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Recent advances in morphing, simulation, and optimization technologies have enabled analytically driven aerodynamic shape optimization to become a reality. This paper will discuss the integration of these technologies into a single process which enables the aerodynamicist to optimize vehicle shape as well as gain a much deeper understanding of the design space around a given exterior theme.

The worldwide automotive industry is currently preparing for a market introduction of hydrogen-fueled powertrains. These powertrains in fuel cell electric vehicles (FCEVs) offer many advantages: high efficiency, zero tailpipe emissions, reduced greenhouse gas footprint, and use of domestic and renewable energy sources. To realize these benefits, hydrogen vehicles must be competitive with conventional vehicles with regards to fueling time and vehicle range. A key to maximizing the vehicle's driving range is to ensure that the fueling process achieves a complete fill to the rated Compressed Hydrogen Storage System (CHSS) capacity. An optimal process will safely transfer the maximum amount of hydrogen to the vehicle in the shortest amount of time, while staying within the prescribed pressure, temperature, and density limits. The SAE J2601 light duty vehicle fueling standard has been developed to meet these performance objectives under all practical conditions.

A tire on a vehicle is not a separate entity to be considered only in itself. The tire is part of the entire vehicle and this entire vehicle must be considered in making tire selections. Just as important are the components of a tire and the various parameters to be considered in tire design. Care must be taken that any attempt to optimize a single parameter does not jeopardize the overall quality.

Optimizing heat protection for underbody and underhood components, using non-CFD heat transfer CAE tools, requires the estimation of local convective heat transfer coefficients. This estimate, in turn requires knowledge of the local air velocity. Currently available methods for obtaining this velocity at several vehicle locations have been impractical and expensive for use in over-the-road testing. This paper presents the design, fabrication, and field testing results of a 26 mm diameter spherical transducer which measures the local heat transfer coefficient directly. The transducer contains three thermocouples and a heater. It is calibrated to correlate the coefficient with the air velocity. Drawing less than 0.1 A, a number of them can be powered by the vehicle battery with negligible drain. The data acquisition consists of sampling three thermocouples per spherical transducer.

This paper describes the design, synthesis-analysis and development of the unique vehicle structure architecture for the fifth generation Chevrolet Corvette, ‘C5’, which starts in the 1997 model year. The innovative structural layout of the ‘C5’ enables torsional rigidity in an open roof vehicle which exceeds that of all current production open roof vehicles by a wide margin. The first structural mode of the ‘C5’ in open roof configuration approaches typical values measured in similar size fixed roof vehicles. Extensive use of CAE and a systems methodology of benchmarking and requirements rolldown were employed to develop the ‘C5’ vehicle architecture. Simple computer models coupled with numerical optimization were used early in the design process to evaluate every design concept and alternative iteration for mass and structural efficiency.

This paper describes the computer implementation and application of an alternative method of computation of structural design sensitivity coefficients. This method is based on a scheme of analysis of strain energy variations and uses Castigliano's Theorem to compute derivatives of nodal displacements with respect to structural design parameters. The method is particularly useful for structural optimization problems with a large number of design variables and a relatively small number of constraints. It is implemented by pre and post processing results from a structural analysis program; therefore, it can be implemented with no changes to the source code of a structural analysis program. The implementation and application of the method with OPUS [1], Ford's in-house structural optimization system, coupled with the MSC/NASTRAN finite element program [2], are described.

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 application of SEA (Statistical Energy Analysis) to the sound package design for a convertible is presented. SEA modeling was used optimize the soft-top construction and the acoustic insulation in the top-stack area (where the soft-top is stored) which were shown to be important transmission paths for tire noise. Correlation between measurement data and predictions from the SEA model is presented and good agreement shown. It is concluded that SEA can be applied to determine the special sound package requirements for convertible vehicles.

The performance of an organic Rankine cycle (ORC) that recovers heat from the exhaust of a heavy-duty diesel engine was simulated. The work was an extension of a prior study that simulated the performance of an experimental ORC system developed and tested at Oak Ridge National laboratory (ORNL). The experimental data were used to set model parameters and validate the results of that simulation. For the current study the model was adapted to consider a 15 liter turbocharged engine versus the original 1.9 liter light-duty automotive turbodiesel studied by ORNL. Exhaust flow rate and temperature data for the heavy-duty engine were obtained from Southwest Research Institute (SwRI) for a range of steady-state engine speeds and loads without EGR. Because of the considerably higher exhaust gas flow rates of the heavy-duty engine, relative to the engine tested by ORNL, a different heat exchanger type was considered in order to keep exhaust pressure drop within practical bounds.

Motivated by a combination of increasing consumer demand for fuel efficient vehicles, more stringent greenhouse gas, and anticipated future Corporate Average Fuel Economy (CAFE) standards, automotive manufacturers are working to innovate in all areas of vehicle design to improve fuel efficiency. In addition to improving aerodynamics, enhancing internal combustion engines and transmission technologies, and developing alternative fuel vehicles, reducing vehicle weight by using lighter materials and/or higher strength materials has been identified as one of the strategies in future vehicle development. Weight reduction in vehicle components, subsystems and systems not only reduces the energy needed to overcome inertia forces but also triggers additional mass reduction elsewhere and enables mass reduction in full vehicle levels.

The selection process of key engine design variables to maximize peak power subject to fuel economy and packaging objectives is formulated as an optimization problem readily solved with nonlinear programming. The merit of this approach lies not in finding a single optimal engine, but in identifying a family of optimal designs dependent on parameter changes in the constraint set. Sensitivity analysis of the optimum to packaging parameters, fuel economy parameters, and manufacturing parameters is presented and discussed in the context of product development decisions.

Sampling-based methods are general but time consuming for solving a Reliability-Based Design Optimization (RBDO) problem. In order to alleviate the computation burden, score function together with the Monte Carlo method was used to compute the stochastic sensitivities of reliability functions. In literature, re-weighting schemes were shown to converge faster than the regular Monte Carlo method. In this paper, a reweighting scheme together with score function is employed to perform sampling-based stochastic sensitivity analysis to improve the computational efficiency and accuracy. An analytical example is used to show the advantages of the proposed method. Comparisons to the conventional methods are made and discussed. Two RBDO problems are solved to demonstrate the use of the proposed method.

Rapid Prototyping, Fabrication and Tooling is a process that blends a series of technologies (machines, tools, and methods) capable of generating physical objects directly from a CAD database. The process dramatically reduces the time spent during product development by allowing for fast visualization, verification, iteration, optimization, and fabrication of parts and tools. Many new techniques of tooling have been and are being developed by using rapid fabricated parts. These are having a dramatic impact on both timing and costs throughout the automotive industry. One area that these methods can be utilized to their full potential is motorsports. Of particular interest is the growing use of bridge tooling to provide first article through production intent parts that promote cost effective changes.

Pareto optimal map concept has been applied to the optimization of the vehicle system control (VSC) strategy for a power-split hybrid electric vehicle (HEV) system. The methodology relies on an inner-loop optimization process to define Pareto maps of the best engine and electric motor/generator operating points given wheel power demand, vehicle speed, and battery power. Selected levels of model fidelity, from simple to very detailed, can be used to generate the Pareto maps. Optimal control is achieved by applying Pontryagin's minimum principle which is based on minimization of the Hamiltonian comprised of the rate of fuel consumption and a co-state variable multiplied by the rate of change of battery SOC. The approach delivers optimal control for lowest fuel consumption over a drive cycle while accounting for all critical vehicle operating constraints, e.g. battery charge balance and power limits, and engine speed and torque limits.

A method to predict gap distribution, can deformation and mounting force of catalytic converter during assembling and operation cycles has been developed using ABAQUS contact algorithm with user subroutine for material properties. Inherent in the methodology is the constitutive model for both vermiculite mat and wire mesh mounting materials, which is able to describe their nonlinear and thermal behaviors and shows good agreement with test results. A design optimization procedure is presented to achieve uniform gap design of can and substrate. The technology will enable engineers to generate robust converter can designs, substrate shape and stamping tools for minimum manufacturing failure rate and maximum durability performance once a mounting material is selected.

A model of Ford's current FTP based OBD-II catalyst monitor has been developed and used in determining the optimal monitored catalyst volume for several LEV applications. The model predictions were found to agree reasonably well with the available experimental data. Furthermore, the results of this study indicate that the optimal monitored catalyst volume for meeting LEV requirements is vehicle application specific. As a result, it is concluded that a general guideline for sizing of the monitored catalyst volume for LEVs will most likely be inadequate and could result in grossly suboptimal catalyst monitor function for some applications. The model which is described in this paper offers a potentially more effective means of determining the best monitored catalyst volume for a given vehicle application. It should be possible to utilize this model during the early phase of a vehicle program in order to provide for the optimal packaging of the catalyst monitor sensor (CMS).

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.

To design a cost, weight, and energy efficient bumper foam energy absorber, it is important to consider optimizing the shape of coring employed in the design of the system. In this paper, a number of foam coring patterns are studied by both empirical and analytical methods. The size and shape of proposed core designs are studied in detail with consideration given to several different densities of expanded polypropylene (EPP) foam. Using the finite element method of structural analysis, it is possible to have an inside look at the stress distribution during deformation of foam structures. An optimization study using the finite element method is conducted using the energy absorption ratio as an efficiency parameter. Several coring patterns are studied and recommended for bumper foam core design based on high energy absorption efficiency and low tear stress.

A method is presented for determining an optimum engine calibration that maximizes fuel economy for the CVS-H cycle subject to a set of emission constraints. The method combines the results of a powertrain simulation with steady state engine dynamometer emissions and fuel flow data to project cycle emission and fuel economy values. Dynamic Programming is used to allocate emission contributions among selected speed/load points to maximize projected cycle fuel economy. The corresponding engine calibration is then directly inferred from the optimum emission allocation. Typical results for 2.3 liter and 400 CID engines are presented.

Design optimization techniques are widely used to drive designs toward a global or a near global optimal solution. However, the achieved optimal solution often appears to be the only choice that an engineer/designer can select as the final design. This is caused by either problem topology or by the nature of optimization algorithms to converge quickly in local/global optimal or both. Problem topology can be unimodal or multimodal with many local and/or global optimal solutions. For multimodal problems, most global algorithms tend to exploit the global optimal solution quickly but at the same time leaving the engineer with only one choice of design. The paper explores the application of genetic algorithms (GA), simulated annealing (SA), and mixed integer problem sequential quadratic programming (MIPSQP) to find multiple local and global solutions using single objective optimization formulation.