基于POD与机器学习的综掘面流场快速预测算法
Fast Prediction Algorithm for Flow Field in Fully Mechanized Excavation Face Based on POD and Machine Learning
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摘要: 为解决综掘面降尘措施难以合理利用的问题,本文提出了一种基于本征正交分解(POD)与机器学习的综掘面流场快速预测算法,该算法首先利用POD将多种工况下风流场数据或粉尘浓度数据进行降维,得到流场工况的基函数模态与模态系数,通过机器学习方法预测不同工况下占总能量达到90%以上的模态系数,从而对未知工况的模态系数进行预测,利用预测得到的模态系数与基函数模态进行重构便可得到未知工况的风流场数据或粉尘浓度场。通过对比得到支持向量机模型对模态系数的预测能力优于其他模型。通过综掘面300种工况下的数值模拟结果作为数据样本,使用支持向量机模型对风流场与粉尘浓度场进行预测仅需数值模拟花费时间的1/816,且对比60种工况下的预测结果与数值模拟结果可知:各网格的风流速度相对误差为0.36m/s,粉尘浓度相对误差为86.24mg/m3。Abstract: To address the challenges associated with effectively implementing dust reduction measures in the fully mechanized excavation face, this study introduces a rapid flow field prediction algorithm based on proper orthogonal decomposition (POD) and machine learning. Initially, the algorithm employs POD to perform a dimensional reduction of flow field data or dust concentration data across multiple working conditions, yielding the basic function modal and modal coefficients of the flow conditions. Utilizing machine learning techniques, the algorithm predicts the modal coefficients for diverse conditions that contribute to over 90% of the total energy, facilitating the estimation of modal coefficients for unknown scenarios. Reconstructing using these predicted modal coefficients alongside basic function modals enables the derivation of flow field data or dust concentration data for unknown conditions. Comparative analysis reveals that the Support Vector Machine (SVM) model exhibits superior capability in predicting modal coefficients compared to other models. Using numerical simulation results from 300 conditions in the comprehensive excavation as data samples, the SVM model can predict the flow field and dust concentration field in just 1/816th of the time required by traditional numerical simulations. Additionally, a comparison between the predicted results and numerical simulations across 60 conditions indicates a relative error of 0.36m/s for flow velocity and 86.24mg/m3 for dust concentration across all grids.
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