Apply for a PhD at BCU - February 2027, May 2027, September 2027 Entry!
Senior Lecturer in Mechanical Engineering/
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
The projects listed below are examples of typical PhD projects available to prospective applicants. The topics generally fall into Mechanical, Energy Systems, Thermal-Fluid & Heat Transfer, Artificial Intelligence & Digital Systems, and Environmental & Sustainable Engineering, and are inherently multidisciplinary. Bespoke project ideas can also be developed in collaboration with individual candidates. For further discussion, please contact me directly at: olusegun.ilori@bcu.ac.uk
1. Physics-Informed AI/ML Models for EV Systems under Extreme Environmental Conditions
The project aims to investigate the coupled thermal, electrochemical, and fluid dynamic processes governing electric vehicle (EV) systems under harsh and transient environmental conditions. EV components, including batteries and thermal management systems, exhibit significant performance variability under extreme temperatures, humidity, and dynamic loading, leading to efficiency losses and accelerated degradation. Current monitoring approaches are limited by insufficient representation of multi-physics interactions and poor generalisation of purely data-driven models beyond observed conditions. This research addresses these challenges by developing physics-informed artificial intelligence (PI-AI) algorithms that integrate first-principles models with data-driven techniques. The study will utilise high-resolution sensor data from instrumented EV subsystems under controlled and real-world conditions to characterise system behaviour and identify key performance indicators. Hybrid modelling frameworks, such as physics-informed neural networks and digital twins, will be developed to enable accurate prediction, fault detection, and adaptive control (Industrial relevance: electric mobility, battery systems, smart diagnostics, and sustainable transport)
2. Assessing Retrofitted Hydrogen-Compliant Boiler for Domestic Heating Decarbonisation
The project aims to investigate the performance, safety, and decarbonisation potential of retrofitted hydrogen-compliant boilers for domestic heating applications. As the UK transitions towards net-zero, hydrogen offers a promising low-carbon alternative to natural gas; however, uncertainties remain regarding combustion characteristics, efficiency, emissions, and system integration in existing housing stock. Current approaches are constrained by limited experimental evidence and insufficient modelling of hydrogen combustion dynamics under realistic operating conditions. This research addresses these gaps through a combined experimental and modelling framework to evaluate thermal performance, flame stability, heat transfer, and pollutant formation in retrofitted systems. The study will utilise instrumented test rigs to generate high-resolution operational data, with numerical simulations to capture combustion and heat-transfer processes. The outcomes will inform optimisation strategies, safety guidelines, and deployment pathways (Industrial relevance: low-carbon heating, hydrogen energy systems, building decarbonisation, and energy policy).
3. Advanced Modelling-Based Optimisation of Constructed Wetlands
The project aims to develop advanced modelling and optimisation frameworks for constructed wetlands as sustainable, nature-based solutions for wastewater treatment. Constructed wetlands involve complex interactions among hydrological, biological, and physicochemical processes that are highly nonlinear and spatially heterogeneous. These interactions govern pollutant removal efficiency but remain insufficiently understood and difficult to optimise under varying environmental and operational conditions. Current design and management approaches are largely empirical, limiting the predictability of performance and scalability. This research addresses these limitations by integrating process-based modelling with data-driven techniques to capture coupled flow, transport, and biogeochemical processes within wetland systems. The study will utilise field and experimental data to calibrate and validate high-fidelity models, enabling robust prediction of system performance under diverse loading and climatic scenarios. Optimisation techniques will be applied to improve design configurations and operational strategies. (Industrial relevance: wastewater treatment, water resource management, nature-based solutions, environmental sustainability, and climate resilience).
4. Advanced Numerical Investigation of Thermal-Fluid Enhancement Using Nanofluids/Optimised Geometry
The project aims to conduct a fundamental numerical investigation of thermal-fluid enhancement mechanisms using nanofluids and optimised geometrical configurations. Conventional heat transfer systems are often limited by low thermal efficiency, necessitating advanced strategies to enhance heat transfer performance without incurring excessive pressure losses. Nanofluids, owing to their improved thermal conductivity, offer significant potential; however, their behaviour remains insufficiently understood due to complex interactions between particle dynamics, flow structure, and thermal transport processes. This challenge is further compounded by geometric effects, in which flow separation, boundary layer development, and mixing play critical roles. The proposed research will employ high-fidelity computational fluid dynamics (CFD) to model turbulent flow and heat-transfer characteristics across varying nanoparticle concentrations and geometrical configurations. Parametric optimisation will be conducted to identify configurations that maximise thermal performance while minimising hydraulic penalties. The outcomes will inform the design of next-generation heat exchangers with improved efficiency and reliability. (Industrial relevance: energy systems, HVAC, process engineering, and emerging thermal management technologies).
5. Investigation of Emission Reduction Pathways in Energy Intensive Industry
The project aims to investigate emission-reduction pathways in energy-intensive industries through an integrated analysis of process-level energy use, thermal systems, and carbon-mitigation technologies. Sectors such as cement, steel, and chemicals are characterised by high-temperature processes and complex energy flows, making decarbonisation particularly challenging. Current approaches are constrained by fragmented modelling of process interactions and limited integration of emerging low-carbon technologies. This research addresses these gaps by developing a comprehensive modelling framework that combines first-principles thermodynamic analysis with data-driven optimisation techniques. The study will utilise industrial datasets and representative process models to evaluate the impact of interventions such as waste heat recovery, electrification, hydrogen integration, and carbon capture. Advanced simulation and optimisation methods will be employed to identify feasible and cost-effective decarbonisation pathways under operational constraints. The outcomes will support robust decision-making and accelerate the transition towards low-carbon industrial systems. (Industrial relevance: heavy industry, net-zero transition, process optimisation, and energy systems decarbonisation)
6. Experimental and Numerical Investigation of Novel Materials for Thermal Energy Storage
The project aims to investigate the thermal performance of novel materials for thermal energy storage (TES) through integrated experimental and numerical approaches. Efficient TES is critical for enhancing the reliability and flexibility of renewable energy systems and improving energy utilisation across industrial and building applications. This research will focus on advanced materials, including phase change materials (PCMs) and composite systems, to evaluate their heat storage capacity, thermal conductivity, and cycling stability. Experimental studies will be conducted to characterise melting and solidification behaviour, heat transfer rates, and long-term performance under repeated thermal cycles. Complementary numerical modelling will be developed to simulate transient heat transfer and optimise material configurations, including encapsulation and enhancement techniques. The integration of experimental data with validated models will improve prediction accuracy and support system-level design. The outcomes will support the development of high-performance TES solutions for low-carbon energy systems. (Applications: renewable energy integration, heat pumps, and industrial waste heat recovery.)
7. Thermodynamic and System-Level Optimisation of Solar-Driven Cooling Technologies
The project aims to investigate the thermodynamic performance and system-level optimisation of solar-driven cooling technologies for medical cold chains and agricultural storage. Reliable cooling is critical for vaccine preservation and post-harvest storage, particularly in off-grid and resource-constrained regions where conventional energy supply is limited. The research will develop integrated modelling frameworks combining first-principles thermodynamic analysis with system-level optimisation to evaluate solar-powered cooling configurations, including absorption, adsorption, and hybrid cooling cycles. The study will assess the impact of key parameters, including solar irradiance variability, thermal storage integration, and load dynamics, on system efficiency, reliability, and cost-effectiveness. Simulation and optimisation techniques will be employed to identify robust system designs capable of maintaining stable temperature conditions under fluctuating environmental and demand profiles. The outcomes will support the development of sustainable, energy-efficient cooling solutions for critical applications. (Industrial relevance: healthcare logistics, agri-food systems, off-grid energy, and sustainable cooling technologies).
Project, Leadership and Management Research
In addition to engineering-focused PhD projects, I also supervise 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 supervision adopts a multidisciplinary, systems-oriented approach, integrating engineering principles with management theory, behavioural science, and data-driven methods. Examples of potential projects are outlined below (Note: specific research directions can be defined and tailored to applicants' interests):
1. Translating Safety Leadership into On-Site Behaviour Using Causal and Machine Learning Models
This project investigates how safety leadership practices influence workforce behaviour in large-scale construction environments. It will develop a causal–machine learning framework that combines structural causal models with advanced predictive methods (e.g., causal forests, double machine learning) to quantify how leadership actions propagate through the safety climate and worker cognition, resulting in observable behaviours such as compliance and risk-taking. Using real-world project data (e.g., site observations, incident reports, safety audits), the study will identify key causal drivers and intervention points. Scenario simulations will evaluate how changes in leadership strategies improve behavioural outcomes under varying operational conditions. The research will deliver evidence-based safety leadership interventions and contribute to data-informed safety management in construction and other high-risk industries.
2. Data-Driven Optimisation of Project Performance Through Leadership and Decision-Making Dynamics
This project examines how leadership behaviours and managerial decision-making influence project performance outcomes such as productivity, cost efficiency, and schedule adherence. The research will develop a systems-based modelling framework that integrates statistical analysis, causal inference, and machine learning to analyse interactions among leadership style, communication patterns, team coordination, and project complexity. Using multi-source project datasets, the study will quantify direct and indirect effects of leadership on performance, identify nonlinear relationships, and detect performance bottlenecks. Optimisation and scenario analysis will be used to design robust management strategies that improve project delivery under uncertainty. The outcomes will support the development of predictive and prescriptive tools for project management, with applications across construction, manufacturing, and energy sectors.