https://bura.brunel.ac.uk/handle/2438/13039 Thu, 13 Aug 2026 12:27:55 GMT 2026-08-13T12:27:55Z https://bura.brunel.ac.uk/handle/2438/33288 Title: Characterisation of Particle Number and Size from a DI SI Engine Operating on Hydrogen versus Gasoline: Operating Sensitivities and Filtration Effects Authors: Harrington, A; Zaman, Z; Nickolaus, C; Zhao, H; Wang, X; Hall, J Abstract: Hydrogen Internal Combustion Engines (H₂ICEs) offer the potential for near-zero carbon emissions. However, while nitrogen oxide (NOₓ) emissions have been extensively studied, particulate emissions, specifically particle number (PN), which are widely attributed to in the literature to lubricant oil pyrolysis and exacerbated by hydrogen’s short quenching distance, remain less well understood. This study investigates exhaust-gas particle emission characteristics from a spark-ignition, single-cylinder research engine based on MAHLE Powertrain’s downsizing engine combustion system. The work was carried out at Brunel University of London and compares gasoline and hydrogen direct-injection strategies (central versus side injection) across a wide range of operating conditions, including variations in engine speed, load, air–fuel ratio (λ), rail pressure, and spark timing. While previous studies have investigated hydrogen particle formation mechanisms under isolated operating conditions, the combined influence of combustion strategy, mechanical engine condition, and exhaust filtration has not been systematically explored within a single experimental framework. This study characterises PN emissions and particle size distributions (PSDs) from a direct-injection spark-ignition research engine operating on hydrogen and gasoline under steady-state conditions. The effects of injection strategy (central versus side), air–fuel ratio (λ), rail pressure, and spark timing are examined, alongside a controlled comparison between a freshly overhauled engine and a mechanically worn configuration to assess sensitivity to oil-control condition. Particle measurements were performed using a fast-response differential mobility spectrometer equipped with a catalytic stripper to isolate solid particles, with results interpreted using SPN₁₀-equivalent metrics for comparative analysis. In addition, a series-production gasoline particulate filter (GPF) was evaluated under hydrogen operation to assess its ability to attenuate the ultrafine particles characteristic of H₂ICE exhaust. The results show that hydrogen combustion produces substantially lower engine-out PN than gasoline under comparable operating points, with particle size distributions strongly biased toward sub-23 nm diameters. PN emissions under hydrogen operation exhibit sensitivity to injection targeting, mixture strength, rail pressure, and engine mechanical condition, consistent with literature linking lubricant oil ingress and near-wall combustion behaviour to hydrogen PN formation. The GPF demonstrated measurable PN reduction under hydrogen operation in the single-cylinder, steady-state configuration examined. Overall, this work provides an internally consistent dataset linking hydrogen combustion behaviour, engine mechanical condition, and injection strategy to PN emissions and filtration response under steady-state conditions. The findings are intended to inform calibration development, hardware design, and future certification-grade studies, rather than to demonstrate regulatory compliance. Description: This paper was presented as: Harrington, A., Zaman, Z., Nickolaus, C., Zhao, H., et al., "Characterisation of Particle Number and Size from a DI SI Engine Operating on Hydrogen versus Gasoline: Operating Sensitivities and Filtration Effects," WCX SAE World Congress Experience, Detroit, Michigan, United States, April 14, 2026, https://doi.org/10.4271/2026-01-0378. Tue, 07 Apr 2026 00:00:00 GMT https://bura.brunel.ac.uk/handle/2438/33288 2026-04-07T00:00:00Z https://bura.brunel.ac.uk/handle/2438/32991 Title: Enhancing Wind Energy Forecasting Efficiency Through Dense and Dropout Networks (DDN): Leveraging Grid Search Optimization Authors: Alazemi, T; Darwish, M; Alaraj, M; Alsisi, E Editors: Arai, K Abstract: The wind power industry has experienced remarkable growth due to technological advancements and innovative business models. In 2020, the global installed wind power capacity reached 93 GW, marking a significant 52.96% increase compared to the previous year. This growth highlights the industry’s pivotal role in addressing energy needs and sustainability challenges. Timely wind energy forecasting is critical due to the nonlinear relationship between wind speed and power generation—however, the complexity and uncertainty of natural wind factors present challenges, necessitating effective forecasting methods. A deep learning-based approach named Dense and Dropout Networks (DDN) is introduced to address these challenges, employing Grid Search Optimization techniques. The model consists of eight dense layers for intricate data pattern recognition and a “ReLU” activation function. A dropout layer with a rate of 0.4 is integrated to enhance generalization and mitigate overfitting. The optimization process combines grid search with cross-validation to determine optimal hyperparameters. The actual “Texas Turbine” dataset evaluates the proposed DDN model based on Mean Squared Error (MSE) and Mean Absolute Error (MAE), revealing a significant improvement in accuracy with an enhanced MSE of 94.013% and an improved MAE of 76.947%. In conclusion, the optimized DDN model is a valuable and reliable tool for forecasting wind turbine energy production. Its impressive accuracy and potential for real-world implementation make it a noteworthy contribution to advancing renewable energy technologies and sustainable practices. Sun, 16 Nov 2025 00:00:00 GMT https://bura.brunel.ac.uk/handle/2438/32991 2025-11-16T00:00:00Z https://bura.brunel.ac.uk/handle/2438/32765 Title: Influence of fluctuating heat sources on multiphase thermal storage process: Thermal performance optimization utilizing the Taguchi method Authors: Huang, X; Li, M; Wang, Q; Xie, Y; Yang, X; Jouhara, H Abstract: The application of thermal energy storage technology in scenarios requiring rapid heat storage and release is of critical importance. This study introduces a novel composite thermal energy storage configuration, comprising a solid-phase change material (PCM) in the lower half and water in the upper section. By utilizing density variations induced by PCM phase change, the design facilitates natural convection between the two media, thereby enhancing heat transfer efficiency. The investigation focuses on the impact of unsteady pulsating heat fluxes on heat and mass transfer dynamics during the charging process. A comparative analysis of experimental and numerical results delineates the evolution of the solid–liquid interface within the PCM and the total melting time, validating the proposed thermal model. The findings demonstrate that the amplitude of the pulsating heat flux significantly influences the mean energy storage rate (ESR), while having a negligible effect on the total thermal energy absorbed by both water and PCM. Compared to a reference configuration with a heat source amplitude of 2.5 K, a half-period of 25 s, and a base temperature of 334.15 K, the Taguchi-optimized thermal storage structure exhibits a 28 % and 30 % improvement in the average ESR for the PCM and water, respectively, along with a 20 % reduction in the total melting time. Description: Highlights: • Effect of unsteady heat source on two-phase flow and heat transfer during melting is studied. • The numerical model of CFD-VOF is verified by experiments. • The parameters of the fluctuating heat source are further optimized by Taguchi method. • Influence of the change of heat source on average energy storage rate of two media is explored.; Data availability: No data was used for the research described in the article. Sat, 27 Dec 2025 00:00:00 GMT https://bura.brunel.ac.uk/handle/2438/32765 2025-12-27T00:00:00Z https://bura.brunel.ac.uk/handle/2438/32619 Title: The Use of Machine Learning in Predicting the Economic Performance of the ASTEP Solar Thermal System Authors: Gobio-Thomas, L; Stojceska, V Abstract: A ridge regression model developed in Python was used to predict the economic performance of an innovative solar thermal system called ASTEP. The system was applied to the industrial processes of two end-users, Mandrekas (MAND) and Arcelor Mittal (AMTP). The ASTEP system was designed to supply thermal energy up to 400 °C and consist of three main components: a novel rotary Fresnel Sundial, thermal energy storage (TES) and a control system. The actual levelized cost of energy (LCOE) of the ASTEP system and four other solar thermal plants as presented in the literature were used to evaluate the ability of the ridge regression model to predict their LCOE values. The plant capacity of the ASTEP system is 25 kW, while the capacities of the other plants are 5 MW–50 MW. The model was trained using data from plants with capacities of 5 MW–50 MW as these were the data available in the literature. The actual and predicted LCOE values were compared and the results showed a prediction error of 2.17–4.72 cents/kWh for the four solar thermal plants, 15.64 cents/kWh for AMTP and 17.98 cents/kWh for MAND’s ASTEP system. These findings indicate that the model has lower prediction error for solar thermal plants with capacities of 5 MW–50 MW, but higher prediction error for smaller capacity plants of less than 1 MW. It is recommended that more studies be conducted on the economic performance of small capacity plants, enabling sufficient data to be available to train machine learning (ML) models, resulting in higher prediction accuracy of the LCOE of these plants. Fri, 02 Jan 2026 00:00:00 GMT https://bura.brunel.ac.uk/handle/2438/32619 2026-01-02T00:00:00Z