A. P. Roberts, A. A. M. Rahat, D. S. Jarman, J. E. Fieldsend, G. R. Tabor
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引用次数: 0
Abstract
The shape of a hydrodynamic particle separator has been optimized using a parallelized and robust formulation of Bayesian optimization, with data from an unsteady Eulerian flow field coupled with Lagrangian particle tracking. The uncertainty due to the mesh, initial conditions, and stochastic dispersion in the Eulerian-Lagrangian simulations was minimized and quantified. This was then translated across to the error term in the Gaussian process model and the minimum probability of improvement infill criterion. An existing parallelization strategy was modified for the infill criterion and customized to prefer exploitation in the decision space. In addition, a new strategy was developed for hidden constraints using Voronoi penalization. In the approximate Pareto Front, an absolute improvement over the base design of \(14\%\) in the underflow collection efficiency and \(10\%\) in the total collection efficiency was achieved, which resulted in the filing of a patent.* The corresponding designs were attributed to the effective distribution of residence time between the trays via the removal of a vertical plume. The plume also reduced both efficiencies by creating a flow path in a direction that acted against effective settling. The concave down and offset tray shapes demonstrated the value of Bayesian optimization in producing useful and non-intuitive designs.
期刊介绍：
Optimization and Engineering is a multidisciplinary journal; its primary goal is to promote the application of optimization methods in the general area of engineering sciences. We expect submissions to OPTE not only to make a significant optimization contribution but also to impact a specific engineering application.
Topics of Interest:
-Optimization: All methods and algorithms of mathematical optimization, including blackbox and derivative-free optimization, continuous optimization, discrete optimization, global optimization, linear and conic optimization, multiobjective optimization, PDE-constrained optimization & control, and stochastic optimization. Numerical and implementation issues, optimization software, benchmarking, and case studies.
-Engineering Sciences: Aerospace engineering, biomedical engineering, chemical & process engineering, civil, environmental, & architectural engineering, electrical engineering, financial engineering, geosciences, healthcare engineering, industrial & systems engineering, mechanical engineering & MDO, and robotics.
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