Aerodynamic Optimization of UAV’S for Increased Endurance

Abstract

This literature paper reviews recent advancements in aerodynamic optimization of Unmanned Aerial Vehicles (UAVs) between 2023 and 2025, focusing on methods aimed at improving endurance through aerodynamic efficiency. The reviewed works cover optimization techniques involving airfoil design, morphing wings, drag reduction strategies, and energy-aerodynamic integration using computational tools such as CFD, genetic algorithms, and machine learning- based design frameworks. The pursuit of extended endurance in unmanned aerial vehicles (UAVs) has led to rapid advancements in aerodynamic optimization methods integrating computational, structural, and intelligent design approaches. This literature review examines research published between 2023 and 2025 focusing on aerodynamic refinements that enhance flight endurance and energy efficiency. The reviewed studies highlight the use of morphing airfoils, optimized planform geometries, and adaptive wing configurations to minimize drag while maintaining lift performance. Emerging optimization frameworks—such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and machine learning-driven surrogate modeling—have been shown to significantly reduce computational cost and improve aerodynamic prediction accuracy. Additionally, co-optimization strategies combining aerodynamics with propulsion and energy management systems have contributed to multi- disciplinary performance improvements. Experimental and CFD-based analyses consistently demonstrate that optimized configurations can increase endurance by 12–30% compared to conventional fixed-wing UAVs. The synthesis of these studies underscores the importance of integrated aerodynamic–energy design for future UAV development. This review identifies key research trends, limitations, and opportunities for further exploration in morphing mechanisms, hybrid optimization, and sustainable UAV design for extended missions. The collective findings highlight a growing shift toward multi-disciplinary design optimization (MDO) and AI-assisted design workflows in UAV research. This review identifies key research trends, methodological advancements, and limitations, outlining opportunities for future exploration in morphing mechanisms, bio-inspired designs, and sustainable UAV configurations tailored for long-endurance operations.

Introduction

Unmanned Aerial Vehicles (UAVs) are increasingly used for surveillance, mapping, logistics, and defense, creating a demand for longer flight endurance. Endurance—how long a UAV can remain airborne—is strongly influenced by aerodynamic efficiency, propulsion, and structural optimization. Improving lift-to-drag ratios (L/D) and reducing drag are central to extending flight duration. Between 2023 and 2025, significant progress has been made in aerodynamic optimization using Computational Fluid Dynamics (CFD), Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Machine Learning (ML).

Problem & Motivation:
Despite advances in materials and energy systems, UAVs still face drag, inefficient lift distribution, and adaptability challenges during long-endurance missions. Integrating aerodynamic optimization with propulsion and energy systems remains complex, motivating a review of recent methods that enhance endurance.

Objectives:

• Analyze recent aerodynamic optimization techniques.

• Examine their impact on endurance, L/D, and flight stability.

• Compare computational and experimental approaches.

• Identify gaps and future research directions.

Theoretical Background:
UAV endurance is governed by lift, drag, and energy efficiency. Key aerodynamic factors include:

• Airfoil shape – affects lift and pressure distribution.

• Aspect ratio – higher ratios reduce induced drag but increase structural weight.

• Wing sweep/taper and surface finish – influence stability and drag.
  Optimization relies on solving Navier–Stokes equations via CFD, often enhanced by ML for faster predictions and multi-disciplinary optimization (MDO) linking aerodynamics, structure, and energy.

Recent Advances (2023–2025):

• Airfoil & planform optimization: Improved L/D and reduced profile drag (~12–15% endurance gains).

• Morphing/adaptive wings: Adjustable geometry reduces drag in different flight phases.

• Energy–aerodynamic co-optimization: Solar and hybrid UAVs gain up to 30% endurance through coupled energy–aero design.

• Propulsion–aerodynamics integration: Optimized propellers reduce cruise power consumption by 5–15%.

• Computational methods: Adjoint methods, multi-fidelity models, and ML surrogates accelerate optimization and reduce CFD cost.

Trends & Insights:

• Shift toward system-level MDO for realistic endurance improvements.

• Hybrid low/high-fidelity modeling balances accuracy and design-space exploration.

• Morphing structures are maturing, though actuator energy, weight, and durability remain challenges.

• Integrated propulsor–airframe optimization is critical for long-endurance missions.

Challenges & Research Gaps:

• Limited long-duration flight validation under real conditions.

• Morphing trade-offs: added mass, actuator energy, and material fatigue.

• Off-design robustness and environmental sensitivity need better modeling.

• Surrogate and low-order models may mispredict outside trained design spaces.

• High computational demands restrict adoption in smaller labs.

Recommendations:

• Adopt holistic, system-level MDO including actuator, energy, and environmental factors.

• Optimize across multiple flight conditions for robustness.

• Combine experimental tests with CFD and surrogate updates.

• Develop energy-aware morphing control strategies.

• Use open, reproducible CFD workflows (e.g., DAFoam) to improve collaboration.

Conclusion

The comprehensive review of recent literature (2023–2025) demonstrates that aerodynamic optimization continues to be one of the most effective strategies for improving the endurance of Unmanned Aerial Vehicles (UAVs). Over the past few years, research efforts have evolved from basic airfoil shaping and planform tuning to advanced multi-disciplinary optimization (MDO) frameworks that integrate aerodynamics, propulsion, structural design, and energy management. Studies by Haider (2023), Sahraoui et al. (2024), and Di et al. (2025) collectively highlight that aerodynamic refinements — when coupled with efficient energy systems and optimized propulsion — can yield endurance improvements of up to 30%, validating the importance of holistic co-design. These findings reflect a paradigm shift from purely geometric optimization toward intelligent, adaptive, and system-level design philosophies that merge computational intelligence, material science, and flight dynamics. The review further emphasizes the growing influence of artificial intelligence (AI), machine learning (ML), and adjoint-based computational methods in UAV aerodynamic research. These tools have enabled engineers to conduct high-fidelity simulations and optimize complex aerodynamic configurations with remarkable efficiency and accuracy. Surrogate modeling and hybrid optimization techniques have accelerated the exploration of large design spaces while maintaining predictive reliability. In parallel, morphing wing technologies have emerged as a promising approach to achieve in-flight adaptability, allowing UAVs to modify camber, span, or twist dynamically according to mission requirements. Although experimental studies such as those by Montaño et al. (2024) and He et al. (2024) have validated the aerodynamic benefits of morphing designs, challenges remain regarding actuator energy consumption, structural weight, and long-term durability. These limitations underline the need for lightweight, energy-efficient, and smart morphing mechanisms that can operate reliably over extended missions. Despite significant progress, several challenges and research gaps continue to limit the practical application of these advancements. Most existing studies remain simulation-based, with limited full-scale flight validation under realistic atmospheric conditions. Additionally, there is an urgent need to incorporate energy-aware optimization that considers the interplay between aerodynamic performance, propulsion efficiency, and onboard energy usage. Future research must also explore robust and multi-point optimization techniques to ensure UAVs perform efficiently across diverse operating regimes, including gusty, turbulent, or high- altitude environments. As renewable energy integration, such as solar and hydrogen fuel systems, becomes more prevalent, aerodynamic optimization must adapt to new power-to- weight constraints and sustainability goals. Aerodynamic optimization remains a cornerstone of UAV endurance enhancement. The synthesis of computational intelligence, adaptive aerodynamics, and energy-efficient design marks a transformative phase in UAV technology—one that will enable the next generation of autonomous, sustainable, and long-endurance aerial systems capable of serving diverse applications in surveillance, environmental monitoring, and communication networks.

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Copyright

Copyright © 2025 Manya N M, Gagana D S, Kruthika R Babu, Harshitha N, Harshitha K. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.