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On the path to decarbonizing road transport, electric commercial vehicles will play a significant role. The first applications were directed to the smaller trucks for distribution traffic with relatively moderate driving and range requirements, but meanwhile, the first generation of a complete portfolio of truck sizes is developed and available on the market. In these early applications, many compromises were accepted to overcome component availability, but meanwhile, the supply chain can address the specific needs of electric trucks. With that, the optimization towards higher usability and lower costs can be moved to the next level. Especially for long-haul trucks, efficiency is a driving factor for the total costs of ownership. Besides the propulsion system, all other systems must be optimized for higher efficiency. This includes thermal management since the thermal management components consume energy and have a direct impact on the driving range.

In electrified vehicles, auxiliary units can be a dominant source of noise, one of which is the refrigerant scroll compressor. Compared to vehicles with combustion engines, e-vehicles require larger refrigerant compressors, as in addition to the interior, also the battery and the electric motors have to be cooled. Currently, scroll compressors are widely used in the automotive industry, which generate one pressure pulse per revolution due to their discontinuous compression principle. This results in speed-dependent pressure fluctuations as well as higher-harmonic pulsations that arise from reflections. These fluctuations spread through the refrigeration cycle and cause the vibration excitation of refrigerant lines and heat exchangers. The sound transmission path in the air conditioning heat exchanger integrated in the dashboard is particularly critical. Various silencer configurations can be used to dampen these pulsations.

With the automotive industry's increasing focus on electromobility and the growing share of electric cars, new challenges are arising for the development of electric motors. The requirements for torque and power of traction motors are constantly growing, while installation space, costs and weight are increasingly becoming limiting factors. Moreover, there is an inherent conflict in the design between power density and efficiency of an electric motor. Thus, a main focus in today's development lies on space-saving and yet effective and innovative cooling systems. This paper presents an approach for a multi-physical optimization that combines the domains of electromagnetics and thermodynamics. Based on a reference machine, this simulative study examins a total of nine different stator cooling concepts varying the cooling duct positions and end-winding cooling concepts.

Homologation is an important process in vehicle development and aerodynamics a main data contributor. The process is heavily interconnected: Production planning defines the available assemblies. Construction defines their parts and features. Sales defines the assemblies offered in different markets, where Legislation defines the rules applicable to homologation. Control engineers define the behavior of active, aerodynamically relevant components. Wind tunnels are the main test tool for the homologation, accompanied by surface-area measurement systems. Mechanics support these test operations. The prototype management provides test vehicles, while parts come from various production and prototyping sources and are stored and commissioned by logistics. Several phases of this complex process share the same context: Production timelines for assemblies and parts for each chassis-engine package define which drag coefficients or drag coefficient contributions shall be determined.

EU legislation provides for only local CO2 emission-free vehicles to be allowed in individual passenger transport by 2035. In addition, the directive provides for fuels from renewable sources, i.e. defossilised fuels. This development leads to three possible energy sources or forms of energy for use in individual transport. The first possibility is charging with electricity generated from renewable sources, the second possibility is hydrogen generated from renewable sources or blue production path. The third possibility is the use of renewable fuels, also called e-fuels. These fuels are produced from atmospheric CO2 and renewable hydrogen. Possible processes for this are, for example, methanol or Fischer-Tropsch synthesis. The production of these fuels is very energy-intensive and large amounts of renewable electricity are needed.

Autonomous driving is a hot topic in the automotive domain, and there is an increasing need to prove its reliability. They use machine learning techniques, which are themselves stochastic techniques based on some kind of statistical inference. The occurrence of incorrect decisions is part of this approach and often not directly related to correctable errors. The quality of the systems is indicated by statistical key figures such as accuracy and precision. Numerous driving tests and simulations in simulators are extensively used to provide evidence. However, the basis of all descriptive statistics is a random selection from a probability space. The difficulty in testing or constructing the training and test data set is that this probability space is usually not well defined. To systematically address this shortcoming, ontologies have been and are being developed to capture the various concepts and properties of the operational design domain.

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.

By building on mature internal combustion engine (ICE) hardware combined with dedicated hydrogen (H2) technology, the H2-ICE has excellent potential to accelerate CO2 reduction. H2-ICE concepts can therefore contribute to realizing the climate targets in an acceptable timeframe. In the landscape of H2-ICE combustion concepts, High Pressure Direct Injection (HPDI™) is an attractive option considering its high thermal efficiency, wide load range and its applicability to on-road as well as off-road heavy-duty equipment. Still, H2-HPDI is characterized by diffusion combustion, giving rise to significant NOx emissions. In this paper, the potential of H2-HPDI toward compliance with future emissions legislation is explored on a 1.8L single-cylinder research engine. With tests on multiple load-speed points, Exhaust Gas Recirculation (EGR) was shown to be an effective measure for reducing engine-out NOx, although at the cost of a few efficiency points.

In the effort to achieve the goal of a climate-neutral transportation system, the use of hydrogen and other synthetic fuels plays a key role. As battery electric vehicles become more widespread, e-fuels could be used to defossilize the hard-to-electrify transportation sectors and to store energy produced from renewable and non-continuous energy sources. Among e-fuels, hydrogen and ammonia are very attractive because they are carbon-neutral and their oxidation does not lead to any CO2 emissions. Furthermore, hydrogen/ammonia mixtures overcome the issues that arise as each of the two fuels is separately used. In the automotive sector, the use of either hydrogen, ammonia or their blends require a characterization of such mixtures under engine-like conditions, that is, at high pressures and temperatures. The aim of this work is to evaluate the Laminar Flame Speed (LFS) of hydrogen/ammonia mixtures by varying the thermodynamic conditions and the molar composition of the reactants.

Gas bearings are an effective solution to high-speed rotor applications for its contamination free, reduced maintenance and higher reliability. However, low viscosity of gas leads to lower dynamic stiffness and damping characteristics resulting in low load carrying capacity and instability at higher speeds. Gas bearings can be enhanced by adding a foil structure commonly known as gas foil bearings (GFBs), whose dynamic stiffness can be tailored by modifying the geometry and the material properties resulting in better stability and higher load carrying capacity. A detailed study is required to assess the performance of high-speed rotor systems supported on GFBs, therefore in this study a bump type GFB is analyzed for its static and dynamic characteristics. The static characteristics are obtained by solving the non-linear Reynolds equation through an iterative procedure.

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.

Airframe section of rockets, missiles and launch vehicles are typically cylindrical in shape. The cylindrical shell is subjected to high axial load and an external pressure during its operation. The design of cylinders subjected to such loads is generally found to be critical in buckling. To minimize the weight of cylinders, it is typically stiffened with rings and stringers on the inner diameter to increase the buckling load factor. Conventionally the buckling load estimated by analytical or numerical means is multiplied by an empirical factor generally called Knockdown factor (kdf) to get the critical buckling load. This factor is considered to account for the variation between theory and experiment and is specified by handbooks or codes. In aerospace industry, NASA SP 8007 is commonly followed and it specifies the kdf as a lower bound fit curve for experimental data .

A structural load estimating methodology was developed for the RLV-TD HEX-01 mission, the maiden winged body technology demonstrator vehicle of ISRO. The technique characterizes atmospheric regime of flight from vehicle loads perspective and ensures adequate structural margin considering atmospheric variations and system level perturbations. Primarily the method evaluates time history of station loads considering effects of vehicle dynamics and structural flexibility. Station loads in the primary structure are determined by superposition of quasi-static aerodynamic loads, dynamic inertia loads, control surface loads and propulsion system loads based on actual physics of the system. Spatial aerodynamic distributions at various Mach numbers along the trajectory have been used in the study. Argumentation in aerodynamic loads due to vehicle flexibility is assessed through the use of spatial aerodynamic distributions.

The study of aerodynamic forces in hypersonic environments is important to ensure the safety and proper functioning of aerospace vehicles. These forces vary with the angle of attack (AOA) and there exists an optimum angle of attack where the ratio of the lift to drag force is maximum. In this paper, computational analysis has been performed on a blunt cone model to study the aerodynamic characteristics when hypersonic flow is allowed to pass through the model. The flow has a Mach number of 8.44 and the angle of attack is varied from 0º to 20º. The commercial CFD solver ANSYS FLUENT is used for the computational analysis and the mesh is generated using the ICEM CFD module of ANSYS. Air is selected as the working fluid. The simulation is carried out for a time duration of 1.2 ms where it reaches a steady state and the lift and drag forces and coefficients are estimated. The pressure, temperature, and velocity contours at different angles of attack are also observed.

The present study discusses about the determination of the Seal drag force in the application where elastomeric seal is used with metallic interface in the presence of different fluids. An analytical model was constructed to predict the seal drag force and experimental test was performed to check the fidelity of the analytical model. A Design of Experiment (DoE) was utilized to perform experimental test considering different factors affecting the Seal drag force. Statistical tools such as Test for Equal Variances and One way Analysis of Variance (ANOVA) were used to draw inferences for population based on samples tested in the DoE test. It was observed that Glycol based fluids lead to lubricant wash off resulting into increased seal drag force. Additionally, non-lubricated seals tend to show higher seal drag force as compared to lubricated seals. Keywords: Seal Drag, DoE, ANOVA

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.

Aluminum and its alloys entered a main role in the engineering sectors because of their applicable characteristics for indispensable applications. To enhance requisite belongings for the components, the composition of variant metal/nonmetal with light metal alloys is essential in the manufacturing industries. To enhance the wear resistance with significant strength property of the aluminum alloy 2024, the reinforcement SiC and fly ash (FA) were added with the designation Al2024 + 10% SiC; Al2024 + 5% SiC + 5% FA; and Al2024 + 10% FA via stir-casting technique. The wear resistance property of the composites was tested in pin-on-disc with a dry-sliding wear test procedure. The experiment trials were designed in Box–Behnken design (BBD) by differing the wear test parameters like % of reinforcement, sliding distance (m), and load (N).

The objectives of this study were to provide insights on how injury risk is influenced by occupant demographics such as sex, age, and size; and to quantify differences within the context of commonly-occurring real-world crashes. The analyses were confined to either single-event collisions or collisions that were judged to be well-defined based on the absence of any significant secondary impacts. These analyses, including both logistic regression and descriptive statistics, were conducted using the Crash Investigation Sampling System for calendar years 2017 to 2021. In the case of occupant sex, the findings agree with those of many recent investigations that have attempted to quantify the circumstances in which females show elevated rates of injury relative to their male counterparts given the same level bodily insult. This study, like others, provides evidence of certain female-specific injuries.

For intelligent vehicles, a robust perception system relies on training datasets with a large variety of scenes. The architecture of federated learning allows for efficient collaborative model iteration while ensuring privacy and security by leveraging data from multiple parties. However, the local data from different participants is often not independent and identically distributed, significantly affecting the training effectiveness of autonomous driving perception models in the context of federated learning. Unlike the well-studied issues of label distribution discrepancies in previous work, we focus on the challenges posed by scene heterogeneity in the context of federated learning for intelligent vehicles and the inadequacy of a single scene for training multi-task perception models. In this paper, we propose a federated learning-based perception model training system.

With the development of internet technology and autonomous vehicles (AVs), the multimodal transportation and distribution model based on AVs will be a typical application paradigm in the smart city scenario. Before AVs carry out logistics distribution, it is necessary to plan a reasonable distribution path based on each customer point, and this is also known as Vehicle Routing Problem (VRP). Unlike traditional VRP, the urban logistics distribution process based on multimodal transportation mode will use a set of different types of AVs, mainly including autonomous ground vehicles and unmanned aerial vehicles (UAVs). It is worth pointing out that there is currently no research on combining the planning of AVs distribution paths with the trajectory planning of UAVs. To address this issue, this article establishes a bilevel programming model. The upper-level model aims to plan the optimal delivery plan for AVs, while the lower-level model aims to plan a driving trajectory for UAVs.