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Available methodologies for model bias identification are mainly regression-based approaches, such as Gaussian process, Bayesian inference-based models and so on. Accuracy and efficiency of these methodologies may degrade for characterizing the model bias when more system inputs are considered in the prediction model due to the curse of dimensionality for regression-based approaches. This paper proposes a copula-based approach for model bias identification without suffering the curse of dimensionality. The main idea is to build general statistical relationships between the model bias and the model prediction including all system inputs using copulas so that possible model bias distributions can be effectively identified at any new design configurations of the system. Two engineering case studies whose dimensionalities range from medium to high will be employed to demonstrate the effectiveness of the copula-based approach.

A global, systems-level model which characterizes the thermal behavior of internal combustion engines is described in this paper. Based on resistor-capacitor thermal networks, either steady-state or transient thermal simulations can be performed. A two-zone, quasi-dimensional spark-ignition engine simulation is used to determine in-cylinder gas temperature and convection coefficients. Engine heat fluxes and component temperatures can subsequently be predicted from specification of general engine dimensions, materials, and operating conditions. Emphasis has been placed on minimizing the number of model inputs and keeping them as simple as possible to make the model practical and useful as an early design tool. The success of the global model depends on properly scaling the general engine inputs to accurately model engine heat flow paths across families of engine designs. The development and validation of suitable, scalable submodels is described in detail in this paper.

The pulleys employed in automotive accessory drive systems very often consist of a two piece assembly; a multitude of fastening techniques are used in completing the assembly. There are numerous assembly methods and a variety of distinct pulley configurations dictated by the various automobile manufacturers in accordance with individual accessory drive needs. An expert system is being developed to evaluate the merit of multiple assembly alternatives for a specific pulley application. The expert system provides a consistent evaluation tool for assembly alternatives, balancing the influence of product cost, strength and quality considerations. The knowledge-based system is implemented in an expert system shell called AGNESS (A Generalized Network-based Expert System Shell). The expert system judges the acceptability of various pulley assembly techniques, assigning a high “merit value” to the better designs and proportionately lower values to less desirable designs.

In applications of the Energy Finite Element Analysis (EFEA) there is an increasing need for developing comprehensive models with a large number of elements which include both structural and interior fluid elements, while certain parts of the structure are considered to be exposed to an external fluid loading. In order to accommodate efficient computations when using simulation models with a large number of elements, joints, and domains, a substructuring computational capability has been developed. The new algorithm is based on dividing the EFEA model into substructures with internal and interface degrees of freedom. The system of equations for each substructure is assembled and solved separately and the information is condensed to the interface degrees of freedom. The condensed systems of equations from each substructure are assembled in a reduced global system of equations. Once the global system of equations has been solved the solution for each substructure is pursued.

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.

When unexpected integrated lubricant-related problems occur, for example, high-speed operating conditions, lubricants can be degraded and even fail to reach certain automatic transmission parts. Dryout of oil films means a serious lack of lubrication, which may damage the power transmission line and key parts. Dryout of ATF is analogous to that in forced convective boiling and condensation. It thus requires special efforts to determine the mechanics that induce such fluid transport phenomena. This paper presents an experimental investigation of lubricant activities in the main shaft. Dimensional analysis is applied, and flow maps of the air-oil flow and dryout regimes are constructed. Correlations closely agree with the data and reveal the possibility of dryout. Heat transfer effect is briefly discussed.

Industrial tasks performed by standing workers are among those most commonly simulated using digital human models. Workers often walk, turn, and take acyclic steps as they perform these tasks. Current h uman modeling tools lack the capability to simulate these whole body motions accurately. Most models simulate walking by replaying joint angle trajectories corresponding to a general gait pattern. Turning is simulated poorly if at all, and violations of kinematic constraints between the feet and ground are common. Moreover, current models do not accurately predict foot placement with respect to loads and other hand targets, diminishing the utility of the associated ergonomic analyses. A new approach to simulating stepping and walking in task-oriented activities is proposed. Foot placements and motions are predicted from operator and task characteristics using empirical models derived from laboratory data and validated using field data from an auto assembly plant.

The need for improved understanding of new magnesium alloys for the automotive industry continues to grow as the application for these lightweight alloys expands to more demanding environments, particularly in drivetrain components. Their use at elevated temperatures, such as in transmission cases, presents a challenge because magnesium alloys generally have lower creep resistance than aluminum alloys currently employed for such applications. In this study, a new die cast magnesium alloy, MEZ, containing rare earth (RE) elements and zinc as principal alloying constituents, was examined for its bolt-load retention (BLR) properties. Preloads varied from 14 to 28 kN and test temperatures ranged from 125 to 175°C. At all test temperatures and preloads, MEZ retained the greatest fraction of the initial imposed preload when compared to the magnesium alloys AZ91D, AE42, AM50, and the AM50+Ca series alloys.

This paper presents an extension of our previous work on decomposition-based assembly synthesis [1], where the 3D finite element model of a vehicle body-in-white (BIW) is optimally decomposed into a set of components considering the stiffness of the assembled structure under given loading conditions, and the manufacturability and assemblability of each component. The stiffness of the assembled structure is evaluated by finite element analyses, where spot-welded joints are modeled as linear torsional springs. In order to allow close examinations of the trade-off among stiffness, manufacturability, and assemblability, the optimal decomposition problem is solved by multi-objective genetic algorithm [2,3], with graph-based crossover [4,5], combined with FEM analyses, generating Pareto optimal solutions. Two software programs are developed to implement the proposed method.

Testing for vehicle emissions and fuel economy certification occurs primarily on chassis dynamometers in a laboratory setting and therefore the actual road conditions, such as forces due to tire rolling resistance and internal friction, must be simulated. Test track coastdown procedures measure vehicle road load forces and produce an equation which relates these forces to velocity. The recent inclusion of onboard anemometry has allowed the coastdown procedure to account for varying wind effects; however, the new anemometer based mechanical loss coefficients do not take into account ambient weather conditions. The two purposes of this study are (1) to determine the new tire rolling resistance temperature correction coefficient that should be used when test ambient temperature is different from the standard reference value of 68°F, and (2) to investigate the effects of auxiliary measurements, such as other ambient conditions and vehicle settings, on this correction coefficient.

The buildup of deposits in EGR coolers causes significant degradation in heat transfer performance, often on the order of 20-30%. Deposits also increase pressure drop across coolers and thus may degrade engine efficiency under some operating conditions. It is unlikely that EGR cooler deposits can be prevented from forming when soot and HC are present. The presence of cooled surfaces will cause thermophoretic soot deposition and condensation of HC and acids. While this can be affected by engine calibration, it probably cannot be eliminated as long as cooled EGR is required for emission control. It is generally felt that “dry fluffy” soot is less likely to cause major fouling than “heavy wet” soot. An oxidation catalyst in the EGR line can remove HC and has been shown to reduce fouling in some applications. The combination of an oxidation catalyst and a wall-flow filter largely eliminates fouling. Various EGR cooler designs affect details of deposit formation.

This paper presents experimental results that validate eco-driving and eco-heating strategies developed for connected and automated vehicles (CAVs). By exploiting vehicle-to-infrastructure (V2I) communications, traffic signal timing, and queue length estimations, optimized and smoothed speed profiles for the ego-vehicle are generated to reduce energy consumption. Next, the planned eco-trajectories are incorporated into a real-time predictive optimization framework that coordinates the cabin thermal load (in cold weather) with the speed preview, i.e., eco-heating. To enable eco-heating, the engine coolant (as the only heat source for cabin heating) and the cabin air are leveraged as two thermal energy storages. Our eco-heating strategy stores thermal energy in the engine coolant and cabin air while the vehicle is driving at high speeds, and releases the stored energy slowly during the vehicle stops for cabin heating without forcing the engine to idle to provide the heating source.

While the results are generally the most exciting aspects of an LCI study, the details of the LCI model that generates the results are equally significant; particularly when modeling the life cycle of an automobile. The modeling challenges faced in conducting the US AMP LCI of a mid-sized vehicle based on the 1995 Lumina, Intrepid and Taurus are highlighted. The number of parts (over 20,000), supply chain complexity, materials composition, and the demanding set of OEM requirements for model features required special LCI methods and solutions. The LCI model and selected results are compared with previous studies, and recommendations for improvements in the USAMP LCI model are also provided. This paper is one of six SAE publications discussing the results and execution of the USCAR AMP Generic Vehicle LCI. The papers in this series are (Overview of results 982160, 982161, 982162, 982168, 982169, 982170).

Simulation of reach movements is an essential component for proactive ergonomic analysis in digital human modeling and for numerous applications in vehicle design. Most studies on reach kinematics described human movements in static conditions. Earlier studies of reach performance in vibration environments focused mainly on fingertip deviation without considering multi-body dynamics. However, for the proper assessment of reach performance under whole-body vibration exposure, a multi-body biodynamic model needs to be developed. This study analyzes three dimensional reach kinematics of the upper extremity during in-vehicle operations, using a multi-segmental model of the upper body in the vibratory environment. The goals are to identify the characteristics of upper body reach movements and to investigate vibration-induced changes in joint kinematics. Thirteen subjects reached to four target directions in the right hemisphere.

A research scheme and preliminary results of a pilot study concerning upper body coordination in reach movements is presented. Techniques for multi-joint arm movements were used to obtain the kinematics of each body segment in reach movements to targets spatially distributed in a horizontal plane. Further understanding of the control mechanisms associated with coordination is investigated by combining the information of gaze orientation and body segment movements during reach activities. The implicit sequence of body segments in reach movement can be derived from their kinematic characteristics. Moreover, an identification of phases composing a reach movement is attempted.

Warpage is the distortion induced by inhomogeneous shrinkage during injection molding of plastic parts. Uncontrolled warpage will result in dimensional instability and bring a lot of challenges to the mold design and part assembly. Current commercial simulation software for injection molding cannot provide consistently accurate warpage prediction, especially for semi-crystalline thermoplastics. In this study, the root cause of inconsistency in warpage prediction has been investigated by using injection molded polypropylene plaques with a wide range of process conditions. The warpage of injection molded plaques are measured and compared to the numerical predictions from Moldex3D. The study shows that with considering cooling rate effect on crystallization kinetics and using of the improved material model for residual stress calculations, good agreements are obtained between experiment and simulation results.