Performance Optimization of a Kirsten–Boeing Turbine by A Metamodel Based on Neural Networks Coupled with CFD

Abstract

The supply of energy is sustainable only if it is predominantly based on renewable or regenerative energies. For this reason, the use of micro-hydropower plants on rivers and streams is considered recently. This is a particular challenge for the preservation of ecologically permeable streams, so that no dams or similar structures can be considered. While the axial turbine design has prevailed in wind power, there is still no consensus for the generation of energy in free water flow conditions. In this work, an existing prototype of an unusual vertical axis Kirsten–Boeing turbine was investigated. A multivariate optimization process was created, in which all important machine parameters were checked and improved. By using neural networks as a metamodel coupled with flow simulations in ANSYS CFX, a broadly applicable optimization strategy is presented that yielded a blade design that is 36% more efficient than its predecessor in experiments. During the process, it was shown how to set up a complex sliding mesh problem with ANSYS expressions while evaluating a free surface problem.

Design of optimal flow concentrator for vertical-axis wind turbines using computational fluid dynamics, artificial neural networks and genetic algorithm

“…Mohammadi et al 7 used computational intelligence to study the relations between the parameters of the Savonius rotor and to perform its single-objective GA optimization. Kuppers et al 17 also coupled CFD with ANN in order to optimize vertical-axis Kirsten–Boeing turbine. Finally, the authors also previously used ANNs to control boundary layers around foils in linear cascades.…”

Calibration of CFD grid for simulating the impeller clearance flow and axial hydraulic force of centrifugal pump

“…As a modern computer-aided method, artificial neural network (ANN) and machine learning can be applied into CFD-based optimization and determination. [34][35][36][37][38][39] By training and checking of the generalization ability, ANN can be an accurate fitting tool of solution space and indicates the determination direction. Therefore, an ANN-based approach is discussed in this study to calibrate the CFD grid for pump impeller clearance flow and axial hydraulic force.…”