https://bura.brunel.ac.uk/handle/2438/33749 2026-10-08T16:25:13Z https://bura.brunel.ac.uk/handle/2438/33871 Title: Computational investigation of installation noise reduction in urban air mobility vehicles using structured porous surface treatments Authors: Naicker, Clinton Jared Abstract: Urban Air Mobility Vehicles (UAMVs) offer a possible solution to the ever-increasing ground-based congestion by utilising the free airspace. However, since these vehicles are expected to operate close to densely populated areas, the noise generated during take-off, landing and low-altitude flight is a major concern, both with respect to certification and public perception. Due to the use of electric propulsion, the contribution of engine noise is reduced, and therefore aerodynamic noise from the rotors, airframe and their interactions becomes increasingly important. This thesis investigates the use of turbulence-resolving Computational Fluid Dynamics (CFD) and computational aeroacoustics to predict the flow and noise associated with UAMVs, alongside the impact of porous coatings for installation noise reduction. The simulations were carried out using OpenFOAM. The computational setup was first assessed using high-performance computing scaling tests, followed by 2-D and 3-D NACA 0012 validation cases. The 2-D Reynolds-Averaged Navier–Stokes (RANS) validation showed good agreement with NASA reference data, with maximum deviations of 2% in lift coefficient and 3.5% in drag coefficient when compared to computational reference codes. The 3-D Large-Eddy Simulation (LES) validation captured the main flow features of the blunt trailing-edge case, including separation, reattachment, transition and vortex shedding. The validated setup was then applied to low-Mach-number aeroacoustic simulations of a tripped NACA 0012 airfoil. The results showed that tripping was required to reproduce the experimental boundary-layer development, and that LES and Zonal LES were able to predict the main flow and acoustic behaviour. In contrast, the Improved Delayed Detached Eddy Simulation (IDDES) produced limited resolved turbulence in the near-wall region and significantly underpredicted the acoustic levels. The acoustic predictions also showed that Ffowcs Williams–Hawkings (FW-H) surface and closing-disc placement had a strong influence on the predicted acoustic spectra. The best placement and closing disc location were identified providing future guidance. Structured porous coatings were then investigated using both fully resolved geometry and reduced-order approaches. Fully resolved LES of a structured porous coated cylinder showed that the coating modified the near-cylinder flow through internal separation, circumferential pore flow, shear-layer diffusion and boundary-layer development on the inner cylinder. A Darcy–Forchheimer approximation was then developed to represent the porous coating at lower computational cost, and was shown to reproduce several of the main wake trends while also highlighting the sensitivity of the results to the selected model coefficients. Finally, the porous coating was assessed for modelled vortex impingement on both a cylinder and an airfoil leading edge. For the cylinder case, the coating diffused the incoming vortex, reduced near-wake turbulence intensity and reduced the Overall Sound Pressure Level by up to 5.8 dB. For the airfoil leading-edge case, the coating weakened the downstream vortex imprint, reduced surface-pressure fluctuations near the leading edge and reduced the Overall Sound Pressure Level at all observer angles, with a maximum reduction of 8.1 dB. Description: This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London 2026-01-01T00:00:00Z https://bura.brunel.ac.uk/handle/2438/33782 Title: Adaptive model of uncertainty in healthcare technology management Authors: Mahgoub, Tasneem Mahmoud Salih Abstract: Healthcare Technology Management (HTM) operates under significant uncertainty arising from technical, managerial, and macro-environmental factors. Existing risk assessment models—including recent data-driven and machine learning–based approaches—focus predominantly on internal maintenance variables and fail to account for broader sources of uncertainty or the dynamic influence of equipment utilization. Addressing this gap, this thesis develops an Adaptive Model of Uncertainty in HTM by integrating expert judgment, fuzzy multi-criteria weighting, and nonlinear utilization–risk modelling. An initial pool of 53 HTM risk factors was systematically screened through expert relevance scoring, after which 41 significant factors were prioritized using a Fuzzy Analytic Hierarchy Process (Fuzzy AHP), yielding stable global weights that reflect region-specific HTM priorities in Saudi Arabia and the Gulf. Building on this weighted risk structure, the thesis formulates the Synergistic Quadratic–Resonant Risk Model (SQRRM) as a nonlinear system-identification law that couples utilization intensity with fuzzy-weighted readiness gaps to adaptively modulate risk magnitude. The proposed model was identified using a five-year hospital imaging dataset and evaluated on the aggregated 2024 cross-section, where it demonstrated superior explanatory performance (R² = 0.943) relative to six competing baseline formulations. To assess the potential cross-domain applicability of the proposed modelling framework, SQRRM was embedded within the BiLSTM and Adaptive Dynamic Graph Neural Network (ADGNN) architectures and evaluated on the NASA C-MAPSS FD001 turbofan degradation dataset. Under the adopted experimental protocol, the SQRRM-enhanced ADGNN achieved the strongest predictive performance among the selected comparator models (RMSE = 12.80, R² = 0.905). These findings provide additional empirical evidence supporting the robustness of the proposed nonlinear modelling framework and its potential applicability beyond the healthcare domain. The contributions of this thesis include: (i) the first region-specific, expert-validated hierarchy of HTM risks; (ii) an integrated risk-quantification framework that unifies readiness gaps, utilization, and operational burden; (iii) the derivation of the SQRRM model as a nonlinear, resonance-modulated risk function; and (iv) successful cross-domain validation through deep learning architectures. Recommendations emphasize the need for improved Computerized Maintenance Management System (CMMS) data governance, evaluation of current mitigation strategies, and expansion of contextual readiness indicators to enhance predictive-risk analytics in HTM. Overall, this thesis proposes and initially validates an integrated Healthcare Technology Management (HTM) risk-modelling framework within the healthcare context of Saudi Arabia and the Gulf region. It establishes a scientifically grounded and operationally implementable foundation for risk-aware, utilization-driven decision making in healthcare technology management, while the cross-domain evaluation provides initial evidence of the framework's potential applicability to other high-reliability engineering systems, including aviation and energy. Description: This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University. 2026-01-01T00:00:00Z