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Multi-objective Bayesian shape optimization of an industrial hydrodynamic separator using unsteady Eulerian-Lagrangian simulations

Andrew Roberts, Alma Rahat Orcid Logo, Daniel Jarman Orcid Logo, Jonathan Fieldsend Orcid Logo, Gavin Tabor Orcid Logo

Optimization and Engineering

Swansea University Author: Alma Rahat Orcid Logo

Abstract

The shape of a hydrodynamic particle separator has been optimized using a parallelized and robust formulation of Bayesian optimization, with data from an unsteady Eulerian flow field coupled with Lagrangian particle tracking. The uncertainty due to the mesh, initial conditions, and stochastic disper...

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Published in: Optimization and Engineering
Published: Spinger-Nature
Online Access: https://link.springer.com/journal/11081
URI: https://cronfa.swan.ac.uk/Record/cronfa67078
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spelling v2 67078 2024-07-12 Multi-objective Bayesian shape optimization of an industrial hydrodynamic separator using unsteady Eulerian-Lagrangian simulations 6206f027aca1e3a5ff6b8cd224248bc2 0000-0002-5023-1371 Alma Rahat Alma Rahat true false 2024-07-12 MACS The shape of a hydrodynamic particle separator has been optimized using a parallelized and robust formulation of Bayesian optimization, with data from an unsteady Eulerian flow field coupled with Lagrangian particle tracking. The uncertainty due to the mesh, initial conditions, and stochastic dispersion in the Eulerian-Lagrangian simulations was minimized and quantified. This was then translated across to the error term in the Gaussian process model and the minimum probability of improvement in infill criterion. An existing parallelization strategy was modified for the infill criterion and customized to prefer exploitation in the decision space. In addition, a new strategy was developed for hidden constraints using Voronoi penalization. In the approximate Pareto Front, an absolute improvement over the base design of 14% in the underflow collection efficiency and 10% in the total collection efficiency was achieved. The corresponding designs were attributed to the effective distribution of residence time between the trays via the removal of a vertical plume. The plume also reduced both efficiencies by creating a flow path in a direction that acted against effective settling. This demonstrates the value of Bayesian optimization in producing non-intuitive designs, which resulted in the filing of a patent. Journal Article Optimization and Engineering Spinger-Nature 0 0 0 0001-01-01 https://link.springer.com/journal/11081 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University Another institution paid the OA fee Innovate UK and EPSRC This work was supported by Innovate UK and the EPSRC (partnership number 11477). 2024-07-12T06:39:08.4128797 2024-07-12T06:24:22.2714530 College of Science Computer Science Andrew Roberts 1 Alma Rahat 0000-0002-5023-1371 2 Daniel Jarman 0000-0001-6582-5239 3 Jonathan Fieldsend 0000-0002-0683-2583 4 Gavin Tabor 0000-0003-3549-228X 5
title Multi-objective Bayesian shape optimization of an industrial hydrodynamic separator using unsteady Eulerian-Lagrangian simulations
spellingShingle Multi-objective Bayesian shape optimization of an industrial hydrodynamic separator using unsteady Eulerian-Lagrangian simulations
Alma Rahat
title_short Multi-objective Bayesian shape optimization of an industrial hydrodynamic separator using unsteady Eulerian-Lagrangian simulations
title_full Multi-objective Bayesian shape optimization of an industrial hydrodynamic separator using unsteady Eulerian-Lagrangian simulations
title_fullStr Multi-objective Bayesian shape optimization of an industrial hydrodynamic separator using unsteady Eulerian-Lagrangian simulations
title_full_unstemmed Multi-objective Bayesian shape optimization of an industrial hydrodynamic separator using unsteady Eulerian-Lagrangian simulations
title_sort Multi-objective Bayesian shape optimization of an industrial hydrodynamic separator using unsteady Eulerian-Lagrangian simulations
author_id_str_mv 6206f027aca1e3a5ff6b8cd224248bc2
author_id_fullname_str_mv 6206f027aca1e3a5ff6b8cd224248bc2_***_Alma Rahat
author Alma Rahat
author2 Andrew Roberts
Alma Rahat
Daniel Jarman
Jonathan Fieldsend
Gavin Tabor
format Journal article
container_title Optimization and Engineering
institution Swansea University
publisher Spinger-Nature
college_str College of Science
hierarchytype
hierarchy_top_id collegeofscience
hierarchy_top_title College of Science
hierarchy_parent_id collegeofscience
hierarchy_parent_title College of Science
department_str Computer Science{{{_:::_}}}College of Science{{{_:::_}}}Computer Science
url https://link.springer.com/journal/11081
document_store_str 0
active_str 0
description The shape of a hydrodynamic particle separator has been optimized using a parallelized and robust formulation of Bayesian optimization, with data from an unsteady Eulerian flow field coupled with Lagrangian particle tracking. The uncertainty due to the mesh, initial conditions, and stochastic dispersion in the Eulerian-Lagrangian simulations was minimized and quantified. This was then translated across to the error term in the Gaussian process model and the minimum probability of improvement in infill criterion. An existing parallelization strategy was modified for the infill criterion and customized to prefer exploitation in the decision space. In addition, a new strategy was developed for hidden constraints using Voronoi penalization. In the approximate Pareto Front, an absolute improvement over the base design of 14% in the underflow collection efficiency and 10% in the total collection efficiency was achieved. The corresponding designs were attributed to the effective distribution of residence time between the trays via the removal of a vertical plume. The plume also reduced both efficiencies by creating a flow path in a direction that acted against effective settling. This demonstrates the value of Bayesian optimization in producing non-intuitive designs, which resulted in the filing of a patent.
published_date 0001-01-01T06:39:09Z
_version_ 1804350520478924800
score 11.016302

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