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Aerodynamic vehicle design improvements require flow simulation driven iterative shape changes. The 3-D flow field simulations (CFD analysis) are not explicitly descriptive in providing the direction for aerodynamic shape changes (reducing drag force or increasing the down-force). In recent times, aerodynamic shape optimization using the adjoint method has been gaining more attention in the automotive industry. The traditional DOE (Design of Experiment) optimization method based on the shape parameters requires a large number of CFD flow simulations for obtaining design sensitivities of these shape parameters. The large number of CFD flow simulations can be significantly reduced if the adjoint method is applied. The main purpose of the present study is to demonstrate and validate the adjoint method for vehicle aerodynamic shape improvements.

Fuel lean combustion and exhaust gas dilution are known to increase the thermal efficiency and reduce NOx emissions. In this study, experiments are performed to understand the effect of equivalence ratio on flame kernel formation and flame propagation around the spark plug for different low turbulent velocities. A series of experiments are carried out for propane-air mixtures to simulate engine-like conditions. For these experiments, equivalence ratios of 0.7 and 0.9 are tested with 20 percent mass-based exhaust gas recirculation (EGR). Turbulence is generated by a shrouded fan design in the vicinity of J-spark plug. A closed loop feedback control system is used for the fan to generate a consistent flow field. The flow profile is characterized by using Particle Image Velocimetry (PIV) technique. High-speed Schlieren visualization is used for the spark formation and flame propagation.

New seal cross-section development is a very tedious and time consuming process if conventional analysis methods are used, as it is very difficult to predict the dimensions of the seal that will satisfy the sealing performance targets. In this study, a generic cross-section is defined and the design constraints are specified. Isight then runs the FEA model, utilizing a custom python script for post-processing. Isight then updates the dimensions of the seal and continues running analyses. Isight was run using two different design exploration techniques. The first was a design of experiments (DOE) to discover how the seal’s response varies with its dimensions. Then, after the analyst examined the results, Isight was run in optimization mode focusing on feasible design areas as determined from the DOE. Thus, after the initial model setup, the user can run the analyses in the background and only needs to interact with the program after Isight has determined a list of feasible designs.

The Cadillac CT6 plug-in hybrid electric vehicle (PHEV) power-split transmission architecture utilizes two motors. One is an induction motor type while the other is a permanent magnet AC (PMAC) motor type referred to as motor A and motor B respectively. Bar-wound stator construction is utilized for both motors. Induction motor-A winding is connected in delta and PMAC motor-B winding is connected in wye. Overall, the choice of induction for motor A and permanent magnet for motor B is well supported by the choice of hybrid system architecture and the relative usage profiles of the machines. This selection criteria along with the design optimization of electric motors, their electrical and thermal performances, as well as the noise, vibration, and harshness (NVH) performance are discussed in detail. It is absolutely crucial that high performance electric machines are coupled with high performance control algorithms to enable maximum system efficiency and performance.

Several application developers are currently faced with the problem of moving a complex system from a single-core to a multicore platform. The problem encompasses several issues that go from modeling issues (the need to represent the system features of interest with sufficient accuracy) to analysis and optimization techniques, to the selection of the right formulations for constraints that relate to time. We report on the initial findings in a case study in which the application of interest is a fuel injection system. We provide an analysis on the limitations of AUTOSAR and the existing modeling tools with respect to the representation of the parameters of interest for timing analysis, and we discuss applicable optimization methods and analysis algorithms.

The headliner system in a vehicle is an important element in vehicle noise control. In order to predict the performance of the headliner, it is necessary to develop an understanding of the substrate performance, the effect of air gaps, and the contribution from any acoustic pads in the system. Current Statistical Energy Analysis (SEA) models for predicting absorption performance of acoustic absorbers are based on material Biot properties. However, the resources for material Biot property testing are limited and cost is high. In this paper, modeling parameters for the headliner substrate are identified from a set of standard absorption measurements on substrates, using curve fitting and optimization techniques. The parameters are then used together with thickness/design information in a SEA model to predict the vehicle headliner system absorption performance.

Response Surface Models are often used as a surrogate for expensive black-box functions during optimization to reduce computational cost. Often, the CAE analysis models are highly nonlinear and multi-modal. A response surface approximation of such analysis as a result is highly multi-modal; i.e. it contains multiple local optima. A gradient-based optimizer working with such a response surface will often converge to the nearest local optimum. There does not exist any method to guarantee a global optima for non-convex multi-modal functions. For such problems, we propose an efficient algorithm to find as many distinct local optima as possible. The proposed method is specifically designed to work in large dimensions (about 100 ∼ 1000 design variables and similar number of constraints) and can identify most of the locally optimal solutions in a reasonable amount of time.

Spot weld separation in vehicle development stage is one of the critical phenomena in structural analyses regarding quasi-static test condition, like roof strength or seat/belt pull. It directly reduces structural performance by losing connected load path and occasionally introduces tearing on surrounding sheet metals. Traditionally many efforts have been attempted to capture parent metal ductile fracture, but not applied to spot weld separations in automotive FEA simulations. [1,2,3] This paper introduces how to develop FFLD failure criteria from a series of parametric study on ultra high strength sheet steel and deals with failure criteria around spot weld and parent metal. Once the fracture strains for sheet steels are determined, those developed values were applied to traditional spot weld coupon FEA simulations and tests. Full vehicle level roof strength FEA simulations on a typical automotive body structure were performed and verified to the physical tests.

This paper presents the Component Dynamic Synthesis (CDS) superelement creation, which contains the loading frequency information and is much faster than the Component Mode Synthesis (CMS) method in the residual run. The Frequency Response Functions (FRFs) are computed using the direct frequency response method and the inversion of dynamic stiffness matrix is done using the singular value decomposition (SVD) method for every discrete frequency in the frequency range of interest. The CDS will be very efficient and economical for design of experiments and robust optimization, where hundreds of runs are required. The CDS super element can be used when there is a large number of residual runs on a very large vehicle model at higher end of the frequency range of study. For the residual analysis to run as fast as possible, all components, except very small ones, need to be converted into CDS superelements.

Rapidly increasing customer, financial, and regulatory pressures are creating clear changes in the calculus of vehicle design for modern automotive OEM's (Original Equipment Manufacturers). Customers continue to demand shorter product lifecycles; the increasingly competitive global market exerts pressure to reduce costs in all stages of development; and environmental regulations drive a continuous need to reduce mass and energy consumption. OEM's must confront these challenges while continuing to satisfy the customer. The foundation to meeting these challenges includes: (1) Continued development of objective metrics to quantify performance; (2) Frontloading vehicle design content and performance synthesis; (3) A precise understanding of the customer and their performance preferences under diverse usage conditions. These combined elements will enable products better optimized amongst competing (and often contradictory) imperatives.

Passenger vehicle engine cooling systems typically fall into surge tank or recovery type systems. Recovery systems rely on an expansion/recovery volume, which operates at atmospheric pressure. Over long periods of time and with elevated temperatures, coolant evaporates from this atmospheric recovery bottle. An experimental study determined the evaporation rate as a function of temperature for one bottle geometry. A 1-D model then projected the total coolant loss to evaporation over several different hypothetical customer duty cycles to evaluate robustness of recommended service intervals.

For a CAE model of the park pawl dynamic system, the engagement speed calculation is done by controlling the input rotational velocity of the vehicle. Usually, it requires multiple adjustment of the input rotational velocity to get the engagement speed and that demands time, effort and file management skill of an analyst. The current objective of this paper is to demonstrate how software Isight, working with ABAQUS Explicit as the solver, can be used to automate the engagement speed calculation procedure and thus reduce the time and effort required of a CAE analyst. The automated system is developed in a way such that the accuracy of the results can be controlled by the end user. It is observed that the automated system significantly saves an analyst's effort. The system design can be optimized easily for modifiable design features such as the torsional spring and the actuator spring stiffness values using the proposed procedure.

This paper presents an overview of a four-year project focused on development of an integrated computational materials engineering (ICME) toolset for third generation advanced high-strength steels (3GAHSS). Following a brief look at ICME as an emerging discipline within the Materials Genome Initiative, technical tasks in the ICME project will be discussed. Specific aims of the individual tasks are multi-scale, microstructure-based material model development using state-of-the-art computational and experimental techniques, forming, toolset assembly, design optimization, integration and technical cost modeling. The integrated approach is initially illustrated using a 980MPa grade transformation induced plasticity (TRIP) steel, subject to a two-step quenching and partitioning (Q&P) heat treatment, as an example.

Emission compliance at the production level has been a challenge for vehicle manufacturers. Diesel oxidation catalyst (DOC) plays a very important role in controlling the emissions for the diesel vehicles. Vehicle manufacturers tend to ‘over design’ the diesel oxidation catalyst to ‘absorb’ the production variations which seems an easier and faster solution. However this approach increases the DOC cost phenomenally which impacts the overall vehicle cost. The main objective of this paper is to address the high variation in CO tail pipe emissions which were observed on a diesel passenger car during development. This variation was posing a challenge in consistently meeting the internal product requirement/specification.

In this paper, we propose algorithms for cost minimization of physical wires that are used to connect electronic devices in the vehicle. The wiring cost is one of the most important drivers of electrical architecture selection. Our algorithms perform wire routing from a source device to a destination device through harnesses, by selecting the optimized wire size. In addition, we provide optimized splice allocation with limited constraints. Based on the algorithms, we develop a tool which is integrated into an off-the-shelf optimization and workflow system-level design tool. The algorithms and the tool provide an efficient, flexible, scalable, and maintainable approach for cost analysis and architecture selection.

The General Motors (GM) 1ET35 drive unit is designed for an optimum combination of efficiency, performance, reliability, and cost as part of the propulsion system for the 2014 Chevrolet Spark Electric Vehicle (EV) [1]. The 1ET35 drive unit is a coaxial transaxle arrangement which includes a permanent-magnet (PM) electric motor and a low loss single-planetary transmission and is the sole source of propulsion for the battery-only electric vehicle (BEV) Spark. The 1ET35 is designed with experience gained from the first modern production BEV, the 1996 GM EV1. This paper describes the design optimization and development of the 1ET35 and its electric motor that will be made in the United States by GM. The high torque density electric motor design is based on high-energy permanent magnets that were originally developed by GM in connection with the EV1 and GM bar-wound stator technology introduced in the 2Mode Hybrid electric transmission, used in the Chevrolet Volt and in GM eAssist systems.

With the ever increasing pressure to improve the fuel economy of vehicles, there has been a corresponding interest in reducing the mass and size of vehicles. While mass is easily quantifiable, vehicle size, particularly the notion of “interior space” as perceived by the customer, is not. This paper explores different ways in which vehicle spaciousness can be quantified and explores new metrics based on customer verbatims. A novel ‘spaciousness calculator’ combines individual metrics to provide a singular holistic rating for spaciousness, useful during vehicle development. Beyond spaciousness, the paper discusses techniques to quantify the ‘packaging efficiency’ of a vehicle; this allows engineers to maximize the interior space for a given exterior size.

Oil canning and initial stiffness of the automotive roofs and panels are considered to be sensitive customer ‘perceived quality’ issues. In an effort to develop more accurate objective requirements, respective simulation methods are continuously being developed throughout automotive industries. This paper discusses a latest development on oil canning predictions using LS-DYNA® Implicit, including BNDOUT request, MORTAR contact option and with the stamping process involved, which resulted in excellent correlations especially when it comes to measurements at immediate locations to the feature lines of the vehicle outer panels. Furthermore, in pursuit of light-weighting vehicles with thinner roofs, a new CAE method was recently developed to simulate severe noise conditions exhibited on some of developmental properties while going through a car wash.

The need to reduce fuel consumption and harmful pollutants from engines is an important task for automotive industry. It has led to technological advances in new engine design, such as engine downsizing. Due to the reduction of displacement, engine power output is reduced and thus its overall performance is limited. In order to increase torque and power, engines are typically boosted by turbochargers or superchargers. Meanwhile, the improvement on turbo design makes it possible to operate VGT (variable geometry turbocharger) at harsher exhaust environment for gasoline engines as well (e.g., with much higher exhaust temperature than that of diesel engines). This makes VGT related control problems more challenging and requires attention to protecting corresponding engine hardware during an entire engine life.

Variable displacement vane pumps are becoming more popular for engine oil circuits due to their fuel savings over traditional fixed displacement pumps. As a result, engineers need to analyze these pumps to ensure the pump design meets the demands of the oil circuit while having good friction characteristics and avoiding issues like high pressure amplitude and resonance. By employing 1D flow simulation to these pumps, the user can analyze the most important issues surrounding vane pumps at a fraction of the time as 3D CFD. This paper showcases the prediction of several major performance quantities of a variable displacement vane pump including flow rate, pressure rise, and friction torque vs. engine speed and temperature. The simulation results show good correlation to measurement data. In addition, the pressure pulsation at several locations including in the vane chamber and at the outlet is compared directly with 3D CFD for a different pump.