Treffer: Developing a fast cloud-based simulation workflow for the full aerodynamic evaluation of airborne vehicles

Title:
Developing a fast cloud-based simulation workflow for the full aerodynamic evaluation of airborne vehicles
Authors:
Schubiger, Alain, van Oerle, Dario, Boiger, Gernot
Source:
ISSN:2409-1669 ; ISSN:2409-7527 ; Multiphysics 2022 ; International Conference of Multiphysics, Oslo, Norway, 15-16 December 2022.
Publisher Information:
International Society of Multiphysics
Publication Year:
2022
Collection:
ZHAW digitalcollection (Repository of the Zurich University of Applied Sciences)
Subject Terms:
OpenFOAM, Massive Simultaneous Cloud Computing (MSCC), Computational Fluid Dynamics (CFD), Aerodynamic analysis, info:eu-repo/classification/ddc/005
Document Type:
Konferenz conference object
File Description:
application/pdf
Language:
English
Relation:
https://digitalcollection.zhaw.ch/handle/11475/27041
DOI:
10.21256/zhaw-27041
Availability:
https://doi.org/10.21256/zhaw-27041
https://static1.squarespace.com/static/5c9f89c101232c1d41297d67/t/639912525a90a93394aa8b71/1670976084271/MULTIPHYSICS+2022-Abstract+Booklet.pdf
https://digitalcollection.zhaw.ch/handle/11475/27041
Rights:
info:eu-repo/semantics/openAccess ; Licence according to publishing contract
Accession Number:
edsbas.448D1486
Database:
BASE

Weitere Informationen

References: [1] Gernot Kurt Boiger, Darren Sharman, Bercan Siyahhan, Viktor Lienhard, Marlon Boldrini, and Dominic Drew. A massive simultaneous cloud computing platform for openfoam. In 9th OpenFOAM Conference, online, 19-20 October 2021. ZHAW Zürcher Hochschule für Angewandte Wissenschaften, 2021.2 ; The field of Computational Science is facing an increasing demand for data-intensive research. Engineering tasks such as parameter-, sensitivity- and optimisation studies require ensemble computing to an ever-increasing extent. At the same time, the field of artificial intelligence (AI) is pushing for ever more extensive, numerically derived learning-, testing- and validation data. With the cloud software KaleidoSim, we can run hundreds of numerical simulations simultaneously [1] and generate large amounts of data in a short time. Whilst KaleidoSim supports various simulation tools, only OpenFOAM is used in this study. In this work, we have developed tools and routines to accelerate, simplify and automate studies with hundreds of simultaneous simulations in the cloud. We performed a full 360° aerodynamics analysis of different aircraft to test our toolbox. The study included 420 OpenFOAM simulation cases. Each case was a steady-state, Reynolds Average Stress (RAS) turbulence model-based, single-phase flow simulation on a 1.5 million cell hexahedral finite volume grid. Drag and lift coefficients were calculated for each case. For the toolbox development, we used a combination of Python and KaleidoSim Application Programming Interface (API) routines. The Python-based graphical user interface (GUI) allows switching between different CAD models to compare multiple aircraft. The GUI also allows mesh sensitivity analysis to determine optimised meshes for each aerodynamic shape. Based on this, we performed a series of mesh sensitivity analyses using snappyHexMesh and CfMesh meshes. This work has shown that a combination of cloud computing via KaleidoSim-based API routines and Python scripting can speed up certain ...