Automated design of gas turbines for the chemical industry

The electricity and heat are the major drives in the chemical and pharmaceutical industries, and currently gas turbines are widely used throughout these industries as they allow for high power output with a satisfactory overall efficiency at relatively reasonable costs. Gas turbines include two major sections: cold section (Intake and Compressor) and hot section (Combustors, Turbine and Exhaust). Usually, they convert the energy from burning fuel and operate in a continuous thermodynamic cycle. Gas turbines accelerate air to create thrust in aero engines, or they drive generators to make electricity, or turn pumps and ship propellers (industrial and marine applications). Usually, they are devices with significantly high specific energy consumption and hence can cause for high emission rates creating environmental issues. Therefore, a slight improvement of their thermal/mechanical efficiency can have significant impact on the global energy saving while cutting down harmful emissions. Also, new tight environmental and health/safety regulations worldwide have already made a huge pressure on turbine manufactures to improve their design(s) to minimize the carbon footprint. In terms of the efficiency of gas turbines, fluid flow and heat transfer through the whole engine unit are critical. This study aims to investigate an automated design of gas turbines for the chemical industry. The heat transfer and fluid flow through an engine will be modelled theoretically and then analyse it for investigating possible modifications/improvements in increasing its efficiency. There is a possibility of modelling each device separately and then come up with a reasonably accurate theoretical model for the whole engine. Then, a comprehensive analysis should be presented on the effects of flow behaviour and heat transfer particularly on the thermal efficiency of the engine. Mutiobjective optimization should be carried out to consider trader-off between different cost functions such as cost, energy efficiency, flame pulsations and emission. A robust design should be identified via sensitivity analysis. Multiobjective optimization will be realized with the use of the Directed Search Domain algorithm. Also, a set of Pareto optimal solutions will be identified. Then, a decision-making algorithm will be implemented for their ranking. The results of the project can be used in the systems of optimal design.

Students with a First class/2.1 degree in Engineering, Physics, or Mathematics subjects are encouraged to apply. A prior knowledge on gas turbine engineering and an MSc in a related filed would also be desirable (but not essential). Experience in computer programming would also be preferable.