Our research is primarily concerned with fluid mechanics, transport phenomena, and chemical reactions in energy and propulsion systems. Energy/propulsion systems that convert chemical energy to heat, electrical or mechanical energy typically involve nonlinear interactions of multiple physical and chemical processes with a wide range of time and length scales.
The objectives of our research are to advance the fundamental understanding of the multiphysics and multiscale phenomena in reacting flows through theory and computation, to develop predictive, reduced-order models for engineering applications, and finally to develop computational tools for the multiscale and multiphysics simulations.
Computation and Modeling of Chemically Reacting Flows in Energy and Propulsion Systems
Our research on combustion is concerned primarily with computational aspects of turbulent combustion. Combustion will remain as the predominant energy supply in the near future, but faces significant challenges due to energy security, global climate change, and health issues, which result in increasingly stringent requirements for efficiency and pollutant emissions. In order to meet such requirements, next-generation combustion devices are expected to utilise low/zero-carbon fuels like Ammonia and Hydrogen. In most cases, practical combustion devices are operated on turbulent flow conditions and there are complex non-linear interactions of fluid flow, molecular transport, chemical reactions, multiphase processes, and heat transfer, among others, in these devices. The current focuses are on advancing a fundamental understanding of such non-linear interactions, through low-order and high-fidelity computation and theory, and developing predictive models for the advanced combustion technologies.
Computational Multi-Physics Modelling and Optimisation of Liquid Cooling System in Electric Motors
With the evolving trend of electrification in transportation, electric machines with higher power density and efficiency are demanded and, thus, more stringent thermal management requirements are needed for future electric vehicles. For accurate thermal management, the simulation and optimisation of the cooling system must be carried out considering all phenomena involved in design of electric motors. Effective numerical models are needed for each involved phenomenon, along with an efficient and computationally cost-effective optimisation strategy for industrial applications. First, three-dimensional modelling and simulation will be used to study heat transfer and obtain detailed temperature field of all the motor components under different operating conditions. Then, optimisation methodology will be employed to optimise cooling system parameters with the aim of minimising losses in the system. During the optimisation, a variety of different design configurations are investigated in order to find the optimal cooling situation. In order to develop a computationally cost-effective numerical modelling, data-driven machine learning models can play an important role. A surrogate machine learning model will be developed to replace the three-dimensional simulations in the optimisation and perform optimisation using this model instead of computationally expensive three-dimensional model.
Optimal fuel blends for ammonia fuelled thermal propulsion systems
The aim of this project is to develop a computationally cost-effective numerical tool for Co-optimisation of fuel blend and combustion system in a systematic way, and to examine how the conflicting requirements can be met by adding gaseous fuels (e.g., hydrogen) to ammonia so that engine can be operated stably and reliably with improved thermal efficiency and minimal NOx emissions. This computational study requires development of a reduced reaction mechanism for fuel blends with the goal of further reduction in the number of reactions under engine relevant conditions. While CFD simulations in combination with reaction mechanisms can capture complex phenomena with high accuracy, they have high computational cost and, therefore, are not efficient for the optimisation studies. Thus, a reliable and comprehensive 0D phenomenological model with reduced chemistry will be developed for combustion modelling of the engine fuelled with ammonia-based fuel blends. A genetic algorithm (GA) optimisation model coupled to the 0D model will be developed to simultaneously optimise fuel composition and engine input parameters.