Abstract
A robust multi-fidelity design algorithm has been developed, focusing to efficiently handle industrial hydraulic runner design considerations. The computational task is split between low- and high-fidelity phases in order to properly balance the CFD cost and required accuracy in different design stages. In the low-fidelity phase, a derivative-free optimization method employs an inviscid flow solver to obtain the major desired characteristics of a good design in a relatively fast iterative process. A limited number of candidates are selected among feasible optimization solutions by a newly developed filtering process. The main function of the filtering process is to select some promising candidates to be sent into the high-fidelity phase, which have significantly different geometries, and also are dominant in their own territories. The high-fidelity phase aims to accurately evaluate those promising candidates in order to select the one which is closest to design targets. A low-head runner case study has shown the ability of this methodology to identify an optimized blade through a relatively low computational effort, which is significantly different from the base geometry.
Highlights
1.1 HydropowerDue to the depletion of non-renewable fossil energy, the enforcement of the Brazil-Kyoto agreements, and considering the high risk associated with nuclear power plants, renewable energy markets are projected to continue to grow strongly in the following decades
NOMAD uses the Mesh Adaptive Direct Search (MADS) algorithm, which has demonstrated its power of local search in complex industrial applications
Since the low-fidelity solver used in the optimization is not accurate enough and the efficiency is not directly reachable, a filtering unit selects a limited number of geometrically different candidates which are dominant in their own neighborhoods
Summary
1.1 HydropowerDue to the depletion of non-renewable fossil energy, the enforcement of the Brazil-Kyoto agreements, and considering the high risk associated with nuclear power plants (especially after Fukushima Daiichi nuclear disaster in 2011), renewable energy markets are projected to continue to grow strongly in the following decades. A full range of CFD methods has been utilized in the optimization of hydraulic turbine runner blades; from low-fidelity inviscid models (e.g. using potential flow [17]) to highfidelity viscous models (e.g. using a turbulent RANS solver [20]). None of these methods can, by itself, entirely fulfil industrial design needs. Georgopoulou et al [63] applied a functional surrogate model based on radial basis functions, to optimize runner blades of hydraulic turbines using an evolutionary algorithm in a hierarchical scheme They obtained design candidates for one test case after 3500 high-fidelity evaluations. Different types of low-cost methods have been employed for the low-level turbomachinery optimization, such as functional surrogate models (e.g. [67]), physics-based surrogates (e.g. [86]), coarser mesh (e.g. [72]), and reduced convergence tolerance (e.g. [71])
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