Computationally efficient optimisation of elbow-type draft tube using neural network surrogates

Sikirica, Ante; Lučin, Ivana; Alvir, Marta; Kranjčević, Lado; Čarija, Zoran

doi:10.1016/j.aej.2024.01.062

Cited by 2 publications

(1 citation statement)

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“…The use of surrogate models in the optimisation of draft tubes is not yet very widespread. Exceptions are the doctoral thesis of D. Marjavaar [17], article by Demirel et al [18] or Sikirica et al [19]. Recently, publications using response surface methodology have appeared more frequently for the optimisation of the draft tubes.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Elbow Draft Tubes for Variable Speed Propeller Turbine

Souček,

Nowak

2024

Water

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The design of the elbow draft tubes is challenging due to the complexity of the flow. The whole turbine unit’s power output strongly depends on the draft tube function, especially for the low-head turbines. The article presents a novel approach to optimizing elbow draft tubes for a variable-speed propeller turbine designed for low-head applications. First, the study addresses the specifics of the propeller variable speed turbine by comparing the classical Kaplan turbine. Then, the grid scaling test is conducted to evaluate the uncertainty of the pressure regeneration. Further, a new approach to parameterising the elbow draft tube geometry is introduced. The study employs ANSYS CFX 2021 R1 software for numerical simulation to optimise the elbow draft tube geometry in the CAESES environment. After the sensitivity test and deselecting the non-sensitive parameters, we perform multi-objective genetic algorithm (MOGA) optimization. The optimization process results in a Pareto front of optimised elbow draft tube shapes with the best pressure regeneration for different draft tube construction heights, enabling the selection of suitable candidates for various locations. Minimal difference in the performance of the selected elbow draft tube shapes with the simple straight draft tube confirms a high-quality draft tube optimization achievement.

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Section: Introductionmentioning

confidence: 99%