Machine learning-based surrogate model for accelerating simulation-driven optimisation of hydropower Kaplan turbine

Wastewater treatment plants (WWTPs) are a significant energy consumer, yet there are several opportunities for implementing on-site power generation systems. Within the treatment process, the high flow rate of effluent is produced and discharged to a nearby water body by gravity. Thus, hydroturbines can be utilized to generate power in such an application due to a difference in elevation and high flow rate. This paper presents a case study of introducing a hydroturbine in a WWTP in Wisconsin and evaluating the power output in addition to determining the energy savings. The WWTP considered in this study has an effluent flow rate of 190 MGD (million gallons per day) and elevation difference of 3 m (10 ft) between the final stage of treatment and the discharge point. Based on the parameters above; hubless rim-drive Kaplan type hydroturbine (RDT) is the optimal choice to be used in such an application. The RDT is designed and optimized by using in-house code. A computational fluid dynamics (CFD) software is applied to evaluate the performance of the proposed model, and the system is simulated through homer software to validate the results generated by the CFD. The expected savings is estimated to be 1564 MWh/yr.

Utilization of Hydroturbines in Wastewater Treatment Plants

This study investigates performance-based design optimization for a Kaplan hydro turbine at a maximum water head of 2.6 m (8.5 ft), a micro-sized horizontal Kaplan turbine with 7.6 cm (3.0 in) diameter that has fixed blades featured to attain the optimum performance for such type and size of hydro turbines. The optimization process includes solving design problems and enhance design development by applying a multi-disciplinary design optimization (MDO) technique. Varying the geometrical parameters of the turbine, i.e., dimensions, number of blades, blade wrap angles, and different rotational speeds (500–3000 rpm), are the relevant proposed disciplines of this study. Two multi-simulation matrices were solved by using the high-performance computing (HPC) cluster of the University of Wisconsin-Milwaukee. The first matrix includes different number of the blades (3, 4, 5, 6, and 7 blades) over six different rotational speeds (500, 1000, 1500, 2000, 2500, and 3000 rpm), while the second matrix includes 121 possible combinations of blade wrap angles starting at 60–60 deg (hub-shroud) angle to 110–110 deg angle with 5 deg increment alternated at both sides, the hub and the shroud.

Investigation of Horizontal Micro Kaplan Hydro Turbine Performance Using Multi-Disciplinary Design Optimization

Bruno Lequesne

Papers

Numerical Optimization of Micro Kaplan Hydro Turbine System

Numerical Investigations on Intake Tube Design of Micro Kaplan Hydro-Turbine System

Optimization of Kaplan Hydro-Turbine at Very Low Head With Rim-Driven Generator