Influence of High Level Requirements in Aircraft Design: from scratch to sketch

Fusaro, Roberta; Viola, Nicole

doi:10.2514/6.2018-4135

Cited by 4 publications

(1 citation statement)

References 6 publications

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“…Four design parameters, namely b a , S a , MTOW, and T A , were selected as they are generally regarded as high-level design parameters and are typically investigated in the preliminary and conceptual sizing portion of a BWB's design cycle. During this phase, design space exploration is conventionally utilized to explore the trade space and understand design sensitives as it relates to both the MR&Os and the aircraft manufacturer's product and technology capability [50]. The bounds for b in and S in design space were selected as 169 ft. ≤ b in ≤ 327 ft. and 4200 ft. 2 ≤ S in ≤ 22,000 ft. 2 , respectively.…”

Section: Database Creationmentioning

confidence: 99%

Mission-Driven Inverse Design of Blended Wing Body Aircraft with Machine Learning

Sharma,

Hosder

2024

Aerospace

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The intent of this work was to investigate the feasibility of developing machine learning models for calculating values of airplane configuration design variables when provided time-series, mission-informed performance data. Shallow artificial neural networks were developed, trained, and tested using data pertaining to the blended wing body (BWB) class of aerospace vehicles. Configuration design parameters were varied using a Latin-hypercube sampling scheme. These data were used by a parametric-based BWB configuration generator to create unique BWBs. Performance for each configuration was obtained via a performance estimation tool. Training and testing of neural networks was conducted using a K-fold cross-validation scheme. A random forest approach was used to determine the values of predicted configuration design variables when evaluating neural network accuracy across a blended wing body vehicle survey. The results demonstrated the viability of leveraging neural networks in mission-dependent, inverse design of blended wing bodies. In particular, feed-forward, shallow neural network architectures yielded significantly better predictive accuracy than cascade-forward architectures. Furthermore, for both architectures, increasing the number of neurons in the hidden layer increased the prediction accuracy of configuration design variables by at least 80%.

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Section: Database Creationmentioning

confidence: 99%

Mission-Driven Inverse Design of Blended Wing Body Aircraft with Machine Learning

Sharma,

Hosder

2024

Aerospace

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Model-Based Requirements (TMBR) of a Satellite TTC Transponder

Wach

Salado

2021

2021 IEEE Aerospace Conference (50100)

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Investigation of Mission-Driven Inverse Aircraft Design Space Exploration with Machine Learning

Sharma

Hosder

2021

Journal of Aerospace Information Systems

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The goal of this work was to investigate the feasibility of developing machine learning models for predicting the values of aircraft configuration design variables when provided with time series of mission-informed performance parameters. Regression artificial neural networks, along with their associated training data, have been generated and tested for aircraft design space exploration scenarios. The bounds of the data used to train the models were partially informed by the configuration characteristics of the Boeing 737 Next Generation family. The effects of varying neural network architecture, along with the application of different data filtering schemes, on the models’ predictive accuracy have been examined. The results obtained demonstrated that cascade-forward shallow neural networks not only exhibited excellent generalization across the design space for which the model was calibrated for, but also showcased versatility when tasked with predicting design variable values for a configuration layout relatively different from the ones used for training. Furthermore, these models had favorable metrics in computational wall-clock time required and number of epochs needed for training.

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Influence of High Level Requirements in Aircraft Design: from scratch to sketch

Cited by 4 publications

References 6 publications

Mission-Driven Inverse Design of Blended Wing Body Aircraft with Machine Learning

Mission-Driven Inverse Design of Blended Wing Body Aircraft with Machine Learning

Model-Based Requirements (TMBR) of a Satellite TTC Transponder

Investigation of Mission-Driven Inverse Aircraft Design Space Exploration with Machine Learning

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