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.

Leakage of hydraulic oils causes long-term damage to the environment. Every year, a significant amount of hydraulic oil is released into the environment through accidents and leakages. Biohydraulic oils minimize the risk of environmental damage due to their biodegradability and lack of toxicity. Mobile machines offer great substitution potential for biohydraulic oils. The majority of machines are operated with mineral hydraulic oils. Furthermore, in view of decreasing energy resources and rising CO₂ emissions, there is a strong interest in reducing the fuel consumption of mobile machines. Triggered by this trend, the power density of hydraulic systems and their components continue to increase. Resulting, the used hydraulic fluid is subject to greater thermal stress and the system thermal of the used fluids are becoming increasingly important. Biohydraulic oils offer a great potential due to their thermal properties, regarding viscosity temperature dependency. Therefore, the content of this article is to consider the influence of hydraulic oil’s viscosity on the efficiency of a crawler excavator. For this purpose, experimental tests are discussed by regarding viscosity related energy losses of hydraulic systems of crawler excavators by using biohydraulic oil. A hypothesis about the dependency of rheological properties of biohydraulic oils on the hydraulic system’s efficiency is formulated.

Gaining insight into tribological systems is crucial for optimizing efficiency and prolonging operational lifespans in technical systems. Experimental investigations are time-consuming and costly, especially for reciprocating seals in fluid power systems. Elastohydrodynamic lubrication (EHL) simulations offer an alternative but demand significant computational resources. Physics-informed neural networks (PINNs) provide a promising solution using physics-based approaches to solve partial differential equations. While PINNs have successfully modeled hydrodynamics with stationary cavitation, they have yet to address transient cavitation with dynamic geometry changes. This contribution applies a PINN framework to predict pressure build-up and transient cavitation in sealing contacts with dynamic geometry changes. The results demonstrate the potential of PINNs for modeling tribological systems and highlight their significance in enhancing computational efficiency.

In many technical applications, understanding the behavior of tribological contacts is pivotal for enhancing efficiency and lifetime. Traditional experimental investigations into tribology are often both costly and time-consuming. A more profound insight can be achieved through elastohydrodynamic lubrication (EHL) simulation models, such as the ifas-DDS, which determines precise friction calculations in reciprocating pneumatic seals. Similar to other distributed parameter simulations, EHL simulations require a labor-intensive resolution process. Physics-informed neural networks (PINNs) offer an innovative method to expedite the computation of such complex simulations by incorporating the underlying physical equations into the neural network’s parameter optimization process. A hydrodynamic PINN framework has been developed and validated for a variant of the Reynolds equation. This paper elucidates the framework’s capacity to handle multi-case scenarios—utilizing one PINN for various simulations—and its ability to extrapolate solutions beyond a limited training domain. The outcomes demonstrate that PINNs can overcome the typical limitation of neural networks in extrapolating the solution space, showcasing a significant advancement in computational efficiency and model adaptability.

Reciprocating seals are widely used in general industrial machines and are designed to endure rigorous working conditions. Among the harshest challenges, the wide temperature range they withstand has a significant influence on their sealing performance. However, most studies of reciprocating seals have focused only on their sealing characteristics at normal temperatures. To investigate the impacts of temperature on the seal's tribological performance, this paper examines the VL seal, a kind of combined seals, and discusses its performance in a wide temperature range. Material properties of the VL seal at temperatures from −55 °C to 135 °C are measured based on the seal product. The thermo-viscosity effect and the influence of thermal expansion and contraction are both considered in the reciprocating seal modelling. A coupling method that combines the finite-element analysis model with a mixed-lubrication procedure is implemented. For deformation calculation, a comparison between the influence coefficient method and the semi-infinite space method is conducted, and finds only minuscule disparities for the VL-seal analysis. To avoid the divergent problem due to high viscosity at low temperatures, this paper proposes the successive approaching method. Details of the sealing zone in the outstroke and instroke are systematically discussed in relation to a wide range of temperatures. The corresponding experiments are conducted and compared with the simulation. The results indicate that the temperature has a tremendous influence on the tribological performance. The leakage and the friction at the high temperature are much higher than those at the normal temperature.

Combined seals, as a kind of reciprocating seals, are widely used in hydraulic systems. The combined seal usually contains two or more elements to achieve the sealing function. Not only the interference fit between the seal and the rod/groove should be considered, but the assembling parameters between the inner sealing elements are also essential to be investigated. Stretching ratio, as an indicator for the interference fit, usually exerts a significant influence on the sealing performance. However, in the studies of stretching effects on combined seals, the traditional axisymmetric modeling method will introduce the overstretching problems, resulting in large errors. To overcome this problem, this paper proposed a putter-movement method, where an extra putter is introduced into the model, and a mixed-lubrication theory was employed to study macro and micro characteristics among the sealing zone. As a typical kind of combined seals, the VL seal is utilized as the research object, and the material’s plastic effects are included in the reciprocating-seal modeling for the first time. The numerical results are compared with the experimental data, and good agreements are obtained. The proposed method is then employed to discuss the sealing characteristics under different interference-fit conditions systematically. The results indicate that the stretching mainly affects the contact pressure on the cavity side. Compared to the performance of the seal with the standard geometry, in the extra-stretching condition, the leakage is increased, but the friction is decreased.