http://bura.brunel.ac.uk/handle/2438/13039 2026-05-26T20:49:21Z 2026-05-26T20:49:21Z Harrington, A Zaman, Z Nickolaus, C Zhao, H Wang, X Hall, J http://bura.brunel.ac.uk/handle/2438/33288 2026-05-15T02:00:59Z 2026-04-07T00:00:00Z

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.

2026-04-07T00:00:00Z Draper, P Kubacka, J Seymour, K Karayiannis, T http://bura.brunel.ac.uk/handle/2438/33097 2026-04-03T02:00:46Z 2026-01-01T00:00:00Z

Title: CLOSED-LOOP GEOTHERMAL SYSTEMS: PARAMETRIC STUDY INCLUDING INTERMITTENT OPERATION Authors: Draper, P; Kubacka, J; Seymour, K; Karayiannis, T Abstract: ...

2026-01-01T00:00:00Z Widgington, JJ Ivanov, A Karayiannis, T http://bura.brunel.ac.uk/handle/2438/33096 2026-04-03T02:00:45Z 2026-01-01T00:00:00Z

Title: FLOW BOILING PRESSURE DROP IN A SINGLE MICROCHANNEL AND COMPARISON WITH CORRELATIONS Authors: Widgington, JJ; Ivanov, A; Karayiannis, T Abstract: ... Description: ...

2026-01-01T00:00:00Z Alazemi, T Darwish, M Alaraj, M Alsisi, E http://bura.brunel.ac.uk/handle/2438/32991 2026-03-17T03:01:02Z 2025-11-16T00:00:00Z

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.

2025-11-16T00:00:00Z