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When a vehicle with a partially filled fuel tank undergoes sudden acceleration, braking, turning or pitching motion, fuel sloshing is experienced. It is important to establish a CAE methodology to accurately predict slosh phenomenon. Fuel slosh can lead to many failure modes such as noise, erroneous fuel indication, irregular fuel supply at low fuel level and durability issues caused by high impact forces on tank surface and internal parts. This paper summarizes activities carried out by the fuel system team at Ford Motor Company to develop and validate such CAE methodology. In particular two methods are discussed here. The first method is Volume Of Fluid (VOF) based incompressible multiphase Eulerian transient CAE method. The CFD solvers used here are Star CD and Star CCM+. The second method incorporates Fluid-Structure interaction (FSI) using Arbitrary Lagrangian-Eulerian (ALE) formulation.

Tappet/bore friction and torque at the camshaft were measured for a direct acting bucket tappet using a cam/tappet friction apparatus. Tappet/bore and cam/tappet friction torque and friction coefficient as a function of cam angle were derived from those measurements. The results showed that, for the particular geometry tested, tappet/bore friction torque accounted for about 13% of the total cam/tappet/bore friction torque at 250 cam rpm. This fraction decreased with increasing speed. Tappet bore friction was greatest at about ± 40 degrees of cam angle, where side loads on the tappet bore were highest. In contrast, earlier results for a center pivot rocker arm design showed tappet bore friction to be negligible.

Over the last decades, the use of computers has become an integral part of the engine development process. Computer-based tools are increasingly used in the design process, and especially the layout of the various subsystems is conducted by means of simulation models. Computer-aided engineering plays a central role e.g. in the design of the combustion process as well as with regards to work performed in the area of engine mechanics, where CFD, FEM, and MBS are applied. As a parallel trend, it can be observed that various engine performance characteristics such as e.g. the specific power output and the power-to-weight ratio have undergone an enormous increase, a trend which to some extent counteracts the increase in safety against malfunction and failure. As yet, due to the constant need for further optimization, mechanical testing and verification processes have not become redundant, and it is assumed that they will remain indispensable for the foreseeable future.

Rapid Prototyping, Fabrication and Tooling is a process that blends a series of technologies (machines, tools, and methods) capable of generating physical objects directly from a CAD database. The process dramatically reduces the time spent during product development by allowing for fast visualization, verification, iteration, optimization, and fabrication of parts and tools. Many new techniques of tooling have been and are being developed by using rapid fabricated parts. These are having a dramatic impact on both timing and costs throughout the automotive industry. One area that these methods can be utilized to their full potential is motorsports. Of particular interest is the growing use of bridge tooling to provide first article through production intent parts that promote cost effective changes.

There are a number of benefits from Ford Motor Company's participation in motorsports. This paper will describe how an engineering team developed a CAE process to assist in the design of a race car to meet impact requirements, with the technology transfer benefit of improved impact performance of composite structures in passenger cars. In 1997/98, a CAE process was developed and applied in the design and test of Formula One race car composite impact structures. For this particular engineering effort, a Ford proprietary software program, COMP-COLLAPSE, was the primary analysis tool that was utilized to successfully predict impact performance. As a result, COMP-COLLAPSE was used extensively in the design of race car composite impact structures. There were two beneficiaries from this effort: Race Vehicles: Improved vehicle impact performance as well as design improvement in crush efficiency, packaging, weight, and manufacturing.

FMVSS 201U, interior head impact performance is required for each new vehicle program. In the laboratory, testing to this requirement includes laying out the target locations, defining additional robustness target points based on targeting variation, positioning the Free Motion Headform (FMH), impacting each location with the headform and measuring HIC values. The tests may involve some conservative strategies and robustness studies to protect for the worst-case scenarios, where an impact might produce the highest HIC(d) within variations of impact conditions. In order to automate the best practices and procedures for both laboratory and CAE, a process automation environment was used to develop the Interior Head Impact Toolkit (IHIT, pronounced as i-hit). The IHIT software addresses several key testing processes and is grouped into four modules.

This paper presents an enhanced Bayesian based model validation method together with probabilistic principal component analysis (PPCA). The PPCA is employed to address multivariate correlation and to reduce the dimensionality of the multivariate functional responses. The Bayesian hypothesis testing is used to quantitatively assess the quality of a multivariate dynamic system. Unlike the previous approach, the differences between test and CAE results are used for dimension reduction though PPCA and then to assess the model validity. In addition, physics-based thresholds are defined and transformed to the PPCA space for Bayesian hypothesis testing. This new approach resolves some critical drawbacks of the previous method and provides desirable properties of a validation method, e.g., symmetry. A dynamic system with multiple functional responses is used to demonstrate this new approach.

Calculating the bearing reliability and behavior is one of the primary tasks which have to be performed to define the main dimensions of the cranktrain of an internal combustion engine. Since the bearing results are essential for the pre-layout of the cranktrain, the conclusion on the bearing safety should be met as early as possible. Therefore detailed simulations like T-EHD or EHD analysis may not be applied to define the dimensions in such an early development phase. In the frame of this study a prediction methodology, based on a HD bearing approach, for bearing reliability of inline-4 crankshafts of passenger cars is proposed. In this way not only the design phase is shortened but also achieving the optimal solution is simplified. Moreover the requirement of a CAD model is eliminated for the preliminary design phase. The influencing parameters on the bearing behavior are first selected and divided into two groups: geometry and loading.

This work presents a statistical approach for simulation based on Monte Carlo method. As an exercise of the method a CAE vehicle dynamics model was specifically created to evaluate the likelihood to meet a given target driving a maneuver for given inputs variations. In the exercise, three different inputs were chosen as stochastic inputs (also called noise factors) and all relevant information about their statistics has been raised, based in components information. The chosen inputs are: front/rear dampers curves, front/rear ride heights and tire surface temperature. A brief description of the Monte Carlo technique is presented. The choice of this method is due to the reduced number of simulations required to have a given accuracy in comparison with other approaches, especially for multivariable system. As output variable for the exercise, the tire patch height was chosen and the resulting probability density function of it is presented.

The behavior knowledge of the vehicle on uphill maneuvers - startability on grade, is an important metric for sizing powertrain components, such as the engine torque, clutch, first and reverse gear ratios, final drive and tire sizes. During the uphill maneuver, all components of the powertrain are subject to efforts that determine the vehicle performance in this condition. The analysis of this maneuver, for a front-wheel-drive vehicle, is evaluated in this article, through a correlation of a computer program developed in Matlab-Simulink, with experimental measurements performed on the vehicle at the track, becoming an important tool for analysis of passenger vehicles subject to these conditions present on Brazilian streets.

As automotive manufacturers continue to increase their use of thermoplastics for interior and exterior components, there is a likelihood of squeaks due to material contacts. To address this issue, Ford's Body Chassis NVH Squeak and Rattle Prevention Engineering Department has developed a tester that can measure friction, and any accompanying audible sound, as a function of sliding velocity, normal load, surface roughness, and environmental factors. The Ford team has been using the tester to address manufacturing plant issues and to develop a database of polymeric material pairings that will be used as a guide for current and future designs to eliminate potential noise concerns. Based upon the database, along with a physical property analysis of the various plastic (viscoelastic) materials used in the interior, we are in the process of developing an analytical model which will be a tool to predict frictional behavior.

Vehicle body with aluminum riveted construction instead of steel welded one will be a big challenge to NVH. In this paper, aluminum and steel rails with the dimensions similar to the rear rail portion of a typical mid-size sedan were fabricated. Rivets were used to assemble the aluminum rails while welds were used to assemble the steel rails. Adhesive, rivet/weld spacing, and rivet/weld location were the three major factors to be studied and their impact on NVH were investigated. The DOE matrix was developed using these three major factors. Modal tests were performed on those rails according to the DOE matrix. The FEA models corresponding to the hardware were built. CAE modal analysis were performed and compared with test data. The current in-house CAE modeling techniques for spot weld and adhesive were evaluated and validated with test data.

The sound quality and performance of an automotive engine are both significantly influenced by the “air-to-air” system, i.e., the intake system, the exhaust system, and the engine gas dynamics. Only a full systems approach can result in an optimized air-to-air system, which fulfills engine performance requirements, overall sound pressure level targets for airborne vehicle noise, as well as sound quality demands. This paper describes an approach, which considers the intake system, engine, and exhaust system within one CAE model that can be utilized for engine performance calculations as well as acoustic simulations. Examples comparing simulated and measured sound are discussed. Finally, the simulated sound (e.g., at the tailpipe of the exhaust system) is combined with an interior noise simulation technique to evaluate its influence inside the vehicle's interior.

Piston slap is a problem that plagues many engines. One of the most difficult aspects of designing to eliminate piston slap is that slight differences in operating conditions and in part geometries from build to build can create large differences in the magnitude of piston slap. In this paper we will describe a design for robustness CAE approach to eliminating piston slap. This approach considers the variations of the significant control factors in the design, e.g. piston pin offset, piston skirt design, etc. as well as the variation in the noise factors the system is subjected to, e.g. assembly clearance, skirt collapse, peak cylinder pressure, cylinder pressure rise rate, and location of peak cylinder pressure. Using analytical knowledge about how these various factors impact the generation of piston slap, a piston design for low levels of piston slap can be determined that is robust to the various noise factors.

Engine control unit (ECU) modeling using engine feature C code is an increasingly important part of new vehicle analysis and development tools. The application areas of feature based ECU models are numerous: a) cold vehicle fuel economy (FE) prediction required for recently introduced 5-cycle certification; b) vehicle thermal modeling; c) evaporative (purge) systems design; d) model-in-the-loop/software-in-the-loop (MIL/SIL) vehicle control development and calibration. The modeling method presented in the paper embeds production C-code directly into Simulink at a feature level using an S-Function wrapper. A collection of features critical to accurate engine behavior prediction are compiled individually and integrated according to the newly developed Engine Control Model Architecture (ECMA). Custom MATLAB script based tools enable efficient model construction.

This paper presents the benefits of following a disciplined thermal management process during the design and development of Ford Light Truck engine cooling systems. The thermal management process described has evolved through the increased use of Computer Aided Engineering (CAE) tools. The primary CAE tool used is a numerical simulation technique within the field of Computational Fluid Dynamics (CFD). The paper discusses the need to establish a heat management team, develop a heat management model, construct a three dimensional CFD model to simulate the thermal environment of the engine cooling system, and presents CFD modeling examples of Ford Light Trucks with engine driven cooling fans.

The Vehicle Electrical System Computer Aided Design (VESCAD) tool is a means by which the vehicle electrical system, including all wiring and the components attached to wiring can be laid out over an outline of the planform (looking down on the vehicle) view of the vehicle. This graphical representation of the vehicle electrical system is linked to a database that contains the definition of all the wiring of the vehicle plus electrical component attributes. The vehicle electrical system can be composed and completely manipulated graphically, using a mouse, and the database is dynamically changed, including automatic re-routing of the wiring in the wiring harnesses. A complete series of reports can be generated once a vehicle electrical system is configured using VESCAD. All of the reports can be keyed by component(s), harness(es), subsystem(s) or the entire vehicle.

This paper presents the benefits of following a disciplined thermal management process during the design and development of Ford Light Truck engine cooling systems. The thermal management process described has evolved through the increased use of Computer Aided Engineering (CAE) tools. The primary CAE tool used is a numerical simulation technique within the field of Computational Fluid Dynamics (CFD). The paper discusses the need to establish a heat management team, develop a heat management model, construct a three dimensional CFD model to simulate the thermal environment of the engine cooling system, and presents CFD modeling examples of Ford Light Trucks with engine driven cooling fans.

Ford Motor Company has developed a Computer Aided Engineering (CAE) process for predicting NVH performance of its forward model vehicles. This process utilizes simplified models to drive the upstream design effort before detailed drawings are available and then verifies the design with detailed models. The process can be used to determine and evaluate the most effective design modifications and to assist in resolving downstream problems after prototype vehicle evaluations. This paper outlines this process and also gives examples from the work which was done to support the new 1992 Econoline design.

After establishing the performance requirements and initial design assumptions, CAE concept models are used to set targets for major structural components to achieve desirable crash performance. When the designs of these major components become available they are analyzed in detail using nonlinear crash finite element models to evaluate their performance. All these components are assembled together later in a full car model to predict the overall vehicle crash performance. If the analysis shows that the targets are met, the design drawings are released for prototype fabrication. When CAE tools are effectively used, it will reduce product development cycle time and the number of prototypes. Crash analysis methodology has been validated and applied for steel automotive product development. Recently, aluminum is replacing steel for lighter and more fuel efficient automobiles. In general aluminum has quite different performance from steel, in particular with lower ductility.