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Tree Models for Design Space Exploration in Aerospace Engineering
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science. Blekinge Institute of Technology.ORCID iD: 0000-0002-3311-2530
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: urn:nbn:se:bth-17852ISBN: 978-91-7295-377-2 (print)OAI: oai:DiVA.org:bth-17852DiVA, id: diva2:1307074
Presentation
2019-06-03, 13:00 (English)
Opponent
Supervisors
Available from: 2019-04-26 Created: 2019-04-25 Last updated: 2019-06-11Bibliographically approved
List of papers
1. Tree-Based Response Surface Analysis
Open this publication in new window or tab >>Tree-Based Response Surface Analysis
2015 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Computer-simulated experiments have become a cost effective way for engineers to replace real experiments in the area of product development. However, one single computer-simulated experiment can still take a significant amount of time. Hence, in order to minimize the amount of simulations needed to investigate a certain design space, different approaches within the design of experiments area are used. One of the used approaches is to minimize the time consumption and simulations for design space exploration through response surface modeling. The traditional methods used for this purpose are linear regression, quadratic curve fitting and support vector machines. This paper analyses and compares the performance of four machine learning methods for the regression problem of response surface modeling. The four methods are linear regression, support vector machines, M5P and random forests. Experiments are conducted to compare the performance of tree models (M5P and random forests) with the performance of non-tree models (support vector machines and linear regression) on data that is typical for concept evaluation within the aerospace industry. The main finding is that comprehensible models (the tree models) perform at least as well as or better than traditional black-box models (the non-tree models). The first observation of this study is that engineers understand the functional behavior, and the relationship between inputs and outputs, for the concept selection tasks by using comprehensible models. The second observation is that engineers can also increase their knowledge about design concepts, and they can reduce the time for planning and conducting future experiments.

Place, publisher, year, edition, pages
Springer, 2015. p. 12
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9432
Keywords
Machine learning, Regression, Surrogate model, Response surface model
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-11442 (URN)10.1007/978-3-319-27926-8_11 (DOI)978-3-319-27925-1 (ISBN)
Conference
The International Workshop on Machine learning, Optimization and big Data (MOD 2015), Taormina - Sicily, Italy
Funder
Knowledge Foundation
Available from: 2016-01-19 Created: 2016-01-19 Last updated: 2019-04-25Bibliographically approved
2. Random Forest Surrogate Models to Support Design Space Exploration in Aerospace Use-case
Open this publication in new window or tab >>Random Forest Surrogate Models to Support Design Space Exploration in Aerospace Use-case
2019 (English)In: IFIP Advances in Information and Communication Technology, Springer-Verlag New York, 2019, Vol. 559Conference paper, Published paper (Refereed)
Abstract [en]

In engineering, design analyses of complex products rely on computer simulated experiments. However, high-fidelity simulations can take significant time to compute. It is impractical to explore design space by only conducting simulations because of time constraints. Hence, surrogate modelling is used to approximate the original simulations. Since simulations are expensive to conduct, generally, the sample size is limited in aerospace engineering applications. This limited sample size, and also non-linearity and high dimensionality of data make it difficult to generate accurate and robust surrogate models. The aim of this paper is to explore the applicability of Random Forests (RF) to construct surrogate models to support design space exploration. RF generates meta-models or ensembles of decision trees, and it is capable of fitting highly non-linear data given quite small samples. To investigate the applicability of RF, this paper presents an approach to construct surrogate models using RF. This approach includes hyperparameter tuning to improve the performance of the RF's model, to extract design parameters' importance and \textit{if-then} rules from the RF's models for better understanding of design space. To demonstrate the approach using RF, quantitative experiments are conducted with datasets of Turbine Rear Structure use-case from an aerospace industry and results are presented.

Place, publisher, year, edition, pages
Springer-Verlag New York, 2019
Series
IFIP Advances in Information and Communication Technology ; 559
Keywords
machine learning, random forests, hyperparameter tuning, surrogate model, meta-models, engineering design, aerospace
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-17743 (URN)10.1007/978-3-030-19823-7_45 (DOI)978-3-030-19822-0 (ISBN)
Conference
15th International Conference on Artificial Intelligence Applications and Innovations (AIAI'19)At: Crete, Greece
Projects
MD3S
Available from: 2019-03-27 Created: 2019-03-27 Last updated: 2020-03-05Bibliographically approved
3. Predictive Modelling to Support Sensitivity Analysis for Robust Design in Aerospace Engineering
Open this publication in new window or tab >>Predictive Modelling to Support Sensitivity Analysis for Robust Design in Aerospace Engineering
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
Aerospace engineering, Machine learning, Meta-models, Random forests, Response surface models, Robust design, Sensitivity analysis, Surrogate models
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-17848 (URN)10.1007/s00158-019-02467-5 (DOI)
Funder
Knowledge Foundation, 20120278, 20140032
Note

open access

Available from: 2019-04-25 Created: 2019-04-25 Last updated: 2020-01-23Bibliographically approved
4. Integration of Value and Sustainability Assessment in Design Space Exploration by Machine Learning: An Aerospace Application
Open this publication in new window or tab >>Integration of Value and Sustainability Assessment in Design Space Exploration by Machine Learning: An Aerospace Application
2020 (English)In: Design ScienceArticle in journal (Refereed) Epub ahead of print
Abstract [en]

The use of decision-making models in the early stages of the development of complex products and technologies is a well-established practice in industry. Engineers rely on well-established statistical and mathematical models to explore the feasible design space and make early decisions on future design configurations. At the same time, researchers in both value-driven design and sustainable product development areas have stressed the need to expand the design space exploration by encompassing value and sustainability-related considerations. A portfolio of methods and tools for decision support regarding value and sustainability integration has been proposed in literature, but very few have seen an integration in engineering practices. This paper proposes an approach, developed and tested in collaboration with an aerospace subsystem manufacturer, featuring the integration of value-driven design and sustainable product development models in the established practices for design space exploration. The proposed approach uses early simulation results as input for value and sustainability models, automatically computing value and sustainability criteria as an integral part of the design space exploration. Machine learning is applied to deal with the different levels of granularity and maturity of information among early simulations, value models, and sustainability models, as well as for the creation of reliable surrogate models for multidimensional design analysis. The paper describes the logic and rationale of the proposed approach and its application to the case of a turbine rear structure for commercial aircraft engines. Finally, the paper discusses the challenges of the approach implementation and highlights relevant research directions across the value-driven design, sustainable product development, and machine learning research fields.

Place, publisher, year, edition, pages
Cambridge University Press, 2020
Keywords
decision-making, value-driven design, sustainable product development, design space exploration, machine learning, surrogate models
National Category
Mechanical Engineering Computer Sciences
Identifiers
urn:nbn:se:bth-17851 (URN)10.1017/dsj.2019.29 (DOI)
Funder
Knowledge Foundation
Note

open access

Available from: 2019-04-25 Created: 2019-04-25 Last updated: 2020-02-06Bibliographically approved

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