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Mission-Driven Inverse-Design of Conventional and Unconventional Airplane Configurations Using Machine Learning.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Mission-Driven Inverse-Design of Conventional and Unconventional Airplane Configurations Using Machine Learning./
Author:	Sharma, Rohan Srinivas.
Description:	1 online resource (118 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
Contained By:	Dissertations Abstracts International85-12B.
Subject:	Aerospace engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9798383098769

Mission-Driven Inverse-Design of Conventional and Unconventional Airplane Configurations Using Machine Learning.
Sharma, Rohan Srinivas.

Mission-Driven Inverse-Design of Conventional and Unconventional Airplane Configurations Using Machine Learning. - 1 online resource (118 pages)

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

Thesis (Ph.D.)--Missouri University of Science and Technology, 2024.

Includes bibliographical references

A key challenge in airplane design has been the long-desired ability to quickly create an airplane design with specific performance properties. To tackle this problem, researchers have traditionally leveraged design space exploration methodologies to iteratively explore a design space via surrogate models. On the other hand, inverse design is a process wherein performance objectives are fixed while a suitable design for satisfying these requirements is calculated. In recent years, artificial neural networks (ANNs) have been explored as viable surrogate models because traditional surrogate models struggle with the large number of output to input mapping needed for inverse design problems. In this study, the feasibility of developing ANNs for calculating airplane configuration design parameters when provided with time-series, mission-informed performance data is investigated. Shallow ANNs were developed, trained, and tested using data pertaining to two design cases: tube-and-wing (TAW) and blended wing body (BWB) configurations. Performance for each configuration was obtained via a physics-based performance assessment in order to construct the training data sets. The effects of varying ANN architecture, along with the application of different data filtering schemes, on the predictive accuracy of the models have been examined. The results for the TAW scenario demonstrated that cascade-forward (CF) shallow ANNs exhibited better generalization across design sites, when trained with a limited amount of training data. Meanwhile, results for the BWB scenario showed that feed-forward (FF), shallow ANNs yielded significantly better predictive accuracy when a larger training dataset is used. The inference based on the results of the two design cases is that the optimal ANN architecture identified for each case was not due to characteristic differences in the airplane configurations; instead, it was a function of the size of the training dataset employed for each scenario. The results presented in this work are expected to assist future efforts aimed at developing surrogate models capable of handling time-series, mission-informed performance data for airplane inverse-design methodologies.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798383098769Subjects--Topical Terms:

686400
Aerospace engineering.
Subjects--Index Terms:

Airplane designIndex Terms--Genre/Form:

554714
Electronic books.

Mission-Driven Inverse-Design of Conventional and Unconventional Airplane Configurations Using Machine Learning.
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520 $a A key challenge in airplane design has been the long-desired ability to quickly create an airplane design with specific performance properties. To tackle this problem, researchers have traditionally leveraged design space exploration methodologies to iteratively explore a design space via surrogate models. On the other hand, inverse design is a process wherein performance objectives are fixed while a suitable design for satisfying these requirements is calculated. In recent years, artificial neural networks (ANNs) have been explored as viable surrogate models because traditional surrogate models struggle with the large number of output to input mapping needed for inverse design problems. In this study, the feasibility of developing ANNs for calculating airplane configuration design parameters when provided with time-series, mission-informed performance data is investigated. Shallow ANNs were developed, trained, and tested using data pertaining to two design cases: tube-and-wing (TAW) and blended wing body (BWB) configurations. Performance for each configuration was obtained via a physics-based performance assessment in order to construct the training data sets. The effects of varying ANN architecture, along with the application of different data filtering schemes, on the predictive accuracy of the models have been examined. The results for the TAW scenario demonstrated that cascade-forward (CF) shallow ANNs exhibited better generalization across design sites, when trained with a limited amount of training data. Meanwhile, results for the BWB scenario showed that feed-forward (FF), shallow ANNs yielded significantly better predictive accuracy when a larger training dataset is used. The inference based on the results of the two design cases is that the optimal ANN architecture identified for each case was not due to characteristic differences in the airplane configurations; instead, it was a function of the size of the training dataset employed for each scenario. The results presented in this work are expected to assist future efforts aimed at developing surrogate models capable of handling time-series, mission-informed performance data for airplane inverse-design methodologies.
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