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Using machine learning to predict 1D steady-state temperature profiles from compressible mantle convection simulations

Agarwal, Siddhant and Tosi, Nicola and Breuer, Doris and Kessel, Pan and Montavon, Grégoire (2019) Using machine learning to predict 1D steady-state temperature profiles from compressible mantle convection simulations. In: APS Division of Fluid Dynamics (Fall) 2019. 72nd Annual Meeting of the American Physical Society’s Division of Fluid Dynamics, 23-26 Nov 2019, Seattle, USA.

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Official URL: https://ui.adsabs.harvard.edu/abs/2019APS..DFDP13008A/abstract

Abstract

Thermal evolution simulations of planetary mantles in 2D and 3D are computationally intensive. A low-fidelity alternative is to use scaling laws based on boundary-layer theory to express Nusselt Number (Nu) as a function of Rayleigh Number (Ra). Such a Ra-Nu relation can be used to run `0D' parametrized evolution models by solving a simple energy balance equation. Yet scaling relations are available only for simple flows that cannot capture the full complexity of mantle dynamics. We propose leveraging Machine Learning to find a higher-dimensional mapping from five different parameters to the entire 1D temperature profile. The parameters are Ra, internal heating Ra, dissipation number and the maximum viscosity contrast between top and bottom due to temperature and pressure. We train a Neural Network (NN) to take these inputs and predict the resulting steady-state temperature profile. The training data comes from a subset of 20,000 compressible simulations on a 2D cylindrical grid. This results in predictions with an average error of 1.6% on the test set. The NN can potentially be used to build a 1D evolution model by stacking several steady-state temperature profiles together, with each prediction serving as an input at the next time-step.

Item URL in elib:https://elib.dlr.de/136679/
Document Type:Conference or Workshop Item (Lecture)
Title:Using machine learning to predict 1D steady-state temperature profiles from compressible mantle convection simulations
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Agarwal, SiddhantSiddhant.Agarwal (at) dlr.dehttps://orcid.org/0000-0002-0840-2114
Tosi, Nicolanicola.tosi (at) dlr.dehttps://orcid.org/0000-0002-4912-2848
Breuer, DorisDoris.Breuer (at) dlr.dehttps://orcid.org/0000-0001-9019-5304
Kessel, Panpan.kessel (at) campus.tu-berlin.deUNSPECIFIED
Montavon, Grégoiregregoire.montavon (at) tu-berlin.deUNSPECIFIED
Date:November 2019
Journal or Publication Title:APS Division of Fluid Dynamics (Fall) 2019
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:Mantle Convection, Machine Learning, Fluid Dynamics, Surrogate Modelling, Neural Networks
Event Title:72nd Annual Meeting of the American Physical Society’s Division of Fluid Dynamics
Event Location:Seattle, USA
Event Type:international Conference
Event Dates:23-26 Nov 2019
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Science and Exploration
DLR - Research area:Raumfahrt
DLR - Program:R EW - Erforschung des Weltraums
DLR - Research theme (Project):R - Vorhaben Exploration des Sonnensystems
Location: Berlin-Adlershof
Institutes and Institutions:Institute of Planetary Research
Institute of Planetary Research > Planetary Physics
Deposited By: Agarwal, Siddhant
Deposited On:16 Oct 2020 08:16
Last Modified:16 Oct 2020 08:16

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