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Predictive Modelling to Support Sensitivity Analysis for Robust Design in Aerospace Engineering
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science. Blekinge Institute of Technology.ORCID iD: 0000-0002-3311-2530
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science. Blekinge Institute of Technology.ORCID iD: 0000-0002-4390-411x
GKN Aerospace Engine Systems, SWE.
2020 (English)In: Structural and multidisciplinary optimization (Print), ISSN 1615-147X, E-ISSN 1615-1488Article in journal (Refereed) Epub ahead of print
Abstract [en]

The design of aircraft engines involves computationally expensive engineering simulations. One way to solve this problem is the use of response surface models to approximate the high-fidelity time-consuming simulations while reducing computational time. For a robust design, sensitivity analysis based on these models allows for the efficient study of uncertain variables’ effect on system performance. The aim of this study is to support sensitivity analysis for a robust design in aerospace engineering. For this, an approach is presented in which random forests (RF) and multivariate adaptive regression splines (MARS) are explored to handle linear and non-linear response types for response surface modelling. Quantitative experiments are conducted to evaluate the predictive performance of these methods with Turbine Rear Structure (a component of aircraft) case study datasets for response surface modelling. Furthermore, to test these models’ applicability to perform sensitivity analysis, experiments are conducted using mathematical test problems (linear and non-linear functions) and their results are presented. From the experimental investigations, it appears that RF fits better on non-linear functions compared with MARS, whereas MARS fits well on linear functions.

Place, publisher, year, edition, pages
Springer, 2020.
Keywords [en]
Aerospace engineering, Machine learning, Meta-models, Random forests, Response surface models, Robust design, Sensitivity analysis, Surrogate models
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-17848DOI: 10.1007/s00158-019-02467-5OAI: oai:DiVA.org:bth-17848DiVA, id: diva2:1307062
Funder
Knowledge Foundation, 20120278, 20140032
Note

open access

Available from: 2019-04-25 Created: 2019-04-25 Last updated: 2020-01-23Bibliographically approved
In thesis
1. Tree Models for Design Space Exploration in Aerospace Engineering
Open this publication in new window or tab >>Tree Models for Design Space Exploration in Aerospace Engineering
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

A crucial issue in the design of aircraft components is the evaluation of a larger number of potential design alternatives. This evaluation involves too expensive procedures, consequently, it slows down the search for optimal design samples. As a result, scarce or small number of design samples with high dimensional parameter space and high non-linearity pose issues in learning of surrogate models. Furthermore, surrogate models have more issues in handling qualitative data (discrete) than in handling quantitative data (continuous). These issues bring the need for investigations of methods of surrogate modelling for the most effective use of available data. 

 The thesis goal is to support engineers in the early design phase of development of new aircraft engines, specifically, a component of the engine known as Turbine Rear Structure (TRS). For this, tree-based approaches are explored for surrogate modelling for the purpose of exploration of larger search spaces and for speeding up the evaluations of design alternatives. First, we have investigated the performance of tree models on the design concepts of TRS. Second, we have presented an approach to explore design space using tree models, Random Forests. This approach includes hyperparameter tuning, extraction of parameters importance and if-then rules from surrogate models for a better understanding of the design problem. With this presented approach, we have shown that the performance of tree models improved by hyperparameter tuning when using design concepts data of TRS. Third, we performed sensitivity analysis to study the thermal variations on TRS and hence support robust design using tree models. Furthermore, the performance of tree models has been evaluated on mathematical linear and non-linear functions. The results of this study have shown that tree models fit well on non-linear functions. Last, we have shown how tree models support integration of value and sustainability parameters data (quantitative and qualitative data) together with TRS design concepts data in order to assess these parameters impact on the product life cycle in the early design phase.

 

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2019. p. 149
Series
Blekinge Institute of Technology Licentiate Dissertation Series, ISSN 1650-2140 ; 8
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-17852 (URN)978-91-7295-377-2 (ISBN)
Presentation
2019-06-03, 13:00 (English)
Opponent
Supervisors
Available from: 2019-04-26 Created: 2019-04-25 Last updated: 2019-06-11Bibliographically approved

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Dasari, Siva KrishnaCheddad, Abbas

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