SUMMARYOne of the basic problems in fluid dynamics shape optimization is mesh generation. When analysis is performed using the finite element method, meshes of sufficient quality need to be constructed automatically. This work presents a structured meshing procedure that creates subdomains for generating good quality structured meshes in critical flow regions around aerodynamic profiles. Techniques of this nature enable other kinds of problems and geometries to be tackled. To demonstrate its capacity, it was applied to a straightforward shape optimization problem in fluid dynamics via genetic algorithms (GAs), including a preliminary efficiency study for different GA parameter combinations.

The study about a variable geometry truss is presented. The first step is a kinematic design and later an analysis using the MSC/Adams code. Then, the finite-element method with the MSC/Nastran code was used to develop a variable mathematical model to characterize its mechanical behaviour. An explanation is given of the solution selected to analyse the most complex joints. A series of measurements were carried out using different experimental techniques. The aim of photogrammetry tests is to study clearance effect in the joints. On the other hand, measurements of the stiffness were carried out to introduce these data in the finite-element model and thus characterize the accurate position of the structure.

The increased need to design higher performing aerodynamic shapes has led to design optimisation cycles requiring high-fidelity CFD models and high-dimensional parametrisation schemes. The computational cost of employing global search algorithms on such scenarios has typically been prohibitive for most academic and industrial environments. In this paper, a novel strategy is presented that leverages the capabilities of Artificial Neural Networks for regressing complex unstructured data, while coupling them with dimensionality reduction algorithms. This approach enables employing global-based optimisation methods on high-dimensional applications through a reduced computational cost. This methodology is demonstrated on the efficiency optimisation of a modern jet engine fan blade with constrained pressure ratio. The outcome is compared against a state-of-the-art adjoint-based approach. Results indicate the strategy proposed achieves comparable improvements to its adjoint counterpart with a reduced computational cost, and can scale better to multi-objective optimisation applications.

This study developed a framework for the shape optimization of aerodynamics profiles using computational fluid dynamics (CFD) and genetic algorithms. A genetic algorithm code and a commercial CFD code were integrated to develop a CFD shape optimization tool. The results obtained demonstrated the effectiveness of the developed tool. The shape optimization of airfoils was studied using different strategies to demonstrate the capacity of this tool with different GA parameter combinations.

Regression-based Polynomial Chaos expansions offer several advantages over projectionbased approaches, including their lower computation cost and greater flexibility. In the presence of expensive function evaluations, such as with computational fluid dynamics and finite element analysis, the availability of gradient information, coming from adjoint solvers, can be used to reduce the cost of least-square estimation. Particular attention needs to be payed to the accuracy of gradient information, as adjoint solvers are often more noisy than their primal counterparts. This paper compares different approaches for gradient-enhanced least-square Polynomial Chaos expansion, both for algebraic test cases, and for real-world test cases, i.e. a transonic compressor and a modern jet engine fan.