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Wave energy harvesting systems mostly have low power production capability because of unoptimized design of the system components. A bidirectional flow impulse-turbine used in such a system has efficiency less than 40%, and it is required to design the turbine for a higher efficiency. Present work finds an optimal design and shows design-variable sensitivity to the turbine efficiency. The problem is solved using numerical analysis technique. The flow through the turbine was analyzed by solving the Reynolds-averaged Navier-Stokes equations (RANSE). The design variables; namely number of rotor blades and number of guide vane, guide vane angle and guide vane profile were modified to maximize the turbine efficiency. Using the Latin hypercube sampling technique, sample points were selected from a design space defined by lower and upper limits of the variables. Then, several surrogates were constructed using the RANSE calculated results, and the turbine performance was optimized. The results show that guide vane angle is the most sensitive parameter, while the guide vane profile has negligible effect on efficiency. A hybrid genetic algorithm searched the optimal design point. The relative mean efficiency enhancement over a wide range of flow coefficient was approximately 24%, while it was 28% at maximum efficiency point. [ABSTRACT FROM AUTHOR]

Real-life engineering problems have multiple objectives, which mostly are conflicting in nature, and these problems can be solved through multi-objective optimization (MOO) procedure. In the present problem, a high-fidelity computational fluid dynamics model coupled with multiple-surrogate assisted genetic algorithm based MOO has been solved for performance enhancement of a wave energy extracting axial impulse turbine. Response surface approximation, Kriging, neural network and a weighted-average surrogate (WAS) were used to generate population for the MOO procedure and Pareto optimal fronts (PoF) of the objectives were produced. The design variables were number of rotor blade and guide vanes and the objectives were minimization of pressure drop and maximization of shaft power of the turbine. It was found that a cross-validation error analysis is inevitable to find the degree of fitness of a surrogate. The WAS-produced PoF shows better performance as compared to that of the other surrogates. The surrogates based on minimum cross-validation errors produce slightly lesser performance than the WAS. The efficiency, which is a function of both the objectives, was relatively increased by ∼11% through the current investigation. [ABSTRACT FROM AUTHOR]

OWC (Oscillating water column) wave power plants are widely used in ocean wave energy conversion. It is universally accepted that for a good efficiency of the plant, the turbine and the chamber should match each other according to the wave climate site. In this work an alternative methodology is presented for coupling the chamber and the turbine efficiently, carrying out a whole dimensional analysis of the OWC plant. Basically, the wave data and the chamber geometry are the input data. The behaviour of the chamber is numerically simulated. From the numerical results, the optimum damping could be identified for every wave condition. Once the optimum chamber damping is found, the objective is to determine which turbine matches this damping. Numerical results, based on the quasi-steady assumption, were used to simulate turbine performance under periodic conditions. The calculation of the diameter is based on the damping caused by the turbine on the OWC, whereas the rotational speed is fixed to maximize the turbine mean efficiency. Furthermore, this methodology is worked out to compare the size and the rotational speed of different kinds of turbines. Finally, the influence of sea state changes on the parameters of the optimum turbine is also studied. [ABSTRACT FROM AUTHOR]

Oscillating water column based wave energy extracting system has a low efficiency due to the poor performance of its principal power extracting component, the bidirectional turbine. In the present work, flow over a bidirectional impulse turbine was simulated using CFD technique and optimized using multiple surrogates approach. The surrogates being problem dependent may produce unreliable results, if a wrong surrogate is selected. Hence, multiple surrogates such as response surface approximation, radial basis function, Kriging and weighted average surrogates were incorporated in this problem. Same design points were used to generate multiple optima via multiple surrogates to enhance the robustness of the optimization process. Numbers of guide vanes and rotor blades were chosen as the design variables, and the objective was to maximize the blade efficiency. Reynolds-averaged Navier–Stokes equations were solved for analyzing the flow physics. The computed results were used to train the surrogates and find the optimal points via hybrid genetic algorithm. The surrogates were further applied to find the optimal flow parameters by changing flow velocity and turbine speed. The relative efficiency enhancement through our present approach was about 16%. Detailed methodologies, analysis of the results and surrogate applicability have been presented in this paper. [ABSTRACT FROM AUTHOR]

Purpose – This paper is concerned with improving the flow pattern in the nozzle-rotor axial gap in impulse turbines using a genetic algorithm (GA) and 3D numerical analysis. The paper aims to discuss these issues. Design/methodology/approach – The appropriate model was used to estimate the turbine performance introduced in the beginning of the work. Then, the nozzle design parameters that are effective in the axial gap flow pattern are optimized using a non-linear optimization code. This code works based on the GA theory. Since the GA results are not conclusive, the selected cases were evaluated using 3D numerical analysis. For a detailed comparison of the flow pattern in initial and improved cases, a transient analysis was done. Experimental tests were performed in order to validate the work. For this purpose, the characteristic curves of the turbines were studied and compared with each other. Findings – Improving the nozzle-rotor axial gap flow pattern leading to increase in the total-to-total efficiency of the turbine by more than two points. Research limitations/implications – Partially injected flow forced to use the full model computational analysis. Practical implications – Weight reduction in a feeding system. Originality/value – New loss modeling method presented for partial admission condition. [ABSTRACT FROM AUTHOR]