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Computer programs and models are playing an increasing role in simulating vehicle crashworthiness, dynamic, and fuel efficiency. To maximize the effectiveness of these models, the validity and predictive capabilities of these models need to be assessed quantitatively. For a successful implementation of Computer Aided Engineering (CAE) models as an integrated part of the current vehicle development process, it is necessary to develop objective validation metric that has the desirable metric properties to quantify the discrepancy between multiple tests and simulation results. However, most of the outputs of dynamic systems are multiple functional responses, such as time history series. This calls for the development of an objective metric that can evaluate the differences of the multiple time histories as well as the key features under uncertainty.

To date, model validation metric is prominently designed for non-dynamic model responses. Though metrics for dynamic responses are also available, they are specifically designed for the vehicle impact application and uncertainties are not considered in the metric. This paper proposes the validation metric for general dynamic system responses under uncertainty. The metric makes use of the popular U-pooling approach and extends it for dynamic responses. Furthermore, shape deviation metric was proposed to be included in the validation metric with the capability of considering multiple dynamic test data. One vehicle impact model is presented to demonstrate the proposed validation metric.

Simulation based design optimization has become the common practice in automotive product development. Increasing computer models are developed to simulate various dynamic systems. Before applying these models for product development, model validation needs to be conducted to assess their validity. In model validation, for the purpose of obtaining results successfully, it is vital to select or develop appropriate metrics for specific applications. For dynamic systems, one of the key obstacles of model validation is that most of the responses are functional, such as time history curves. This calls for the development of a metric that can evaluate the differences in terms of phase shift, magnitude and shape, which requires information from both time and frequency domain. And by representing time histories in frequency domain, more intuitive information can be obtained, such as magnitude-frequency and phase-frequency characteristics.

Design optimization methods are commonly used for weight reduction subjecting to multiple constraints in automotive industry. One of the major challenges remained is to deal with a large number of design variables for large-scale design optimization problems effectively. In this paper, a new approach based on fuzzy rough set is proposed to address this issue. The concept of rough set theory is to deal with redundant information and seek for a reduced design variable set. The proposed method first exploits fuzzy rough set to screen out the insignificant or redundant design variables with regard to the output functions, then uses the reduced design variable set for design optimization. A vehicle body structure is used to demonstrate the effectiveness of the proposed method and compare with a traditional weighted sensitivity based main effect approach.

One of the major challenges in multiobjective, multidisciplinary design optimization (MDO) is the long computational time required in evaluating the new designs' performances. To shorten the cycle time of product design, a data mining-based strategy is developed to improve the efficiency of heuristic optimization algorithms. Based on the historical information of the optimization process, clustering and classification techniques are employed to identify and eliminate the low quality and repetitive designs before operating the time-consuming design evaluations. The proposed method improves design performances within the same computation budget. Two case studies, one mathematical benchmark problem and one vehicle side impact design problem, are conducted as demonstration.

The r-value is a very important parameter in the forming simulations of high strength steels, especially for steels with prominent anisotropy. R-values for sheet steels conventionally measured by extensometers were found neither consistent nor accurate due to difficulties in measuring the width strain. In this study, the Digital Image Correlation (DIC) technique was applied to determine r-values in Longitudinal (L), Transverse (T) and Diagonal (D) directions for cold rolled DP980 GI, DP780 GI, DP600 GI and BH250 GI sheet steels. The r-values measured from DIC were validated by finite element analysis (FEA) of a uniaxial tensile test for BH250. The simulation results of the load-displacement for two plasticity models were compared to experimental data, with one being the isotropic yield (von-Mises) and the other being an anisotropic model (Hill-48) using the r-value measured from DIC.

Reducing friction in crankshaft bearings during cold engine operation by heating the oil supply to the main gallery has been investigated through experimental investigations and computational modelling. The experimental work was undertaken on a 2.4l DI diesel engine set up with an external heat source to supply hot oil to the gallery. The aim was to raise the film temperature in the main bearings early in the warm up, producing a reduction in oil viscosity and through this, a reduction in friction losses. The effectiveness of this approach depends on the management of heat losses from the oil. Heat transfer along the oil pathway to the bearings, and within the bearings to the journals and shells, reduces the benefit of the upstream heating.

As vehicle fuel economy continues to grow in importance, the ability to accurately measure the level of efficiency on all driveline components is required. A standardized test procedure enables manufacturers and suppliers to measure component losses consistently and provides data to make comparisons. In addition, the procedure offers a reliable process to assess enablers for efficiency improvements. Previous published studies have outlined the development of a comprehensive test procedure to measure transfer case speed-dependent parasitic losses at key speed, load, and environmental conditions. This paper will take the same basic approach for the Power Transfer Units (PTUs) used on Front Wheel Drive (FWD) based All Wheel Drive (AWD) vehicles. Factors included in the assessment include single and multi-stage PTUs, fluid levels, break-in process, and temperature effects.

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.

Finite Element (FE) models are widely used in automotive for vehicle design. Even with increasing speed of computers, the simulation of high fidelity FE models is still too time-consuming to perform direct design optimization. As a result, response surface models (RSMs) are commonly used as surrogates of the FE models to reduce the turn-around time. However, RSM may introduce additional sources of uncertainty, such as model bias, and so on. The uncertainty and model bias will affect the trustworthiness of design decisions in design processes. This calls for the development of stochastic model interpolation and extrapolation methods that can address the discrepancy between the RSM and the FE results, and provide prediction intervals of model responses under uncertainty.

This study presents the utilization of the hardware-in-the-loop (HIL) approach for regenerative braking (regen) control enhancement efforts for the power split hybrid vehicle architecture. The HIL stand used in this study includes a production brake control module along with the hydraulic brake system, constituted of an accelerator/brake pedal assembly, electric vacuum booster and pump, brake hydraulic circuit and four brake calipers. This work presents the validation of this HIL simulator with real vehicle data, during mild and heavy braking. Then by using the HIL approach, regen control is enhanced, specifically for two cases. The first case is the jerk in deceleration caused by the brake booster delay, during transitions from regen to friction braking. As an example, the case where the regen is ramped out at a low speed threshold, and the hydraulic braking ramped in, can be considered.

Pending reductions in light duty vehicle PM emissions standards from 10 to 3 mg/mi and below will push the limits of the gravimetric measurement method. At these levels the PM mass collected approaches the mass of non-particle gaseous species that adsorb onto the filter from exhaust and ambient air. This introduces an intrinsic lower limit to filter based measurement that is independent of improvements achieved in weighing metrology. The statistical variability of back-up filter measurements at these levels makes them an ineffective means for correcting the adsorption artifact. The proposed subtraction of a facility based estimate of the artifact will partially alleviate the mass bias from adsorption, but its impact on weighing variability remains a problem that can reach a significant fraction of the upcoming 3 and future 1 mg/mi standards. This paper proposes an improved PM mass method that combines the gravimetric filter approach with real time aerosol measurement.

Diesel combustion and emissions formation is spray and mixing controlled and understanding spray parameters is key to determining the impact of fuel injector operation and nozzle design on combustion and emissions. In this study, both spray visualization and computational fluid dynamics (CFD) modeling were undertaken to investigate key mechanisms for liquid length fluctuations. For the experimental portion of this study a common rail piezoelectric injector was tested in an optically accessible constant volume combustion vessel. Liquid penetration of the spray was determined via processing of images acquired from Mie back scattering under vaporizing conditions by injecting into a charge gas at elevated temperature with a 0% oxygen environment. Tests were undertaken at a gas density of 34.8 kg/m₃, 2000 bar injection pressure, and at ambient temperatures of 900, 1100, and 1300 K.

The helical spring is one of fundamental mechanical elements used in various industrial applications such as valves, suspension mechanisms, shock and vibration absorbers, hand levers, etc. In high speed applications, for instance in the internal combustion engine or in reciprocating compressor valves, helical springs are subjected to dynamic and impact loading, which can result in a phenomenon called “surge”. Hence, proper design and selection of helical springs should consider modeling the dynamic and impact response. In order to correctly characterize the physics of a helical spring and its response to dynamic excitations, a comprehensive model of spring elasticity for various spring coil and wire geometries, spring inertial effects as well as contacts between the windings leading to a non-linear spring force behavior is required. In practical applications, such models are utilized in parametric design and optimization studies.

There are benefits of using ultra thin wall (UTW) substrates (i.e., 900/2, 400/4, etc) in lowering cost and emission level. However, the more fragile mechanical characteristics of the UTW present a challenge to design and manufacture of robust catalytic converters. This paper describes a method of canning trial, where a combined Design of Experiment / Monte-Carlo analysis method was used, to develop and validate a canning method for ultra thin wall substrates. Canning trials were conducted in two stages-- Prototype Canning Trial and Production Canning Trial. In Prototype Canning Trial, the root cause of substrate failure was identified and a model for predicting substrate failure was established. Key factors affecting scrap rate and gap capability were identified and predictions were performed on scrap rate and gap capability with the allowed variations in the key factors. The results provided guidelines in designing production line and process control.

The SAE Fuel Cell Vehicle (FCV) Safety Working Group has been addressing FCV safety for over 11 years. In the past couple of years, significant attention has been directed toward a revision to the standard for vehicular hydrogen systems, SAE J2579(1). In addition to streamlining test methodologies for verification of Compressed Hydrogen Storage Systems (CHSSs) as discussed last year,(2) the working group has been considering the effect of vehicle fires, with the major focus on a small or localized fire that could damage the container in the CHSS and allow a burst before the Pressure Relief Device (PRD) can activate and safely vent the compressed hydrogen stored from the container.

A benchmark study was conducted to assess the capability of an open source CFD based process to accurately simulate the physics of the flow field around various vehicle types. The ICON FOAMpro process was used to simulate the flow field of four baseline geometries of a Truck, CD-Car, B-Car and an SUV. Further studies were carried out to assess the effects of geometry variations on the predicted aerodynamic lift and drag. A Detached-Eddy Simulation (DES) approach was chosen for the benchmarks. In addition to aerodynamic lift and drag values, the results for surface pressure data, surface and wake flow fields were calculated. These results were compared with values obtained using Ford's existing CFD processes.

A new diesel engine, called the 6.7L Power Stroke® V-8 Turbo Diesel and code named "Scorpion" was designed and developed by Ford Motor Company for the full-size pickup truck and light commercial vehicle markets. A high pressure Exhaust Gas Recirculation (EGR) layout in combination with a Variable Geometry Turbine (VGT) is used to deliver cooled EGR for in-cylinder NOx reduction. The cylinder-to-cylinder variation of EGR and swirl ratio is tightly controlled by the careful design of the EGR mixer and intake system flow path to reduce variability of cylinder-out PM and NOx emissions. 3D-CFD studies were used to quickly screen several EGR mixer designs based on mixing efficiency and pressure drop considerations. To optimize the intake system, 1D-3D co-simulation methodology with AVL-FIRE and AVL-BOOST has been used to assess the cylinder-to-cylinder EGR distribution and dynamic swirl.

Analytical models (math or computer simulation models) are typically built on the basis of many assumptions and simplifications and hence model prediction could be inaccurate in intended applications. Model validation is thus critical to quantify and improve the degree of accuracy of these models. So far, little work considers model validation for various design configurations so that model prediction is accurate in the intended design space. Furthermore, there is a lack of effective approaches that can be used to quantify model accuracy considering different number of experimental data. To overcome these limitations, objective of this paper is to develop a model validation approach for various design configurations with a reference metric for model accuracy check considering different number of experimental data.

ACOUSTOMIZE™ is a new method of acoustic evaluation used for the purpose of understanding and optimizing NVH performance of vehicles. The following paper documents a case study of the ACOUSTOMIZE™ test methodology on a passenger car BIW. This study includes an analysis of noise flow through BIW locations, a comparison of noise sound levels through BIW cavities with and without a sound treatment package and a comparison of the original cavity sealing design package consisting of baffles, tapes and baggies to low density polyurethane NVH Foam. The results of the study show detection of complex BIW pass throughs that the body leakage test (BLT) was not able to find. In addition, the data shows improved noise reduction with the low density polyurethane foam versus the original cavity sealing design package.