications are vital for operational efficiency, but connectivity challenges persist, particularly in remote or crowded areas. Drone Base Station (DBS) architecture, proposed for Beyond fifth Generation (B5G) and Sixth Generation (6G) multi-cell networks, offers on-demand hotspot coverage, addressing connectivity gaps in remote or crowded environments. DBSs provide a promising solution to meet the demanding requirements of high data rates, real-time responsiveness, low latency, and extended network coverage, particularly for real-time e-business applications. A critical challenge in this context involves efficiently allocating the needed number of DBSs to the different hotspot service areas, referred to as cells, to optimize the operator’s total profit under unpredictable user demands, varying area-specific service costs, and price dependence real-time e-service. The objective is to achieve the highest total revenue while minimizing the cost (cost savings) throughout the multi-cell system. This challenge is formulated as a profit-maximization discount return problem, integrating the coverage constraint, the variable cell-dependent operational cost, the e-service-based price and the uncertain demands of users across cells. Traditional optimization methods fail due to environmental uncertainty, which leads to the need to reformulate the problem as a Markov Decision Problem (MDP). We introduce a cloud-based Reinforcement Learning (RL) algorithm for DBS dispatch to address the MDP formulation. This algorithm dynamically adjusts to uncertain per-cell user distributions, considering variable operating costs and service-dependent prices across cells. Through extensive evaluation, the RL-based dispatch approach is compared with reference drone dispatch algorithms, demonstrating superior performance in maximizing operator profit through cost savings by optimizing DBS dispatch decisions based on learned user behaviors, variable operational costs, and e-service types.