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Driving simulators allow the testing of driving functions, vehicle models and acceptance assessment at an early stage. For a real driving experience, it's necessary that all immersions are depicted as realistically as possible. When driving manually, the perceived haptic steering wheel torque plays a key role in conveying a realistic steering feel. To ensure this, complex multi-body systems are used with numerous of parameters that are difficult to identify. Therefore, this study shows a method how to generate a realistic steering feel with a nonlinear open-loop model which only contains significant parameters, particularly the friction of the steering gear. This is suitable for the steering feel in the most driving on-center area. Measurements from test benches and real test drives with an Electric Power Steering (EPS) were used for the Identification and Validation of the model.

The optimization and further development of automated driving functions offers great potential to relieve the driver in various driving situations and increase road safety. Simulative testing in particular is an indispensable tool in this process, allowing conclusions to be drawn about the design of automated driving functions at a very early stage of development. In this context, the use of driving simulators provides support so that the driving functions of tomorrow can be experienced in a very safe and reproducible environment. The focus of the acceptance and optimization of automated driving functions is particularly on vehicle lateral control functions. As part of this paper, a test person study was carried out regarding manual vehicle lateral control on the dynamic vehicle road simulator at the Institute of Automotive Engineering.

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.

In pursuing sustainable automotive technologies, exploring alternative fuels for hybrid vehicles is crucial in reducing environmental impact and aligning with global carbon emission reduction goals. This work compares methanol and naphtha as potential suitable alternative fuels for running in a battery-driven light-duty hybrid vehicle by comparing their performance with the diesel baseline engine. This work employs a 0-D vehicle simulation model within the GT-Power suite to replicate vehicle dynamics under the Worldwide Harmonized Light Vehicles Test Cycle (WLTC). The vehicle choice enables the assessment of a delivery application scenario using distinct payload capacities: 0%, 25%, 50%, and 100%. The model is fed with engine maps derived from previous experimental work conducted in the same engine, in which a full calibration was obtained that ensures the engine's operability in a wide region of rotational speed and loads.

Although structural intensity was introduced in the 80's, this concept never found practical applications, neither for numerical nor experimental approaches. Quickly, it has been pointed out that only the irrotational component of the intensity offers an easy interpretation of the dynamic behavior of structures by visualizing the vibration energy flow. This is especially valuable at mid and high frequency where the structure response understanding can be challenging. A new methodolodgy is proposed in order to extract this irrotational intensity field from the Finite Element Model of assembled structures such as Bodies In White. This methodology is hybrid in the sense that it employs two distinct solvers: a dynamic solver to compute the structural dynamic response and a thermal solver to address a diffusion equation analogous to the thermal conduction built from the previous dynamic response.

Water management in PEMFC power generation systems is a key point to guarantee optimal performances and durability. It is known that a poor water management has a direct impact on PEMFC voltage, both in drying and flooding conditions: furthermore, water management entails phenomena from micro-scale, i.e., formation and water transport within membrane, to meso-scale, i.e., water capillary transport inside the GDL, up to the macro-scale, i.e., water droplet formation and removal from the GFC. Water transport mechanisms through the membrane are well known in literature, but typically a high computational burden is requested for their proper simulation. To deal with this issue, the authors have developed an analytical model for the water membrane content simulation as function of stack temperature and current density, for fast on-board monitoring and control purposes, with good fit with literature data.

Computer modelling, virtual prototyping and simulation is widely used in the automotive industry to optimize the development process. While the use of CAE is widespread, on its own it lacks the ability to provide observable acoustics or tactile vibrations for decision makers to assess, and hence optimize the customer experience. Subjective assessment using Driver-in-Loop simulators to experience data has been shown to improve the quality of vehicles and reduce development time and uncertainty. Efficient development processes require a seamless interface from detailed CAE simulation to subjective evaluations suitable for high level decision makers. In the context of perceived vehicle vibration, the need for a bridge between complex CAE data and realistic subjective evaluation of tactile response is most compelling. A suite of VI-grade noise and vibration simulators have been developed to meet this challenge.

In applications demanding high performance under extreme conditions of pressure and temperature, a range of Mechanically Attached Fittings (MAFs) is offered by various Multinational Corporations (MNCs). These engineered fittings have been innovatively designed to meet the rigorous requirements of the aerospace industry, offering a cost-effective and lightweight alternative to traditional methods such as brazing, welding, or other mechanically attached tube joints. One prominent method employed for attaching these fittings to tubing is through Internal Swaging, a mechanical technique. This process involves the outward formation of rigid tubing into grooves within the fitting. One of the methods with which this intricate operation is achieved is by using a drawbolt - expander assembly within an elastomeric swaging machine.

Launch vehicle structures in course of its flight will be subjected to dynamic forces over a range of frequencies up to 2000 Hz. These loads can be steady, transient or random in nature. The dynamic excitations like aerodynamic gust, motor oscillations and transients, sudden application of control force are capable of exciting the low frequency structural modes and cause significant responses at the interface of launch vehicle and satellite. The satellite interface responses to these low frequency excitations are estimated through Coupled Load Analysis (CLA). The analysis plays a crucial role in mission as the satellite design loads and Sine vibration test levels are defined based on this. The perquisite of CLA is to predict the responses with considerable accuracy so that the design loads are not exceeded in the flight. CLA validation is possible by simulating the flight experienced responses through the analysis.

Faults if not detected and processed will create catastrophe in closed loop system for safety critical applications in automotive, space, medical, nuclear, and aerospace domains. In aerospace applications such as stall warning and protection/prevention system (SWPS), algorithms detect stall condition and provide protection by deploying the elevator stick pusher. Failure to detect and prevent stall leads to loss of lives and aircraft. Traditional Functional Hazard and Fault Tree analyses are inadequate to capture all failures due to the complex hardware-software interactions for stall warning and protection system. Hence, an improved methodology for failure detection and identification is proposed. This paper discusses a hybrid formal method and model-based technique using STPA to identify and diagnose faults and provide monitors to process the identified faults to ensure robust design of the indigenous stall warning and protection system (SWPS).

This specification covers a titanium alloy in the form of welding wire (see 8.5).

Abstract AISI H13 hot work tool steel is commonly used for applications such as hot forging and hot extrusion in mechanical working operations that face thermal and mechanical stress fluctuations, leading to premature failures. Cryogenic treatment was applied for AISI H13 steel to improve the surface hardness and thereby fatigue resistance. This work involves failure analysis of H13 steel specimens subjected to cryogenic treatment and gas nitriding. The specimens were heated to 1020°C, oil quenched followed by double tempering at 550°C for 2 h, and subsequently, deep cryogenically treated at −185°C in the cryochamber. Gas nitriding was carried out for 24 h at 500°C for 200 μm case depth in NH3 surroundings. The specimens were subjected to rotating bending fatigue at constant amplitude loading at room temperature.

This procurement specification covers solid rivets and hollow end rivets made from a corrosion and heat resistant steel of the type identified under the Unified Numbering System as UNS S66286 and of 80 ksi single shear strength at room temperature.

Roundabouts are one of the most complex traffic environments in urban roads, and a key challenge for intelligent driving decision-making. Deep reinforcement learning, as an emerging solution for intelligent driving decisions, has the advantage of avoiding complex algorithm design and sustainable iteration. For the decision difficulty in roundabout scenarios, this paper proposes an artificial potential field based Soft Actor-Critic (APF-SAC) algorithm. Firstly, based on the Carla simulator and Gym framework, a reinforcement learning simulation system for roundabout driving is built. Secondly, to reduce reinforcement learning exploration difficulty, global path planning and path smoothing algorithms are designed to generate and optimize the path to guide the agent.

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 economic development and the increasing number of vehicles in cities, urban transport systems have become an important issue in urban development. Efficient traffic signal control is a key part of achieving intelligent transport. Reinforcement learning methods show great potential in solving complex traffic signal control problems with multidimensional states and actions. Most of the existing work has applied reinforcement learning algorithms to intelligently control traffic signals. In this paper, we investigate the agent-based reinforcement learning approach for the intelligent control of ramp entrances and exits of urban arterial roads, and propose the Proximal Policy Optimization (PPO) algorithm for traffic signal control. We compare the method controlled by the improved PPO algorithm with the no-control method.

Fracture characterization of automotive metals under simple shear deformation is critical for the calibration of advanced fracture models employed in forming and crash simulations. In-plane shear fracture tests of high ductility materials have proved challenging since the sample edge fails first in uniaxial tension before the fracture limit in shear is reached at the center of the gage region. Although through-thickness machining is undesirable, it appears required to promote higher strains within the shear zone. The present study seeks to adapt existing in-plane shear geometries, which have otherwise been successful for many automotive materials, to have a local shear zone with a reduced thickness. It is demonstrated that a novel shear zone with a pocket resembling a “peanut” can promote shear fracture within the shear zone while reducing the risk for edge fracture. An emphasis was placed upon machinability and surface quality for the design of the pocket in the shear zone.

Multiple hybrid bead designs were investigated in this study to control the springback on DP780 samples using post-stretching technique. The performance of the four different hybrid bead designs was evaluated by measuring the minimum blank-lock tonnage required to control the springback during a U-channel stamping process. A finite element (FE) model of the U-channel stamping process was developed to simulate the process and predict the minimum blank-lock tonnage required for springback control using each of the hybrid bead designs. It is shown that the developed FE model predicts both the required minimum blank-lock tonnage for post-stretching, and the springback profile, with good accuracy.

During the vehicle lifecycle, customers are able to directly perceive the outer panel stiffness of vehicles in various environmental conditions. The outer panel stiffness is an important factor for customers to perceive the robustness of the vehicle. In the real test of outer panel stiffness after prototype production, evaluators manually press the outer panel in advance to identify vulnerable areas to be tested and evaluate the performance only in those area. However, when developing the outer panel stiffness performance using FEA (Finite Element Analysis) before releasing the drawing, it is not possible to filter out these areas, so the entire outer panel must be evaluated. This requires a significant amount of computing resources and manpower. In this study, an approach utilizing artificial intelligence was proposed to streamline the outer panel stiffness analysis and improve development reliability.

Heavy Vehicle Event Data Recorders (HVEDRs) have the ability to capture important data surrounding an event such as a crash or near crash. Efforts by many researchers to analyze the capabilities and performance of these complex systems can be problematic, in part, due to the challenges of obtaining a heavy truck, the necessary space to safely test systems, the inherent unpredictability in testing, and the costs associated with this research. In this paper, a method for simulating vehicle speed sensor (VSS) inputs to HVEDRs to trigger events is introduced and validated. Full-scale instrumented testing is conducted to capture raw VSS signals during steady state and braking conditions. The recorded steady state VSS signals are injected into the HVEDR along with synthesized signals to evaluate the response of the HVEDR. Brake testing VSS signals are similarly captured and injected into the HVEDR to trigger an event record.