Clean transport requires tailored energy carriers. For heavy-duty transportation, synthetic fuels are promising but must fulfil the key challenges of achieving carbon neutrality while reducing air pollution and ensuring scalability through compatibility with existing infrastructure. Here we show that hydroformylated Fischer–Tropsch (HyFiT) fuels composed of optimized alkane–alcohol blends simultaneously address these challenges. First, the design of the HyFiT fuel process flexibly closes the carbon cycle by employing biomass or carbon dioxide as feedstock, while being scalable through mature technologies. Second, fuel testing shows that HyFiT fuels comply with global fuel standards. Material compatibility is demonstrated for two standard sealing materials, enabling the retrofit of today’s vehicle fleets. Third, vehicle testing shows that HyFiT fuels substantially reduce combustion-induced particulate matter and nitrogen oxides. Fourth, a well-to-wheel life cycle assessment finds that HyFiT fuels enable the transition to net-zero greenhouse gas emissions, showing simultaneously a favourable profile in other environmental parameters. HyFiT fuels can thus complement electrification for heavy-duty transportation.

Precise flow measurement is crucial in fluid power systems. Especially in combination with pressure, hydraulic power can be particularly beneficial for predictive maintenance and control applications. However, conventional flow sensors in fluid power systems are often invasive, thus disrupting the flow and yielding unreliable measurements, especially under transient conditions. A common alternative is to estimate the flow rate using pressure differentials along a pipe and the Hagen–Poiseuille law, which is limited to steady, laminar, and incompressible flows. This study advances a previously introduced analytical soft sensor, demonstrating its ability to accurately determine the transient pipe flow beyond laminar conditions, without requiring a dedicated flow rate sensor. This method provides a robust and computationally efficient solution for real-world hydraulic systems by applying two pressure transducers. A key contribution of this work is the investigation of signal filtering, revealing that even a simple first-order low-pass filter with a 100 Hz cutoff frequency significantly improves accuracy, which is demonstrated for pulsation frequencies of 5, 10, and 15 Hz, where the filtered results closely match experimental data from a test rig. These findings underscore the soft sensor’s potential as a reliable alternative to traditional flow sensors, offering high accuracy with minimal computational overhead for a wide range of flow conditions.

Bio-hybrid fuels, derived from sustainable raw materials and green energies, offer a promising alternative to conventional fuels made of mineral oil. Within the cluster of excellence “The Fuel Science Center (FSC)” at RWTH Aachen bio-hybrid fuels are investigated on a holistic level, including an examination of their compatibility with sealing materials. Previous time-consuming experiments revealed that many bio-hybrid fuels show poor material compatibility with elastomer sealing materials (e.g. NBR & FKM) leading to issues such as volume expansion, hardness alteration, or chemical reactions upon immersion. These incompatibilities could result in catastrophic failures during practical applications. Due to the high number of possible fuels, fuel-mixtures and therefore fuel/seal combinations a solely empirical approach is impractical. Consequently, this work presents a hands-on framework of machine learning (ML) regression models that predicts material compatibility based on experimental results and selected fluid properties, thereby enabling the preselection of possible fuel/seal combinations for subsequent immersion tests.

In oil-hydraulic tribological contacts, hard-soft material pairings of steel and non-ferrous metal are usually used, whose main component copper is often alloyed with lead. These materials have significant disadvantages in terms of oil ageing and toxicity, which is why research is being carried out into alternatives. This article examines whether modern high-performance polymers can replace conventional non-ferrous metal alloys in oil-hydraulic contacts. Experimental studies on the media compatibility of various PEEK, PAI, PI and POM-H polymers in mineral and ester oil are discussed. In addition, the friction and wear behavior of PEEK and PAI is quantified by tribometer tests under typical oil-hydraulic conditions.

Thermo-hydrodynamic (THD) lubrication is a key mechanism in injection pumps, where frictional heating and heat transfer strongly influence lubrication performance. Accurate numerical modeling remains challenging due to the nonlinear coupling of temperature- and pressure-dependent fluid properties and the high computational cost of iterative solvers. The rising relevance of bio-hybrid fuels, however, demands the investigation of a great number of fuel mixtures and conditions, which is currently infeasible with traditional solvers. Physics-informed neural networks (PINNs) have recently been applied to lubrication problems; existing approaches are typically restricted to stationary cases or rely on data to improve training. This work presents a novel, purely physics-based PINN framework for solving coupled, transient THD lubrication problems in injection pumps. By embedding the Reynolds equation, energy conservation laws, and temperature- and pressure-dependent fluid models directly into the loss function, the proposed approach eliminates the need for any simulation or experimental data. The PINN is trained solely on physical laws and validated against an iterative solver for 16 transient test cases across two fuels and eight operating scenarios. The good agreement of PINN and iterative solver demonstrates the strong potential of PINNs as efficient, scalable surrogate models for transient THD lubrication and iterative design applications.