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Physics-informed machine learning models for predicting the progress of reactive-mixing

机译:物理知识机器学习模型,用于预测反应性混合进展

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This paper presents a physics-informed machine learning (ML) framework to construct reduced-order models (ROMs) for reactive-transport quantities of interest (QoIs) based on high-fidelity numerical simulations. QoIs include species decay, product yield, and degree of mixing. The ROMs for QoIs are applied to quantify and understand how the chemical species evolve over time. First, high-resolution datasets for constructing ROMs are generated by solving anisotropic reaction-diffusion equations using a non-negative finite element formulation for different input parameters. The reactive-mixing model input parameters are: time-scale associated with flipping of velocity, spatial-scale controlling small/large vortex structures of velocity, perturbation parameter of the vortex-based velocity, anisotropic dispersion contrast, and molecular diffusion. Second, random forests, F-test, and mutual information criterion are used to evaluate the importance of model inputs/features with respect to QoIs. We observed that anisotropic dispersion contrast is the most important feature and time-scale associated with flipping of velocity is the least important feature. Third, Support Vector Machines (SVM) and Support Vector Regression (SVR) are used to construct ROMs based on the model inputs. The constructed SVR-ROMs are then used to predict scaling of QoIs. We also present estimates and inequalities on the QoIs, which inform that the species decay, mix, and produce in an exponential fashion. These inequalities also inform that a radial basis function is the most suitable kernel for the SVM/SVR models for QoIs. It is observed that R-2-score for SVR-ROMs on unseen data is greater than 0.9, implying that the SVR-ROMs are able to predict the reaction-diffusion system state reasonably well. Finally, in terms of the computational cost, the proposed SVM-ROMs are O(10(7)) times faster than running a high-fidelity finite element simulation for evaluating QoIs. This makes the proposed ML-based ROMs attractive for reactive-transport sensing and real-time monitoring applications as they are significantly faster yet reasonably accurate. (C) 2020 Elsevier B.V. All rights reserved.
机译:本文介绍了物理知识的机器学习(ML)框架,用于构建基于高保真数值模拟的可反应运输量(QOIS)的阶数模型(ROM)。 QOI包括物种衰减,产品产量和混合程度。 Qois的ROM适用于量化和理解化学物种随时间的发展方式。首先,通过用于不同输入参数的非负有限元制剂来解决各向异性反应扩散方程来产生用于构建ROM的高分辨率数据集。反应混合模型输入参数是:与速度翻转,空间尺度控制小/大涡流结构,涡旋基速度,各向异性分散对比度和分子扩散的空间尺度。其次,随机森林,F检验和互信息标准用于评估模型输入/特征相对于Qois的重要性。我们观察到,各向异性色散对比度是与速度翻转相关的最重要的特征和时间尺度是最不重要的特征。第三,支持向量机(SVM)和支持向量回归(SVR)用于基于模型输入构造ROM。然后使用构建的SVR-ROM来预测Qois的缩放。我们还向Qois提供了估计和不平等,这为物种衰减,混合和生产以指数时尚。这些不等式还通知,径向基函数是QOIS的SVM / SVR模型中最合适的内核。观察到,看不见数据上的SVR-ROM的R-2分数大于0.9,这意味着SVR-ROM能够合理地预测反应扩散系统状态。最后,就计算成本而言,所提出的SVM-ROM是o(10(7))倍,而不是运行用于评估Qois的高保真有限元模拟。这使得所提出的基于ML的ROM对于无功传感和实时监测应用具有吸引力,因为它们明显更快但合理准确。 (c)2020 Elsevier B.v.保留所有权利。

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