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The hydrogen engine is one of the promising technologies that enables carbon-neutral mobility, especially in heavy-duty on- or off-road applications. In this paper, a methodological procedure for the design of the combustion system of a hydrogen-fueled, direct injection spark ignited commercial vehicle engine is described. In a preliminary step, the ability of the commercial 3D computational fluid dynamics (CFD) code AVL FIRE classic to reproduce the characteristics of the gas jet, introduced into a quiescent environment by a dedicated H2 injector, is established. This is based on two parts: Temporal and numerical discretization sensitivity analyses ensure that the spatial and temporal resolution of the simulations is adequate, and comparisons to a comprehensive set of experiments demonstrate the accuracy of the simulations. The measurements used for this purpose rely on the well-known schlieren technique and use helium as a safe substitute for H2.

For the purpose of achieving carbon-neutrality in the mobility sector by 2050, hydrogen can play a crucial role as an alternative energy carrier, not only for direct usage in fuel cell-powered vehicles, but also for fueling internal combustion engines. This paper focuses on the numerical investigation of high-pressure hydrogen injection and the mixture formation inside a high-tumble engine with a conventional liquid fuel injector for passenger cars. Since the traditional 3D-CFD approach of simulating the inner flow of an injector requires a very high spatial and temporal resolution, the enormous computational effort, especially for full engine simulations, is a big challenge for an effective virtual development of modern engines. An alternative and more pragmatic lagrangian 3D-CFD approach offers opportunities for a significant reduction in computational effort without sacrificing reliability.

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.

The automotive industry is continuously evolving, demanding innovative approaches to enhance testing methodologies and preventive identify potential issues. This paper proposes an advancement test approach in the area of the overall vehicle system included steering system and power train on a “Road to Rig” test bench. The research aims to revolutionize the conventional testing process by identifying faults at an early stage and eliminating the need to rely solely on field tests. The motivation behind this research is to optimize the test bench setup and bring it even closer to real field tests. Key highlights of the publication include the introduction of an expanded load spectrum, incorporating both steering angle and speed parameters along the test track. The load includes different route and driving profiles like on a freeway, overland and city drive in combination with the steering angles.

Ducted Fuel Injection (DFI) engines have emerged as a promising technology in the pursuit of a clean and efficient combustion process. This article aims at elucidating the effect of piston geometry on the engine performance and emissions of a metal DFI engine. Three different types of pistons were investigated and the main piston design features including the piston bowl diameter, piston bowl slope angle, duct angle and the injection nozzle position were examined. To achieve the target, computational fluid dynamics (CFD) simulations were conducted coupled to a reduced chemical kinetics mechanism. Extensive validations were performed against the measured data from a conventional diesel engine. To calibrate the soot model, genetic algorithm and machine learning methods were utilized. The simulation results highlight the pivotal role played by piston bowl diameter and fuel injection angle in controlling soot emissions of a DFI engine.

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.

In recent years, the urgent need to fully exploit the fuel economy potential of the Electrified Vehicles (xEVs) through the optimal design of their Energy Management System (EMS) have led to an increasing interest in Machine Learning (ML) techniques. Among them, Reinforcement Learning (RL) seems to be one of the most promising approaches thanks to its peculiar structure, in which an agent is able to learn the optimal control strategy through the feedback received by a direct interaction with the environment. Therefore, in this study, a new Soft Actor-Critic agent (SAC), which exploits a stochastic policy, was implemented on a digital twin of a state-of-the-art diesel Plug-in Hybrid Electric Vehicle (PHEV) available on the European market. The SAC agent was trained to enhance the fuel economy of the PHEV while guaranteeing its battery charge sustainability.

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.

Nowadays, the push for more ecological low-carbon propulsion systems is high in all mobility sectors, including the recreational or light-commercial boating, where propulsion is usually provided by internal combustion engines derived from road applications. In this work, the effects of replacing conventional fossil-derived B7 diesel with Hydrotreated Vegetable Oil (HVO) were experimentally investigated in a modern Medium-Duty Engine, using the advanced biofuel as drop-in and testing according to the ISO 8178 marine standard. The compounded results showed significant benefits in terms of NOx, Soot, mass fuel consumption and WTW CO2 thanks to the inner properties of the aromatic-free, hydrogen-rich renewable fuel, with no impact on the engine power and minimal deterioration of the volumetric fuel economy.

For electric vehicles, road noise, together with wind noise, is the most important contributor for vehicle interior noise. Road noise is very dependent on the NVH behavior of axle system including wheels and tires. Axle system is part of vehicle platform which should be compatible with different body variants. Therefore, il is important to characterize the NVH performance of an axle system independently of car body structure, so that the design the axle can be optimized at the early stage according to the global requirements of all the related vehicles. The best way to characterize the NVH performance of an axle system is to measure the blocked forces on an appropriate test rig. However, the measurement of blocked forces from an axle system requires very stiff boundary conditions which is difficult to achieve in practice. For axles with rigid mountings, it is nearly impossible to measure the blocked forces on test rig.

This research aims to develop an inverse control method capable of adaptively simulating dynamic models of car subsystems in the rig-test condition. Accurate simulation of the actual vibration conditions is one of the most crucial factors in realizing reliable rig-test platforms. However, most typical rig tests are conducted under simple random or harmonic sweep conditions. Moreover, the conventional test methods are hard to directly adapt to the actual vibration conditions when switching the dynamic characteristics of the subsystem in the rig test. In the present work, we developed an inverse controller to adaptively control the vibration exciter referring to the target vibration signal. An adaptive LMS filter, employed for the control algorithm, updated the filter weights in real time by referring to the target and the measured acceleration signals.

Severe problem of aerodynamic heating and drag force are inherent with any hypersonic space vehicle like space shuttle, missiles etc. For proper design of vehicle, the drag force measurement become very crucial. Ground based test facilities are employed for these estimates along with any suitable force balance as well as sensors. There are many sensors (Accelerometer, Strain gauge and Piezofilm) reported in the literature that is used for evaluating the actual aerodynamic forces over test model in high speed flow. As per previous study, the piezofilm also become an alternative sensor over the strain gauges due to its simple instrumentation. For current investigation, the piezofilm and strain gauge sensors have mounted on same stress force balance to evaluate the response time as well as accuracy of predicted force at the same instant. However, these force balance need to be calibrated for inverse prediction of the force from recorded responses.

Dimensional optimization has always been a time consuming process, especially for aerodynamic bodies, requiring much tuning of dimensions and testing for each sample. Aerodynamic auxiliaries, especially wings, are design dependent on the primary model attached, as they influence the amount of lift or reduction in drag which is beneficial to the model. In this study CFD analysis is performed to obtain pressure counter of wings. For a wing, the angle of attack is essential in creating proper splits to incoming winds, even under high velocities with larger distances from the separation point. In the case of a group of wings, each wing is then mentioned as a wing element, and each wing is strategically positioned behind the previous wing in terms of its vertical height and its self-angle of attack to create maximum lift. At the same time, its drag remains variable to its shape ultimately maximizing the C L /C D ratio.

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

A novel method for Single Event Effect (SEE) Radiation Testing using Built-In Self-Test (BIST) feature of indigenously developed Vikram1601 processor is discussed. The novelty is that the usage of BIST avoids need of exhaustive test vectors to ensure test coverage of all the internal registers and physical memory to store them. So processor is the only element vulnerable to radiation damage during testing. The test design was carried out at VSSC, Trivandrum and the testing was carried out at IUAC, Delhi. In the first part, a brief introduction, need and methods of radiation testing of electronics especially SEE of radiation on Silicon based devices, different radiation effects, radiation damage mechanisms and testing methods are described. A brief introduction to Vikram1601 processor, the instruction – TST, used as BIST and testing scheme implementation using TST for studying the SEE is explained.

RAMBHA-LP (Radio Anatomy of Moon Bound Hypersensitive Ionosphere and Atmosphere - Langmuir Probe) is one of the key scientific payloads onboard the Indian Space Research Organization’s (ISRO) Chandrayaan-3 mission. Its objectives were to estimate the plasma density and its variations on the near lunar surface. The probe was initially kept in a stowed condition attached to the lander. A mechanism was designed and realized to meet the functional requirement of deploying the probe at a distance of 1 meter, equivalent to the Debye length of the probe in the moon’s plasma environment. The probe deployment mechanism consists of the Titanium alloy spherical probe with a Titanium Nitride coating on its surface to achieve a constant work function, a long carbon-fiber-reinforced polymer boom, a double torsion spring, a dust-protection box, and a shape-memory alloy-based Frangibolt actuator for low-shock separation. The entire mechanism weighed less than 1.5 kilograms.

It is important to accurately predict the aerodynamic properties for designing applications which involves fluid flows, particularly in the aerospace industry. Traditionally, this is done through complex numerical simulations, which are computationally expensive, resource-intensive and time-consuming, making them less than ideal for iterative design processes and rapid prototyping. Machine learning, powered by vast datasets and advanced algorithms, offers an innovative approach to predict airfoil characteristics with remarkable accuracy, speed, and cost-effectiveness. Machine learning techniques have been applied to fluid dynamics and have shown promising results. In this study, machine learning model called the back-propagation neural network (BPNN) is used to predict key aerodynamic coefficients of lift and drag for airfoils.

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).

With globalization, vehicles are sold across the world throughout different markets and their automotive brake systems must function across a range of environmental conditions. Currently, there is no current standardized test that analyzes brake pads’ robustness against severe cold and humid environmental conditions. The purpose of this proposed test method is to validate brake system performance under severe cold conditions, comparing the results with ambient conditions to evaluate varying lining materials’ functional robustness. The goal of this paper is to aid in setting a standardized process and procedure for the testing of automotive brakes’ environmental robustness. Seven candidate friction materials were selected for analysis. The friction materials are kept confidential. Design of experiment (DOE) techniques were used to create a full-factorial test plan that covered all combinations of parameters.