Hostname: page-component-848d4c4894-r5zm4 Total loading time: 0 Render date: 2024-06-15T09:40:17.619Z Has data issue: false hasContentIssue false
  • English
  • Français

A SUSTAINABLE COMPUTATIONAL DESIGN CONCEPT USING WEB SERVICE METHODS

Published online by Cambridge University Press:  19 June 2023

James Gopsill ,
Ben Hicks ,
Oliver Schiffmann  and
Adam McClenaghan
James Gopsill*
Affiliation:
University of Bristol, UK; Centre for Modelling and Simulation, UK
Ben Hicks
Affiliation:
University of Bristol, UK;
Oliver Schiffmann
Affiliation:
University of Bristol, UK;
Adam McClenaghan
Affiliation:
University of Bristol, UK;
*
Gopsill, James, University of Bristol, United Kingdom, james.gopsill@bristol.ac.uk

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Simulation is fundamental to many engineering design processes and powers the field of computational design. Simulation inherently consumes energy resulting in CO2 emissions that impact our environment. While one can source energy from renewable sources and use energy efficient hardware, efforts need to also be made in how we can use simulation in a sustainable manner.

This paper presents a sustainable simulation framework that borrows concepts from web services. The framework makes it easy for engineering firms to adopt and embed sustainable simulation practices thereby removing the burden from the designer tin thinking about how to design sustainably. An illustrative example reveals a 25% reduction in computational effort can be achieved by adopting the framework.

Keywords

SimulationComputational design methodsSustainability
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 425 - 434
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

References

al Handawi, K., Andersson, P., Panarotto, M., Isaksson, O., Kokkolaras, M., 2020. Scalable Set-Based Design Optimization and Remanufacturing for Meeting Changing Requirements. Journal of Mechanical Design 143. https://doi.org/10.1115/1.4047908Google Scholar
Argyres, N.S., 1999. The Impact of Information Technology on Coordination: Evidence from the B-2 “Stealth” Bomber. Organization Science 10, 162–180. https://doi.org/10.1287/orsc.10.2.162CrossRefGoogle Scholar
Autodesk. 2023. Tokens for Fusion 360 Simulation Studies. [WWW Document] URL https://help.autodesk.com/view/fusion360/ENU/?guid=GUID-97268379-A0C4-4426-8929-2FB3F3FB439C (accessed 16.2.23).Google Scholar
Fisher, A., Levy, A., Kratz, J., 2023. Effects of heat transfer coefficient variations on composite curing. J Compos Mater 57, 363–376. https://doi.org/10.1177/00219983221145506CrossRefGoogle Scholar
Gopsill, J., Hicks, B., 2022. Through-Lifecycle Whole-Design Optimisation Using Software Deployment Toolchains. Proceedings of the Design Society 2, 1855–1864. https://doi.org/10.1017/pds.2022.188CrossRefGoogle Scholar
Research, Grand View, 2022. Data Center Virtualization Market Size, Share & Trends Analysis Report By Component, By Organization Size, By End Use Industry, By Region, And Segment Forecasts, 2022 - 2030 [WWW Document]. Grand View Research. URL https://www.grandviewresearch.com/industry-analysis/data-center-virtualization-market-report (accessed 11.2.22).Google Scholar
Hanna, B.N., Dinh, N.T., Youngblood, R.W., Bolotnov, I.A., 2020. Machine-learning based error prediction approach for coarse-grid Computational Fluid Dynamics (CG-CFD). Progress in Nuclear Energy 118, 103140. https://doi.org/https://doi.org/10.1016/j.pnucene.2019.103140CrossRefGoogle Scholar
Honour, E., 2010. 11.4.2 Systems Engineering Return on Investment. INCOSE International Symposium 20, 1422–1439. https://doi.org/https://doi.org/10.1002/j.2334-5837.2010.tb01150.xCrossRefGoogle Scholar
Jeon, K., Yang, S., Kang, D., Na, J., Lee, W.B., 2019. Development of surrogate model using CFD and deep neural networks to optimize gas detector layout. Korean Journal of Chemical Engineering 36, 325–332. https://doi.org/10.1007/s11814-018-0204-8CrossRefGoogle Scholar
Kautz, O., Roth, A., Rumpe, B., 2018. Achievements, Failures, and the Future of Model-Based Software Engineering, in: Volker, Gruhn and Striemer, R. (Ed.), The Essence of Software Engineering. Springer International Publishing, Cham, pp. 221–236. https://doi.org/10.1007/978-3-319-73897-0_13Google Scholar
LaSorda, M., Borky, J.M., Sega, R.M., 2018. Model-based architecture and programmatic optimization for satellite system-of-systems architectures. Systems Engineering 21, 372–387. https://doi.org/https://doi.org/10.1002/sys.21444CrossRefGoogle Scholar
MacAlpine, J., 2022. Driven by Simulation, a Regulations Overhaul Promises a New Era of Racing in Formula 1 [WWW Document]. engineering.com. URL https://www.engineering.com/story/driven-by-simulation-a-regulations-overhaul-promises-a-new-era-of-racing-in-formula-1 (accessed 11.28.22).Google Scholar
McCormack, J., Dorin, A., Innocent, T., 2004. Generative Design: A Paradigm for Design Research.Google Scholar
Muller, D., Reichert, M., Herbst, J., Poppa, F., 2007. Data-Driven Design of Engineering Processes with COREPROModeler, in: 16th IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE 2007). pp. 376–378. https://doi.org/10.1109/WETICE.2007.4407191CrossRefGoogle Scholar
Nelder, J.A., Mead, R., 1965. A Simplex Method for Function Minimization. Comput J 7, 308–313. https://doi.org/10.1093/comjnl/7.4.308CrossRefGoogle Scholar
Parraguez, P., Maier, A., 2017. Data-driven engineering design research: opportunities using open data, in: DS 87-7 Proceedings of the 21st International Conference on Engineering Design (ICED 17) Vol 7: Design Theory and Research Methodology, Vancouver, Canada, 21-25.08. 2017. pp. 41–50.Google Scholar
Powell, M.J.D., 1964. An efficient method for finding the minimum of a function of several variables without calculating derivatives. Comput J 7, 155–162. https://doi.org/10.1093/comjnl/7.2.155CrossRefGoogle Scholar
Reynolds, J., 2019. 2021 F1 RULES: The Key Changes Explained [WWW Document]. Formula One. URL https://www.formula1.com/en/latest/article.2021-f1-rules-the-key-changes-explained.2dCtCkxNofk20K1B4rJwTk.html (accessed 11.28.22).Google Scholar
Selivanova, S., 2022. Computational Complexity of Classical Solutions of Partial Differential Equations. pp. 299–312. https://doi.org/10.1007/978-3-031-08740-0_25CrossRefGoogle Scholar
Timperley, L., Berthoud, L., Snider, C., Tryfonas, T., Prezzavento, A., Plamer, K., 2023. Towards Improving the Design Space Exploration Process Using Generative Design With MBSE, in: 44th IEEE Aerospace Conference.CrossRefGoogle Scholar
Tyflopoulos, E., Tollnes, F.D., Steinert, M., Olsen, A., 2018. State of the art of generative design and topology optimization and potential research needs. DS 91: Proceedings of NordDesign 2018, Linköping, Sweden, 14th-17th August 2018.Google Scholar