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Associate Professor
Director of Recruitment (Academic)
Birmingham City University, Department of Engineering,
School of Architecture, Built Environment, Computing, and Engineering
Millennium Point, Curzon Street, Birmingham, B4 7XG, United Kingdom
Email: olusegun.ilori@bcu.ac.uk; Tel: +44 (0) 778 023 8995
I lead research characterised by integrated experimental-computational frameworks for clean energy and thermal-fluid systems, combining high-fidelity CFD/FEA, physics-informed AI/ML and experimental validation. This enables robust prediction and optimisation of complex systems under real-world operating conditions, supporting translational solutions for decarbonisation, energy efficiency, and digital transformation priorities.
My research expertise/interests span:
Clean Energy Systems (including renewable, heat recovery, storage, utilisation): My research in clean energy systems focuses on advancing fundamental understanding and system-level optimisation of the thermodynamic processes underpinning energy conversion, recovery, storage, and utilisation. It investigates coupled thermal-fluid phenomena in advanced energy systems, where controlled interactions among pressure, temperature, and flow fields enable efficient cyclic energy-transfer mechanisms analogous to Stirling-type processes. A key aspect of the work is the development of integrated experimental and computational frameworks, grounded in first-principles modelling, to analyse and optimise complex energy systems. This includes applications in waste-heat recovery and upgrading, renewable-energy integration (solar and geothermal), and advanced thermal energy storage. The research further extends to hybrid and multi-vector energy systems, where heat, power, and cooling are co-optimised to improve efficiency, flexibility, and resilience. Through this, it contributes to the development of scalable, low-carbon technologies supporting industrial decarbonisation and the transition to net-zero energy systems. (see examples HERE)
Digital HVACR Systems, with an emphasis on performance optimisation: This research area focuses on developing advanced modelling, optimisation, and control frameworks to enhance the performance, efficiency, and reliability of next-generation HVACR (Heating, Ventilating, Air-Conditioning, and Refrigeration) technologies, particularly heat pump systems. It examines the integration of first-principles thermal-fluid models with data-driven and AI-enabled techniques to enable intelligent, adaptive operation under dynamic environmental and load conditions. A central component is the development of hybrid digital twins for HVACR and heat pump systems, where physics-based models are coupled with real-time data from IoT-enabled sensors to support predictive control, fault detection, and continuous performance optimisation. This is particularly critical for improving seasonal performance, defrost strategies, and operation under low ambient temperatures. The work extends to system-level optimisation, including integration with renewable energy sources, thermal storage, and smart grids, contributing to scalable, low-carbon heating solutions aligned with decarbonisation and net-zero targets. (see examples HERE or HERE)
Heat Exchangers and Emerging Thermoacoustic Technologies: This research area focuses on advancing fundamental understanding and optimising heat transfer and fluid flow processes in conventional and emerging thermal systems. It investigates thermo-fluid mechanisms governing the performance of heat exchangers and thermoacoustic devices, where oscillatory, compressible flows and strong coupling among pressure, temperature, and velocity fields influence energy-transfer characteristics. A key aspect of the work is the development of integrated experimental and computational approaches to characterise transport phenomena in complex geometries, including porous media and regenerators. Advanced diagnostic techniques and high-fidelity simulations are employed to resolve transient and three-dimensional flow structures, thereby improving predictions of heat transfer and system behaviour. The research further explores the design and optimisation of high-performance heat exchangers and the application of thermoacoustic principles for energy conversion, heat pumping, and cooling. These technologies offer the potential for efficient, low-maintenance, and environmentally sustainable solutions. (see examples HERE or HERE).
Application of AI/ML, IoT, and Data-driven Approaches to Engineering: My research in this area focuses on developing intelligent, adaptive systems for monitoring, prediction, and optimisation of complex engineering processes. I investigate how data-centric methods can be effectively integrated with first-principles engineering models to address challenges associated with nonlinearity, uncertainty, and real-time system dynamics. A central aspect of my work is developing hybrid modelling frameworks that combine physics-based representations with machine learning techniques to enhance predictive accuracy, robustness, and interpretability. Leveraging IoT-enabled sensing and high-resolution data streams, I design scalable solutions for condition monitoring, fault detection, and performance optimisation across energy, thermal-fluid, and industrial systems. The research further explores edge-AI deployment and digital twin architectures, enabling real-time decision-making and adaptive control in distributed engineering environments. Through this, I contribute to advancing data-driven engineering methodologies that support efficiency, resilience, and sustainability in next-generation engineered systems. (see examples HERE or HERE).
Multiphase Flows and Processes: This research integrates advanced experimental and modelling approaches to develop a fundamental and predictive understanding of complex multiphase flow systems across a wide range of industrial applications. These include gas–solid, solid–liquid, and gas–liquid flows encountered in pneumatic and hydraulic conveying, centrifugal separation, fluidised beds, hopper and silo discharge, wastewater treatment, mining operations, and liquid–liquid systems such as oil–water separation. The work addresses key scientific challenges related to particle-scale dynamics, interphase interactions, microstructural evolution, and the rheology of complex suspensions and polydispersed mixtures. Particular emphasis is placed on resolving the coupling between flow physics and process conditions in transient and heterogeneous environments. By systematically quantifying the influence of operating parameters on system behaviour, this research provides mechanistic insight and predictive capability essential for process optimisation. The outcomes directly support the design, scale-up, and intensification of next-generation industrial processes, thereby improving efficiency, reliability, and sustainability. (see examples HERE)
Advanced Engineering Modelling and Simulation: My research focuses on developing and applying high-fidelity computational frameworks, including CFD and FEA, to investigate complex multiphysics systems. I address challenges associated with strongly coupled thermal, fluid, and structural phenomena under transient, nonlinear, and industrially relevant operating conditions. A core aspect of this work is the integration of physics-based modelling with data-driven and reduced-order approaches to enhance predictive capability, computational efficiency, and scalability. This includes rigorous model validation against experimental data and the incorporation of uncertainty quantification to ensure robustness. The research extends to industrial process systems such as mixing tanks, reactors, separation units, and multiphase flow equipment, where detailed simulation supports design optimisation, scale-up, and performance enhancement. Through this, I advance digital engineering methodologies that enable accelerated innovation, improved efficiency, and informed decision-making in complex engineering and manufacturing environments. (see examples HERE or HERE)
Sustainable Engineering, Environmental Impact Assessment, and LCA of Industrial Processes: My research in sustainable engineering focuses on the quantitative assessment and optimisation of industrial and construction processes to minimise environmental impact and support the transition to net-zero systems. I develop integrated frameworks that combine life cycle assessment (LCA), process modelling, and data-driven techniques to evaluate environmental performance across the full value chain, from raw material extraction to end-of-life. A key aspect of this work is linking process-level engineering models with system-level sustainability metrics, enabling the identification of hotspots in energy use, emissions, and resource consumption. The research incorporates multi-criteria assessment, uncertainty analysis, and scenario modelling to evaluate decarbonisation strategies, including electrification, material substitution, waste heat recovery, and circular economy approaches. Applications span industrial and construction sectors, including manufacturing, energy systems, and the built environment, informing design, optimisation, and policy-relevant decision-making to deliver low-impact, resource-efficient, and sustainable engineering solutions. (see examples HERE, HERE or HERE).
Energy Efficiency in Buildings: In general terms, building energy systems operate across a range of spatial and temporal scales, involving complex interactions between heat transfer, fluid flow, and occupant-driven demand. Energy flows occur through building envelopes, HVAC systems, and internal loads, often under highly variable environmental and operating conditions. Applications span residential, commercial, and public buildings, where maintaining thermal comfort while minimising energy use is a key challenge. Controlling thermal and airflow conditions within buildings is inherently complex due to dynamic boundary conditions, system nonlinearities, and the influence of occupant behaviour. My research interests focus on the application of advanced thermal-fluid modelling and intelligent control strategies to precisely regulate temperature distributions, heat fluxes, and ventilation performance with high spatial and temporal resolution. This includes integrating low-carbon technologies such as heat pumps and thermal energy storage systems. Additionally, airflow management and mixing processes in indoor environments, particularly under low-velocity (low-Reynolds-number) conditions, are critical for both energy efficiency and indoor air quality. These can be enhanced through optimised system design, adaptive control, and data-driven approaches, enabling improved performance, comfort, and sustainability in next-generation buildings. (see examples HERE or HERE)
Project, Leadership and Management: In addition to engineering-focused interests, I conduct research in project management, leadership, and organisational performance, drawing on my academic background and extensive industry experience across the construction, manufacturing, consultancy, and energy sectors. My work adopts a multidisciplinary, systems-oriented approach, integrating engineering principles with management theory, behavioural science, and data-driven methods. Large-scale project systems operate across multiple organisational and temporal scales, involving complex interactions between leadership practices, decision-making, workforce behaviour, and operational environments. Performance outcomes such as productivity, safety, and risk emerge from these interactions under dynamic and uncertain conditions, making effective control and optimisation inherently challenging. My research focuses on developing causal, statistical, and machine learning frameworks to understand and predict how leadership and management interventions translate into measurable outcomes. This includes identifying key performance drivers and designing evidence-based, data-informed strategies to improve decision-making, safety, and operational efficiency in complex environments (see examples HERE or HERE.