Simulation Modelling Practice and Theory最新文献

The South African sunflower industry is considering transferring to a quality-based marketing system driven by an incentive. However, the ability of silos to offer necessary segregation services is critical in such a transition (Baker et al., 1997) and the silo industry is concerned about the negative impact segregation could have on operations and finances. This paper proposes a multi-method simulation approach to quantify the impact of quality-based segregation and sunflower farmer response to the incentive on silo bin utilisation and the ability of the silo to store contents of arriving trucks (service level). A combination of agent-based simulation, discrete event simulation and Bayesian network sampling is used to capture system behaviour where data is scarce. Therefore, in this study, a mixed methods ABM and DES model is implemented in a new environment: a grade-based segregation problem in the South African silo industry. Several scenarios are modelled to cross-validate methods and to tease out the impact of farmer response on the results. The model is applied to a case study silo complex to test the concept. Results obtained for the case study silo show a significant negative impact on costs due to lower service levels and bin utilisation, incurring relocation and opportunity costs. Overall, this study highlights that it is necessary to consider the impact that sunflower segregation could have on each unique silo complex and provides a method to quantify the stated impact.

3D printing technology has been developing rapidly in recent years, but product quality control has become one of the main obstacles to its widespread use in manufacturing. A new stochastic computer model and robust optimization method are proposed for the highly fluctuating 3D printing process to improve the stability of the printed product quality. Firstly, the signal and noise are jointly modeled, and the idea of latent variables in machine learning is incorporated to overcome the limitation that the replication times of the stochastic Kriging model must be greater than one. Then, the chain rule and Woodbury identity are utilized to reduce the time required for hyperparameter estimation of the model. Finally, the optimization objective function is constructed based on the Taguchi quality loss function, and optimal process parameters are found using a genetic algorithm. The numerical simulation results demonstrate that the robust optimization method based on heteroskedasticity Gaussian process model proposed in this paper can estimate model hyperparameters faster and predict results more accurately. Furthermore, the prediction and validation results of 3D printing experiments verify the effectiveness of the proposed method.

Hybrid track/wheel-legged robots combine the advantages of wheel-based and leg-based locomotion, granting adaptability across varied terrains through efficient transitions between rolling and walking modes. However, automating these transitions remains a significant challenge. In this paper, we introduce a method designed for autonomous mode transition in a quadruped hybrid robot with a track/wheel-legged configuration, especially during step negotiation. Our approach hinges on a decision-making mechanism that evaluates the energy efficiency of both locomotion modes using a proposed energy-based criterion. To guarantee a smooth negotiation of steps, we incorporate two climbing gaits designated for the assessment of energy usage in walking locomotion. Simulation results validate the method’s effectiveness, showing successful autonomous transitions across steps of diverse heights. Our suggested approach has universal applicability and can be modified to suit other hybrid robots of similar mechanical configuration, provided their locomotion energy performance is studied beforehand.

As demand for computing power and low latency in intelligence applications grows, the efficient management and coordination of resources in computing power networks become crucial. This paper presents a telemetry-aided multi-agent cooperation framework for DAG task scheduling in computing power networks. Utilizing distributed agents with network telemetry, the framework accurately assesses local network state information, formulates scheduling policies, and assigns tasks to edge servers. An online learning algorithm for DAG task scheduling is also introduced to enhance the cooperation strategy in decision-making, enabling rapid task scheduling and resource allocation decisions. Simulation results demonstrate a minimum 13.5% reduction in total task execution time compared to sub-optimal methods, along with improved node and link load balancing.

There is no lubrication in the contact area, which leads to a significant influence of the thermal characteristics on the friction dynamics and life of the helicopter intermediate reducer because the helicopter experiences severe loss of lubrication. In this study, for evaluating the non-linear friction dynamics and transient thermal network models of the intermediate gearbox, a multi-time scale coupling method was proposed to establish a proportional allocation method between the oil film region and the rough peak contact region of the tooth surface by considering the effect of temperature and to study the adsorption film under the condition of severe lubrication loss. The coupled thermal and kinetic characteristics of the severe lubrication loss condition were obtained by calculating the friction coefficients in the lubricated and dry-friction regions. The results showed that the friction coefficient of the bevel gear tooth surface fluctuated between 0.1 and 0.6, and the degree of the gear axis trajectory shift was more evident owing to the increase in the friction force excitation caused by the increase in the friction coefficient.

While providing efficient and convenient cloud services, data center also brings great pressure to energy consumption and environment. The rise of server temperature not only increases the refrigeration cost, but also seriously affects the operation safety of the data center. Effective analysis and prediction of data center temperature is not only conducive to preventing server overheating and shutdown, but also crucial to data center task scheduling, resource allocation optimization and energy efficiency improvement of data center. Therefore, this article proposes a Two-stage Gated Recurrent Unit (GRU) temperature prediction algorithm with self-healing mechanism. The algorithm establishes a prediction model for the important parameters affecting temperature prediction - CPU utilization, and takes the output of the model as the input parameter of the server temperature prediction model, which fits the changes of each parameter more accurate. To avoid the decrease in prediction accuracy caused by new operating conditions that have not been learned before and changes in physical environmental factors during the operation of the model, a self-healing mechanism is proposed to ensure the prediction accuracy of the model. Experiments show that our prediction model can accurately predict the inlet temperature evolution of the server with dynamic workload. It reduces the prediction error (RSME) to 0.280, and the average prediction temperature difference is only 0.675, which is 10 % higher than the single stage prediction accuracy. The use of Two-stage prediction methods in other machine learning methods can also improve prediction accuracy. Based on the prediction model, this paper proposes a task scheduling algorithm that minimizes temperature difference. The algorithm can make the temperature between servers more balanced after task allocation, effectively reducing the number of servers running at high and low temperatures in the data center, avoiding refrigeration waste, and achieving energy conservation in the data center.

The ramp-up of new geometries, process parameters, and materials can be enormously time and cost-intensive in Additive Manufacturing. Especially for Laser-Directed Energy Deposition (DED-L), the extreme physical environment at the melt pool results in the need for multiple trial-and-error tests to quantify the process behavior. These tests significantly raise manufacturing expenses. A Digital Twin (DT) of the DED-L process can therefore be of substantial value if the amount of experimental testing is hereby reduced. In the present study, a multiscale DT based on coupling a global and local model has been investigated. The global model simulates the heating of the entire part, whereas the local model represents only a specific region of this global geometry. Using a high-density mesh for the local model enables the simulation of the specific laser-powder interactions and fast-cooling rates typical in DED-L. The results of the global model are used to integrate context awareness about the changing process conditions during the print job into the local model. This process evolvement is impossible to obtain with models of smaller dimensions and is of elemental necessity for accurately simulating multi-clad depositions. The DT was validated on an industrial-grade DED-L machine with in-situ process monitoring capabilities. In all cases, the DT shows a high resemblance with the experimental data and metallographic inspections at a reasonable computational cost.

To study the stress corrosion characteristics of wire with different wear conditions, namely depth, cross angle and length, Finite Element (FE) models of worn wires are developed based on multi-physics coupling analysis. The electromechanical parameters required for the FE simulations are determined from experimental testing. Then, the stress corrosion characteristics, i.e. stress, corrosion voltage, and corrosion current density, of the wear surface under different tensile displacements are obtained. The results show that the stress increases uniformly under different tensile displacements. However, different wear states present different stress corrosion characteristics. When the tensile displacement or wear state is sufficient to cause plastic deformation of worn wire, the corrosion characteristics changes dramatically.

Electric scooters are becoming popular in public spaces, and autonomous robots will join soon. However, integrating these Personal Mobility Vehicles (PMV) without proper provisions challenges the safety and comfort of all users. While Social Force Model (SFM) commonly replicates pedestrians’ movements, directly applying it to PMVs is challenging and inaccurate. We propose a heterogeneous SFM considering the dynamic personal spaces of various agents on futuristic sidewalks, addressing the impracticalities of SFM. Additionally, subjective safety estimation relaxes the constant desired-velocity assumption, and the influence weight reduces the complexity by omitting pairwise calibration. Experiments calibrate the model for e-scooters, validating it in realistic scenarios with multiple e-scooters passing through pedestrians. The proposed model has higher accuracy than previous models regarding behavioral naturalness metrics. In addition, the models’ performance in replicating experimental observations is analyzed. This research contributes to safer and more efficient transportation with PMVs, particularly e-scooters, and provides a novel approach to modeling multi-type agents on heterogeneous sidewalks.