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Multi-fidelity data-driven design and analysis of reactor and tube simulations

Lookup NU author(s): Dr Jonathan McDonough

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

Optimizing complex reactor geometries is vital to promote enhanced efficiency. We present a framework to solve this nonlinear, computationally expensive, and derivative-free problem. Gaussian processes are used to learn a multi-fidelity model of reactor simulations correlating multiple continuous mesh fidelities. The search space of reactor geometries is explored through lower fidelity simulations, evaluated based on a weighted acquisition function, trading off information gain with cost. Within our framework, DARTS, we derive a novel criteria for dictating optimization termination, ensuring a high fidelity solution is returned before budget is exhausted. We investigate the design of helical-tube reactors under pulsed-flow conditions, which have demonstrated outstanding mixing characteristics. To validate our results, we 3D print and experimentally validate the optimal reactor geometry, confirming mixing performance. Our approach is applicable to a broad variety of expensive simulation-based optimization problems, enabling the design of novel parameterized chemical reactors.

Publication metadata

Author(s): Savage T, Basha N, McDonough J, Matar OK, del Rio Chanona EA

Publication type: Article

Publication status: Published

Journal: Computers & Chemical Engineering

Year: 2023

Volume: 179

Pages: 108410

Online publication date: 16/09/2023

Acceptance date: 07/09/2023

Date deposited: 21/11/2023

ISSN (print): 0098-1354

ISSN (electronic): 1873-4375

Publisher: Elsevier Ireland Ltd.

URL: https://doi.org/10.1016/j.compchemeng.2023.108410

DOI: 10.1016/j.compchemeng.2023.108410

ePrints DOI: 10.57711/mp4x-2a65

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