Advanced UAV Design Optimization Through Deep Learning-Based Surrogate Models

Hasan Karali, Gökhan İnalhan, Antonios Tsourdos

Aerospace 2024 article cited by 37

Source Open-access PDF More in Engineering & Materials

importance 4/10 ai assisted Advanced Multi-Objective Optimization Algorithms aerospace engineering stage: research

Problem

Conceptual UAV design requires simultaneous optimization of aerodynamics, structure, stealth and propulsion, which is computationally expensive with traditional methods.

Why it matters

Faster design evaluation cuts the cost and time of developing UAVs for varied missions.

What the AI did

Deep neural network surrogate models were integrated with a multiobjective genetic algorithm to estimate key performance metrics and optimize UAV configurations across mission scenarios.

Results

Computational time improved by more than three orders of magnitude over traditional methods

Impact

Framework demonstrated for ISR, combat air patrol and SEAD mission scenarios per the abstract.

Record

Venue	Aerospace
Published	2024
Work type	article
Authors	Hasan Karali, Gökhan İnalhan, Antonios Tsourdos
Citations	37 (OpenAlex, at import time)
References	55
Model or method	Deep neural network surrogate models
Organization	not stated in source
Primary source	https://doi.org/10.3390/aerospace11080669
Open access	gold
Keywords	UAV design, surrogate model, deep learning, genetic algorithm, multiobjective optimization

Importance rating

4 / 10

real but narrower advance

Cutting cost/time of multidisciplinary UAV conceptual design is a real but narrow engineering advance; abstract claims transformative impact for aerospace design but quantifies only speedup, not problem scale.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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