shape optimization

This paper presents a machine-learning-based approach that enables simultaneous surrogate modeling and dimension reduction and applies it to aerodynamic parametric shape optimization.  Aerodynamic shape optimization is a crucial process in various industries, including aerospace, automotive, and renewable energy.  It involves iteratively improving the properties of a system by evaluating an objective function and driving its minimization or maximization using an optimization algorithm.  However, the evaluation of aerodynamic objective functions requires computationally

Aerodynamic shape optimization is a very active area of research that faces the challenges of highly demanding Computational Fluid Dynamics (CFD) problems, optimization with Partial Differential Equations (PDEs) as constraints, and the appropriate treatment of uncertainties.  This includes the development of robust design methodologies that are computationally efficient while maintaining the desired level of accuracy in the optimization process.  This paper addresses aerodynamic shape optimization problems involving uncertain operating conditions.  After a review of pos

Bézier based parametrisations in shape optimization have the drawback of using high degree polynomials to draw more complex shapes.  To overcome this drawback, Non-Uniform Rational B-Splines (NURBS) are usually used.  But, by considering the NURBS weights, in addition to the locations of the control points, as optimization variables, the dimension of the problem greatly increases and this would make the optimization process stiffer.  In this work we propose, then, an algorithm to adapt the weights of NURBS in the parametrization of shape optimization problems.  Unlike t