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The Composite Lightweight Automotive Suspension System is a composite rear suspension knuckle/tieblade consisting of UD prepreg (epoxy resin), SMC (vinylester resin) carbon fibre and a steel insert to reduce the weight of the component by 35% and reduce Co2. The compression moulding manufacturing process and CAE optimisation are unique and ground-breaking for this product and are designed to allow high volume manufacture of approx. 30,000 vehicles per year. The manufacturing techniques employed allow for multi-material construction within a five minute cycle time to make the process viable for volume manufacture. The complexities of the design lie in the areas of manufacturing, CAE prediction and highly specialised design methods. It is a well-known fact that the performance of a composite part is primarily determined by the way it is manufactured.

In this paper, a soft computing approach to a model-free vehicle stability control (VSC) algorithm is presented. The objective is to create a fuzzy inference system (FIS) that is robust enough to operate in a multitude of vehicle conditions (load, tire wear, alignment), and road conditions while at the same time providing optimal vehicle stability by detecting and minimizing loss of traction. In this approach, an adaptive neuro-fuzzy inference system (ANFIS) is generated using previously collected data to train and optimize the performance of the fuzzy logic VSC algorithm. This paper outlines the FIS detection algorithm and its benefits over a model-based approach. The performance of the FIS-based VSC is evaluated via a co-simulation of MATLAB/Simulink and CarSim model of the vehicle under various road and load conditions. The results showed that the proposed algorithm is capable of accurately indicating unstable vehicle behavior for two different types of vehicles (SUV and Sedan).

A CAE method has been developed to address engine tonal noise and whine due to the excitation from a gerotor oil pump. The method involves a multidisciplinary approach including CFD, frequency-response structural analysis and acoustic analysis. The results from the application of the method applied to a couple of pumps with different designs are discussed. Engine tonal noise improvement through reduction in the excitation source from the pump and also stiffening the excitation path from the pump to the engine are studied. The effect of component modal alignment with oil pump orders is addressed as well.

Lightweighting of vehicle panels enclosing vehicle cabin causes NVH degradation since engine, road, and wind noise acoustic sources propagate to the vehicle interior through these panels. In order to reduce this NVH degradation, there is a need to develop new NVH sound package materials and designs for use in lightweight vehicle design. Statistical Energy Analysis (SEA) model can be an effective CAE design tool to develop NVH sound packages for use in lightweight vehicle design. Using SEA can help engineers recover the NVH deficiency created due to sheet metal lightweighting actions. Full vehicle SEA model was developed to evaluate the high frequency NVH performance of “Vehicle A” in the frequency range from 200 Hz to 10 kHz. This correlated SEA model was used for the vehicle sound package optimization studies. Full vehicle level NVH laboratory tests for engine and tire patch noise reduction were also conducted to demonstrate the performance of sound package designs on “Vehicle A”.

The Multi Material Lightweight Vehicle (MMLV) developed by Magna International and Ford Motor Company is a result of a US Department of Energy project DE-EE0005574. The project demonstrates the lightweighting potential of a five passenger sedan, while maintaining vehicle performance and occupant safety. Prototype vehicles were manufactured and limited full vehicle testing was conducted. The MMLV vehicle design, comprised of commercially available materials and production processes, achieved a 364kg (23.5%) full vehicle mass reduction, enabling the application of a 1.0-liter three-cylinder engine resulting in a significant environmental benefit and fuel reduction. The three key requirements of structural performance evaluation for vehicle development are NVH, durability and safety.

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.

Long glass fiber reinforced (LGFR) composites have been widely used in automotive industry to reduce vehicle weight and maintain relatively high mechanical performances. Due to the injection molding process, the distribution of fiber orientations varies at different locations and through the panel thickness, resulting in anisotropic and non-uniform mechanical properties. The current practice of computer modeling of these materials is generally using isotropic properties adjusted by a certain scale factor. The effect of fiber orientation is not carefully considered due to the complexity of fiber orientation distribution in the LGFR parts. The purpose of this paper is to identify key factors affecting vehicle attribute performances where LGFR composites are used; and provide an efficient way for accurate CAE modeling of LGFR composites. In this study, tensile coupons cut from a simple geometric injection molded plaque are tested.

The ability to operate a spark-ignition (SI) engine near the knock limit provides a net reduction of engine fuel consumption. This work presents a real-time knock control system based on stochastic knock detection (SKD) algorithm. The real-time stochastic knock control (SKC) system is developed in MATLAB Simulink, and the SKC software is integrated with the production engine control strategy through ATI's No-Hooks. The SKC system collects the stochastic knock information and estimates the knock level based on the distribution of knock intensities fitting to a log-normal (LN) distribution. A desired knock level reference table is created under various engine speeds and loads, which allows the SKC to adapt to changing engine operating conditions. In SKC system, knock factor (KF) is an indicator of the knock intensity level. The KF is estimated by a weighted discrete FIR filter in real-time.

The present investigation details an experimental procedure for frontal impact responses of a generic steel front bumper crush can (FBCC) assembly subjected to a rigid full and 40% offset impact. There is a paucity of studies focusing on component level tests with FBCCs, and of those, speeds carried out are of slower velocities. Predominant studies in literature pertain to full vehicle testing. Component level studies have importance as vehicles aim to decrease weight. As materials, such as carbon fiber or aluminum, are applied to vehicle structures, computer aided models are required to evaluate performance. A novel component level test procedure is valuable to aid in CAE correlation. All the tests were conducted using a sled-on-sled testing method. Several high-speed cameras, an IR (Infrared) thermal camera, and a number of accelerometers were utilized to study impact performance of the FBCC samples.

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.

NVH requirements are critical in new driveline developments. Failure modes due to resonances must be carefully analyzed and potential root causes must have adequate countermeasures. One of the most common root causes is the modal alignment. This work shows the steps to design and optimize a new plastic bracket for an automotive half shaft bearing. This bracket replaces a very stiff bracket, made of cast iron. The initial design of plastic bracket was not stiff enough to bring natural frequency of the system above engine second order excitation at maximum speed. The complete power pack was modeled and NVH CAE analysis was performed. The CAE outputs included Driving Point Response, Frequency Response Function and Modal analysis. The boundary conditions were discussed deep in detail to make sure the models represented actual system.

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.

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.

Meeting increasingly stringent targets for vehicle performance, economy and emissions requires a deep understanding of the overall IC engine system behavior and the ability to optimize it considering all control and noise factors and their variations. The tradeoffs in exhaust gas temperature, exhaust valve temperature, engine performance, economy and emissions demand a combination of capable CAE analytical tools and a methodology capable of leading the design to a reliable and robust solution. This paper presents a newly developed methodology that uses a Ford in-house quasi-dimensional combustion model called GESIM (General Engine Simulation Program) and a 3D conjugate heat transfer (CHT) model to predict crank angle resolved exhaust gas temperatures and cycle average valve temperatures in a 6-Sigma context, which considers a wide range of engine factors and their variations, to determine a feasible robust design solution.

The Ford GT is a high performance sports car designed to compete with the best that the global automotive industry has to offer. A critical enabler for the performance that a vehicle in this class must achieve is the stiffness and response of the frame structure to the numerous load inputs from the suspension, powertrain and occupants. The process of designing the Ford GT spaceframe started with a number of constraints and performance targets derived through vehicle dynamics CAE modeling, crash performance requirements, competitive benchmarking and the requirement to maintain the unique styling of the GT40 concept car. To achieve these goals, an aluminum spaceframe was designed incorporating 35 different extrusion cross-sections, 5 complex castings, 4 smaller node castings and numerous aluminum stampings.

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.

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.