SIMULATION: Transactions of The Society for Modeling and Simulation International

Morrissey, John P; Totoo, Prabhat; Hanley, Kevin J; Papanicolopulos, Stefanos-Aldo; Ooi, Jin Y; Gonzalez, Iván Cores; Raffin, Bruno; Mostajabodaveh, Seyedmorteza; Gierlinger, Thomas

doi: 10.1177/0037549720906465pmid: N/A

Regardless of its origin, in the near future the challenge will not be how to generate data, but rather how to manage big and highly distributed data to make it more easily handled and more accessible by users on their personal devices. VELaSSCo (Visualization for Extremely Large-Scale Scientific Computing) is a platform developed to provide new visual analysis methods for large-scale simulations serving the petabyte era. The platform adopts Big Data tools/architectures to enable in-situ processing for analytics of engineering and scientific data and hardware-accelerated interactive visualization. In large-scale simulations, the domain is partitioned across several thousand nodes, and the data (mesh and results) are stored on those nodes in a distributed manner. The VELaSSCo platform accesses this distributed information, processes the raw data, and returns the results to the users for local visualization by their specific visualization clients and tools. The global goal of VELaSSCo is to provide Big Data tools for the engineering and scientific community, in order to better manipulate simulations with billions of distributed records. The ability to easily handle large amounts of data will also enable larger, higher resolution simulations, which will allow the scientific and engineering communities to garner new knowledge from simulations previously considered too large to handle. This paper shows, by means of selected Discrete Element Method (DEM) simulation use cases, that the VELaSSCo platform facilitates distributed post-processing and visualization of large engineering datasets.

Lin, Hongzhi; Zhang, Yongping

doi: 10.1177/0037549720920374pmid: N/A

Urban development usually deteriorates the transportation system. For sustainable urban development, policymakers often face the challenging problem of how to optimally allocate overall land use quotas across a number of residential locations according to the performance of the transportation system. This is a kind of Stackelberg competition, where policymakers make land use decisions and travelers make behavioral responses. A novel bi-level model is formulated to solve this problem. The upper-level model minimizes the total system travel time by land use allocation, while at the lower level are sequential models with feedback for transportation system equilibrium. The Dirichlet allocation algorithm, a simulation-based heuristic algorithm, is designed to solve this bi-level model. A simulation experiment using the Nguyen–Dupuis network is then used to verify the proposed model and algorithm. The results from the simulation experiment demonstrate that not only are the model and algorithm operational but that they also provide an effective tool for policymakers to plan for land use development.

Ugurlu, Omer Faruk; Kumral, Mustafa

doi: 10.1177/0037549720923751pmid: N/A

In recent years, commodity prices have swiftly decreased, narrowing the profit margin for many mining operations and forcing them to find effective cost management strategies to respond to low prices. Given that equipment is one of the most significant assets of a mining company, efficient equipment utilization has strong potential to reduce costs. This paper focuses on the relationship between the number of available drilling machines based on reliability analysis and the number of holes to be created on a bench of an open pit mining operation. Since equipment availability is random in nature, a range of holes to be drilled corresponding to a specified probability level was determined. To assess the performance of the proposed approach, a case study was carried out using two stochastic modeling techniques. Evolutions of reliabilities of 10 rotary drilling machines over a specific time were simulated by Markov chain Monte Carlo and mean reverting processes, using historical data. Multiple simulations were then used for risk quantification. Results show that the proposed approach can be used as a tool to assist production scheduling and assess the associated risk.

Ahmadi, Abdollah; Mavalizadeh, Hani; Esmaeel Nezhad, Ali; Siano, Pierluigi; Shayanfar, Heidar Ali; Hredzak, Branislav

doi: 10.1177/0037549720915773pmid: N/A

This paper presents the application of information gap decision theory (IGDT) to deal with uncertainties associated with load forecasting in dynamic, environment constrained, coordinated generation and transmission expansion planning. Traditionally, the gaseous emissions are constrained over the whole system. Conventional methods cannot guarantee a practical expansion plan since huge emissions can still occur on some buses in the power system. This paper introduces a per-bus emission limit to avoid extreme emissions in highly populated areas. The effect of nodal emission limits is fully discussed and compared to a conventional method. The model is kept linear using the big M approach to decrease the model computational burden. Reliability is considered by limiting the estimated load not served in each year over the planning horizon. The cost of fuel transportation and fuel limits are considered in order to make the model more realistic and practical. The effectiveness of the proposed model is verified by implementation on Garver 6 bus, IEEE 30 bus, and 118 bus test systems.

Liu, Xiang; Lv, Jian; Xie, Qingsheng; Huang, Haisong; Wang, Weixing

doi: 10.1177/0037549720915261pmid: N/A

The optimization of man–machine systems is a critical component in the research and development of products but it has been a struggle to improve the optimization accuracy. This study presents an ergonomic optimization method driven by a posture load regulatory network (PLRN). Considering that the differences in work-related musculoskeletal disorders are caused by different occupations, human body part data collection is completed using the deconstructions of man–machine task sequences, which applies in partial theory of the complex network to build the PLRN model. Then the approach connects the human body part data with the regulatory network to calculate the cumulative load tendency and the performance of the load group; results of the analysis provide support for the ranking of human body part loads. In addition, we derive the mapping relationship between the man–machine workload and product engineering modules based on the quality function deployment theory, which can reflect the man–machine system problems of products and assist designers in optimizing and making decisions in man–machine systems. To this end, this paper provides a case study research for evaluating the feasibility of the PLRN simulation optimization method. Results show that our method is capable of explaining the change and the predicting the tendency of human load during the man–machine operation. Compared with the traditional subjective analytic hierarchy process, the PLRN simulation optimization method provides more accurate and objective evaluation on product ergonomics, and new research opportunities on ergonomic optimization.