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PRELIMINARY DESIGN OF NON-LINEAR SYSTEMS BASED ON GLOBAL SENSITIVITY ANALYSIS AND MODELICA LANGUAGE

Published online by Cambridge University Press:  19 June 2023

Bruno Vuillod ,
Enrico Panettieri ,
Ludovic Hallo  and
Marco Montemurro
Bruno Vuillod*
Affiliation:
French Atomic Energy Commission, Route des Gargails, BP2, Le Barp Cedex, France; Arts et Metiers Institute of Technology, Univ. Bordeaux, CNRS, Bordeaux INP, Hesam Universite, I2M, UMR 5295, F-33400 Talence, France
Enrico Panettieri
Affiliation:
Arts et Metiers Institute of Technology, Univ. Bordeaux, CNRS, Bordeaux INP, Hesam Universite, I2M, UMR 5295, F-33400 Talence, France
Ludovic Hallo
Affiliation:
French Atomic Energy Commission, Route des Gargails, BP2, Le Barp Cedex, France;
Marco Montemurro
Affiliation:
Arts et Metiers Institute of Technology, Univ. Bordeaux, CNRS, Bordeaux INP, Hesam Universite, I2M, UMR 5295, F-33400 Talence, France
*
Vuillod, Bruno, CEA - I2M, France, bruno.vuillod@ensam.eu

Abstract

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In the last few years, the growing need of highly reliable and time-effective strategies to perform preliminary design of complex systems has led industries to adopt the Model Based System Engineering (MBSE) approach. In MBSE, systems are split into multiple sub-systems and the relevant physical phenomena are described via analytical or numerical models. When a significant number of design variables are to be considered, a smart approach to reduce the number of analyses to perform would be to make use of the Global Sensitivity Analysis (GSA) to higlight those variables that have a more significant influence on the system output. Moreover, an even more significant reduction of computational cost to perform the GSA can be achieved if the complex system modelled via the MBSE approach is exported under the Functional Mock-Up Interface (FMI) norm. In this context, this paper proposes an original approach to address the study of two constructive solutions of an acceleration measuring device typically used on airbags for which the use of a new solution characterized by a porous material is compared with a classical one.

Keywords

Systems Engineering (SE)Functional modellingOpen source designGlobal Sensitivity AnalysisFunctional Mock-up Interface
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 2925 - 2934
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

Cukier, R.I. et al. (1973), “Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. Theory”, The Journal of Chemical Physics, Vol. 59 No. 8. http://doi.org/10.1063/L1680571.CrossRefGoogle Scholar
Gjengedal, S., Brotan, V., Buset, O.T., Larsen, E., Berg, O., Torsster, O., Ramstad, R.K., Hilmo, B.O. and Frengstad, B.S. (2020), Fluid flow through 3D-printed particle beds: a new technique for understand¬ing, validating, and improving predictability of permeability from empirical equations, Vol. 134. http://doi.org/10.1007/s11242-020-01432-x.CrossRefGoogle Scholar
Goda, T. (2021), “A simple algorithm for global sensitivity analysis with Shapley effects”, Reliability Engineering & System Safety, pp. 107–702. http://doi.org/10.1016Zj.ress.2021.107702.Google Scholar
Hoeffding, W. (1948), “A Class of Statistics with Asymptotically Normal Distribution”, Annals of Mathematical Statistics, Vol. 19 No. 3, pp. 293–325. http://doi.org/10.1214/aoms/1177730196.CrossRefGoogle Scholar
Iooss, B. and Prieur, C. (2019), “Shapley effects for sensitivity analysis with correlated inputs: Comparisons with Sobol' indices, numerical estimation and applications”, International Journal for Uncertainty Quantification, Vol. 9 No. 5, pp. 493–514. http://doi.org/10.1615/IntJ.UncertaintyQuantification.2019028372.CrossRefGoogle Scholar
Kleijnen, J.P. (1995), “Sensitivity analysis and optimization of system dynamics models: Regression analysis and statistical design of experiments”, System Dynamics Review, Vol. 11 No. 4, pp. 275–288. http://doi.org/10.1002/sdr.4260110403.CrossRefGoogle Scholar
Morris, M.D. (1991), “Factorial sampling plans for preliminary computational experiments”, Technometrics, Vol. 33 No. 2, pp. 161–174. http://doi.org/10.1080/00401706.1991.10484804.CrossRefGoogle Scholar
Owen, A.B. (2014), “Sobol' indices and shapley value”, SIAM-ASA Journal on Uncertainty Quantification, Vol. 2 No. 1. http://doi.org/10.1137/130936233.Google Scholar
Razavi, S. and Gupta, H.V. (2016a), “A new framework for comprehensive, robust, and efficient global sensitivity analysis: 1. Theory”, Water Resources Research, Vol. 52 No. 1, pp. 423–439. http://doi.org/10.1002/2015WR017558.CrossRefGoogle Scholar
Razavi, S. and Gupta, H.V. (2016b), “A new framework for comprehensive, robust, and efficient global sensitivity analysis: 2. Application”, Water Resources Research, Vol. 52 No. 1, pp. 440–455. http://doi.org/10.1002/2015WR017559.CrossRefGoogle Scholar
Razavi, S., Jakeman, A., Saltelli, A., Prieur, C., Iooss, B., Borgonovo, E., Plischke, E., Lo Piano, S., Iwanaga, T., Becker, W., Tarantola, S., Guillaume, J.H., Jakeman, J., Gupta, H., Melillo, N., Rabitti, G., Chabridon, V., Duan, Q., Sun, X., Smith, S., Sheikholeslami, R., Hosseini, N., Asadzadeh, M., Puy, A., Kucherenko, S. and Maier, H.R. (2021), “The future of sensitivity analysis: An essential discipline for systems modeling and policy support”, Environmental Modelling and Software, Vol. 137. http://doi.org/10.1016/j.apm.2021.01.022.CrossRefGoogle Scholar
Saltelli, A. (2002), “Making best use of model evaluation to compute sensitivity indices”, Computer Physics Communications, Vol. 145 No. 2, pp. 280–297. http://doi.org/10.1016/S0010-4655(02)00280-1.CrossRefGoogle Scholar
Saltelli, A., Annoni, P., Azzini, I., Campolongo, F., Ratto, M. and Tarantola, S. (2010), “Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index”, Computer Physics Communications, Vol. 181 No. 2, pp. 259–270. http://doi.org/10.1016/jxpc.2009.09.018.CrossRefGoogle Scholar
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M. and Tarantola, S. (2008), Global Sensitivity Analysis: The Primer, 3. http://doi.org/10.1111/j.1751-5823.2008.00062-17.x.Google Scholar
Shapley, L.S. (1953), “A Value for n-Person Games”, Contributions to the Theory of Games (AM-28), Volume II, p. 317. http://doi.org/10.1515/9781400881970-018.Google Scholar
Sobol, I.M. (1993), “Sensitivity Estimates for Nonlinear Mathemitical Models”, MMCE, Vol. 1 No. 4, pp. 407–414.Google Scholar
Tarantola, S., Gatelli, D. and Mara, T.A. (2006), “Random balance designs for the estimation of first order global sensitivity indices”, Reliability Engineering and System Safety, Vol. 91 No. 6, pp. 717–727. http://doi.org/10.1016/j.ress.2005.06.003.CrossRefGoogle Scholar
Tissot, J.Y. and Prieur, C. (2012), “Bias correction for the estimation of sensitivity indices based on random balance designs”, Reliability Engineering and System Safety, Vol. 107, pp. 205–213. http://doi.org/10.1016/j.ress.2012.06.010.CrossRefGoogle Scholar
Vuillod, B., Montemurro, M., Panettieri, E. and Hallo, L. (2023), “A comparison between sobol's indices and shapley's effect for global sensitivity analysis of systems with independent input variables”, Reliability Engineering & System Safety, Vol. 234 No. 109177, p. 15. http://doi.org/10.1016/jj.ress.2023.109177.CrossRefGoogle Scholar
Zhou, C., Shi, Z., Kucherenko, S. and Zhao, H. (2022), “A unified approach for global sensitivity analysis based on active subspace and kriging.”, Reliability Engineering & System Safety. http://doi.org/10.1016/jj.ress.2021.108080.Google Scholar