Surrogate-based model parameter optimization based on gas explosion experimental data. Issue 2 (1st February 2019)

• Record Type: Journal Article
• Authors: Both, Anna-Lena; Hisken, Helene; Rückmann, Jan-J.; Steihaug, Trond
• Is Part Of: Engineering optimization. Volume 51:Issue 2(2019)
• Page Start: 301
• Page End: 316
• Publication Date: 2019-02-01
• Subjects: Surrogate-based optimization -- parameter optimization -- neural networks -- CFD modelling -- gas explosions; Engineering design -- Periodicals; Mathematical optimization -- Periodicals; 620.0042
• DOI: 10.1080/0305215X.2018.1450399
• Languages: English
• ISSNs: 0305-215X

Abstract

ABSTRACT: This article presents a method for optimizing the predictive capabilities of a computational fluid dynamics (CFD) tool used for consequence analysis in the process industries. A procedure for surrogate-based optimization of empirically determined sub-grid model parameters is implemented. The objective is to yield the best fit between certain outputs of a CFD model and corresponding experimental values. The present study investigates whether the optimization approach is applicable for improving the predictions of gas explosions when optimizing (i) only for experiments involving similar physical phenomena and (ii) for a wider range of experiments including symmetric modules as well as realistic offshore modules. Employing optimization may detect model limitations, supporting the further development of complex CFD models to represent a wider range of physical phenomena. The optimization process significantly improves the prediction of the maximum overpressure and pressure impulse at specific monitor points. Online supplemental data for this article can be accessed athttps://doi.org/10.1080/0305215X.2018.1450399 .