Research Areas
Our research addresses energy, environment and sustainable engineering challenges.
Our Research Areas
Fluid mechanics and aerodynamics
Fundamental and applied fluid dynamics for turbines, vehicles, ducts, ventilation systems, process equipment and sustainable engineering technologies.
Heat transfer and thermodynamics
Thermal systems, heat exchangers, refrigeration, cooling, waste heat recovery, energy storage and thermodynamic efficiency.
Renewable and low-carbon energy
Wind energy, solar thermal systems, clean energy technologies, energy efficiency and reduction of climatic impacts.
Multiphase and complex flows
Multiphase, multi-component and complex fluid flows for industrial, environmental, process and biomedical applications.
High-performance computing and CFD
Advanced modelling and high-performance computing to solve fundamental and engineering problems across thermofluids and energy systems.
Digital twins and smart infrastructure
Digital twinning, augmented/virtual reality and data-driven modelling for sustainable infrastructure, smart buildings and engineering optimisation.
Biofluid Dynamics
We investigate respiratory airflow, pulmonary and airway fluid dynamics, blood flow, aerosol and particle transport, drug delivery and fluid interactions within biological systems.
Structural Health Monitoring
We focus on inspection and monitoring of safety-critical engineering structures, with particular emphasis on green energy technologies, including hydrogen and wind structures and systems.
Fluid Mechanics and Aerodynamics
Our research in fluid mechanics and aerodynamics advances the understanding, prediction and control of complex fluid-flow phenomena across energy, transport, industrial and environmental systems. We combine fundamental fluid mechanics with state-of-the-art computational, experimental and data-driven approaches to resolve challenging turbulent, unsteady and multiphase flows and translate fundamental insight into higher-performing, more efficient and more sustainable engineering technologies.
Research spans the aerodynamic analysis, design and optimisation of wind turbines, rotating machinery, vehicles, ducting, ventilation systems and complex industrial flows. We investigate flow separation, turbulence, vortex dynamics, wake interaction, aerodynamic loading, pressure losses, drag, flow-induced instability and transient flow behaviour. Advanced Computational Fluid Dynamics (CFD) methods, including RANS, URANS, LES and high-fidelity numerical approaches, are integrated with experimental measurements, optimisation and data-driven modelling to create robust, validated and predictive engineering tools.
A major focus is the development of next-generation fluid-engineering solutions that deliver measurable improvements in performance, energy efficiency and reliability. Through flow control, design optimisation, uncertainty quantification, artificial intelligence and physics-informed modelling, our research supports cleaner renewable-energy technologies, low-energy ventilation, advanced transportation and more sustainable industrial processes.
Heat Transfer and Thermodynamics
Our research in heat transfer and thermodynamics addresses one of the central engineering challenges of the net-zero transition: how thermal energy can be generated, transferred, stored, recovered and utilised with maximum efficiency and minimum environmental impact. We combine fundamental thermodynamics and heat-transfer science with advanced modelling, experimentation and optimisation to develop high-performance thermal and energy systems.
Research encompasses conduction, convection, radiation, phase-change processes, thermal management, heat exchangers, refrigeration and cooling technologies, thermal-energy storage and waste-heat recovery. Particular emphasis is placed on understanding coupled thermal-fluid phenomena and translating this knowledge into improved component and system performance.
Our work bridges fundamental thermal science and system-level energy optimisation, enabling the development of technologies capable of operating efficiently and reliably under demanding conditions. Advanced numerical simulation, experimental validation, optimisation and AI-enabled modelling are used to accelerate thermal-system design, reduce energy consumption and support the integration of low-carbon technologies.
By developing smarter approaches to thermal management and energy utilisation, our research contributes directly to industrial decarbonisation, energy resilience and the transition towards sustainable and net-zero engineering systems.
Renewable and Low-Carbon Energy
Our research in renewable and low-carbon energy develops advanced engineering solutions to accelerate the transition towards cleaner, more efficient, resilient and economically viable energy systems. We address both the fundamental performance of renewable-energy technologies and the wider challenge of integrating them effectively within buildings, industries and energy infrastructure.
A major research strength is wind-energy engineering, encompassing aerodynamic performance, wake dynamics, flow control and optimisation of horizontal- and vertical-axis wind turbines. Our wider research includes solar-thermal technologies, energy conversion, energy storage and energy-efficient engineering systems. Experimental investigation, CFD, mathematical modelling, optimisation and artificial intelligence are combined to improve efficiency, reliability, operational performance and cost-effectiveness.
Beyond individual technologies, we investigate how renewable-energy systems interact with the wider built and industrial environment. This includes energy efficiency, operational optimisation, lifecycle considerations and integration with low-carbon infrastructure.
Through multidisciplinary, simulation-led and data-driven research, CEESE develops practical pathways from renewable-energy innovation to real-world deployment, supporting cleaner energy generation, reduced carbon emissions and progress towards net-zero energy systems.
Multiphase and Complex Flows
Our research in multiphase and complex flows addresses some of the most challenging transport phenomena encountered in engineering, environmental and biomedical systems. These flows involve the dynamic interaction of liquids, gases, particles, droplets, bubbles and other phases, frequently under turbulent, transient and strongly coupled operating conditions.
We investigate multiphase and multi-component systems relevant to industrial processing, energy technologies, environmental engineering, transport phenomena and biomedical applications. Research includes particle and droplet transport, slurry flows, deposition processes, fluid-particle interaction, flow through complex geometries and the behaviour of coupled multiphase systems across a wide range of operating conditions.
Advanced CFD, high-fidelity numerical simulation, experimental investigation and data-driven modelling are integrated to uncover the physical mechanisms governing these complex systems and develop predictive tools for engineering design and optimisation. Our work supports improved process efficiency, reduced energy consumption, greater system reliability and enhanced control of transport, mixing and deposition phenomena.
The research extends strongly into healthcare and biomedical engineering, where multiphase behaviour governs aerosol transport, respiratory flows, particle deposition and interactions between biological fluids and medical technologies. By combining advanced fluid mechanics with computational modelling, experimental validation and artificial intelligence, we address complex flow problems that cannot be resolved effectively through conventional engineering approaches alone.
High-Performance Computing and CFD
Our research in High-Performance Computing (HPC) and Computational Fluid Dynamics (CFD) provides the advanced computational capability required to solve some of the most complex problems in modern engineering, energy, environment and healthcare. We develop and apply high-fidelity numerical methods alongside AI-driven modelling approaches to investigate, predict and optimise physical phenomena that are difficult, costly or impossible to characterise through experiments alone.
Our computational expertise encompasses RANS, URANS, Large Eddy Simulation (LES), multiphase-flow modelling, transient analysis, moving and deforming meshes, and large-scale three-dimensional simulations. These capabilities are complemented by machine learning, deep learning, artificial neural networks, surrogate modelling, reduced-order modelling and physics-informed artificial intelligence. We develop sophisticated computational and AI-enabled workflows for complex geometries and demanding physical systems, with particular emphasis on accuracy, scalability, computational efficiency, predictive capability and data-driven discovery.
HPC enables us to resolve increasingly complex spatial and temporal phenomena, providing a deeper understanding of turbulence, heat and mass transfer, multiphase interactions, aerodynamic behaviour, biofluid dynamics and energy conversion. By integrating high-fidelity CFD with AI and machine learning, we develop hybrid physics–data-driven frameworks capable of learning from large simulation and experimental datasets, identifying complex nonlinear relationships, accelerating computational prediction and enabling rapid exploration of high-dimensional design spaces.
Our research also advances physics-informed machine learning and AI-accelerated simulation, combining governing physical principles with data-driven algorithms to improve model robustness, generalisation and computational efficiency. These approaches enable the development of intelligent surrogate models, real-time prediction tools, automated optimisation strategies and digital twins that can reproduce complex system behaviour at a fraction of the computational cost of conventional high-fidelity simulations.
Our objective extends beyond conventional simulation. We develop AI-enabled predictive engineering, digital twin and decision-support platforms capable of virtual prototyping, real-time prediction, rapid design-space exploration, multi-objective optimisation, uncertainty quantification and data-informed decision-making. These approaches reduce computational and experimental development time, minimise the need for costly physical prototyping and provide industry, healthcare and research partners with powerful intelligent tools for accelerating innovation and supporting next-generation engineering design.
Digital Twins and Smart Infrastructure
Our research in digital twins and smart infrastructure is focused on creating the next generation of intelligent, connected and adaptive engineering systems. We integrate physics-based modelling, sensing, real-time data, artificial intelligence, advanced analytics and immersive visualisation to develop digital representations capable of monitoring, predicting and optimising the behaviour of physical assets and environments throughout their lifecycle.
Research encompasses digital twins for buildings, energy systems, infrastructure and engineering assets, with applications including condition monitoring, predictive maintenance, energy management, operational optimisation and performance forecasting. Real-time and historical sensor data are integrated with CFD, physics-based models, machine learning and AI to create dynamic and predictive representations of complex physical systems.
We also investigate augmented and virtual reality technologies to transform the way complex engineering information is visualised and interpreted. These approaches support engineering design, maintenance, training, remote monitoring and stakeholder engagement by creating more intuitive interfaces between physical assets, digital models and decision-makers.
A central objective is the development of smart, sustainable and resilient infrastructure capable of learning from operational data and adapting to changing conditions. By connecting physical systems with intelligent digital environments, our research supports lower energy consumption, reduced operational costs, improved reliability, predictive asset management and more informed decision-making.
The long-term vision is to move from passive monitoring towards autonomous and continuously optimised engineering systems, where digital twins become active tools for prediction, intervention and lifecycle performance improvement.
Biofluid Dynamics
Our research in biofluid dynamics applies advanced fluid mechanics, computational modelling and experimental science to some of the most important challenges in healthcare, biomedical engineering and human physiology. We investigate the behaviour of biological fluids and their interactions with tissues, anatomical structures and medical devices, with the aim of transforming fundamental flow understanding into safer, more effective and more personalised healthcare technologies.
Research encompasses respiratory airflow, pulmonary and airway fluid dynamics, cardiovascular flows, blood flow, aerosol and particle transport, drug delivery and fluid interactions within biological systems. We investigate highly complex, transient and patient-dependent flows in anatomically realistic geometries, examining how anatomical variation, physiological conditions, disease-related changes and medical-device configuration influence flow behaviour, pressure distribution and transport phenomena.
Advanced Computational Fluid Dynamics (CFD), including transient simulation, turbulence modelling, multiphase modelling and high-fidelity numerical approaches, is integrated with medical imaging, experimental measurements, machine learning and data-driven methods. This enables the development of patient-specific computational models and digital representations of biological systems, providing detailed insight into respiratory airflow, particle deposition, inhaled drug delivery, haemodynamics and fluid-device interaction.
A major focus is the application of engineering science to clinical and medical-device challenges, including respiratory health, airway management, inhalation therapies and biomedical technologies. By combining biofluid mechanics with artificial intelligence and predictive modelling, we aim to support the development and optimisation of medical devices, improve understanding of patient-specific physiological behaviour and enable more informed healthcare decision-making.
Our ambition is to position biofluid dynamics as a strong bridge between engineering innovation and healthcare impact, translating advanced computational and experimental research into technologies and predictive tools that contribute to improved diagnosis, treatment, device performance and patient outcomes.
Structural Health Monitoring
The Smart Structural Health Monitoring (SHM) & Nondestructive Evaluation (NDE) Research Team focuses on the inspection, monitoring, and assessment of safety-critical engineering structures, with particular emphasis on green energy technologies, including hydrogen and wind structures and systems.
The team’s research combines Ultrasonic Guided Waves (UGW), Acoustic Emission (AE), and Electromagnetic Induction (EMI)-based inspection techniques with the development of smart sensing technologies, cyber-physical systems, and digital twins for advanced structural condition monitoring, damage detection, and predictive maintenance.
The Smart SHM & NDE Research Facility within the CEESE provides dedicated facilities for research and technology development in smart inspection and monitoring of engineering and energy infrastructure.