https://bura.brunel.ac.uk/handle/2438/33748 Thu, 08 Oct 2026 16:27:13 GMT 2026-10-08T16:27:13Z https://bura.brunel.ac.uk/handle/2438/33914 Title: Additive manufacturing for microscale heat exchangers operating in flow boiling Authors: Bekir, Enes; Askounis, Alexandros; Karayiannis, Tassos G Abstract: Micro scale heatsinks provide a promising thermal solution for high-power electronic components. However, the capabilities of current top-to-bottom subtractive manufacturing techniques are constraining the production of complex geometric features required for optimised micro-scale heatsink designs. Thus, additive manufactured heatsinks are introduced as a bottom-up alternative for producing complex surface geometries. The effect of these two different manufacturing techniques, particularly surface characteristics and their effect on heatsink performance, should be evaluated. Sun, 06 Sep 2026 00:00:00 GMT https://bura.brunel.ac.uk/handle/2438/33914 2026-09-06T00:00:00Z https://bura.brunel.ac.uk/handle/2438/33910 Title: EFFECTS OF CEMENT CONDUCTIVITY ON THE THERMAL PERFORMANCE OF AGS SYSTEMS Authors: Kalkisim, AT; Yavuzdogan, A; Ozturk, Z; Sewell, P; Karayiannis, TG Abstract: Geothermal energy applications have gained significant importance within the renewable energy sector. This growth is caused primarily by supply constraints in fossil-fuel-based energy sources. Recent advancements focus on developing various techniques to extract subsurface heat. Advanced designs and innovative materials are constantly emerging to support these novel applications. This highlights the necessity to carefully analyse all system components. Closed-loop geothermal systems also referred to as Advanced Geothermal Systems (AGS), can be configured in various design methods to extract this energy. This study evaluates the influence of casing cement thermal conductivity on the thermal performance of closed-loop systems. The study compares three cement options using fixed design parameters as low-conductivity (0.8 W/mK) and high-conductivity (2.8 W/mK), and a hybrid model. Also, the surrounding rock formation is modelled with a homogeneous thermal gradient along the wellbore, analysed both for low (35 K/km) and high (75 K/km ) thermal gradient scenarios. The results demonstrate that varying cement thermal conductivity impacts the overall thermal output by 8.3 to 12.6%. The hybrid configuration provides a further 1 to 1.5% improvement compared to uniform high conductivity cement, demonstrating the benefit of selecting cement conductivity based on the local direction of heat transfer. Sun, 06 Sep 2026 00:00:00 GMT https://bura.brunel.ac.uk/handle/2438/33910 2026-09-06T00:00:00Z https://bura.brunel.ac.uk/handle/2438/33804 Title: Graph-Centric Deep Q-Learning for Interference-Aware Resource Allocation in Rsma-Enabled 5G Slicing Authors: Ahmed, Aya Kh; Al-Aboody, Nadia; Al-Raweshidy, Hamed S Abstract: The emergence of 5G and 6G advanced ecosystems demands highly adaptive resource management to orchestrate the specialised requirements of eMBB, URLLC, and mMTC network slices. In dense multi-cell environments, capturing complex spatial interdependencies and mitigating dynamic interference is paramount for maintaining Quality of Service (QoS). This paper introduces a robust GNN-DQN framework designed for Rate Splitting Multiple Access (RSMA) based networks. By representing the network topology as a graph, the framework leverages Graph Neural Networks (GNNs) to extract highdimensional spatial features and model inter-cell interference patterns. These insights enable a Deep Q-Network (DQN) agent to perform intelligent resource partitioning and dynamic power splitting of the RSMA common stream. Experimental results demonstrate that the proposed GNN-DQN framework achieves a connectivity success ratio exceeding 90% across all slices, representing an average improvement of over 60% compared to non-graph-based reinforcement learning and supervised baselines. Notably, the framework demonstrates exceptional spectral efficiency, maintaining near-total connectivity while utilising less than 10% of the normalised system bandwidth, a 4× reduction in resource overhead compared to traditional methods. Furthermore, the GNN-driven architecture ensures stable convergence during training, yielding a 1.6× higher system reward score. Our findings validate GNN-DQN as a high-performance, scalable, and resource-efficient paradigm for intelligent orchestration in 5G and 6G networks. Wed, 22 Jul 2026 00:00:00 GMT https://bura.brunel.ac.uk/handle/2438/33804 2026-07-22T00:00:00Z https://bura.brunel.ac.uk/handle/2438/33759 Title: Online Parameter-Reconfigured Model Predictive Control for Integrated Trajectory Tracking of Distributed Four-Wheel Steering Vehicles Authors: Zhang, Hao; Li, Gang; Zhang, Jingxue; Zhang, Dong Abstract: To overcome the limitations of conventional model predictive control (MPC) for trajectory tracking of distributed-drive four-wheel-steering (4WS) vehicles, particularly its fixed weighting matrices and prediction and control horizons, this study investigates the integrated trajectory tracking and stability control of an automated distributed-drive electric vehicle equipped with four independently controlled in-wheel motors and a four-wheel-steering system. The main novelty of this study lies in the simultaneous online adaptation of the MPC weighting matrices and reconfiguration of the prediction and control horizons, together with the coordinated integration of four-wheel steering and direct yaw moment control (DYC) within a unified trajectory tracking framework. Unlike conventional adaptive MPC methods that primarily adjust weighting parameters, the proposed adaptive prediction and control horizon adjustment (APCHA) strategy jointly updates the prediction and control horizons according to the integrated tracking error, error variation rate, and control input variation rate. Meanwhile, a fuzzy adaptive weighting mechanism adjusts the MPC weighting matrices online. At the lower control layer, a torque allocation method considering both the tire load ratio and vertical tire loads is employed to realize the required direct yaw moment. Finally, CarSim–Simulink co-simulation is conducted to verify the effectiveness of the proposed control strategy. Simulation results demonstrate that, at a vehicle speed of 60 km/h and a road adhesion coefficient of μ=0.5, the proposed Improved MPC-4WS controller reduces the maximum lateral tracking error by 34.9% compared with the conventional MPC-4WS controller, thereby demonstrating superior trajectory tracking performance. Furthermore, the ablation study verifies the effectiveness of the proposed hierarchical architecture by quantifying the contributions of the DYC module and the optimized torque allocation strategy. Description: Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. Mon, 10 Aug 2026 00:00:00 GMT https://bura.brunel.ac.uk/handle/2438/33759 2026-08-10T00:00:00Z