Abstract
In the present paper, an optimized design procedure capable of providing the geometry of a high efficiency compact hydraulic propeller turbine for low head is proposed and developed. The turbine preliminary design is based on fundamental turbomachinery mean-line equations and on the employment of statistical correlations, which relate the main geometrical parameters to the fundamental design parameters. The first obtained geometry represents the starting point of an automated aerodynamic single point optimization procedure based on a genetic algorithm generating and updating a wide database of turbine geometries. The approach employs a three-dimensional (3D) Reynolds averaged Navier–Stokes (RANS) solver for the construction of the corresponding database of performance. A meta-model, such as an artificial neural network (ANN), is used to speed up the design optimization process. The procedure has been applied on the practical case of a novel simplified hydraulic propeller turbine prototype for very low heads. The adopted design optimization procedure is able to modify the turbine blade and vane geometries in order to achieve automatically the targeted net head and the maximum for the total to total internal efficiency once diameter, mass flow rate, and rotational speed are assigned.
Highlights
Hydraulic energy from gravitational fields remains the most developed renewable energy source worldwide, due to an incomparable ability to efficiently adapt its power production to the grid time varying power request, the good availability and harmlessness of the conversion fluid, water, the non-polluting conversion process, and the highest specific energy among any other removable energy sources
Due to the remaining scarce availability of high heads, currently, very low heads or even kinetic energy in rivers or tidal applications [3] have started to be considered for hydraulic energy conversion
The main technical and economic problem linked to the low specific energy associated with the low head can be solved by following two different approaches: strongly limiting the technological complexity and the costs related to the machine, such as in [4], or delivering large flow rates to obtain a reasonable power
Summary
Hydraulic energy from gravitational fields remains the most developed renewable energy source worldwide, due to an incomparable ability to efficiently adapt its power production to the grid time varying power request, the good availability and harmlessness of the conversion fluid, water, the non-polluting conversion process, and the highest specific energy among any other removable energy sources. The main hydraulic turbines manufacturers, such as Voith [5] and Andritz Hydro [6], have recently developed new small power turbines, compact, simplified, and characterized by a modular structure for the exploitation of very low heads. As a demonstration of the growing interest in low net heads, several works have recently been developed concerning the analysis of small hydraulic turbines considering microturbines [7], small turbines [8], and innovative components [9]. These realizations concern high specific speed propellers made of three or four not adjustable blades. Once the appropriate boundary conditions are assigned, the optimization process, based on genetic algorithm and artificial neural network, minimizes a penalty function with objectives being the achievement of the targeted net head and the maximization of the internal efficiency compatible with the constraints of the simplified geometry
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