Non-Intrusive Model Order Reduction for Sintering Applications Article Swipe

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R. Dhopeshawar , Harshit Bansal , Karen Veroy , Hao Shi , Diletta Giuntini ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.23967/c.coupled.2023.010

We are interested in thermo-mechanical problems arising in the context of a practically relevant manufacturing process called sintering. These models can be defined using a non-linear material model, namely the Skorohod-Olevsky Viscous Sintering (SOVS) constitutive model. This SOVS model is used to predict macroscopic sintering behavior, such as shrinkage and density evolution. Also, it relies on material properties such as temperature-dependent viscosity and surface tension. However, high-fidelity simulations of coupled, macroscopic, thermo-mechanical models are computationally intensive. Furthermore, developing reducedorder models addressing the non-linearities is challenging due to the history dependence and presence of internal variables. Performing parametric studies, optimization, real-time control, or parameter estimation for such problems, thus, becomes infeasible. In order to accelerate sintering simulations for such multi-query scenarios, a surrogate model is vital. Here, we present a non-intrusive reduced-order modelling framework based on proper orthogonal decomposition and Gaussian process regression. Furthermore, we discuss the performance of such a surrogate model using different metrics for the two-parameter Arrhenius-type viscosity function

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Context (archaeology) Surrogate model Mathematical optimization Model order reduction Computer science Viscosity Sintering Parametric statistics Gaussian process Materials science Applied mathematics Gaussian Algorithm Mathematics Physics Composite material Biology Statistics Projection (relational algebra) Paleontology Quantum mechanics

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Language: en
Landing Page: https://doi.org/10.23967/c.coupled.2023.010
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DOI: https://doi.org/10.23967/c.coupled.2023.010

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Title: Non-Intrusive Model Order Reduction for Sintering Applications

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Authors: R. Dhopeshawar, Harshit Bansal, Karen Veroy, Hao Shi, Diletta Giuntini

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Concepts: Context (archaeology), Surrogate model, Mathematical optimization, Model order reduction, Computer science, Viscosity, Sintering, Parametric statistics, Gaussian process, Materials science, Applied mathematics, Gaussian, Algorithm, Mathematics, Physics, Composite material, Biology, Statistics, Projection (relational algebra), Paleontology, Quantum mechanics

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abstract_inverted_index.Performing	95
abstract_inverted_index.accelerate	113
abstract_inverted_index.addressing	80
abstract_inverted_index.dependence	89
abstract_inverted_index.developing	77
abstract_inverted_index.estimation	103
abstract_inverted_index.evolution.	51
abstract_inverted_index.intensive.	75
abstract_inverted_index.interested	2
abstract_inverted_index.non-linear	25
abstract_inverted_index.orthogonal	136
abstract_inverted_index.parametric	96
abstract_inverted_index.properties	57
abstract_inverted_index.scenarios,	119
abstract_inverted_index.sintering.	17
abstract_inverted_index.variables.	94
abstract_inverted_index.challenging	84
abstract_inverted_index.infeasible.	109
abstract_inverted_index.macroscopic	43
abstract_inverted_index.multi-query	118
abstract_inverted_index.performance	146
abstract_inverted_index.practically	12
abstract_inverted_index.regression.	141
abstract_inverted_index.simulations	67, 115
abstract_inverted_index.Furthermore,	76, 142
abstract_inverted_index.constitutive	34
abstract_inverted_index.macroscopic,	70
abstract_inverted_index.reducedorder	78
abstract_inverted_index.decomposition	137
abstract_inverted_index.high-fidelity	66
abstract_inverted_index.manufacturing	14
abstract_inverted_index.non-intrusive	129
abstract_inverted_index.optimization,	98
abstract_inverted_index.reduced-order	130
abstract_inverted_index.two-parameter	157
abstract_inverted_index.Arrhenius-type	158
abstract_inverted_index.computationally	74
abstract_inverted_index.non-linearities	82
abstract_inverted_index.Skorohod-Olevsky	30
abstract_inverted_index.thermo-mechanical	4, 71
abstract_inverted_index.temperature-dependent	60
cited_by_percentile_year
corresponding_author_ids	https://openalex.org/A5093230715
countries_distinct_count	1
institutions_distinct_count	5
citation_normalized_percentile.value	0.14653139
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	False