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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.

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.

A copula-based approach for model bias characterization was previously proposed [18] aiming at improving prediction accuracy compared to other model characterization approaches such as regression and Gaussian Process. This paper proposes an adaptive copula-based approach for model bias identification to enhance the available methodology. The main idea is to use cluster analysis to preprocess data, then apply the copula-based approach using information from each cluster. The final prediction accumulates predictions obtained from each cluster. Two case studies will be used to demonstrate the superiority of the adaptive copula-based approach over its predecessor.

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.

Ultrasonic fatigue tests (testing frequency around 20 kHz) have been conducted on four different cast aluminum alloys each with a distinct composition, heat treatment, and microstructure. Tests were performed in dry air, laboratory air and submerged in water. For some alloys, the ultrasonic fatigue lives were dramatically affected by the environment humidity. The effects of different factors like material composition, yield strength, secondary dendrite arm spacing and porosity were investigated; it was concluded that the material strength may be the key factor influencing the environmental humidity effect in ultrasonic fatigue testing. Further investigation on the effect of chemical composition, especially copper content, is needed.

Particle swarm optimization (PSO) is a relatively new stochastic optimization algorithm and has gained much attention in recent years because of its fast convergence speed and strong optimization ability. However, PSO suffers from premature convergence problem for quick losing of diversity. That is to say, if no particle discovers a new superiority position than its previous best location, PSO algorithm will fall into stagnation and output local optimum result. In order to improve the diversity of basic PSO, design of experiment technique is used to initialize the particle swarm in consideration of its space-filling property which guarantees covering the design space comprehensively. And the optimization procedure of PSO is divided into two stages, optimization stage and improving stage. In the optimization stage, the basic PSO initialized by Optimal Latin hypercube technique is conducted.

Accurate determination of the forming limit strain of aluminum sheet metal is an important topic which has not been fully solved by industry. Also, the effects of draw beads (enhanced forming limit behaviors), normally reported on steel sheet metals, on aluminum sheet metal is not fully understood. This paper introduces an experimental study on draw bead effects on aluminum sheet metals by measuring the forming limit strain zero (FLD0) of the sheet metal. Two kinds of aluminum, AL 6016-T4 and AL 5754-0, are used. Virgin material, 40% draw bead material and 60% draw bead material conditions are tested for each kind of aluminum. Marciniak punch tests were performed to create a plane strain condition. A dual camera Digital Image Correlation (DIC) system was used to record and measure the deformation distribution history during the punch test. The on-set necking timing is determined directly from surface shape change. The FLD0 of each test situation is reported in this article.

In this paper, thermal stress analysis for powertrain component is carried out using two in-house developed elasto-viscoplastic models (i.e. Chaboche model and Sehitoglu model) that are implemented into ABAQUS via its user subroutine UMAT. The model parameters are obtained from isothermal cyclic tests performed on standard samples under various combinations of strain rates and temperatures. Models' validity is verified by comparing to independent non-isothermal tests conducted on similar samples. Both models are applied to the numerical analysis of exhaust manifold subject to temperature cycling as a result of vehicle operation. Due to complexity, only four thermal cycles of heating-up and cooling-down are simulated. Results using the two material models are compared in terms of accuracy and computational efficiency. It is found that the implemented Chaboche model is generally more computationally efficient than Sehitoglu model, though they are almost identical in regard to accuracy.

High silicon molybdenum (HiSiMo) ductile cast iron (DCI) is commonly used for high temperature engine components, such as exhaust manifolds, which are also subjected to severe thermal cycles during vehicle operation. It is imperative to understand the thermomechanical fatigue (TMF) behavior of HiSiMo DCI to accurately predict the durability of high temperature engine components. In this paper, the effect of the minimum temperature of a TMF cycle on TMF life and failure behavior is investigated. Tensile and low cycle fatigue data are first presented for temperatures up to 800°C. Next, TMF data are presented for maximum temperatures of 800°C and minimum cycle temperatures ranging from 300 to 600°C. The data show that decreasing the minimum temperature has a detrimental effect on TMF life. The Smith-Watson-Topper parameter applied at the maximum temperature of the TMF cycle is found to correlate well with out-of-phase (OP) TMF life for all tested minimum temperatures.

SIF value around weld nugget changes when specimen width is different. To investigate the influence of specimen width on SIF value around weld nugget of coach peel specimen (CP), a finite element model was established in this paper. In this model, a contour integral crack was used, and the area around the nugget was treated as crack tip. Results indicated that when specimen width was below 50mm, SIF decreased rapidly with the increase of specimen width. When specimen width was larger than 50mm, SIF almost remained constant with the variation of specimen width. To further study the influences of nugget diameter and sheet thickness on the Width-SIF curves, CP specimens with different nugget diameters (5mm, 6mm and 7mm) and sheet thicknesses (1.2mm, 1.6mm and 2.0mm) were established in ABAQUS. Simulation results of all CP specimens showed a similar relationship between specimen width and SIF.

Long fiber reinforced plastics (LFRP) have exhibited superior mechanical performance and outstanding design flexibility, bringing them with increasing popularity in the automotive structural design. Due to the injection molding process, the distribution of long fibers varies at different locations throughout the part, resulting in anisotropic and non-uniform mechanical properties of the final LFRP parts. Images from X-ray CT scan of the materials show that local volume fraction of the long fibers tends to be higher at core than at skin layer. Also fibers are bundled and tangled to form clusters. Most of the current micromechanical material models used for LFRP are extended from those for short fibers without adequate validation. The effect of the complexity of long fibers on the material properties is not appropriately considered. Thus, modeling of these materials is lagging behind the material manufacturing and design development, which in turn limits their further development.

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.

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.

Corrosion tendency is one of the major inhibitors for increased use of magnesium alloys in automotive structural applications. Moreover, systematic or standardized methods for evaluation of both general and galvanic corrosion of magnesium alloys, either as individual components or eventually as entire subassemblies, remains elusive, and receives little attention from professional and standardization bodies. This work reports outcomes from an effort underway within the U.S. Automotive Materials Partnership - ‘USAMP’ (Chrysler, Ford and GM) directed toward enabling technologies and knowledge base for the design and fabrication of magnesium-intensive subassemblies intended for automotive “front end” applications. In particular, subassemblies consisting of three different grades of magnesium (die cast, sheet and extrusion) and receiving a typical corrosion protective coating were subjected to cyclic corrosion tests as employed by each OEM in the consortium.

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.

Many combustion researchers use peak pressure rise rate or ringing intensity to indicate combustion noise in lieu of microphone data or using a combustion noise meter that simulates the attenuation characteristics of the engine structure. In this paper, peak pressure rise rate and ringing intensity are compared to combustion noise using a fully documented algorithm similar to the ones used by combustion noise meters. Data from multiple engines operating under several low-temperature combustion strategies were analyzed. The results suggest that neither peak pressure rise rate nor ringing intensity provides a direct correlation to engine noise over a wide range of operating conditions. Moreover, the estimation of both metrics is often accompanied by the filtering of the pressure data, which changes the absolute value of the results.

As a renewable energy source, the ethanol fuel was employed with a diesel fuel in this study to improve the cylinder charge homogeneity for high load operations, targeting on ultra-low nitrogen oxides (NOx) and smoke emissions. A light-duty diesel engine is configured to adapt intake port fuelling of the ethanol fuel while keeping all other original engine components intact. High load experiments are performed to investigate the combustion control and low emission enabling without sacrificing the high compression ratio (18.2:1). The intake boost, exhaust gas recirculation (EGR) and injection pressure are independently controlled, and thus their effects on combustion and emission characteristics of the high load operation are investigated individually. The low temperature combustion is accomplished at high engine load (16~17 bar IMEP) with regulation compatible NOx and soot emissions.