Minimization of Loss in Small Scale Axial Air Turbine Using CFD Modelling and Evolutionary Algorithm Optimization

Small scale axial air driven turbine (less than 10 kW) is the crucial component in distributed power generation cycles and in compressed air energy storage systems driven by renewable energies. Efficient small axial turbine design requires precise loss estimation and geometry optimization of turbine blade profile for maximum performance. Loss predictions are vital for improving turbine efficiency. Published loss prediction correlations were developed based on large scale turbines; therefore, this work aims to develop a new approach for losses prediction in a small scale axial air turbine using computational fluid dynamics (CFD) simulations. For loss minimization, aerodynamics of turbine blade shape was optimized based on fully automated CFD simulation coupled with Multi-objective Genetic Algorithm (MOGA) technique. Compare to other conventional loss models, results showed that the Kacker & Okapuu model predicted the closest values to the CFD simulation results thus it can be used in the preliminary design phase of small axial turbine which can be further optimized through CFD modeling. The combined CFD with MOGA optimization for minimum loss showed that the turbine efficiency can be increased by 12.48% compare to the baseline design

Prediction of Losses in Small Scale Axial Air Turbine Based on CFD Modelling

AbstractEfficient small scale axial air turbines play a major role in determining the overall conversion efficiency in certain energy cycles using renewable energy sources. Loss predictions are vital for the development and optimization of such small scale turbines. Since all published loss prediction schemes were developed for large scale turbines, therefore there is a need for an effective approach to predict such losses for the small scale axial turbines. This work aims to develop a new approach to predict the losses in a small scale axial air turbine using both conventional loss models and computational fluid dynamics (CFD) simulations. Results showed that the Kacker & Okapuu model gave the closest values to the CFD simulation results thus it can be used to produce the initial turbine design that can be further optimised through CFD simulations

Numerical analysis of small scale axial and radial turbines for solar powered Brayton cycle application

In the current work two types of turbines, axial and radial turbine, with their three configurations, Single Stage Axial, Dual Stage Axial and Single Stage Radial turbines, for solar Brayton cycle applications have been parametrically investigated with the aim of figuring out their performance in terms of efficiency and power output. The mean line design for each turbine was effectively completed in order to figure out the initial guess for the dimensions, the power output and the efficiency. Consequently, the Computational Fluid Dimension CFD analysis was employed for the sake of visualising the 3-Dimentions behaviour of the fluid inside the turbine as well as determining the main output like the power output and the efficiency at different boundary conditions. These boundary conditions were selected to be compatible with a small scale solar powered Brayton cycle. An evaluation for some types of losses such as tip clearance and trailing edge losses as well as the total loss coefficient of the rotor of each configuration, in terms of pressure losses, has been established as well. The current paper deals with Small Scale Turbines SST ranged from 5 to 50 kW as a power output. The outcomes showed that the Dual stage axial turbine performances better at the off design conditions. By contrast, the single stage radial turbine achieved higher power output during the same operating conditions. The results of the CFD analysis have been successfully validated against the experimental work done by the researchers for small scale (axial) compressed air turbine in the lab

Development and optimization of efficient small-scale turbines for organic rankine cycle powered by low-temperature heat sources

In this this research, ORC systems using axial, radial-inflow and radial-outflow turbines are investigated for various low-power generation (1-15 kW) applications like domestic, rural and remote off-grid communities. This work presents a new integrated mathematical model for developing efficient axial, radial-inflow and radial-outflow (centrifugal) turbines with low mass flow rate (0.1-0.5 kg/s) using a range of organic working fluids (R14lb, R1234yf, R245fa, R365mfc, isobutene, n-butane and n-pentane). The new mathematical approach integrates mean-line design and 3D CFD analysis with ORC modelling. RANS equations for three-dimensional steady state and viscous flow were solved with k-ro SST turbulence model to predict 3D viscous turbulent flow and turbine performance. With the aim of enhancing the ORC performance by increasing its pressure ratio, novel small-scale two-stage axial and radial outflow turbines are modelled and compared with single-stage axial, radial-inflow and radial-outflow turbines. New performance maps in terms of isentropic efficiency and power output for each turbine configuration are developed in terms of expansion ratio, mass flow rate, rotational speed and turbine size. Novel optimization technique using multi-objective genetic algorithm was applied to optimize small-scale single stage axial, radial-inflow and radial-outflow turbines with this flow rate. Experimental study of the ORC radial-inflow turbine was carried out