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Zhang, J., Li, L., Dong, X., Zhang, Z., Zhang, Y. and Lu, X. (2023) A Discrete Adjoint Framework Coupled with Adaptive PCE for Robust Aerodynamic Optimization of Turbomachinery under Flow Uncertainty. Aerospace Science and Technology, 142, Article ID: 108592.
https://doi.org/10.1016/j.ast.2023.108592

has been cited by the following article:

• TITLE: A Comprehensive Review of Modern Aerodynamic Optimization Techniques

  AUTHORS: Agastya Mundhe, Sananjay Biswas

  KEYWORDS: Aerodynamic Optimization, Adjoint & Evolutionary Methods, Machine Learning & Surrogate Modeling, Multidisciplinary Design Optimization

  JOURNAL NAME: World Journal of Nano Science and Engineering, Vol.16 No.3, September 11, 2026

  ABSTRACT: In order to improve performance, efficiency, and sustainability in energy, automotive, and aerospace systems, aerodynamic optimization is an essential part of contemporary engineering design. Current advances in aerodynamic optimization are covered in this paper, including gradient-based methods such adjoint techniques for efficient sensitivity analysis in high-dimensional design spaces and gradient-free and evolutionary algorithms for complex and nonlinear situations. The increased incorporation of machine learning and surrogate modeling has reduced the computing cost of high-fidelity CFD simulations, enabling faster and more efficient design exploration. The essay also discusses multidisciplinary and multi-objective optimization frameworks for aerodynamics, propulsion systems, and structures. We examine high-dimensional design spaces, computational costs, uncertainty quantification, and novel research avenues like physics-informed models and hybrid CFD-AI techniques. This page compiles trends and methods for aerodynamic optimization.