Browse Topic: Combustion and combustion processes
Ammonia is emerging as a promising energy vector for decarbonising the maritime sector. However, its low flame speed can lead to incomplete combustion, reduced engine efficiency, and increased emissions of unburned ammonia (NH3). Blending hydrogen with ammonia helps to address these issues, but the fundamental combustion characteristics of such mixtures remain insufficiently understood. This study examines the combustion dynamics of an NH3–H2 blend containing 30% hydrogen at 3 bar initial pressure. Experiments were performed in a 1.2 L optically accessible constant-volume combustion chamber fitted with a wall-mounted surface spark plug. High-speed shadowgraph imaging with 6,000 fps captured the flame evolution throughout the combustion process. The pressure and temperature values were monitored using piezoresistive pressure transducers and K-type thermocouples. Combustion times and flame extensions were extracted via post-processing of flame images using custom MATLAB algorithms. The
Accurate prediction of equilibrium combustion products and thermodynamic properties is essential for optimizing engine performance, enhancing combustion efficiency, and reducing emissions in diesel-powered systems. Traditional methods for combustion modeling often involve solving complex chemical equilibrium equations or thermodynamic relations, which could be computationally expensive and time-consuming. In this study, we present a data-driven approach using a deep neural network (DNN) model to predict the equilibrium combustion products and key thermodynamic characteristics of diesel under varying thermodynamic conditions. The proposed DNN model is trained on a comprehensive dataset generated from equilibrium calculations. The inputs include pressure, temperature, and equivalence ratio, covering a relatively wide range to encompass diesel equilibrium combustion under various conditions. Outputs are equilibrium combustion products and thermodynamic properties, including enthalpy
This paper presents the emissions development of a heavy-duty hydrogen internal-combustion engine (H₂ICE) targeting ultra-low NOx with a design goal of 20 mg/hp-hr. The approach integrates advanced thermal management of the engine and aftertreatment, including engine out NOx management through air-fuel ratio controls and an electric heater to accelerate catalyst light-off and sustain activity at low-load/idle conditions. A diesel-derived aftertreatment system (ATS) is selected to maximize practicality and component commonality, and an integrated controls strategy spanning the engine and ATS is implemented to demonstrate ultra-low NOx capability over EPA certification cycles. The paper concludes with considerations for periodic SCR regeneration to ensure emission compliance.
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