Browse Topic: Fuel systems
The global automotive industry is facing an unprecedented convergence of uncertainties driven by geopolitical tensions, evolving trade policies, emissions related regulations, and increasingly volatile consumer demand. Shifting emissions legislation, including the EU’s tightened CO2 targets and long-term plans to phase out internal combustion engines, is imposing strategic and financial pressures on automakers and suppliers as they navigate divergent regional regulatory trajectories. Demand side volatility further complicates the landscape. Consumer preferences are fluctuating due to economic pressures, infrastructure constraints, and uneven EV adoption patterns. While some markets show stagnation in battery electric vehicle uptake, hybrids are rising as consumers seek cost efficient alternatives amid uncertain energy and regulatory environments. Within this unstable context, the transition toward Software Defined Vehicles (SDVs) is emerging as a critical strategic response. SDVs, characterized by centralized computing, updatable software architectures, and over the air feature deployment, offer automakers greater adaptability in addressing regulatory shifts and market dynamics. By decoupling hardware from software cycles, SDVs enable faster innovation, reduced development risk, and new digital revenue models, while virtualization and AI driven analytics enhance development efficiency and lifecycle value.
Multiphase compressible flow problems are widespread in aviation, aerospace, transportation, military, and industrial fields, for instance, in underwater explosion bubble dynamics, fuel injection for hypersonic vehicles, liquid sloshing in propellant tanks, and supercavitating underwater vehicles. This paper proposes an improved THINC (Tangent of Hyperbola for Interface Capturing) method for multiphase flow simulations, based on a selective reconstruction strategy for the dominant material. The core of the strategy is to apply the THINC reconstruction exclusively to the material with the largest volume fraction within a multiphase mixed cell, which numerically governs the local interface evolution. The volume fractions of non-dominant materials are then obtained through a proportional distribution that inherently ensures the summation (Σαk = 1) and boundedness (0 ≤ αk> ≤ 1) constraints are met without explicit corrections. This approach reduces the number of THINC reconstructions for each time step in a multiphase mixed cell from Nm (the number of materials) to one, significantly simplifying the algorithm and lowering computational cost. It thereby avoids the error accumulation and complex renormalization procedures associated with conventional schemes that reconstruct all materials. While strictly maintaining volume fraction conservation, the proposed method preserves interface sharpness through the underlying THINC framework. The method is implemented in a diffuse-interface, multiphase Eulerian framework and validated with a series of challenging benchmarks, including shock-helium bubble interaction, triple-point problem, gas impact, and the more complex modified gas impact. Numerical results show that, compared with conventional multiphase THINC approaches that reconstruct every material, the proposed scheme can reduce CPU time by about 40.0% without compromising the accuracy of key physical quantities.
In the conversation surrounding electrification, the vehicle itself typically dominates the headlines. But those operating on remote jobsites in the mining, construction and agriculture sectors know the machine is only half the equation. These industries prioritize reliability and uptime and require machines that can handle grueling shifts in demanding environments without compromise. Power providers in these heavy-duty, off-highway markets must move beyond the battery itself to explore a holistic approach to infrastructure when it comes to powering remote jobsites. The first step to success is understanding the fundamental differences between off-highway duty cycles and on-highway applications. While on-highway applications like long-hauling trucks benefit from steady-state operation and passive airflow for cooling, off-highway machines often operate at high torque for extended periods, with little to no forward movement. In these scenarios, there is no passive cooling to rely on or regular refueling stations at the next exit. Success, therefore, is defined by the engineering required to ensure that electric machines deliver the same productivity as diesel, even when operated at their limits in the most rugged, remote conditions.
How to ensure off-highway combustion systems operate with sufficient control to meet tightening emissions standards and evolving fuel landscapes without sacrificing reliability. Off-highway equipment is being asked to do more with less. Less margin for emissions, less tolerance for downtime and less room for inefficiency, while operating under some of the most demanding duty cycles in the transport sector. Tier 4 and Tier 5 emissions standards have reshaped engine calibration strategies. Renewable diesel and biodiesel blends are entering worksites and farms at scale. At the same time, construction, mining and agricultural machines are expected to run for 20-25 years, often at sustained high load and far from service infrastructure. In this environment, combustion systems are far from being phased out.
German startup Blackwave is building carbon parts for rocket tanks. Technical University of Munich, Munich, Germany Carbon fiber has become indispensable in high-performance industries such as automotive engineering and aerospace. It's lightweight, extremely durable, and can be shaped in almost any way. The start-up Blackwave, founded at the Technical University of Munich (TUM), specializes in this versatile composite material. What began with custom components for sports cars and aircraft has evolved into the development of high-pressure tanks for space applications. As is so often the case in engineering, a small detail determines technological progress. In the case of rockets, it is the high-pressure tanks that are specially designed for the fuel systems. As rockets are designed to be as light as possible, they lose structural stability when the fuel tanks, known as primary tanks, are emptied. A trick is used to counteract this: alongside fuel combustion, noble gases are released from internal high-pressure tanks, known as secondary tanks. These gases fill the resulting empty space, maintaining structural integrity.
The Korea Research Institute of Standards and Science (KRISS, President: Dr. Lee Ho Seong) has developed equipment that monitors the quality of hydrogen fuel supplied to vehicles through hydrogen refueling stations in real-time. This equipment is expected to prevent hydrogen vehicle accidents caused by impurities in the hydrogen fuel and improve the quality of hydrogen production.
This study presents a fully integrated, vehicle-level thermal management model for gasoline fuel tanks, designed to predict transient fuel temperatures, tank wall heating, and vapor generation under real-world driving conditions. The model simulates coupled thermal contributions from exhaust radiation, transient underbody airflow, conductive heat transfer, in-tank pump heating, and dynamic changes in fuel composition and level. Validation against on-road measurements shows strong agreement for fuel temperature and vapor flow profiles. Results confirm that exhaust radiative heating is the dominant thermal load, particularly during the post-shutdown heat soak period. A well-designed heat shield reduced peak tank wall temperature by approximately 27 °C, significantly lowering fuel heating and evaporation. Parametric analysis indicates that while fuel Reid Vapor Pressure (RVP) and tank material influence evaporation, their effect is secondary to external heat mitigation. While this model employs simplifications, such as assuming a uniform bulk fuel temperature and using empirically based convective correlations, these assumptions proved adequate for vehicle-level thermal management analysis. This adequacy is supported by the strong correlation between the model’s predictions and experimental field data across realistic driving scenarios. As a practical tool, the model successfully supports the optimization of thermal protection strategies and guides heat shield design decisions. Future work to incorporate measurement uncertainties, localized thermal stratification, and experimental validation of vapor composition would further strengthen predictive accuracy and extend the model's applicability to more detailed design phases.
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