Data-Informed Emulators for Multi-Physics Simulations

Lu, Hannah; Ermakova, Dinara; Wainwright, Haruko M.; Zheng, Liange; Tartakovsky, Daniel M.

doi:10.1615/jmachlearnmodelcomput.2021038577

Cited by 8 publications

(3 citation statements)

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“…In this simplified domain, the canister is modeled as a heat source with steel's mechanical properties. The mesh (Figure 3) used in the numerical simulation was taken from Cao et al [21,23,24]; the applicability of this model is limited to the interpretation and prediction of the behavior of TH, primarily in the vicinity of the canister where thermal effects are significant. We adopted an axisymmetric mesh to represent the symmetrical heating process of the EBS.…”

Section: Th Model Implementation In Numerical Modelsmentioning

confidence: 99%

Harnessing Geothermal Energy Potential from High-Level Nuclear Waste Repositories

Sarsenbayev,

Zheng,

Ermakova

et al. 2024

Energies

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The disposal of high-level nuclear waste (HLW) has been one of the most challenging issues for nuclear energy utilization. In this study, we have explored the potential of extracting decay heat from HLW, taking advantage of recent advances in the technologies to utilize low-temperature geothermal resources for the co-generation of electricity and heat. Given that geothermal energy entails extracting heat from natural radioactivity within the Earth, we may consider that our approach is to augment it with an anthropogenic geothermal source. Our study—for the first time—introduces a conceptual model of a binary-cycle geothermal system powered by the heat produced by HLW. TOUGHREACT V3.32 software was used to model the heat transfer resulting from radioactive decay to the surrounding geological media. Our results demonstrate the feasibility of employing the organic Rankine cycle (ORC) to generate approximately 108 kWe per HLW canister 30 years after emplacement and a heat pump system to produce 81 kWth of high-potential heat per canister for HVAC purposes within the same timeframe. The proposed facility has the potential to produce carbon-free power while ensuring the safe disposal of radioactive waste and removing the bottleneck in the sustainable use of nuclear energy.

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Section: Th Model Implementation In Numerical Modelsmentioning

confidence: 99%

Harnessing Geothermal Energy Potential from High-Level Nuclear Waste Repositories

Sarsenbayev,

Zheng,

Ermakova

et al. 2024

Energies

Self Cite

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“…Such emulation approaches in predicting the outcomes of physical systems have a long history, including applications in physics-based animation (Grzeszczuk et al, 1998), complex multi-physics simulators (Lu et al, 2021;Bianchi et al, 2016), climate models (Beucler et al, 2019;Krasnopolsky et al, 2005;Castruccio et al, 2014;Kashinath et al, 2021), and emulating fluid flow through dolomite using a neural network (Li et al, 2022). In an emulator approach, the underlying physical system is approximated by a statistical model (the emulator) which can be evaluated more quickly than a conventional forward model.…”

Section: Introductionmentioning

confidence: 99%

An emulation-based approach for interrogating reactive transport models

Fotherby,

Bradbury,

Druhan

et al. 2023

Geosci. Model Dev.

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Abstract. We present an emulation-based approach to understand the interactions among different chemical and biological processes modelled in environmental reactive transport models (RTMs) and explore how the parameterisation of these processes influences the results of multi-component RTMs. We utilise a previously published RTM consisting of 20 primary species, 20 secondary complexes, 17 mineral reactions, and 2 biologically mediated reactions; this RTM describes bio-stimulation using sediment from a contaminated aquifer. We choose a subset of the input parameters to vary over a range of values. The result is the construction of a new dataset that describes the model behaviour over a range of environmental conditions. Using this dataset to train a statistical model creates an emulator of the underlying RTM. This is a condensed representation of the original RTM that facilitates rapid exploration of a broad range of environmental conditions and sensitivities. As an illustration of this approach, we use the emulator to explore how varying the boundary conditions in the RTM describing the aquifer impacts the rates and volumes of mineral precipitation. A key result of this work is the recognition of an unanticipated dependency of pyrite precipitation on pCO2 in the injection fluid due to the stoichiometry of the microbially mediated sulfate reduction reaction. This complex relationship was made apparent by the emulator, while the underlying RTM was not specifically constructed to create such a feedback. We argue that this emulation approach to sensitivity analysis for RTMs may be useful in discovering such new coupled sensitives in geochemical systems and for designing experiments to optimise environmental remediation. Finally, we demonstrate that this approach can maximise specific mineral precipitation or dissolution reactions by using the emulator to find local maxima, which can be widely applied in environmental systems.

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“…Here, we demonstrate an approach to explore a wide variety of potential model parameters, by adapting an emulation method similar to that previously applied in physics-based animation (Grzeszczuk et al, 1998) to complex multi-physics simulators (Lu et al, 2021;Bianchi et al, 2016) and climate models (Beucler et al, 2019;Krasnopolsky et al, 2005;Castruccio et al, 2014;Kashinath et al, 2021) as well as applied to emulating fluid flow through Dolomite using a neural network (Li et al, 2022). In this emulation approach, the underlying physical system is approximated by a statistical model (the emulator) which can be evaluated more quickly than a conventional forward model.…”

Section: Introductionmentioning

confidence: 99%

An emulation-based approach for interrogating reactive transport models

Fotherby

Bradbury

Druhan

et al. 2022

Preprint

View full text Add to dashboard Cite

Abstract. We present a new approach to understand the interactions among different chemical and biological processes modelled in environmental reactive transport models (RTMs) and explore how the parameterisation of these processes influences the results of multi-component RTMs. We utilize a previously published RTM consisting of 20 primary species, 20 secondary complexes, 17 mineral reactions and 2 biologically-mediated reactions which describes bio-stimulation using sediment from a contaminated aquifer. We choose a subset of the input parameters to vary over a range of values. The result is the construction of a new dataset that describes the model behaviour over a range of environmental conditions. Using this dataset to train a statistical model creates an emulator of the underlying RTM. This is a condensed representation of the original RTM that facilitates rapid exploration of a broad range of environmental conditions and sensitivities. As an illustration of this approach, we use the emulator to explore how varying the boundary conditions in the RTM describing the aquifer impacts the rates and volumes of mineral precipitation. A key result of this work is the recognition of an unanticipated dependency of pyrite precipitation on pCO2 in the injection fluid due to the stoichiometry of the microbially-mediated sulphate reduction reaction. This complex relationship was made apparent by the emulator, while the underlying RTM was not specifically constructed to create such a feedback. We argue that this emulation approach to sensitivity analysis for RTMs may be useful in discovering such new coupled sensitives in geochemical systems and for designing experiments to optimise environmental remediation. Finally, we demonstrate that this approach can maximise specific mineral precipitation or dissolution reactions by using the emulator to find local maxima, which can be widely applied in environmental systems.

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Data-Informed Emulators for Multi-Physics Simulations

Cited by 8 publications

References 0 publications

Harnessing Geothermal Energy Potential from High-Level Nuclear Waste Repositories

Harnessing Geothermal Energy Potential from High-Level Nuclear Waste Repositories

An emulation-based approach for interrogating reactive transport models

An emulation-based approach for interrogating reactive transport models

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