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Software usability is a quality attribute defined as ?the extent to which a product can be used by specified users to achieve specified goals with effectiveness, efficiency and satisfaction in a specific context of use? (ISO 9241, 1998), usability is also referred to as ?quality in use? (ISO 14598, 1999). Presenter Anabell Beltran, Stoneridge Electronics North America

The paper describes the approach, addresses integration challenges and discusses capabilities of the Hybrid Powertrain-in-the-Loop (H-PIL) facility for the series/hydrostatic hydraulic hybrid system. We describe the simulation of the open-loop and closed-loop hydraulic hybrid systems in H-PIL and its use for concurrent engineering and development of advanced supervisory strategies. Presenter Fernando Tavares, Univ. of Michigan

This paper describes the development of the fixed seat eyellipse in the October 2008 revision of SAE Recommended Practice J941. The eye locations of 23 men and women with a wide range of stature were recorded as they sat in each of three second-row bench seats in a laboratory mockup. Testing was conducted at 19-, 23-, and 27-degree seat back angles. Regression analysis demonstrated that passenger eye location was significantly affected by stature and by seat back angle. The regression results were used to develop an elliptical approximation of the distribution of adult passenger eye locations, applying a methodology previously used to develop the driver eyellipse in SAE J941-2002.

This paper investigates series plug-in hybrid electric vehicle (PHEV) behavior during one-day with synthesized representative one-day missions. The amounts of electric energy and fuel consumption are predicted to assess the PHEV impact on the grid with respect to the driving distance and different charging scenarios: (1) charging overnight, (2) charging whenever possible. The representative cycles are synthesized using the extracted information from the real-world driving data in Southeast Michigan gathered through the Field Operational Tests (FOT) conducted by the University of Michigan Transportation Research Institute (UMTRI). The real-world driving data include 4,409 trips covering 830 independent days and temporal distributions of departure and arrival times. The sample size is large enough to represent real-world driving.

Model validation is a process to assess the validity and predictive capabilities of a computer model by comparing simulation results with test data for its intended use of the model. One of the key difficulties for model validation is to evaluate the quality of a computer model at different test configurations in design space, and interpolate or extrapolate the evaluation results to untested new design configurations. In this paper, an integrated model interpolation and extrapolation framework based on Bayesian inference and Response Surface Models (RSM) is proposed to validate the designs both within and outside of the original design space. Bayesian inference is first applied to quantify the distributions' hyper-parameters of the bias between test and CAE data in the validation domain. Then, the hyper-parameters are extrapolated from the design configurations to untested new design. They are then followed by the prediction interval of responses at the new design points.

This paper contains an analysis of the potential safety benefits of electronic stability control (ESC) for single unit trucks and tractor semitrailers within the U.S. operating environment. It is based on research projects [1,2] which combined hardware-in-the-loop simulation and vehicle testing with the analysis of independent crash datasets using engineering and statistical techniques to estimate the probable safety benefits of stability control technologies for 5-axle tractor-semitrailer vehicles and single unit trucks. The characteristics of ESC-relevant crashes involving these two vehicle classes were found to be very different as were the control strategies needed for crash avoidance. Rollover was the dominant ESC relevant crash type for tractor semitrailers while loss of control was the dominant ESC relevant crash for straight trucks.

Seat comfort is driven in part by the fit between the sitter and seat. Traditional anthropometric data provide little information about the size and shape of the torso that can be used for backrest design. This study introduces a methodology for using three-dimensional computer models of the human torso based on a statistical analysis of body shapes for conducting automated fit assessments. Surface scan data from 296 men and 417 women in a seated posture were analyzed to create a body shape model that can be adjusted to a range of statures, body shape, and postures spanning those typical of vehicle occupants. Finite-element models of two auto seat surface were created, along with custom software that generates body models and postures them in the seat. A simple simulation technique was developed to rapidly assess the fit of the torso relative to the seat back.

Model validation is a process of determining the degree to which a model is an accurate representation of the real world from the perspective of the intended uses of the model. In reliability based design, the intended use of the model is to identify an optimal design with the minimum cost function while satisfying all reliability constraints. It is pivotal that computational models should be validated before conducting the reliability based design. This paper presents an ensemble approach for model bias prediction in order to correct predictions of computational models. The basic idea is to first characterize the model bias of computational models, then correct the model prediction by adding the characterized model bias. The ensemble approach is composed of two prediction mechanisms: 1) response surface of model bias, and 2) Copula modeling of a series of relationships between design variables and the model bias, between model prediction and the model bias.

Understanding the lightweighting potential of materials is important in making strategic decisions for material selection for a new vehicle program. Frequently benchmarking is done to support these decisions by selecting a reference vehicle which is believed to be mass efficient, then using the teardown mass data to set targets for the vehicle under design. In this work, rather then considering a single benchmark vehicle or a small set of vehicles, we looked at a large sample of vehicles over a range of sizes and segments (approximately 200 vehicles). Statistical methods were used to identify mass drivers for each subsystem. Mass drivers are the attributes of the vehicle and subsystem which determine subsystem mass. Understanding mass-drivers allows comparisons across vehicle size, segments, and materials. Next, we identified those vehicles which had subsystems which were much lighter than the average after adjusting for mass drivers. This set was defined as mass-efficient designs.

The fore-aft location of the steering wheel relative to the pedals is a critical determinant of driving posture and comfort. Current SAE practices lack quantitative guidance on steering wheel positioning. This paper presents a model of subjective preference for fore-aft steering wheel position across a range of seat heights. Sixty-eight men and women evaluated the steering wheel positions in a total of 9 package conditions differentiated by seat height and fore-aft steering wheel position. Numerical responses were given on a 7-point scale anchored with the words “Too Close”, “Just Right”, and “Too Far”. A statistical analysis of the results demonstrated that the preferred fore-aft steering wheel position was affected by seat height and driver stature. An ordinal logistic regression model was created that predicts the distribution of subjective responses to steering wheel location. The model can be used to calculate the preferred steering wheel position for individuals or populations.

Seat belt anchorage locations have a strong effect on occupant protection. Federal Motor Vehicle Safety Standard (FMVSS) 210 specifies requirements for the layout of the anchorages relative to the seating reference point and seat back angle established by the SAE J826 H-point manikin. Sled testing and computational simulation has established that belt anchorage locations have a strong effect on occupant kinematics, particularly for child occupants using the belt as their primary restraint. As part of a larger study of vehicle geometry, the locations of the anchorage points in the second-row, outboard seating positions of 83 passenger cars and light trucks with a median model year of 2005 were measured. The lower anchorage locations spanned the entire range of lap belt angles permissible under FMVSS 210 and the upper anchorages (D-ring locations) were distributed widely as well.

The number of control actuators available on spark-ignition engines is rapidly increasing to meet demand for improved fuel economy and reduced exhaust emissions. The added complexity greatly complicates control strategy development because there can be a wide range of potential actuator settings at each engine operating condition, and map-based actuator calibration becomes challenging as the number of control degrees of freedom expand significantly. Many engine actuators, such as variable valve actuation and flow control valves, directly influence in-cylinder combustion through changes in gas exchange, mixture preparation, and charge motion. The addition of these types of actuators makes it difficult to predict the influences of individual actuator positioning on in-cylinder combustion without substantial experimental complexity.

An oil-lubricated wet clutch has a direct impact on the drivability and fuel economy of a vehicle equipped with an automatic transmission system. However, a reliable analysis of clutch behavior still remains a challenge. The purpose of this study is to advance the state-of the-art in CFD methodology for modeling transient clutch behavior. First, a new iterative scheme is developed, in combination with commercial CFD software, which is capable of simulating the squeeze film process in a wet clutch. The numerical results are then validated using analytical solutions of the Reynolds equation for simplified clutch geometry and various boundary conditions. It is found that the choice of boundary conditions has a strong influence on squeeze film simulation. The iterative scheme is further validated by comparison to clutch engagement experiments.

Stiffened panels are encountered in many engineering systems since the stiffeners comprise the mechanism which provides support and rigidity to the panel's skin. Either a mechanical excitation or an acoustic load can be applied on a stiffened panel creating vibration that is transmitted in all panel components. Mechanical excitation tends to be localized in nature, originating from operating machinery mounted on the panel, while the acoustic excitation tends to be distributed over the entire panel, since it typically originates from an external acoustic source which creates an acoustic field impinging on the entire panel. In the Energy Finite Element Analysis (EFEA) various degrees of fidelity are possible when modeling the response of a stiffened panel. In this paper, the theoretical background and the corresponding implications associated with each alternative modeling approach are presented first.

Vehicle design is a complex process requiring interactions and exchange of information among multiple disciplines such as fatigue, strength, noise, safety, etc. Simulation models are employed for assessing and potentially improving a vehicle's performance in individual technical areas. Challenges arise when designing a vehicle for improving mutually competing objectives, satisfying constraints from multiple engineering disciplines, and determining a single set of values for the vehicle's characteristics. It is of interest to engage simulation models from the various engineering disciplines in an organized and coordinated manner for determining a design configuration that provides the best possible performance in all disciplines. The multi-discipline design process becomes streamlined when the simulation methods integrate well with finite element or computer aided design models.

A supervisory controller strategy for a hybrid vehicle coordinates the operation of the two power sources onboard of a vehicle to maximize objectives like fuel economy. In the past, various control strategies have been developed using heuristics as well as optimal control theory. The Stochastic Dynamic Programming (SDP) has been previously applied to determine implementable optimal control policies for discrete time dynamic systems whose states evolve according to given transition probabilities. However, the approach is constrained by the curse of dimensionality, i.e. an exponential increase in computational effort with increase in system state space, faced by dynamic programming based algorithms. This paper proposes a novel approach capable of overcoming the curse of dimensionality and solving policy optimization for a system with very large design state space.

In this paper a Multi-Level System (MLS) optimization algorithm is presented and utilized for the multi-discipline design of a ground vehicle track. The MLS can guide the decision making process for designing a complex system where many alternatives and many mutually competing objectives and disciplines need to be considered and evaluated. Mathematical relationships between the design variables and the multiple discipline performance objectives are developed adaptively as the various design considerations are evaluated and as the design is being evolved. These relationships are employed for rewarding performance improvement during the decision making process by allocating more resources to the disciplines which exhibit the higher level of improvement. The track analysis demonstrates how a multi-discipline design approach can be pursued in ground vehicle applications.

The present paper describes two crash tests designed to validate a computer simulation developed for predicting the large dynamic plastic response of vehicle structures under crash conditions. The test structures were idealized quarter scale models consisting of frame and rigid body elements. Both direct and oblique pole impacts are reported. Impact speed was 30 MPH. Predicted and experimental results are compared for the crush displacements, impact force at the pole barrier, and acceleration histories at two points on the “passenger compartment” mass. Good agreement is obtained for the symmetric test. Results for the oblique test are not as uniformly good, but quantitative agreement is still satisfactory. Comparison of dynamic variables are sensitive to both the filtering of the raw test data and the numerical integration procedure employed in the simulation.

In this study, two sled series were conducted with a sled buck representing a compact vehicle. The first series of tests focused on the effects of crash pulse, impact angle, occupant size, and front seat location on rear seat occupant restraint with a generic rear-seat belt system without pre-tensioner or load limiter. The second series of tests focused on investigating the benefit of using advanced features for rear-seat occupant restraint in the most severe crash condition in the first sled series. The first series of tests include 16 test conditions with two impact angles (0° and 15°), two sled pulse (soft and severe), and four ATD sizes (HIII 6YO, HIII 5th female, HIII 95th male, and THOR-NT) with two ATDs in each test. The driver seat was located at the mid position, while the front passenger seat was positioned such that a constant distance between the ATD knee and the front seat is achieved.

New vehicle control algorithms are needed to meet future emissions and fuel economy mandates that are quite likely to require a measurement of ambient specific humidity (SH). Current practice is to obtain the SH by measurement of relative humidity (RH), temperature and barometric pressure with physical sensors, and then to estimate the SH using a fit equation. In this paper a novel approach is described: a system of neural networks trained to estimate the SH using data that already exists on the vehicle bus. The neural network system, which is referred to as a virtual SH sensor, incorporates information from the global navigation satellite system such as longitude, latitude, time and date, and from the vehicle climate control system such as temperature and barometric pressure, and outputs an estimate of SH. The conclusion of this preliminary study is that neural networks have the potential of being used as a virtual sensor for estimating ambient and intake manifold's SH.