Customized data-driven RANS closures for bi-fidelity LES–RANS optimization

Multi-fidelity optimization methods promise a high-fidelity optimum at a cost only slightly greater than a low-fidelity optimization. This promise is seldom achieved in practice, due to the requirement that low- and high-fidelity models correlate well. In this article, we propose an efficient bi-fidelity shape optimization method for...

MultiTune: Dynamic budget allocation for hyperparameter tuning

Hyperparameter optimization(HPO) forms a critical aspect for machine learning applications to attain superior performance. BOHB (Bayesian Optimization and HyperBand) is a state of the art HPO algorithm that approaches HPO in a multi-armed bandit strategy, augmented with Bayesian optimization to drive configuration sampling. However, BOHB...

Design of Swirl Recovery Vanes for Wing-Mounted Tractor Propeller Propulsion System

Increasing pressure of reducing fuel consumption has led to renewed interests in propeller propulsion devices for their high propulsive efficiency. In a propeller propulsion system, the torque applied to the propeller results in angular momentum in propeller slipstream. This angular momentum is not only a form of energy loss that does not...

Multi-fidelity wing aerostructural optimization using a trust region filter-SQP algorithm

A trust region filter-SQP method is used for wing multi-fidelity aerostructural optimization. Filter method eliminates the need for a penalty function, and subsequently a penalty parameter. Besides, it can easily be modified to be used for multi-fidelity optimization. A low fidelity aerostructural analysis tool is presented, that computes the...