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矿井通风系统数字孪生模型的不确定性分析

Uncertainty analysis of digital twin model of mine ventilation system.

作者信息

Cao Peng, Liu Jian, Wang Honglin, Wang Yu, Liu Xue, Wang Dong

机构信息

College of Safety Science and Engineering, Liaoning Technical University, Huludao, 125105, Liaoning, China.

Key Laboratory of Mine Thermo-Motive Disaster and Prevention, Ministry of Education, Huludao, 125105, China.

出版信息

Sci Rep. 2024 Nov 4;14(1):26558. doi: 10.1038/s41598-024-77978-3.

DOI:10.1038/s41598-024-77978-3
PMID:39489805
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11532341/
Abstract

The digital twin model of mine ventilation system (DTMVS) plays an important role in intelligent safety management. However, the uncertainty of the ventilation resistance coefficient, which is the core parameter of the model, makes it challenging to accurately construct a DTMVS. In this study, Latin Hypercube Sampling (LHS) and ventilation resistance coefficient estimation models (VRCEMs) are used to analyze the uncertainty. First, the LHS method was used to explore the effect of uncertainty in the simulated airflow by continuously increasing the level of uncertainty in the ventilation resistance coefficients. Subsequently, the ventilation resistance coefficients were estimated using the VRCEMs, and the uncertainty of the ventilation resistance coefficient and the simulated airflow was analyzed. The results showed that the ventilation resistance coefficients with a 5% coefficient of variation can cause the DTMVS to lose 34% of the real airflow data points. The degree of uncertainty in the ventilation resistance coefficients estimated by the VRCEM-GA (VRCEM using genetic algorithm) and VRCEM-DE (VRCEM using differential evolution algorithm) methods was enhanced by 27.4% and 4.4%, respectively, compared with VRCEM-ES (VRCEM using evolutionary strategy algorithm). The VRCEM-ES model had the least influence on the uncertainty of the simulated airflow of DTMVS. The simulated airflow of the DTMVS constructed based on VRCEMs fluctuated normally within the confidence interval. VRCEMs had a higher sensitivity to the ventilation resistance coefficients of branches with low coefficients of variation.

摘要

矿井通风系统数字孪生模型（DTMVS）在智能安全管理中发挥着重要作用。然而，作为该模型核心参数的通风阻力系数具有不确定性，这使得准确构建DTMVS具有挑战性。在本研究中，采用拉丁超立方抽样（LHS）和通风阻力系数估计模型（VRCEMs）来分析不确定性。首先，通过不断提高通风阻力系数的不确定性水平，利用LHS方法探究其对模拟风流不确定性的影响。随后，使用VRCEMs估计通风阻力系数，并分析通风阻力系数和模拟风流的不确定性。结果表明，变异系数为5%的通风阻力系数会使DTMVS失去34%的实际风流数据点。与使用进化策略算法的VRCEM-ES相比，使用遗传算法的VRCEM-GA和使用差分进化算法的VRCEM-DE方法估计的通风阻力系数的不确定性程度分别提高了27.4%和4.4%。VRCEM-ES模型对DTMVS模拟风流不确定性的影响最小。基于VRCEMs构建的DTMVS的模拟风流在置信区间内呈正态波动。VRCEMs对变异系数较低的分支的通风阻力系数具有较高的敏感性。

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