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This paper compares the material consumption and fire patterns which developed on four nearly identical compact sedans when each was burned for exactly the same amount of time, but with different wind speed and direction during the burns. This paper will also compare the effects of environmental exposure to the fire patterns on the vehicles. The burn demonstrations were completed at an outdoor facility in southeast Michigan on four late model compact sedans. The wind direction was controlled by placing the subject vehicle with either the front facing into the wind, or rear facing into the wind. Two of the burns were conducted when the average observed wind speed was 5-6kph and two of the burns were conducted at an average observed wind speed of 19kph.

Several application developers are currently faced with the problem of moving a complex system from a single-core to a multicore platform. The problem encompasses several issues that go from modeling issues (the need to represent the system features of interest with sufficient accuracy) to analysis and optimization techniques, to the selection of the right formulations for constraints that relate to time. We report on the initial findings in a case study in which the application of interest is a fuel injection system. We provide an analysis on the limitations of AUTOSAR and the existing modeling tools with respect to the representation of the parameters of interest for timing analysis, and we discuss applicable optimization methods and analysis algorithms.

Development of the software using fixed-point arithmetic is known to be tedious and error-prone. Difficulty of selecting the correct data type can outwear software developers. The common retreats often sought after include manual calculation of the approximate ranges, exhaustive simulations with extreme input values and conservative development approach by using excessive word length. The first two retreats - manual calculation and exhaustive simulations - increase the software development time, and the third retreat - conservative development - leads to the excessive memory (RAM and ROM) utilization by the software. The model-based development environment such as the Simulink has graphical nature to the software with flow of data defined by connecting signal lines. The model-based software therefore gives an opportunity to trace signal flow in the software. Input-tracing method is presented to trace the flow of the input signals of the user selected block in the software model.

A GT-Power Fast Run Model simplified from detail model for HIL is verified with a bench test using the dSPACE Simulator. Firstly, the conversion process from a detailed model to FRM model is briefly described. Then, the spark timing, fuel pulse with control for FAR, and torque level control are developed for proof of concept. Moreover a series of FRM/Simulink co-simulation and HIL tests are conducted. In the summary, the test results are presented and compared with GT detailed model simulations. The test results show that the FRM/dSPACE HIL stays consistent in most variables of interest under 0.7-0.9 real-time factor condition between 1000 - 5000 RPM. The same steady-state can be reached by RCP controllers or with GT-Power internal controllers. The transient states are close using different control algorithm. The main purpose of HIL application is achieved, despite inconsistencies in performance data like fuel consumption.

Response Surface Models are often used as a surrogate for expensive black-box functions during optimization to reduce computational cost. Often, the CAE analysis models are highly nonlinear and multi-modal. A response surface approximation of such analysis as a result is highly multi-modal; i.e. it contains multiple local optima. A gradient-based optimizer working with such a response surface will often converge to the nearest local optimum. There does not exist any method to guarantee a global optima for non-convex multi-modal functions. For such problems, we propose an efficient algorithm to find as many distinct local optima as possible. The proposed method is specifically designed to work in large dimensions (about 100 ∼ 1000 design variables and similar number of constraints) and can identify most of the locally optimal solutions in a reasonable amount of time.

This paper presents an overview of a four-year project focused on development of an integrated computational materials engineering (ICME) toolset for third generation advanced high-strength steels (3GAHSS). Following a brief look at ICME as an emerging discipline within the Materials Genome Initiative, technical tasks in the ICME project will be discussed. Specific aims of the individual tasks are multi-scale, microstructure-based material model development using state-of-the-art computational and experimental techniques, forming, toolset assembly, design optimization, integration and technical cost modeling. The integrated approach is initially illustrated using a 980MPa grade transformation induced plasticity (TRIP) steel, subject to a two-step quenching and partitioning (Q&P) heat treatment, as an example.

An area of brake system design that has remained continually resistant to objective, computer model based predictive design and has instead continued to rely on empirical methods and prior history, is that of sizing the brake pads to insure satisfactory service life of the friction material. Despite advances in CAE tools and methods, the ever-intensifying pressures of shortened vehicle development cycles, and the loss of prototype vehicle properties, there is still considerable effort devoted to vehicle-level testing on public roads using “customer-based” driving cycles to validate brake pad service life. Furthermore, there does not appear to be a firm, objective means of designing the required pad volume into the calipers early on - there is still much reliance on prior experience.

In this paper, we propose algorithms for cost minimization of physical wires that are used to connect electronic devices in the vehicle. The wiring cost is one of the most important drivers of electrical architecture selection. Our algorithms perform wire routing from a source device to a destination device through harnesses, by selecting the optimized wire size. In addition, we provide optimized splice allocation with limited constraints. Based on the algorithms, we develop a tool which is integrated into an off-the-shelf optimization and workflow system-level design tool. The algorithms and the tool provide an efficient, flexible, scalable, and maintainable approach for cost analysis and architecture selection.

A rough road ride assessment provides an insightful evaluation of vehicle responses beyond the frequency range of suspension or steering modes. This is when body structure influence on the vehicle performance can be detected by vehicle occupants. In this paper, a rough road is used to evaluate vehicle ride performance and multi-body simulation (MBS) models are developed along with finite-element (FE) representations of the vehicle body and structure. To produce high fidelity simulation results in the frequency range of interest, various vehicle subsystem modeling contents are examined. A case study of a vehicle model with two different structures is provided. Time histories and frequency based analyses are used to obtain insights into the effects of body structure on vehicle responses. Finally, two metrics (‘Isolation’ and ‘Shake’) are used to distinguish the vehicle ride performance.

Oil canning and initial stiffness of the automotive roofs and panels are considered to be sensitive customer ‘perceived quality’ issues. In an effort to develop more accurate objective requirements, respective simulation methods are continuously being developed throughout automotive industries. This paper discusses a latest development on oil canning predictions using LS-DYNA® Implicit, including BNDOUT request, MORTAR contact option and with the stamping process involved, which resulted in excellent correlations especially when it comes to measurements at immediate locations to the feature lines of the vehicle outer panels. Furthermore, in pursuit of light-weighting vehicles with thinner roofs, a new CAE method was recently developed to simulate severe noise conditions exhibited on some of developmental properties while going through a car wash.

Classic, hot-spot induced pre-ignition is a phenomenon that has been observed in gasoline spark ignited engines over the past 60-70 years. With the development of turbocharged, direct-injected (DI) gasoline engines, a new pre-ignition phenomenon occurring at low engine speeds and high loads has been encountered. Termed Stochastic Pre-ignition (SPI), it has become a significant issue to address in allowing for the full potential of gasoline turbo DI technology to improve powertrain efficiency. Many researchers are studying all aspects of the causes of Stochastic Pre-ignition, including causes by oil, fuel and engine hardware systems. The focus of this specific research was to study the relationship of fuel octane and volatility to Stochastic Pre-ignition behavior utilizing a GM 2.0L Gasoline Turbocharged DI engine (LHU).

The automotive industry commonly uses two definitions of the suspension roll center, the Kinematic Roll Center (KRC) - of interest in studying suspension geometry, and the Force-based Roll Center (FRC) - of interest in studying steady-state vehicle dynamics. This paper introduces a third definition, the Dynamic Roll Axis (DRA) - of interest in studying transient vehicle dynamics. The location of each one of these roll centers has a unique application to vehicle design and development. Although the physical meaning of each roll center is significantly different, the generic term “roll center” is often used without proper specification. This can lead to confusion about how roll centers influence vehicle behavior.

In a math-based control algorithm design, model-based simulation and testing are very important as an integral part of design process. There are many advantages of using modeling and simulation in the algorithm design. In this paper, Algorithm-in-the-Loop and Hardware-in-the-Loop approaches are adopted for a transmission control algorithm development. A practical approach is introduced on how to test the control algorithms with a reliable plant (virtual engine, transmission, and vehicle) model in the closed-loop simulation. In using combination of open-loop and closed-loop simulations, various key behavior test cases are developed and documented for the success of control algorithms development. Secondly, the same test cases are reused and verified against the production codes, which are automatically generated from the math-based control algorithm models.