{"title":"Calibration method of discrete element parameters of crushed coal based on mechanical and engineering tests","authors":"Haozhou Ma , Xuewen Wang , Rui Xia , Bo Li","doi":"10.1016/j.advengsoft.2024.103759","DOIUrl":null,"url":null,"abstract":"<div><p>Current discrete element parameter calibration methods primarily rely on mechanical tests to analyze particle properties and often overlook the machinery's interaction with the particles. The extensive variation in particle sizes of crushed coal poses challenges in accurately applying parameters derived from mechanical test calibrations to industrial simulations. DEM models of mechanical tests for coal were developed to examine how parameters influence coal's mechanical properties through factor analysis. Simplified engineering test models were developed based on mining equipment, with the equipment responses used as indicators to optimize mechanical test calibration parameters. On this basis, a calibration method of discrete elemental parameters of coal based on the crushing simulation of mining equipment was proposed. This method was validated through mechanical and engineering simplification tests, resulting in a mean error of <10 % in the time-varying response. The research findings enable calibration of discrete element parameters for crushed coal in industrial simulation.</p></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"197 ","pages":"Article 103759"},"PeriodicalIF":4.0000,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Engineering Software","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0965997824001662","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Current discrete element parameter calibration methods primarily rely on mechanical tests to analyze particle properties and often overlook the machinery's interaction with the particles. The extensive variation in particle sizes of crushed coal poses challenges in accurately applying parameters derived from mechanical test calibrations to industrial simulations. DEM models of mechanical tests for coal were developed to examine how parameters influence coal's mechanical properties through factor analysis. Simplified engineering test models were developed based on mining equipment, with the equipment responses used as indicators to optimize mechanical test calibration parameters. On this basis, a calibration method of discrete elemental parameters of coal based on the crushing simulation of mining equipment was proposed. This method was validated through mechanical and engineering simplification tests, resulting in a mean error of <10 % in the time-varying response. The research findings enable calibration of discrete element parameters for crushed coal in industrial simulation.
目前的离散元件参数校准方法主要依靠机械测试来分析颗粒特性,但往往忽略了机械与颗粒之间的相互作用。碎煤的颗粒大小差异很大,这给将机械测试校准得出的参数准确应用于工业模拟带来了挑战。我们开发了煤炭机械测试的 DEM 模型,通过因素分析来研究参数如何影响煤炭的机械性能。根据采矿设备开发了简化的工程测试模型,并将设备响应作为优化机械测试校准参数的指标。在此基础上,提出了基于采矿设备破碎模拟的煤炭离散元素参数校准方法。该方法通过机械和工程简化测试进行了验证,结果显示时变响应的平均误差为 10%。研究成果有助于在工业模拟中校准碎煤的离散元素参数。
期刊介绍:
The objective of this journal is to communicate recent and projected advances in computer-based engineering techniques. The fields covered include mechanical, aerospace, civil and environmental engineering, with an emphasis on research and development leading to practical problem-solving.
The scope of the journal includes:
• Innovative computational strategies and numerical algorithms for large-scale engineering problems
• Analysis and simulation techniques and systems
• Model and mesh generation
• Control of the accuracy, stability and efficiency of computational process
• Exploitation of new computing environments (eg distributed hetergeneous and collaborative computing)
• Advanced visualization techniques, virtual environments and prototyping
• Applications of AI, knowledge-based systems, computational intelligence, including fuzzy logic, neural networks and evolutionary computations
• Application of object-oriented technology to engineering problems
• Intelligent human computer interfaces
• Design automation, multidisciplinary design and optimization
• CAD, CAE and integrated process and product development systems
• Quality and reliability.