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Society is moving towards climate neutrality where hydrogen fuelled combustion engines (H2 ICE) could be considered a main technology. These engines run on hydrogen (H2) so carbon-based emission are only present at a very low level from the lube oil. The most important pollutants NO and NO2 are caused by the exhaust aftertreatment system as well as CO2 coming from the ambient air. For standard measurement technologies these low levels of CO2 are hard to detect due to the high water content. Normal levels of CO2 are between 400-500 ppm which is very close or even below the detection limit of commonly used non-dispersive-infrared-detectors (NDIR). As well the high water content is very challenging for NOx measuring devices, like chemiluminescence detectors (CLD), where it results in higher noise and therefore a worse detection limit. Even for Fourier-transformed-infrared-spectroscopy-analysers (FT-IR) it is challenging to deal with water content over 15% without increased noise.

The issue of greenhouse gas (GHG) emissions from the transportation sector is widely acknowledged. Recent years have witnessed a push towards the electrification of cars, with many considering it the optimal solution to address this problem. However, the substantial battery packs utilized in electric vehicles contribute to a considerable embedded ecological footprint. Research has highlighted that, depending on the vehicle's size, tens or even hundreds of thousands of kilometers are required to offset this environmental burden. Human-powered vehicles (HPVs), thanks to their smaller size, are inherently much cleaner means of transportation, yet their limited speed impedes widespread adoption for mid-range and long-range trips, favoring cars, especially in rural areas. This paper addresses the challenge of HPV speed, limited by their low input power and non-optimal distribution of the resistive forces.

Fossil fuels such as natural gas used in engines still play the most important role worldwide despite such measures as the German energy transition which however is also exacerbating climate change as a result of carbon dioxide emissions. One way of reducing carbon dioxide emissions is the choice of energy sources and with it a more favourable chemical composition. Natural gas, for instance, which consist mainly of methane, has the highest hydrogen to carbon ratio of all hydrocarbons, which means that carbon dioxide emissions can be reduced by up to 35% when replacing diesel with natural gas. Although natural gas engines show an overall low CO2 and pollutant emissions level, methane slip due to incomplete combustion occurs, causing methane emissions with a more than 20 higher global warming potential than CO2.

Let’s start with the uncomfortable truth, climate change is happening, and the automotive industrial network is one of the main industries contributing to greenhouse gas emissions. SKF is an energy intensive business – directly using energy, mainly in the form of electricity and gas, in its operations around the world. In addition, SKF utilizes materials, predominantly steel, and services which can be energy and carbon intensive – such as transports and raw material in production and processing. The combined impact of these direct and indirect energy uses (scope 1, 2 and 3 upstream) generates an excess of over two million metric tons of CO2e per year. This figure would however be significantly higher were it not for the actions SKF has taken to reduce both energy and carbon intensity. In 2000, we were one of the first companies to actually start to report and set climate targets.

Electrified vehicles are particularly quiet, especially at low speeds due to the absence of combustion noises. This is why there are laws worldwide for artificial driving sounds to warn pedestrians. These sounds are generated using a so-called Acoustic Vehicle Alerting System (AVAS) which must maintain certain minimum sound pressure levels in specific frequency ranges at low speeds. The creation of the sound currently involves an iterative and sometimes time-consuming process that combines composing the sound on a computer with measuring the levels with a car on an outside noise test track. This continues until both the legal requirements and the subjective demands of vehicle manufacturers are met. To optimize this process and reduce the measurement effort on the outside noise test track, the goal is to replace the measurement with a simulation for a significant portion of the development.

A GE Aviation Systems report documents that the National Oceanic and Atmospheric Administration (NOAA) provided weather forecast data has a bias of 15 knots and a standard deviation of 13.3 knots for the 40 flights considered for the research. It also had a 0.47 bias in the temperature with a standard deviation of 0.27. The temperature errors are not as significant as the wind. There is a potential opportunity to reduce the operational cost by improving the weather forecast. The flight management system (FMS) currently uses the weather forecast, available before takeoff, to identify an optimized flight path with minimum operational costs depending on the selected speed mode. Such a flight plan could be optimum for a shorter flight because these flight path planning algorithms are very less susceptible to the accuracy of the weather forecast.

Thermo-mechanical fatigue and natural aging due to environmental conditions are difficult to simulate in an actual test with the advanced fiber-reinforced composites, where their fatigue and aging behavior is little understood. Predictive modeling of these processes is challenging. Thermal cyclic tests take a prohibitively long time, although the strain rate effect can be scaled well for accelerating the mechanical stress cycles. Glass fabric composites have important applications in aircraft and spacecraft structures including microwave transparent structures, impact-resistant parts of wing, fuselage deck and many other load bearing structures. Often additional additively manufactured features and coating on glass fabric composites are employed for thermal and anti-corrosion insulations. In this paper we employ a thermo-mechanical fatigue model based accelerated fatigue test and life prediction under hot to cold cycles.

Bio-composites have gained significant attention within the aerospace industry due to their potential as a sustainable solution that addresses the demand for lightweight materials with reduced environmental impact. These materials blend natural fibers sourced from renewable origins, such as plant-based fibers, with polymer matrices to fabricate composite materials that exhibit desirable mechanical properties and environmental friendliness. The aerospace sector's growing interest in bio-composites originates from those composites’ capacity to mitigate the industry's carbon footprint and decrease dependence on finite resources. This study aims to investigate the suitability of utilizing plant derived flax fabric/PLA (polylactic acid) matrix-based bio-composites in aerospace applications, as well as the recyclability potential of these composites in the circular manufacturing economy.

Industries have been increasingly adopting AI based computer vision models for automated asset defect inspection. A challenging aspect within this domain is the inspection of composite assets consisting of multiple components, each of which is an object of interest for inspection, with its own structural variations, defect types and signatures. Training vision models for such an inspection process involves numerous challenges around data acquisition such as insufficient volume, inconsistent positioning, poor quality and imbalance owing to inadequate image samples of infrequently occurring defects. Approaches to augmenting the dataset through Standard Data Augmentation (SDA) methods (image transformations such as flipping, rotation, contrast adjustment, etc.) have had limited success. When dealing with images of such composite assets, it is challenging to correct the data imbalance at the component level using image transformations as they apply to all the components within an image.

The present study focuses on the impacts of pistachio shell particles (2–10 wt.%) on the mechanical and microstructures properties of Al–Cu–Mg/pistachio shell particulate composites. To inspect the impact of the pistachio shell powder content with Al–Cu–Mg alloys, the experimentation was carried out with different alloy samples with constant copper (Cu) and magnesium (Mg) content. Parameters such as hardness, tensile strength with yield strength and % elongation, impact energy, and microstructure were analyzed. The outcomes demonstrated that the uniform dissemination of the pistachio shell particles with the microstructure of Al–Cu–Mg/pistachio shell composite particulates is the central point liable for the enhancement of the mechanical properties. Incorporating pistachio shell particles, up to 10 wt.%, is a cost-effective reinforcement in the production of metal matrix composites for various manufacturing applications.

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