Multiple surrogate based optimization of a bidirectional impulse turbine for wave energy conversion

Badhurshah, Rameez; Samad, Abdus

doi:10.1016/j.renene.2014.09.001

Cited by 60 publications

(24 citation statements)

References 19 publications

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“…For a higher pressure drop ( P), the performance of the turbine is poor because of the high separation on the SS, and the operating range of the turbine is reduced (Badhurshah & Samad, 2015). Hence, the minimization of was chosen as an objective function (f 1 ).…”

Section: Numerical Model and Problem Descriptionmentioning

confidence: 99%

“…In this case, the turbine dissipates energy. The minimization of pressure drop will increases the operating range which will increases the power extracting capability (Badhurshah & Samad, 2015). Hence, the objectives f 1 and f 2 are justified.…”

Section: Numerical Model and Problem Descriptionmentioning

confidence: 99%

“…Several works are done for optimization to modify parameters to improve the CONTACT Abdus Samad samad@iitm.ac.in performance. The parameters include blade thickness by camber line modification (Gomes, Henriques, Gato, & Falcão, 2012), number of RBs and GVs (Badhurshah & Samad, 2015), tip clearance (Thakker & Dhanasekaran, 2004), RB solidity (Cui & Liu, 2014), blade setting angle (Liu, Cui, Kim, & Shi, 2016), guide vane shape (Thakker, Dhanasekaran, & Ryan, 2005), hub-to-tip ratio (Thakker, Khaleeq, Takao, & Setoguchi, 2003), etc. for the impulse turbine.…”

Section: Introductionmentioning

confidence: 99%

“…Gomes et al (2012) optimized the RB thickness distribution by modifying the camber line and they generated the stacking optimized sections along the span. Badhurshah and Samad (2015) optimized the number of RBs and GVs of the turbine for single objective and found that the turbine with 38 rotor blades and 24 guide vanes as the optimal design. The RB with 0.25% tip clearance performs similar to the turbine without any tip clearance (Thakker & Dhanasekaran, 2004).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Shape optimization of a bidirectional impulse turbine via surrogate models

Ezhilsabareesh

Rhee

Samad

2017

Engineering Applications of Computational Fluid Mechanics

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In this work, a bidirectional impulse turbine used in a wave energy device is simulated using a CFD technique and a shape optimization performed with multiple surrogates-assisted multi-objective evolutionary algorithm. The surrogates have been generated using the validated CFD technique. The objectives for the optimization are to maximize the shaft power and to minimize the pressure drop across the turbine. The hub and the tip thickness of the rotor blade profiles are modified and considered as the design variables. The Latin hypercube sampling technique generated design points are evaluated in the CFD solver, the objective responses are used to construct multiple surrogates, and a Pareto optimal front is generated through a multi-objective optimization approach. The turbine efficiency, which is the function of pressure drop and shaft power, is relatively increased by 10.4%.

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Section: Numerical Model and Problem Descriptionmentioning

confidence: 99%

Section: Numerical Model and Problem Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Shape optimization of a bidirectional impulse turbine via surrogate models

Ezhilsabareesh

Rhee

Samad

2017

Engineering Applications of Computational Fluid Mechanics

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“…Surrogate-based optimization (SBO) methods have proven themselves to be effective in solving complex optimization problems and are increasingly being used in different fields [35][36][37][38][39]. SBO methods may directly solve the optimization problem (e.g., the EGO [40] or EMO [41] algorithms) or may train a surrogate model to be used in lieu of expensive simulators with traditional optimization algorithms.…”

Section: Surrogate Modelsmentioning

confidence: 99%

Multi-objective Geometry Optimization of a Gas Cyclone Using Triple-Fidelity Co-Kriging Surrogate Models

Singh

Couckuyt

Elsayed

et al. 2017

J Optim Theory Appl

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Cyclone separators are widely used in a variety of industrial applications. A low-mass loading gas cyclone is characterized by two performance parameters, namely the Euler and Stokes numbers. These parameters are highly sensitive to the geometrical design parameters defining the cyclone. Optimizing the cyclone geometry therefore is a complex problem. Testing a large number of cyclone geometries is impractical due to time constraints. Experimental data and even computational fluid dynamics simulations are time-consuming to perform, with a single simulation or experiment taking several weeks. Simpler analytical models are therefore often used to expedite the design process. However, this comes at the cost of model accuracy. Existing techniques used for cyclone shape optimization in literature do not take multiple fidelities into account. This work combines cheap-to-evaluate well-known mathematical models of cyclones, available data from computational fluid dynamics simulations and experimental data to build a triple-fidelity recursive co-Kriging model. This model can be used as a surrogate with a multi-objective optimization algorithm to identify a Pareto set of a finite number of solutions. The proposed scheme is applied to optimize the cyclone geometry, parametrized by seven design variables.Keywords Cyclone separator · Surrogate-based optimization (SBO) · Multi-fidelity data · Sparse Kriging · Recursive co-Kriging (2017) 175:172-193

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Optimization of Fluid Machinery for Renewable Energy Systems

2019

Design Optimization of Fluid Machinery

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Multiple surrogate based optimization of a bidirectional impulse turbine for wave energy conversion

Cited by 60 publications

References 19 publications

Shape optimization of a bidirectional impulse turbine via surrogate models

Shape optimization of a bidirectional impulse turbine via surrogate models

Multi-objective Geometry Optimization of a Gas Cyclone Using Triple-Fidelity Co-Kriging Surrogate Models

Optimization of Fluid Machinery for Renewable Energy Systems

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