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Casting technology developmentshave led to a manufacturing process that allows the casting of thin wall (2-3mm) heat resistant ferritic stainless steel exhaust manifolds which can replace stamped and tubular weldments as well as iron castings where temperature requirements are increased. This casting process combines the thin wall and clean metal benefits of the counter gravity, vacuum-assist casting process using thin, light-weight bonded sand molds supported by vacuum-ridgidized sand. This combination is called the LSVAC (Loose Sand Vacuum Assisted Casting) process, a patented process. This process will significantly contribute to the growth of near-net shape steellstainless steel castings for automotive and allied industries. For exhaust manifolds, a modified grade of ferritic stainless steel with good oxidation resistance to 950°C in high dew point synthetic exhaust gas atmospheres was developed.

This paper assesses the potential improvement of automotive powertrain technologies 25 years into the future. The powertrain types assessed include naturally-aspirated gasoline engines, turbocharged gasoline engines, diesel engines, gasoline-electric hybrids, and various advanced transmissions. Advancements in aerodynamics, vehicle weight reduction and tire rolling friction are also taken into account. The objective of the comparison is the potential of anticipated improvements in these powertrain technologies for reducing petroleum consumption and greenhouse gas emissions at the same level of performance as current vehicles in the U.S.A. The fuel consumption and performance of future vehicles was estimated using a combination of scaling laws and detailed vehicle simulations. The results indicate that there is significant potential for reduction of fuel consumption for all the powertrains examined.

As component and systems sophistication in both cars and trucks increase, improved diagnostic capabilities are required to assure proper and expedient serviceability. Replacement of electrical modules, starter motors, carburetors, fuel injectors and even whole engines or transmissions is encouraged by high labor costs and continued vehicle mobility mandates. The remanufacturing business has grown and components previously discarded now provide valuable core elements to feed the industry. To achieve efficient utilization of capital, equipment and labor, remanufacturers must estimate when this supply of core elements will be available and plan their production schedules accordingly. In order to properly service private individuals and commercial fleets, minimize vehicle downtime and reduce life cycle costs, adaptation of available analytical tools must be made.

Rare earths are a group of elements whose availability has been of concern due to monopolistic supply conditions and environmentally unsustainable mining practices. To evaluate the risks of rare earths availability to automakers, a first step is to determine raw material content and value in vehicles. This task is challenging because rare earth elements are used in small quantities, in a large number of components, and by suppliers far upstream in the supply chain. For this work, data on rare earth content reported by vehicle parts suppliers was assessed to estimate the rare earth usage of a typical conventional gasoline engine midsize sedan and a full hybrid sedan. Parts were selected from a large set of reported parts to build a hypothetical typical mid-size sedan. Estimates of rare earth content for vehicles with alternative powertrain and battery technologies were made based on the available parts' data.

Accurately predicting real-world vehicle energy consumption is essential for optimizing vehicle designs, enhancing energy efficiency, and developing effective energy management strategies. This paper presents a data-driven approach that utilizes machine learning techniques and a comprehensive dataset of vehicle parameters and environmental factors to create precise energy consumption prediction models. The methodology involves recording real-world vehicle data using data loggers to extract information from the CAN bus systems for ICE and hybrid electric, as well as hydrogen and battery fuel cell vehicles. Data cleaning and cycle-based analysis are employed to process the dataset for accurate energy consumption prediction. This includes cycle detection and analysis using methods from statistics and signal processing, and then pattern recognition based on these metrics.

This paper discusses an analytical technique for computing the failure rate of steel springs used in automotive part applications. Preliminary computations may be performed and used to predict spring failure rates quickly at a very early stage of a product development cycle and to establish program reliability impact before commitment. The analytical method is essentially a combination of various existing procedures that are logically sequenced to compute a spring probability of failure under various operational conditions. Fatigue life of a mechanical component can be computed from its S-N curve. For steels, the S-N curve can be approximated by formulae which describe the fatigue life as a function of its endurance limit and its alternating stress. Most springs in service are preloaded and the actual stress fluctuates about a mean level. In order to compute an equivalent alternating stress with zero mean, an analytical method based on the Goodman Diagram is used.

Advances in avionics and display technology are significantly changing the cockpit environment in current transport aircraft. The MIT Aeronautical Systems Lab (ASL) has developed a part-task flight simulator specifically to study the effects of these new technologies on flight crew situational awareness and performance. The simulator is based on a commercially-available graphics workstation, and can be rapidly reconfigured to meet the varying demands of experimental studies. The simulator has been successfully used to evaluate graphical microburst alerting displays, electronic instrument approach plates, terrain awareness and alerting displays, and ATC routing amendment delivery through digital datalinks.

The challenge of developing a robust, real-time driver gaze classification system is that it has to handle difficult edge cases that arise in real-world driving conditions: extreme lighting variations, eyeglass reflections, sunglasses and other occlusions. We propose a single-camera end-toend framework for classifying driver gaze into a discrete set of regions. This framework includes data collection, semi-automated annotation, offline classifier training, and an online real-time image processing pipeline that classifies the gaze region of the driver. We evaluate an implementation of each component on various subsets of a large onroad dataset. The key insight of our work is that robust driver gaze classification in real-world conditions is best approached by leveraging the power of supervised learning to generalize over the edge cases present in large annotated on-road datasets.

This paper quantifies the potential of electric propulsion systems to reduce petroleum use and greenhouse gas (GHG) emissions in the 2030 U.S. light-duty vehicle fleet. The propulsion systems under consideration include gasoline hybrid-electric vehicles (HEVs), plug-in hybrid vehicles (PHEVs), fuel-cell hybrid vehicles (FCVs), and battery-electric vehicles (BEVs). The performance and cost of key enabling technologies were extrapolated over a 25-30 year time horizon. These results were integrated with software simulations to model vehicle performance and tank-to-wheel energy consumption. Well-to-wheel energy and GHG emissions of future vehicle technologies were estimated by integrating the vehicle technology evaluation with assessments of different fuel pathways. The results show that, if vehicle size and performance remain constant at present-day levels, these electric propulsion systems can reduce or eliminate the transport sector's reliance on petroleum.