Title: A comparative study of machine learning models for predicting the state of reactive mixing

Abstract

Mixing phenomena are important mechanisms controlling flow, species transport, and reaction processes in fluids and porous media. Accurate predictions of reactive mixing are critical for many Earth and environmental science problems such as contaminant fate and remediation, macroalgae growth, and plankton biomass evolution. Here, to investigate the evolution of mixing dynamics under different scenarios (e.g., anisotropy, fluctuating velocity fields), a finite-element-based numerical model was built to solve the fast, irreversible bimolecular reaction-diffusion equations to simulate a range of reactive-mixing scenarios. A total of 2,315 simulations were performed using different sets of model input parameters comprising various spatial scales of vortex structures in the velocity field, time-scales associated with velocity oscillations, the perturbation parameter for the vortex-based velocity, anisotropic dispersion contrast (i.e., ratio of longitudinal-to-transverse dispersion), and molecular diffusion. The outputs comprised concentration profiles of reactants and products. The inputs to and outputs from these simulations were concatenated into feature and label matrices, respectively, to train 20 different machine learning (ML) models intended to emulate system behavior. These 20 ML emulators, based on linear methods, Bayesian methods, ensemble learning methods, and multilayer perceptrons (MLPs), were trained to classify the state of mixing and predict three quantities of interest (QoIs) characterizing speciesmore » production, decay (i.e., average concentration, square of average concentration), and degree of mixing (i.e., variances of species concentration). Unsurprisingly, linear classifiers and regressors failed to reproduce the QoIs; however, ensemble methods (classifiers and regressors) and the MLP model accurately classified the state of reactive mixing and the QoIs. Among ensemble methods, random forest and decision-tree-based AdaBoost faithfully predicted the QoIs. At run time, trained ML emulators produced results times faster than the finite-element simulations. Due to their low computational expense and high accuracy, ensemble and MLP models are excellent emulators for these numerical simulations and great utilities in uncertainty quantification exercises, which can require 1,000s of forward model runs.« less

Authors:

Ahmmed, Bulbul ^[1]; Mudunuru, Maruti K. ^[2]; Karra, Satish ^[3]; James, Scott C. ^[4]; Vesselinov, Velimir V. ^[3]

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+ Show Author Affiliations

1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Baylor Univ., Waco, TX (United States)
2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
3. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
4. Baylor Univ., Waco, TX (United States)

Publication Date:

Tue Jan 19 00:00:00 EST 2021

Research Org.:

Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Org.:

USDOE Office of Fossil Energy (FE); USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Biological and Environmental Research (BER); USDOE Office of Science (SC), Biological and Environmental Research (BER). Earth and Environmental Systems Science Division

OSTI Identifier:

1765121

Alternate Identifier(s):

OSTI ID: 1762732; OSTI ID: 1775931; OSTI ID: 1778767

Report Number(s):

PNNL-SA-157340; LA-UR-20-21737
Journal ID: ISSN 0021-9991; TRN: US2206433

Grant/Contract Number:  

AC05-76RL01830; 20150693ECR; 20190020DR; 89233218CNA000001; AC02-05CH11231

Resource Type:

Accepted Manuscript

Journal Name:

Journal of Computational Physics

Additional Journal Information:

Journal Volume: 432; Journal ID: ISSN 0021-9991

Publisher:

Elsevier

Country of Publication:

United States

Language:

English

Subject:

97 MATHEMATICS AND COMPUTING; surrogate modeling; machine learning; reaction-diffusion equations; random forests; ensemble methods; artificial neural networks; computer science; earth sciences; energy sciences; information science; mathematics