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RMS (Reliability-Maintainability-Safety-Supportability) engineering is emerging as the newest discipline in product development due to new credible, accurate, quantitative methods. Weibull Analysis is foremost among these new tools. New and advanced Weibull techniques are a significant improvement over the original Weibull approach. This workshop, originally developed by Dr. Bob Abernethy, presents special methods developed for these data problems, such as Weibayes, with actual case studies in addition to the latest techniques in SuperSMITH® Weibull for risk forecasts with renewal and optimal component replacement.

The 2-day foundational-level Fundamentals of GD&T course teaches the terms, rules, symbols, and concepts of geometric dimensioning and tolerancing, as prescribed in the ASME Y14.5-2018 Standard. The class offers an explanation of geometric tolerances, including their symbols, tolerance zones, applicable modifiers, common applications, and limitations. It explains Rules #1 and #2, the datum system, form and orientation controls, tolerance of position (RFS and MMC), runout, and profile controls. Newly acquired learning is reinforced throughout the class with more than 130 practice exercises, including more than 60 application problems. 

The 2-day foundational-level Fundamentals of GD&T course teaches the terms, rules, symbols, and concepts of geometric dimensioning and tolerancing, as prescribed in the ASME Y14.5-2009 Standard. The class offers an explanation of geometric tolerances, their symbols, tolerance zones, applicable modifiers, common applications, and limitations. It explains Rules #1 and #2, form and orientation controls, the datum system, tolerance of position (RFS and MMC), runout, and profile controls. Newly acquired learning is reinforced throughout the class with more than 80 practice exercises. 

This full-day course is designed to equip engineering professionals with the knowledge and tools needed to combine the strengths of Design Engineering and Systems Engineering into Systems Design Engineering (SDE) principles. These principles will improve engineering efficiency and practically design more sustainable system-level products, all while strategically aligning with digital transformation objectives.

Electric and hybrid vehicle engineers and designers are faced with the important issue of how to adequately configure required powertrain system components to achieve needed performance, occupant accommodation, and operational objectives. This course enables participants to fully comprehend vehicle architectural/configurational design requirements to enable efficient structural design, effective packaging of required components, and efficient vehicle performance for shared and autonomous operation. The importance of integrating these design requirements with specific vehicle user needs and expectations will be emphasized.

Due to its physical and chemical properties, hydrogen is an attractive fuel for internal combustion engines, providing grounds for studies on hydrogen engines. It is common practice to use a mathematical model for basic engine design and an essential part of this is the simulation of the combustion cycle, which is the subject of the work presented here. One of the most widely used models for describing combustion in gasoline and diesel engines is the Wiebe model. However, for cases of hydrogen combustion in DI engines, which are characterized by mixture stratification and in some cases significant incomplete combustion, practically no data can be found in the literature on the application of the Wiebe model. Based on Wiebe's formulas, a mathematical model of hydrogen combustion has been developed. The model allows making computations for both DI and PFI hydrogen engines. The parameters of the Wiebe model were assessed for three different engines in a total of 26 operating modes.

The global time that is propagated and synchronized in the vehicle E/E architecture is used in safety-critical, security-critical, and time-critical applications (e.g., driver assistance functions, intrusion detection system, vehicle diagnostics, external device authentication during vehicle diagnostics, vehicle-to-grid and so on). The cybersecurity attacks targeting the global time result in false time, accuracy degradation, and denial of service as stated in IETF RFC 7384. These failures reduce the vehicle availability, robustness, and safety of the road user. IEEE 1588 lists four mechanisms (integrated security mechanism, external security mechanism, architectural solution, and monitoring & management) to secure the global time. AUTOSAR defines the architecture and detailed specifications for the integrated security mechanism "Secured Global Time Synchronization (SGTS)" to secure the global time on automotive networks (CAN, FlexRay, Ethernet).

The emergence of connected vehicles is driven by increasing customer and regulatory demands. To meet these, more complex software applications, some of which require service-based cloud and edge backends, are developed. Due to the short lifespan of software, it becomes necessary to keep these cloud environments and their applications up to date with security updates and new features. However, as new behavior is introduced to the system, the high complexity and interdependencies between components can lead to unforeseen side effects in other system parts. As such, it becomes more challenging to recognize whether deviations to the intended system behavior are occurring, ultimately resulting in higher monitoring efforts and slower responses to errors. To overcome this problem, a simulation of the cloud environment running in parallel to the system is proposed. This approach enables the live comparison between simulated and real cloud behavior.

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.

Nowadays, green hydrogen can play a crucial role in a successful clean energy transition, thus reaching net zero emissions in the transport sector. Moreover, hydrogen exploitation in internal combustion engines is favoured by its suitable combustion properties and quasi-zero harmful emissions. High flame speeds enable a lean combustion approach, which provides high efficiency and reduces NOx emissions. However, high air flow rates are required to achieve the load levels typical of heavy-duty applications. In this framework, the present study aims to investigate the required boosting system of a 6-cylinder, 13-liter heavy-duty spark ignition engine through 1D numerical simulation. A comparison among various architectures of the turbocharging system and the size of each component is presented, thus highlighting limitations and potentialities of each architecture and providing important insights for the selection of the best turbocharging system.

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.

Modeling thermal systems in Battery Electric Vehicles (BEVs) is crucial for enhancing energy efficiency through predictive control strategies, thereby extending vehicle range. A major obstacle in this modeling is the often limited availability of detailed system information. This research introduces a methodology using neural networks for system identification, a powerful technique capable of approximating the physical behavior of thermal systems with minimal data requirements. By employing black-box models, this approach supports the creation of optimization-based operational strategies, such as Model Predictive Control (MPC) and Reinforcement Learning-based Control (RL). The system identification process is executed using MATLAB Simulink, with virtual training data produced by validated Simulink models to establish the method's feasibility. The neural networks utilized for system identification are implemented in MATLAB code.

In order to improve the efficiency of passenger cars, developments focus on decreasing their aerodynamic drag, part of which is caused by cooling air. Thus, car manufacturers try to seal the cooling air path to prevent leakage flows. Nevertheless, gaps between the single components of the cooling air path widen due to the deformation of components under aerodynamic load. For simulating the cooling airflow utilization ratio (CAUR), computational fluid dynamics (CFD) simulations are used, which neglect component deformation. In this paper, a computational method aiming at sufficient gap resolution and determining the CAUR of passenger cars under the consideration of component deformation is developed. Therefore, a partitioned approach of fluid structure interaction (FSI) simulations is used. The fluid field is simulated in OpenFOAM, whereas the structural simulations are conducted using Pam-Crash.

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.

Increasing urbanisation and the growing environmental awareness in society require new and innovative vehicle concepts. In the present work, the design freedoms of additive manufacturing (AM) are used to develop a front axle wheel suspension for a novel modular vehicle concept. The development of the suspension components is based on a new method using industry standard load cases for the strength design of the components. To design the chassis components, first the available installation space is determined and a suitable configuration of the chassis components is defined. Furthermore, numerical methods are used to identify component geometries that are suitable for the force flow. The optimisation setup is selected in a way that allows to integrate information, energy and material-carrying conductors into the suspension arms. The conductors even serve as load-bearing structures because of the matching design of the components.

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.