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Design Optimization of a Fluidic Diode for a Wave Energy Converter via Artificial Intelligence-Based Technique

  • Research Article-Mechanical Engineering
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Abstract

A pair of turbines can harness power from a wave energy Converter. Their performance is poor than individual turbines due to flow reversal. A fluidic diode (FD) which offers variable resistance to the flow, can be used to prevent flow reversal and improve the performance of these units. Its performance is given by diodicity (ratio of reverse to forward flow pressure drop). A higher diodicity enables it to prevent flow reversal better and improve the turbine unit’s overall efficiency. In this work, the geometrical shape of the FD is optimized to obtain higher diodicity. Six geometrical variables of the FD are varied to obtain sample points using the sampling technique, which is numerically investigated by solving steady-state Reynolds averaged Navier–Stokes (RANS) equations. These numerical results were fed into a neural network code that produced an optimal FD design. The optimum model showed a 36.5% improvement in diodicity at 0.35 m3/s. The fluid flowing through the optimized model experience higher resistance in the reverse direction because of the increased vortex strength than the base model. Among all the design variable considered, nozzle angle is a highly sensitive parameter in the optimization process. The optimum FD model enhanced the overall efficiency of the turbine unit by 13.3.

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Abbreviations

ANN:

Artificial neural network

RANS:

Reynolds averaged Navier–Stokes

BFD:

Base fluidic model

OWC:

Oscillating water column

CSB:

Crested shape body

OFD:

Optimized fluidic model

CFD:

Computational fluid dynamics

SC:

Spread constant

DOE:

Design of experiment

TKE:

Turbulent kinetic energy

EG:

Error goal

TU:

Unidirectional turbine

FD:

Fluidic diode

WEC:

Wave energy converter

GCI:

Grid convergence index

RBNN:

Radial basis neural network

\({C}_{A}\) :

Input flow coefficient

\(u\) :

Angular velocity (m/s)

\({C}_{T}\) :

Torque coefficient

\(v\) :

Axial flow velocity (m/s)

D :

Diameter of the duct (m)

\(\varphi\) :

Flow co-efficient

\(f\) :

Friction factor

\(v\prime\) :

Fluctuation of velocity in the y-direction

L N :

Normalized length of the nozzle

\(w^{\prime}\) :

Fluctuation of velocity in the z-direction

Q :

Flow rate (m3/s)

ω :

Angular speed (rad/s)

R :

Normalized radius

\(\psi\) :

Diodicity

\({R}_{r}\) :

Mean radius of the turbine (m)

\(\eta\) :

Efficiency

\(T\) :

Time (s)

\(\gamma\) :

Nozzle angle (degree)

\(TR\) :

Torque (N-m)

\(\Delta p\) :

Pressure drop (Pa)

TU :

Turbine

\({\Delta p}_{T}\) :

Pressure drop across turbine (Pa)

\(U\) :

Velocity of fluid inside the duct (m/s)

ε :

Turbulent energy dissipation (m2/s3)

\(u\prime\) :

Fluctuation of velocity in the x-direction

\(k\) :

Turbulent kinetic energy (m2/s2)

B :

Bluff body

\(fr\) :

Forward

\(re\) :

Reverse

To :

Toroidal

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Acknowledgements

The high-performance computing facility at IIT Madras is acknowledged for its computational support. Mr. Keito Matsumoto of the National Institute of Technology, Matsue College, Japan, is acknowledged for his valuable analytical assessment suggestions.

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Authors and Affiliations

  1. Ocean Engineering Department, Wave Energy and Fluids Engineering Lab, IIT Madras, Chennai, 600036, India

    Doddamani Hithaish, Tapas K. Das & Abdus Samad

  2. Department of Mechanical Engineering, National Institute of Technology, Matsue College, Shimane, 6908518, Japan

    Manabu Takao

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Correspondence to Abdus Samad.

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Hithaish, D., Das, T.K., Takao, M. et al. Design Optimization of a Fluidic Diode for a Wave Energy Converter via Artificial Intelligence-Based Technique. Arab J Sci Eng 48, 11407–11423 (2023). https://doi.org/10.1007/s13369-022-07467-0

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