Optimization of small-scale axial turbine for distributed compressed air energy storage system

Small scale distributed compressed air energy storage (D-CAES) has been recognized as promising technology which can play major role in enhancing the use of renewable energy. Due to the transient behavior of the compressed air during the discharging phase, there are significant variations in air pressure, temperature and mass flow rate resulting in low turbine efficiency. This research aims to improve the expansion process of the small scale D-CAES system through optimization of a small scale axial turbine. A small scale axial air turbine has been developed using 1D Meanline approach and CFD simulation using ANSYS CFX 16.2. For improving the turbine efficiency, different optimization approaches like single and multi-operating point optimization have been performed. The turbine blade profiles for both stator and rotor have been optimized for minimum losses and maximum power output based on 3D CFD modelling and Multi Objective Genetic Algorithm (MOGA) optimization for single and multi-operating points. Using multi-operating point optimization, the maximum turbine efficiency of 82.767 % was achieved at the design point and this approach improved the overall efficiency of D-CAES system by 8.07% for a range of inlet mass flow rate indicating the potential of this optimization approach in turbine design development

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

Investigate a hybrid open-Rankine cycle small-scale axial nitrogen expander by a camber line control point parameterization optimization technique

During the last few decades, low-grade heat sources such as solar energy and wind energy have enhanced the efficiency of advanced renewable technologies such as the combined Rankine cycle, with a significant reduction in CO2 emissions. To address the problem of the intermittent nature of such renewable sources, energy storage technologies have been used to balance the power demand and smooth out energy production. In this study, a detailed thermodynamic analysis of a hybrid open Rankine cycle was conducted by using engineering equation solver (EES) software in order to investigate the performance of such a cycle using a liquid nitrogen energy storage system. In this cycle configuration, the conventional closed loop Rankine cycle (topping cycle) is combined with a direct open Rankine cycle (bottoming cycle) for a more efficient system which can solve the problem of discontinuous renewable sources. In the direct open-Rankine cycle, the small expander is the main component that can improve the cycle’s performance and as a result, this small expander needs to be optimized for maximum efficiency to achieve high system performance levels. In this work a small-scale nitrogen axial expander has been optimized and modeled to be incorporated into a hybrid open-Rankine cycle, using a one-dimensional preliminary design and CFD three-dimensional ANSYS design exploration and a novel camber line control point parametrization technique, which is outlined in detail. The design optimization approach has been proven as an effective tool that could enhance turbine efficiency from 72% to 76.3% and output power from 2076 W to 2597.6 W. The optimized turbine using the control points’ approach could also improve the cycle’s thermal efficiency by 3.38% compared with the baseline design. Such results underline the potential of full simulation optimization by using a blade camber line control point’s parametrization technique for a small-scale expander with low flow rate and rotational speed

Preliminary Mean-line Design and Optimization of a Radial Turbo-Expander for Waste Heat Recovery Using Organic Rankine Cycle

AbstractThis study presents an optimized modelling approach for ORC based on radial turbo-expander, where the constant expander efficiency is replaced by dynamic efficiency and is unique for each set of cycle operating conditions and working fluid properties. The model was used to identify the key variables that have significant effects on the turbine overall size. These parameters are then included in the optimization process using genetic algorithm to minimize the turbine overall size for six organic fluids. Results showed that, dynamic efficiency approach predicted considerable differences in the turbine efficiencies of various working fluids at different operating conditions with the maximum difference of 7.3% predicted between the turbine efficiencies of n-pentane and R245fa. Also, the optimization results predicted that minimum turbine overall size was achieved by R236fa with the value of 0.0576m. Such results highlight the potential of the optimized modeling technique to further improve the performance estimation of ORC and minimize the size

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 simulation of film cooling over flat plate

The effect of film cooling over flat plate is investigated using the commercial CD code; Fluent 6.3. The computational domain includes the coolant supply tube as well as the main mixing region. A tube L/D of 4 and injection angles of (30o , 60o , and 90o ) were employed for blowing ratio of (0.33, 0.5, and 1.67), and a density ratio of 1.14. Adiabatic film cooling effectiveness distributions were also determined for inline and staggered arrangements. The main observation from this study that the 30o hole gave larger effectiveness values than 60o and 90o at the blowing ratio of 0.33 with the same length-to-diameter ratio. The maximum effectiveness was achieved with a blowing ratio of 0.5. The results show that the increase of blowing ratio negatively affects film cooling, such that for the blowing ratio of 1.67 the injected coolant tends to lift off from the wall due to the increase of the wall normal momentum. The comparisons for numerical results with experimental data are presented

Development of micro-scale axial and radial turbines for low-temperature heat source driven organic Rankine cycle

Most studies on the organic Rankine cycle (ORC) focused on parametric studies and selection working fluids to maximize the performance of organic Rankine cycle but without attention for turbine design features which are crucial to achieving them. The rotational speed, expansion ratio, mass flow rate and turbine size have markedly effect on turbine performance. For this purpose organic Rankine cycle modeling, mean-line design and three-dimensional computational fluid dynamics analysis were integrated for both micro axial and radial-inflow turbines with five organic fluids (R141b, R1234yf, R245fa, n-butane and n-pentane) for realistic low-temperature heat source <100 °C like solar and geothermal energy. Three-dimensional simulation is performed using ANSYS R17-CFX where three-dimensional Reynolds-averaged Navier-Stokes equations are solved with k-omega shear stress transport turbulence model. Both configurations of turbines are designed at wide range of mass flow rate (0.1-0.5) kg/s for each working fluid. The results showed that n-pentane has the highest performance at all design conditions where the maximum total-to-total efficiency and power output of radial-inflow turbine are 83.85% and 8.893 kW respectively. The performance of the axial turbine was 83.48% total-to-total efficiency and 8.507 kW power output. The maximum overall size of axial turbine was 64.685 mm compared with 70.97 mm for radial-inflow turbine. R245fa has the lowest overall size for all cases. The organic Rankine cycle thermal efficiency was about 10.60% with radial-inflow turbine and 10.14% with axial turbine. Such results are better than other studies in the literature and highlight the potential of the integrated approach for accurate prediction of the organic Rankine cycle performance based on micro-scale axial and radial-inflow turbines