Development and optimization of small-scale radial inflow turbine for waste heat recovery with organic rankine cycle

This thesis is an investigation of different strategies for efficient development and optimization of radial-inflow turbines (RIT) for small-scale ORC systems. A novel methodology based on mean-line modelling, multi-level optimization and experimental study was proposed and validated for a small-scale compressed air RIT. Extending the proposed approach to organic fluids necessitated the use of real-gas equations. Deficiencies of constant turbine efficiency assumption that was commonly used in the literature were highlighted. A novel approach for integrated modelling of organic RIT with ORC coupled with genetic algorithm optimization technique was developed to alleviate the errors during fluid selection and cycle analysis and also optimize the ORC performance. A novel dual-stage transonic RIT was developed to further improve the ORC performance. The efficiency of such turbine was improved further using 3-D CFD optimization technique. Such optimization proved to be very efficient as it substantially improved the turbine efficiency of both stages by about 10%. CFD results for the optimized dual-stage turbine at design point showed the turbine efficiency of 87.12% and ORC thermal efficiency of 13.19%. Such results were considerably higher than the reported values in the literature and highlighted the effectiveness of the combined mean-line and CFD optimizations developed in thesis

Mathematical modeling and performance study of Fischer-Tropsch synthesis of liquid fuel over cobalt-silica

AbstractA numerical one-dimensional pseudo-homogeneous mathematical model of a fixed bed reactor for Fischer-Tropsch (FT) synthesis was developed over a simulated nitrogen-rich syngas (33% hydrogen, 17% carbon monoxide and 50% nitrogen (volume basis)), on a cobalt-silica catalyst. An algorithm was developed and the MATLAB codes were written in order to predict the product selectivity (H2O, CO2 and hydrocarbons i.e. CH4, C2, C3, C4 and C5+) and syngas conversion (CO and H2). In order to predict the kinetic parameters, the global search optimization subroutine (from MATLAB Global Optimization) was used. The model was fitted with experimental data at five different operating conditions with respect to conversion and selectivity. Discrimination between the model and the experiments was determined by the mean absolute relative residuals percentage (MARR %) and the value was 13.29%. The Effects of operating conditions such as reaction temperature, total pressure, flow rate and H2/CO molar ratio were investigated on the catalytic performance of the cobalt-silica for synthesis of liquid fuel. The model was studied in the range of 200-260°C, 1-25bar, reduced gas flow rate (per unit mass of catalyst) of 2.4-3.6 NL gcat-1 h-1 and H2/CO = 1.75-2.75 (mole basis)

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