Browse Topic: Engine mechanical components
In recent years, especially in high-performance spark-ignition engines, the thermal stress of pistons has gradually increased due to the implementation of various technologies, aimed at meeting emission reduction and specific power increase requirements. If the heat is not properly dissipated, cracking and plastic deformation of the material as well as formation of hot spots triggering pre-ignition in the combustion chamber mixture can occur. This last aspect is even more true considering innovative fuels such as hydrogen. To overcome these problems, one or more jets of oil are directed towards the piston under-crown region, impacting at high speed. This technique ensures immediate cooling and allows the engine performance to be increased without compromising the useful life. In order to optimize the oil jet effectiveness, 3D-CFD can be proficiently adopted. In this regard, the aim of this work is to define a robust numerical methodology able to simulate oil jet impingement and piston
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
The automotive industry's future hinges on a new AI-native engineering workflow that accelerates iteration, strengthens system thinking, and preserves human judgment. Automotive development cycles are compressing at a pace the industry has never seen. The shift to all-electric fleets of software-defined vehicles is moving faster than traditional processes can absorb. In parallel, regulatory pressure and customer expectations keep rising, demanding greater performance, higher safety, better energy efficiency, and sharper competitiveness. In this environment, OEMs R&D competitiveness depends on three factors: How quickly teams can explore and iterate on design choices while delivering differentiated value, product performance, and cost efficiency. How early system-level interactions can be detected, before they turn into delivery friction or costly late-stage failures. How effectively a company can encode and scale its internal engineering know-how into lean development processes.
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