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We propose a composite thermal model of the vehicle passenger compartment that can be used to predict and analyze thermal comfort of the occupants of a vehicle. Physical model is developed using heat flow in and out of the passenger compartment space, comprised of glasses, roof, seats, dashboard, etc. Use of a model under a wide variety of test conditions have shown high sensitivity of compartment air temperature to changes in the outside air temperature, solar heat load, temperature and mass flow of duct outlet air from the climate control system of a vehicle. Use of this model has subsequently reduced empiricism and extensive experimental tests for design and tuning of the automatic climate control system. Simulation of the model allowed several changes to the designs well before the prototype hardware is available.

There is randomness in both the applied loads and the strength of systems. Therefore, to account for the uncertainty, the safety of the system must be quantified using its reliability. Monte Carlo Simulation (MCS) is widely used for probabilistic analysis because of its robustness. However, the high computational cost limits the accuracy of MCS. Smarslok et al. [2010] developed an improved sampling technique for reliability assessment called Separable Monte Carlo (SMC) that can significantly increase the accuracy of estimation without increasing the cost of sampling. However, this method was applied to time-invariant problems involving two random variables. This paper extends SMC to problems with multiple random variables and develops a novel method for estimation of the standard deviation of the probability of failure of a structure. The method is demonstrated and validated on reliability assessment of an offshore wind turbine under turbulent wind loads.

This article presents an approach for comparing alternative repairable systems and calculating the value of information obtained by testing a specified number of such systems. More specifically, an approach is presented to determine the value of information that comes from field testing a specified number of systems in order to appropriately estimate the reliability metric associated with each of the respective repairable systems. Here the reliability of a repairable system will be measured by its failure rate. In support of the decision-making effort, the failure rate is translated into an expected utility based on a utility curve that represents the risk tolerance of the decision-maker. The algorithm calculates the change of the expected value of the decision with the sample size. The change in the value of the decision represents the value of information obtained from testing.

There is an increasing interest in transient thermal simulations of automotive brake systems. This paper presents a high-fidelity CFD tool for modeling complete braking cycles including both the deceleration and acceleration phases. During braking, this model applies the frictional heat at the interface on the contacting rotor and pad surfaces. Based on the conductive heat fluxes within the surrounding parts, the solver divides the frictional heat into energy fluxes entering the solid volumes of the rotor and the pad. The convective heat transfer between the surfaces of solid parts and the cooling airflow is simulated through conjugate heat transfer, and the discrete ordinates model captures the radiative heat exchange between solid surfaces. It is found that modeling the rotor rotation using the sliding mesh approach provides more realistic results than those obtained with the Multiple Reference Frames method.

Autonomous Vehicles (AVs) operate based on image information and 3D maps generated by sensors like cameras, LIDARs and RADARs. This information is processed by the on-board processing units to provide the right actuation signals to drive the vehicle. For safe operation, these sensors should provide continuous high quality data to the processing units without interruption in all driving conditions like dust, rain, snow and any other adverse driving conditions. Any contamination on the sensor surface/lens due to rain droplets, snow, and other debris would result in adverse impact to the quality of data provided for sensor fusion and this could result in error states for autonomous driving. In particular, snow is a common contamination condition during driving that might block a sensor surface or camera lens. Predicting and preventing snow accumulation over the sensor surface of an AV is important to overcome this challenge.

A Reliability-Based Design Optimization (RBDO) method for multiple failure regions is presented. The method uses a Probabilistic Re-Analysis (PRRA) approach in conjunction with an approximate global metamodel with local refinements. The latter serves as an indicator to determine the failure and safe regions. PRRA calculates very efficiently the system reliability of a design by performing a single Monte Carlo (MC) simulation. Although PRRA is based on MC simulation, it calculates “smooth” sensitivity derivatives, allowing therefore, the use of a gradient-based optimizer. An “accurate-on-demand” metamodel is used in the PRRA that allows us to handle problems with multiple disjoint failure regions and potentially multiple most-probable points (MPP). The multiple failure regions are identified by using a clustering technique. A maximin “space-filling” sampling technique is used to construct the metamodel. A vibration absorber example highlights the potential of the proposed method.

Importance Sampling is a popular method for reliability assessment. Although it is significantly more efficient than standard Monte Carlo simulation if a suitable sampling distribution is used, in many design problems it is too expensive. The authors have previously proposed a method to manage the computational cost in standard Monte Carlo simulation that views design as a choice among alternatives with uncertain reliabilities. Information from simulation has value only if it helps the designer make a better choice among the alternatives. This paper extends their method to Importance Sampling. First, the designer estimates the prior probability density functions of the reliabilities of the alternative designs and calculates the expected utility of the choice of the best design. Subsequently, the designer estimates the likelihood function of the probability of failure by performing an initial simulation with Importance Sampling.

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.

With improvements in casting technology, cast iron can be an alternative to steel in some applications due to its similar strength. One objective of this study was to analyze cast iron data obtained from the literature and evaluate predictive correlations between its tensile, microstructural, and fatigue properties. Reasonably good correlation of tensile strength and yield strength were found with hardness. However, fatigue strength could not be correlated with hardness or tensile properties. Another objective of this study was to evaluate tensile and compressive means stress effects on fatigue behavior of 120-90-02 ductile cast iron experimentally, as well as analytically by using predictive models. Mean stress levels were chosen such that R ratios in load-controlled tests were −7, −3, −1, 0, 1/3, 0.5, and 0.75. Modified Goodman, Smith-Watson-Topper, FKM and the Fatemi-Socie mean stress parameters were used to account for the mean stress effect on fatigue life.

Finite element analysis is a well-established methodology in structural dynamics. However, optimization and/or probabilistic studies can be prohibitively expensive because they require repeated FE analyses of large models. Various reanalysis methods have been proposed in order to calculate efficiently the dynamic response of a structure after a baseline design has been modified, without recalculating the new response. The parametric reduced-order modeling (PROM) and the combined approximation (CA) methods are two re-analysis methods, which can handle large model parameter changes in a relatively efficient manner. Although both methods are promising by themselves, they can not handle large FE models with large numbers of DOF (e.g. 100,000) with a large number of design parameters (e.g. 50), which are common in practice. In this paper, the advantages and disadvantages of the PROM and CA methods are first discussed in detail.

This paper describes the application of Statistical Energy Analysis (SEA) and Experimental SEA (ESEA) to calculating the transmission of air-borne and structure-borne noise in a mid-sized sedan. SEA can be applied rapidly in the early stages of vehicle design where the degree of geometric detail is relatively low. It is well suited to the analysis of multiple paths of vibrational energy flow from multiple sources into the passenger compartment at mid to high frequencies. However, the application of SEA is made difficult by the geometry of the vehicle's subsystems and joints. Experience with current unibody vehicles leads to distinct modeling strategies for the various frequency ranges in which airborne or structure-borne noise predominates. The theory and application of ESEA to structure-borne noise is discussed. ESEA yields loss factors and input powers which are combined with an analytical SEA model to yield a single hybrid model.

Although rollover crashes represent a small fraction (approximately 3%) of all motor vehicle crashes, they account for roughly one quarter of crash fatalities to occupants of cars, light trucks, and vans (NHTSA Traffic Safety Facts, 2004). Therefore, the National Highway Traffic Safety Administration (NHTSA) has identified rollover injuries as one of its safety priorities. Motor vehicle manufacturers are developing technologies to reduce the risk of injury associated with rollover collisions. This paper describes the development by General Motors Corporation (GM) of a suite of laboratory tests that can be used to develop sensors that can deploy occupant protection devices like roof rail side air bags and pretensioners in a rollover as well as a discussion of the challenges of conducting this suite of tests.

A methodology involving Design for Six Sigma (DFSS) and Multi-body dynamic simulation is employed to tune a body-on-frame vehicle, for improved ride (shake) performance. The design space is limited to four sets of symmetric body mounts for a vehicle. The stiffness and damping characteristics of the mounts are the control factors in the virtual experiment. Variation of these design parameters from the nominal settings, as well as axle size, tire and wheel combinations, tire pressure, shock damping, and vehicle speed constitute the noise factors. This approach proves to be an excellent predictor of the vehicle behavior, by which much insight as to influence of each parameter on vehicle performance is gained. Ultimately, specific recommendations for the control factor settings are provided. Subsequent hardware builds show excellent agreement with the analytical model and suggested tuning.

Since 1997, General Motors Body Manufacturing Engineering - Die Engineering Services (BME-DES) has been working jointly with our software vendor to develop and implement a parallel version of stamping simulation software for mass production analysis applications. The evolution of this technology and the insight gained through the implementation of DMP/MPP technology as well as performance benchmarks are discussed in this publication.

Historically, the long development time required to produce a new automobile has meant that the electronics in that vehicle might lag the state-of-the-art by several years. For traditional vehicle electronics, this was certainly an appropriate delay, ensuring through extensive testing and qualification that the quality and reliability of the electronic systems met rigorous standards. However, with the growing consumer-oriented electronics content in today's vehicles, it is becoming more difficult for the automotive manufacturers to meet consumers' expectations with older technology. Couple this with the fast-paced consumer product cycle, typically nine to eighteen and the result is increasing pressure on the vehicle manufacturers from after-market electronics suppliers, who can update their product lines as fast as the component manufacturers can produce new models.

The problem of predicting the quality of weld is critical to manufacturing. A great deal of data is collected under multiple conditions to predict the quality. The data generated at Daimler Chrysler has been used to develop a model based on grammatical evolution. Grammatical Evolution Technique is based on Genetic Algorithms and generates rules from the data which fit the data. This paper describes the development of a software tool that enables the user to choose input variables such as the metal types of top and bottom layers and their thickness, intensity and speed of laser beam, to generate a three dimensional map showing weld quality. A 3D weld quality surface can be generated in response to any of the two input variables picked from the set of defining input parameters. This tool will enable the user to pick the right set of input conditions to get an optimal weld quality. The tool is developed in Matlab with Graphical User Interface for the ease of operation.

This paper presents a computer generated model of a hydraulic circuit that is typically seen in hybrid hydraulic vehicles (HHV's). HHV's have shown considerable potential for increasing fuel economy and decreasing emissions for mid-size and commercial trucks that exhibit urban driving habits. The aim of the present model is to aid in further optimizing the performance of the hybrid powertrain and serve as a baseline for future studies into the noise and vibration (NV) associated with the system. The model simulates the regenerative braking process of a parallel type hybrid hydraulic propulsion system. Regenerative braking captures and stores otherwise lost energy into an accumulator. This stored energy can then later be used to propel the vehicle, thus reducing the vehicle's reliance on a conventional internal combustion engine (ICE). This model will serve as a baseline for future developments and will be expanded and validated experimentally in ensuing research.

This paper describes the results of an ongoing project in the GM Motorsports Safety Technology Research Program to investigate Indianapolis-type (Indy car) race car crashes using an on-board impact recorder as the primary data collection tool. The paper discusses the development of specifications for the impact-recording device, the selection of the specific recorder and its implementation on a routine basis in Indy car racing. The results from incidents that produced significant data (crashes with peak decelerations above 20 G) during the racing seasons from 1993 through the first half of 1998 are summarized. The focus on Indy car crashes has proven to provide an almost laboratory-like setting due to the similarity of the cars and to the relative simplicity of the crashes (predominantly planar crashes involving single car impacts against well-defined impact surfaces).

The use of regrind acrylonitrile-butadiene-styrene (ABS) for automotive parts and components results in two types of financial savings. The first is the shared monetary savings between General Motors and the molder for the difference in the virgin resin price versus price of the ABS regrind. The second is a societal energy savings seen in the life cycle of virgin ABS versus reground ABS. An added benefit is the preservation of natural resources used to produce virgin ABS.

A complete probabilistic model of uncertainty in probabilistic analysis and design problems is the joint probability distribution of the random variables. Often, it is impractical to estimate this joint probability distribution because the mechanism of the dependence of the variables is not completely understood. This paper proposes modeling dependence by using copulas and demonstrates their representational power. It also compares this representation with a Monte-Carlo simulation using dispersive sampling.