Artificial Intelligent Research Assistant for Aerospace Design Synthesis—Solution Logic

McCall, Thomas; Alavi, K.; Rana, Loveneesh; Chudoba, Bernd

doi:10.2514/6.2018-5387

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…This capability is identified as the Gen-VI in ASDS evolution as shown in Figure 24. The AVD Laboratory is currently working towards augmenting the DBMS with an AI capability and is developing a true forecasting capability [25].…”

Section: Discussionmentioning

confidence: 99%

A Paradigm-Shift in Aerospace Vehicle Design Synthesis and Technology Forecasting

Rana

McCall

Haley

et al. 2018

2018 AIAA SPACE and Astronautics Forum and Exposition

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Section: Discussionmentioning

confidence: 99%

A Paradigm-Shift in Aerospace Vehicle Design Synthesis and Technology Forecasting

Rana

McCall

Haley

et al. 2018

2018 AIAA SPACE and Astronautics Forum and Exposition

Self Cite

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“…Early applications in the aerospace domain include the use of support vector machines [41] and kriging [42] and co-kriging [43] methods for metamodels as well as the application of Bayesian [44] and neural networks [45] for the classification of architectures and topologies. More recently, the field of aerospace design has been enhanced by a broader use of machine learning applications for a multitude of specific design problems and applications [46][47][48][49][50]. Previous work in the context of the MISSION project focuses on the integration of the system-level dynamics with the aircraft-level [51] and the design of controls for multiple aircraft systems [52].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Enabled Multi-Fidelity Platform for the Integrated Design of Aircraft Systems

Garriga

Mainini

Ponnusamy

2019

Journal of Mechanical Design

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The push toward reducing the aircraft development cycle time motivates the development of collaborative frameworks that enable the more integrated design of aircraft and their systems. The ModellIng and Simulation tools for Systems IntegratiON on Aircraft (MISSION) project aims to develop an integrated modelling and simulation framework. This paper focuses on some recent advancements in the MISSION project and presents a design framework that combines a filtering process to down-select feasible architectures, a modeling platform that simulates the power system of the aircraft, and a machine learning-based clustering and optimization module. This framework enables the designer to prioritize different designs and offers traceability on the optimal choices. In addition, it enables the integration of models at multiple levels of fidelity depending on the size of the design space and the accuracy required. It is demonstrated for the electrification of the Primary Flight Control System (PFCS) and the landing gear braking system using different electric actuation technologies. The performance of different architectures is analyzed with respect to key performance indicators (fuel burn, weight, power). The optimization process benefits from a data-driven localization step to identify sets of similar architectures. The framework demonstrates the capability of optimizing across multiple, different system architectures in an efficient way that is scalable for larger design spaces and larger dimensionality problems.

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Application of Artificial Intelligence in Aerospace Engineering and Its Future Directions: A Systematic Quantitative Literature Review

Hassan,

Thakur,

Singh

et al. 2024

Arch Computat Methods Eng

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Artificial Intelligent Research Assistant for Aerospace Design Synthesis—Solution Logic

Cited by 3 publications

References 7 publications

A Paradigm-Shift in Aerospace Vehicle Design Synthesis and Technology Forecasting

A Paradigm-Shift in Aerospace Vehicle Design Synthesis and Technology Forecasting

A Machine Learning Enabled Multi-Fidelity Platform for the Integrated Design of Aircraft Systems

Application of Artificial Intelligence in Aerospace Engineering and Its Future Directions: A Systematic Quantitative Literature Review

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