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Using Open Source Code Libraries for Robust Design Analysis

Part of: Design Methods and Tools

Published online by Cambridge University Press:  26 July 2019

Kevin Otto ,
Jiahui Wang  and
Tekin Uyan

Abstract

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The design of systems today often involves computer simulation to assess performance and design margins. Understanding how variability erases design margin is important to assure adequacy of margins, especially in optimization efforts. In this paper, we develop a toolchain using open source code libraries in Python, and encapsulate it in Jupyter notebooks, to provide an open source, interactive uncertainty quantification and sensitivity analysis toolchain. This works generally with simulation tools, where a reference folder is created containing a script that reads an input file of parameter values and runs the simulation. With that easily created, the toolchain executes the necessary uncertainty quantification steps with replicates of that reference folder. This approach fits within a broader workflow outlined that defines the variation modes to study, maps to simulation inputs, and screens the variables for sensitivity before conducting an uncertainty quantification. An example is shown in the simulation analysis of a Stirling engine.

Keywords

Robust designSimulationOpen source design
Type
Article
Information
Proceedings of the Design Society: International Conference on Engineering Design , Volume 1 , Issue 1 , July 2019 , pp. 1733 - 1742
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) 2019

References

Arendt, P.D., Apley, D.W. and Chen, W. (2013), “Objective-oriented sequential sampling for simulation based robust design considering multiple sources of uncertainty”, Journal of Mechanical Design, Vol. 135 No. 5, p. 051005.Google Scholar
Arvidsson, M., Gremyr, I. and Johansson, P. (2003), “Use and knowledge of robust design methodology: a survey of Swedish industry”, Journal of engineering design, Vol. 14 No. 2, pp. 129–143.Google Scholar
Baudin, M. 2015. “pyDOE. https://github.com/tisimst/pyDOEGoogle Scholar
Chen, W., Jin, R. and Sudjianto, A. (2006), “Analytical global sensitivity analysis and uncertainty propagation for robust design”, Journal of quality technology, Vol. 38 No. 4, pp. 333–348.Google Scholar
Çınar, C., Aksoy, F., Solmaz, H., Yılmaz, E. and Uyumaz, A. (2018), “Manufacturing and testing of an α-type Stirling engine”, Applied Thermal Engineering, Vol. 130, pp. 1373–1379.Google Scholar
Eifler, T., Ebro, M. and Howard, T.J. (2013). “A classification of the industrial relevance of robust design methods”, In Proceedings of the 19th International Conference on Engineering Design, Vol. 9: Design Methods and Tools, Seoul, pp. 427–436.Google Scholar
Fang, K.T., Li, R. and Sudjianto, A. (2005), Design and modeling for computer experiments, Chapman and Hall/CRC.Google Scholar
De Rainville, F., (2017), ghalton 0.6.1, A Generalized Halton Number Generator. pypi.org/project/ghalton/Google Scholar
Forslund, A., Madrid, J., Söderberg, R., Isaksson, O., Lööf, O. and Frey, D. (2018), “Evaluating How Functional Performance in Aerospace Components Is Affected by Geometric Variation”, SAE International Journal of Aerospace, 01-11-01-0001.Google Scholar
Freund, T., Würtenberger, J., Lotz, J., Rommel, C. and Kirchner, E. (2017), “Design for robustness-Systematic application of design guidelines to control uncertainty”, In Proceedings of the 21st International Conference on Engineering Design, Vancouver, Canada, pp. 277–286.Google Scholar
Garud, S.S., Karimi, I.A. and Kraft, M. (2017), “Design of computer experiments: A review”, Computers & Chemical Engineering, Vol. 106, pp. 71–95.Google Scholar
Herman, J. and Usher, W. (2017), “SALib: an open-source Python library for sensitivity analysis”, The Journal of Open Source Software, Vol. 2 No. 9.Google Scholar
Howard, T., Eifler, T., Pedersen, S., Göhler, S., Boorla, S. and Christensen, M. (2017), “The variation management framework (VMF): A unifying graphical representation of robust design”, Quality Engineering, Vol. 29 No. 4, pp. 563–572.Google Scholar
Jin, R., Chen, W. and Simpson, T. (2001), “Comparative studies of metamodelling techniques under multiple modelling criteria”, Structural and multidisciplinary optimization, Vol. 23 No. 1, pp. 1–13.Google Scholar
Johansson, P., Chakhunashvili, A., Barone, S. and Bergman, B. (2006), “Variation mode and effect analysis: a practical tool for quality improvement”, Quality and reliability engineering international, Vol. 22 No. 8, pp. 865–876.Google Scholar
Kluyver, T., Ragan-Kelley, B., Pérez, F., Granger, B.E., Bussonnier, M., Frederic, J., Kelley, K., Hamrick, J.B., Grout, J., Corlay, S. and Ivanov, P. (2016), May “Jupyter Notebooks-a publishing format for reproducible computational workflows”, ELPUB, pp. 87–90.Google Scholar
Lawless, C. (2015), sobol_seq 0.1.2, pypi.org/project/sobol_seq/.Google Scholar
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V. and Vanderplas, J. (2011), “Scikit-learn: Machine learning in Python”, Journal of machine learning research, Vol. 12 No. Oct, pp. 2825–2830.Google Scholar
Qian, Z., Seepersad, C.C., Joseph, V.R., Allen, J.K. and Wu, C.J. (2006), “Building surrogate models based on detailed and approximate simulations”, Journal of Mechanical Design, Vol. 128 No. 4, pp. 668–677.Google Scholar
Söderberg, R., Lindkvist, L. and Dahlström, S. (2006), “Computer-aided robustness analysis for compliant assemblies”, Journal of Engineering Design, Vol. 17 No. 5, pp. 411–428.Google Scholar
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M. and Tarantola, S. (2008), Global sensitivity analysis: the primer, John Wiley & Sons.Google Scholar
Tan, J., Otto, K. and Wood, K. (2017), “Relative Impact of Early versus Late Design Decisions in Systems Development”, Design Science Journal.Google Scholar
Ureili, I. (2010), Stirling Cycle Machine Analysis, www.ohio.edu/mechanical/stirling/Google Scholar
Wallace, K. (2011), “Transferring design methods into practice”, In The future of design methodology, Springer, London, pp. 239–248.Google Scholar