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Surrogate-Based Optimization of a Biplane Wells Turbine

  • Tapas K. DasEmail author
  • Abdus Samad
Conference paper
Part of the Lecture Notes in Civil Engineering book series (LNCE, volume 23)

Abstract

Oscillating Water Column (OWC) is one of the most popular wave energy converters being used for the last two decades. The pneumatic energy from water waves inside the air chamber of OWC is converted into mechanical energy with the help of Wells turbine. Biplane Wells turbine has inherent advantage over the monoplane turbine in terms of starting characteristics and operating range. The main parameters affecting the performance of biplane Wells turbine are the gap between the planes and the offset angle between blades in two planes. Surrogate-based optimization represents the optimization methodologies that use surrogate modelling techniques to find out maxima or minima. Surrogate modelling techniques are very useful for design analysis that uses computationally expensive codes such as Computational Fluid Dynamics (CFD). In the present work, flow over a biplane Wells turbine is simulated using CFD and optimized using surrogate approach. Radial Basis Neural Network (RBNN) method is used to create the surrogate. Blade thickness and the offset angle defining the circumferential position of blades in two planes are considered as the two variables and the objective function is taken as efficiency of the turbine rotor. The comparison of performance between the reference blade and the optimized blade is presented in this article.

Keywords

Wells turbine Biplane Surrogate model Radial basis function 

Nomenclature

ρ

Air density (kg/m3)

ω

Angular velocity (rad/s)

c

Chord length (m)

η

Efficiency

\( \varphi \)

Flow coefficient

R

Rotor tip radius (m)

T

Torque (N-m)

CT

Torque coefficient

\( \Delta P_{0} \)

Total pressure drop (Pa)

\( \Delta P_{0}^{*} \)

Pressure drop coefficient

Q

Volume flow rate (m3/s)

ua

Inlet air velocity (m/s)

ut

Tip speed velocity (m/s)

Abbreviation

OWC

Oscillating Water Column

RMS

Root Mean Square

SST

Shear Stress Transport

RSM

Response Surface Methodology

ANN

Artificial Neural Network

KRG

Kriging

RBNN

Radial Basis Neural Network

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Wave Energy and Fluids Engineering Lab, Ocean Engineering DepartmentIndian Institute of Technology MadrasChennaiIndia

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