Abstract
The MDAO framework has become an essential part of almost all fields, apart from mechanical, transportation, and aerospace industries, for efficient energy conversion or otherwise. It enables rapid iterative interaction among several engineering disciplines at various fidelities using automation tools for design improvement. An advanced framework from low to high fidelity is developed for ducted and unducted turbomachinery blade designs. The parametric blade geometry tool is a key feature which converts low-fidelity results into 3D blade shapes and can readily be used in high-fidelity multidisciplinary simulations as part of an optimization cycle. The geometry generator and physics solvers are connected to DAKOTA, an open-source optimizer with parallel computation capability. The entire cycle is automated and new design iterations are generated with input parameter variations controlled by DAKOTA. Single- and multi-objective genetic algorithm and gradient method-based optimization cases are demonstrated for various applications. B-splines are used to define smooth perturbation of parametric variables chordwise and spanwise of the blade. The ability to create parametric 3D blade shapes quickly from low-fidelity analyses with advanced control is demonstrated to be unique and enables a rapid 3D design cycle. Non-intuitive designs are feasible in this framework and designers can really benefit from parametric geometry manipulation. Optimization at each fidelity is realized through automation. As part of the multidisciplinary analysis, 3D structural analysis is also performed using the unidirectional fluid−structure interaction for a few cases with imported pressure loads from the 3D RANS solution. Examples of axial turbofans, compressor rotors, turbines, radial compressors, propellers, wind and hydrokinetic turbines are demonstrated to prove generality.