Quantifying Subsurface Parameter and Transport Uncertainty Using Surrogate Modeling and Environmental Tracers

We combine physics-based groundwater reactive transport modeling with machine learning techniques to quantify hydrogeologic model and solute transport predictive uncertainties. We train an artificial neural network (ANN) on a dataset of groundwater hydraulic heads and H concentrations generated using a high-fidelity groundwater reactive transport model. Using the trained ANN as a surrogate model to reproduce the input-output response of the high-fidelity reactive transport model, we quantify the posterior distributions of hydrogeologic parameters and hydraulic forcing conditions using Markov-chain Monte Carlo (MCMC) calibration against field observations of groundwater hydraulic heads and H concentrations. We demonstrate the methodology with a model application that predicts Chlorofluorocarbon-12 (CFC-12) solute transport at a contaminated site in Wyoming, USA. Our results show that including H observations in the calibration dataset reduced the uncertainty in the estimated permeability field and infiltration rates, compared to calibration against hydraulic heads alone. However, predictive uncertainty quantification shows that CFC-12 transport predictions conditioned to the parameter posterior distributions cannot reproduce the field measurements. We found that calibrating the model to hydraulic head and H observations results in groundwater mean ages that are too large to explain the observed CFC-12 concentrations. The coupling of the physics-based reactive transport model with the machine learning surrogate model allows us to efficiently quantify model parameter and predictive uncertainties, which is typically computationally intractable using reactive transport models alone.

Download Full-text

Use of a reactive transport model to describe reductive dechlorination (RD) as a remediation design tool: application at a CAH-contaminated site

Environmental Science and Pollution Research ◽

10.1007/s11356-013-2035-9 ◽

2013 ◽

Vol 21 (2) ◽

pp. 1514-1527 ◽

Cited By ~ 10

Author(s):

Paolo Viotti ◽

Paolo Roberto Di Palma ◽

Federico Aulenta ◽

Antonella Luciano ◽

Giuseppe Mancini ◽

...

Keyword(s):

Reductive Dechlorination ◽

Reactive Transport ◽

Transport Model ◽

Design Tool ◽

Contaminated Site ◽

Reactive Transport Model ◽

Remediation Design

Download Full-text

Accelerating phase-field-based microstructure evolution predictions via surrogate models trained by machine learning methods

npj Computational Materials ◽

10.1038/s41524-020-00471-8 ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

David Montes de Oca Zapiain ◽

James A. Stewart ◽

Rémi Dingreville

Keyword(s):

Neural Network ◽

Machine Learning ◽

Microstructure Evolution ◽

Phase Field ◽

Surrogate Model ◽

Multivariate Adaptive Regression Splines ◽

Machine Learning Techniques ◽

Phase Field Method ◽

High Fidelity ◽

Integration Schemes

AbstractThe phase-field method is a powerful and versatile computational approach for modeling the evolution of microstructures and associated properties for a wide variety of physical, chemical, and biological systems. However, existing high-fidelity phase-field models are inherently computationally expensive, requiring high-performance computing resources and sophisticated numerical integration schemes to achieve a useful degree of accuracy. In this paper, we present a computationally inexpensive, accurate, data-driven surrogate model that directly learns the microstructural evolution of targeted systems by combining phase-field and history-dependent machine-learning techniques. We integrate a statistically representative, low-dimensional description of the microstructure, obtained directly from phase-field simulations, with either a time-series multivariate adaptive regression splines autoregressive algorithm or a long short-term memory neural network. The neural-network-trained surrogate model shows the best performance and accurately predicts the nonlinear microstructure evolution of a two-phase mixture during spinodal decomposition in seconds, without the need for “on-the-fly” solutions of the phase-field equations of motion. We also show that the predictions from our machine-learned surrogate model can be fed directly as an input into a classical high-fidelity phase-field model in order to accelerate the high-fidelity phase-field simulations by leaping in time. Such machine-learned phase-field framework opens a promising path forward to use accelerated phase-field simulations for discovering, understanding, and predicting processing–microstructure–performance relationships.

Download Full-text

Design Space Exploration of Turbulent Multiphase Flows Using Machine Learning-Based Surrogate Model

Energies ◽

10.3390/en13174565 ◽

2020 ◽

Vol 13 (17) ◽

pp. 4565 ◽

Cited By ~ 1

Author(s):

Himakar Ganti ◽

Manu Kamin ◽

Prashant Khare

Keyword(s):

Machine Learning ◽

Surrogate Model ◽

Momentum Flux ◽

Design Space Exploration ◽

Design Space ◽

Machine Learning Techniques ◽

Dispersed Phase ◽

High Fidelity ◽

Test Conditions ◽

Learning Techniques

This study focuses on establishing a surrogate model based on machine learning techniques to predict the time-averaged spatially distributed behaviors of vaporizing liquid jets in turbulent air crossflow for momentum flux ratios between 5 and 120. This surrogate model extends a previously developed Gaussian-process-based framework applicable to laminar flows to accommodate turbulent flows and demonstrates that in addition to detailed fields of primitive variables, second-order turbulence statistics can also be predicted using machine learning techniques. The framework proceeds in 3 steps—(1) design of experiment studies to identify training points and conducting high-fidelity calculations to build the training dataset; (2) Gaussian process regression (supervised training) for the range of operating conditions under consideration for gaseous and dispersed phase quantities; and (3) error quantification of the surrogate model by comparing the machine learning predictions with the truth model for test conditions (i.e., conditions not used for training). The framework was trained using data generated by high-fidelity large eddy simulation (LES)-based calculations (also referred to as the truth model), which solves the complete set of conservation equations for mass, momentum, energy, and species in an Eulerian reference frame, coupled with a Lagrangian solver that tracks the dispersed phase. Simulations were conducted for the range of momentum flux ratios between 5 and 120 for liquid water injected into crossflowing air at a pressure of 1 atm and temperature of 600 K. Results from the machine-learned surrogate model, also called emulations, were compared with the truth model under testing conditions identified by momentum flux ratios of 7 and 40. L1 errors for time-averaged field quantities, including velocity magnitudes, pressure, temperature, vapor fraction of the evaporated liquid, and turbulent kinetic energy in the gas phase, and spray penetration and Sauter mean diameters in the dispersed phase are reported. Speedup of 65 was achieved with this emulator when compared against LES simulation of the same test conditions with errors for all quantities below 14%, thus demonstrating the potential benefits of using machine learning techniques for design space exploration of devices that are based on turbulent multiphase fluid flows. This is the first effort of its kind in the literature that demonstrates the application of machine learning techniques on turbulent multiphase flows.

Download Full-text