Uncertainty quantification for fat-tailed probability distributions in aircraft engine simulations

Ahlfeld, R, Montomoli, F, Scalas, E and Shahpar, S (2017) Uncertainty quantification for fat-tailed probability distributions in aircraft engine simulations. Journal of Propulsion and Power. ISSN 0748-4658

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Abstract

Rare event simulation is vital for industrial design because some events, so-called black swans, can have fatal consequences despite their low probability of occurrence. Finding low-probability events far off the mean design is a challenging task for realistic engineering models because they are characterized by high computational demands, many input variables, and often insufficient statistical information to build parametric probability distributions. Therefore, an adaptive and arbitrary polynomial chaos method, called sparse approximation of moment-based arbitrary polynomial chaos, is suggested in this work. Sparse approximation of moment-based arbitrary polynomial chaos creates custom polynomial basis functions and grids based on statistical moments to avoid incorrect statistical assumptions. The contribution of this work is that it is derived how rare event simulation can conveniently be integrated into adaptive sparse grid methods by calculating polynomial chaos expansions based on the statistical moments of truncated fat-tailed distributions. Moreover, the use of tempered alpha-stable distributions is suggested to avoid discontinuous tail cutoffs. Sparse approximation of moment-based arbitrary polynomial chaos is compared to other statistical methods in two industrial aircraft engine simulations: a simulation of transient cycle temperature in a turbine cavity and hot-gas ingestion in the interwheel region. In both cases, sparse approximation of moment-based arbitrary polynomial chaos agrees with previous results but obtains them with lower computational effort.

Item Type: Article
Schools and Departments: School of Mathematical and Physical Sciences > Mathematics
Subjects: Q Science > QA Mathematics > QA0273 Probabilities. Mathematical statistics
T Technology > TL Motor vehicles. Aeronautics. Astronautics > TL0500 Aeronautics. Aeronautical engineering
Depositing User: Enrico Scalas
Date Deposited: 16 Mar 2017 09:06
Last Modified: 06 Apr 2017 21:37
URI: http://sro.sussex.ac.uk/id/eprint/67107

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