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The computer-aided engineering (CAE) automation study requires a large disk space and a premium processor. If all finite element (FE) models run locally, it may crash the local machine, and if the FE model runs on high-performance computing (HPC), transferring data from the server to the local machine to do the optimization may cause latency issues. This automation study provides a unique road map to optimize the design by working efficiently using the initial setup on the local machine, running an analysis of a large number of FE models on HPC, and performing optimization on the server. CAE Automation process has been demonstrated using a case study on a driveline component, crush spacer. Crush spacer is a very critical engineering design because, first, it provides the minimum required preload to the bearing inner races to keep them in position and, second, it endures a number of duty cycles.

This study evaluates powertrain technologies capable of reducing light duty vehicle fuel consumption for compliance with 2025 CAFE standards. A fully integrated GT-Power engine model with physics based sub-models was developed to capture any positive or negative synergies between the technologies. The two zone multi-cylinder engine model included typical thermodynamic subroutines, with predictive combustion, flame quench and knock models, along with map-based turbocharger models to capture key combustion and efficiency behaviors. The engine model was calibrated to data from a boosted GDI engine and exercised through one series of current and production viable technology configurations for 2025 regulations.

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.

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.

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.

Diesel combustion and emissions is largely spray and mixing controlled. Spray and combustion models enable characterization over a range of conditions to understand optimum combustion strategies. The validity of models depends on the inputs, including the rate of injection profile of the injector. One method to measure the rate of injection is to measure the momentum, where the injected fuel spray is directed onto a force transducer which provides measurements of momentum flux. From this the mass flow rate is calculated. In this study, the impact of impingement distance, the distance from injector nozzle exit to the anvil connected to the force transducer, is characterized over a range of 2 - 12 mm. This characterization includes the impact of the distance on the momentum flux signal in both magnitude and shape. At longer impingement distances, it is hypothesized that a peak in momentum could occur due to increasing velocity of fuel injected as the pintle fully opens.

One of the passive methods to reduce drag on the unshielded underbody of a passenger road vehicle is to use a vertical deflectors commonly called air dams or chin spoilers. These deflectors reduce the flow rate through the non-streamlined underbody and thus reduce the drag caused by underbody components protruding in to the high speed underbody flow. Air dams or chin spoilers have traditionally been manufactured from hard plastics which could break upon impact with a curb or any solid object on the road. To alleviate this failure mode vehicle manufacturers are resorting to using soft plastics which deflect and deform under aerodynamic loading or when hit against a solid object without breaking in most cases. This report is on predicting the deflection of soft chin spoiler under aerodynamic loads. The aerodynamic loads deflect the chin spoiler and the deflected chin spoiler changes the fluid pressure field resulting in a drag change.

The heat rejection rates and skin temperatures of a liquid cooled exhaust manifold on a 3.5 L Gasoline Turbocharged Direct Injection (GTDI) engine are determined experimentally using an external cooling circuit, which is capable of controlling the manifold coolant inlet temperature, outlet pressure, and flow rate. The manifold is equipped with a jacket that surrounds the collector region and is cooled with an aqueous solution of ethylene-glycol-based antifreeze to reduce skin temperatures. Results were obtained by sweeping the manifold coolant flow rate from 2.0 to 0.2 gpm at 12 different engine operating points of increasing brake power up to 220 hp. The nominal coolant inlet temperature and outlet pressure were 85 °C and 13 psig, respectively. Data were collected under steady conditions and time averaged. For the majority of operating conditions, the manifold heat rejection rate is shown to be relatively insensitive to changes in manifold coolant flow rate.

Failure mode and fatigue behavior of dissimilar laser welds in lap-shear specimens of aluminum and copper sheets are investigated. Quasi-static tests and fatigue tests of laser-welded lap-shear specimens under different load ranges with the load ratio of 0.1 were conducted. Optical micrographs of the welds after the tests were examined to understand the failure modes of the specimens. For the specimens tested under quasi-static loading conditions, the micrograph indicates that the specimen failed through the fusion zone of the aluminum sheet. For the specimens tested under cyclic loading conditions, two types of failure modes were observed under different load ranges. One failure mode has a kinked crack initiating from the interfacial surface between the aluminum and copper sheets and growing into the aluminum fusion zone at an angle close to 90°.

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.

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.

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.

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.

Variable nozzle turbine (VNT) technology has become a popular technology for diesel engine application. To pivot the nozzle vane and adjust the turbine operating condition, nozzle clearances are inevitable on both the hub and shroud side of turbine housing. Leakage flow formed inside the nozzle clearance leads to extra flow loss and makes the nozzle exit flow less uniform, thus further affects downstream aerodynamic performance of the rotor. As the leakage mixing with nozzle wake flow, the process is highly unsteady, which increases the fluctuation amplitude of transient load on the rotating turbine wheels. In present paper, firstly steady CFD analysis of a turbocharger turbine was performed at different nozzle openings. Then unsteady simulation of the turbine was carried out to investigate the interaction between the leakage flow through nozzle clearance and the main flow. Nozzle clearance's effect on turbine performance was investigated.

In this paper, a microstructure-based three-dimensional (3D) finite element modeling method is adopted to investigate the effects of porosity in thin-walled high pressure die-cast (HPDC) magnesium alloys on their ductility. For this purpose, the cross-sections of AM60 casting samples are first examined using optical microscope and X-ray tomography to obtain the general information on the pore distribution features. The experimentally observed pore distribution features are then used to generate a series of synthetic microstructure-based 3D finite element models with different pore volume fractions and pore distribution features. Shear and ductile damage models are adopted in the finite element analyses to induce the fracture by element removal, leading to the prediction of ductility.

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.

A Ford 2.4-liter 115PS light-duty diesel engine was modified to allow solenoid control of the oil feed to the piston cooling jets, enabling these to be switched on or off on demand. The influence of the jets on piston temperatures, engine thermal state, gaseous emissions and fuel economy has been investigated. With the jets switched off, piston temperatures were measured to be between 23 and 88°C higher. Across a range of speed-load points, switching off the jets increased engine-out emissions of NOx typically by 3%, and reduced emissions of CO by 5-10%. Changes in HC were of the same order and were reductions at most conditions. Fuel consumption increased at low-speed, high-load conditions and decreased at high-speed, low-load conditions. Applying the results to the NEDC drive cycle suggests active on/off control of the jets could reduce engine-out emissions of CO by 6%, at the expense of a 1% increase in NOx, compared to the case when the jets are on continuously.

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.