2026-06-01T11:52:37Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/228322026-02-03T12:10:17Zcom_10630_2254col_10630_37959

00925njm 22002777a 4500 dc Granados-Ortiz, Francisco-Javier author Ortega-Casanova, Joaquín author 2021 Computational design is a key part in most engineering applications, thanks to the possibility to create new designs in a safer, quicker and reliable environment. The recent developments in engineering are also guiding the classical design life cycle to a more sophisticated frameworks, such as the implementation of Machine Learning methods to support the design process. This work shows the potential of using the namely Machine Learning-Aided Design Optimisation framework to optimise vortex-shedding based applications, and it is applied as example to a vortex shedding aerodynamic-based design extendable to other applications. This framework consisted of using a predictive model to discard useless computations and speed up the efficient construction of surrogate models. The method is applied to the optimisation of a mechanical vortex shedding-based passive mixer achieving a successful design in terms of minimisation of pressure drop and maximisation of mixing efficiency. https://hdl.handle.net/10630/22832 Ingeniería mecánica - Congresos Machine Learning-Aided Design Optimisation(MLADO) in Vortex Shedding-Based Engineering Applications