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In this paper a design methodology for automotive heat exchangers has been applied which brings robustness into the design process and helps to optimize the design goals: as to maintain an optimal coolant temperature and to limit the vehicle underhood air temperature within a tolerable limit. The most influential design factors for the heat exchangers which affect the goals have been identified with that process. The paper summarizes the optimization steps necessary to meet the optimal functional goals for the vehicle as mentioned above. Taguchi's [1] Design for Six Sigma (DFSS) methods have been employed to conduct this analysis in a robust way.

Taguchi method is a technology to prevent quality problems at early stages of product development and product design. Parameter design method is an important part in Taguchi method which selects the best control factor level combination for the optimization of the robustness of product function against noise factors. The air induction system (AIS) provides clean air to the engine for combustion. The noise radiated from the inlet of the AIS can be of significant importance in reducing vehicle interior noise and tuning the interior sound quality. The porous duct has been introduced into the AIS to reduce the snorkel noise. It helps with both the system layout and isolation by reducing transmitted vibration. A CAE simulation procedure has been developed and validated to predict the snorkel noise of the porous ducted AIS. In this paper, Taguchi's parameter design method was utilized to optimize a porous duct design in an AIS to achieve the best snorkel noise performance.

Design optimization methods are commonly used for weight reduction subjecting to multiple constraints in automotive industry. One of the major challenges remained is to deal with a large number of design variables for large-scale design optimization problems effectively. In this paper, a new approach based on fuzzy rough set is proposed to address this issue. The concept of rough set theory is to deal with redundant information and seek for a reduced design variable set. The proposed method first exploits fuzzy rough set to screen out the insignificant or redundant design variables with regard to the output functions, then uses the reduced design variable set for design optimization. A vehicle body structure is used to demonstrate the effectiveness of the proposed method and compare with a traditional weighted sensitivity based main effect approach.

One of the major challenges in multiobjective, multidisciplinary design optimization (MDO) is the long computational time required in evaluating the new designs' performances. To shorten the cycle time of product design, a data mining-based strategy is developed to improve the efficiency of heuristic optimization algorithms. Based on the historical information of the optimization process, clustering and classification techniques are employed to identify and eliminate the low quality and repetitive designs before operating the time-consuming design evaluations. The proposed method improves design performances within the same computation budget. Two case studies, one mathematical benchmark problem and one vehicle side impact design problem, are conducted as demonstration.

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.

Two finite element optimization techniques are presented for minimizing automotive engine air induction structural radiated noise and mass. Air induction systems are generally made of thin wall plastic which is exposed to high levels of pulsating engine noise. Weak air induction system walls vibrate excessively creating noise that can be heard by the driver. The conventional approach is to add ribs (many times through trial and error) which increase part weight or by adding “kiss-offs,” which restrict air flow. The finite element optimization methods considered here are shape optimization and topometry optimization. Genesis, a fully integrated finite element analysis and optimization package by Vanderplaats Research & Development, was used to perform finite element optimization. Choice of optimization method is primarily dependent on several factors which are appearance, part interference and flow restriction requirements.

Advanced high strength steels (AHSS) offer a good balance of strength, durability, crash energy absorption and formability. Applications of AHSS for lightweight designs of automotive structures are accelerating in recent years to meet the tough new CAFE standard for vehicle fuel economy by 2025. At the same time, the new generation pickup cargo box is to be designed for a dramatic increase in payload. Upgrading the box material from conventional mild steels to AHSS is necessary to meet the conflicting requirements of vehicle light weighting and higher payload. In this paper, typical AHSS grades such as DP590 and DP780 were applied to selected components of the pickup cargo box for weight reduction while meeting the design targets for fatigue, strength and local stiffness.

Environmental concerns and government regulations are factors that have led to an increased focus on fuel economy in the automotive industry. This paper identifies a method used to improve the efficiency of a front-wheel-drive (FWD) automatic transmission. In order to create improvements in large complex systems, it is key to have a large scope, to include as much of the system as possible. The approach taken in this work was to use Design for Six Sigma (DFSS) methodology. This was done to optimize as many of the front-wheel-drive transmission components as possible to increase robustness and efficiency. A focus of robustness, or consistency in torque transformation, is as important as the value of efficiency itself, because of the huge range of usage conditions. Therefore, it was necessary to find a solution of the best transmission component settings that would not depend on specific usage conditions such as temperatures, system pressures, or gear ratio.

ACOUSTOMIZE™ is a new method of acoustic evaluation used for the purpose of understanding and optimizing NVH performance of vehicles. The following paper documents a case study of the ACOUSTOMIZE™ test methodology on a passenger car BIW. This study includes an analysis of noise flow through BIW locations, a comparison of noise sound levels through BIW cavities with and without a sound treatment package and a comparison of the original cavity sealing design package consisting of baffles, tapes and baggies to low density polyurethane NVH Foam. The results of the study show detection of complex BIW pass throughs that the body leakage test (BLT) was not able to find. In addition, the data shows improved noise reduction with the low density polyurethane foam versus the original cavity sealing design package.

Improvements to 1D engine modeling accuracy and computational speed have led to greater reliance on this simulation technology during the engine development process. The benefits of modeling show up in many ways: increased simulation iterations for better optimization, reduction in prototype hardware iterations, reduction in program timing and overall cost. In this study a 1D GT-Power model of a turbocharged engine system was used to assist in the initial design phase and throughout the program. The model was developed using Chrysler Group LLC proprietary modeling features for predictive combustion and knock event prediction. In all stages of this project the model's accuracy was improved through regular correlation with dynamometer data. This paper mainly focuses on engine compression ratio selection, turbocharger selection, and cycle-to-cycle variation/cylinder-to-cylinder variation reduction through the combination of 1D GT-Power model optimization and dynamometer tests.

In recent trend, there is a huge demand for lightweight chassis frame, which improves fuel efficiency and reduces cost of the vehicle. Stiffness based optimization process is simple and straightforward while durability (life) based optimizations are relatively complex, time consuming due to a two-step (Stress then life) virtual engineering process and complicated loading history. However, durability performances are critical in chassis design, so a process of optimization with simplified approach has been developed. This study talks about the process of chassis frame weight optimization without affecting current durability performance where complex durability load cases are converted to equivalent static loadcases and life targets are cascaded down to simple stress target. Sheet metal gauges and lightening holes are the parameters for optimization studies. The optimization design space is constrained to chassis unique parts.

Interference assessments of a stepped-radius power-train component moving within a deformed stepped bore often arise during engine and transmission development activities. For example, when loads are applied to an engine block, the block distorts. This distortion may cause a cam or crankshaft to bind or wear prematurely in its journals as the part rotates within them. Within an automatic transmission valve body, care must be taken to ensure valve body distortion under oil pressure, assembly, and thermal load does not cause spool valves to stick as they translate within the valve body. In both examples, the mechanical scenario to be assessed involves a uniform or stepped radius cylindrical part maintaining a designated clearance through a correspondingly shaped but distorted bore. These distortions can occur in cross-sections (“out-of-round”) or along the bore (in an “s” or “banana” shaped distortions).

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.

The performance of door assembly is very significant for the vehicle design and door sag/set is one of the important attribute for design of door assembly. This paper provides an overview of conventional approach for door sag/set study based on door-hinge-BIW assembly (system level approach) and its limitation over new approach based on subassembly (subsystem level approach). The door sag/set simulation at system level is the most common approach adopted across auto industry. This approach evaluates only structural adequacy of door assembly system for sag load. To find key contributor for door sagging is always been time consuming task with conventional approach thus there is a delay in providing design enablers to meet the design target. New approach of door sag/set at “subsystem level” evaluates the structural stiffness contribution of individual subsystem. It support for setting up the target at subsystem level, which integrate and regulate the system level performance.

The 2013 SRT Viper Carbon Fiber X-Brace, styled by Chrysler's Product Design Office (PDO), is as much of a work of art as it is an engineered structural component. Presented in this paper is the design evolution, development and performance refinement of the composite X-Brace (shown in Figure 1). The single-piece, all Carbon Fiber Reinforced Plastic (CFRP) X-Brace, an important structural component of the body system, was developed from lightweight carbon fiber material to maximize weight reduction and meet performance targets. The development process was driven extensively by virtual engineering, which applied CAE analysis and results to drive the design and improve the design efficiency. Topology optimization and section optimization were used to generate the initial design's shape, form and profile, while respecting the package requirements of the engine compartment.

With the increased market share of electric vehicles, the demand for energy-efficient routing algorithms specifically optimized for electric vehicles has increased. Traditional routing algorithms are focused on optimizing the shortest distance or the shortest time in finding a path from point A to point B. These traditional methods have been working well for fossil fueled vehicles. Electric vehicles, on the other hand, require different route optimization techniques. Negative edge costs, battery power limits, battery capacity limits, and vehicle parameters that are only available at query time, make the task of electric vehicle routing a challenging problem. In this paper, we present an ant colony based, energy-efficient routing algorithm that is optimized and designed for electric vehicles. Simulation results show improvements in the energy consumption of electric vehicles when applied to a start-to-destination routing problem.

Brake judder is a brake induced vibration that a vehicle driver experiences in the steering wheel or floor panel at highway speeds during vehicle deceleration. The primary cause of this disturbance phenomenon is the brake torque variation (BTV). Virtual CAE tools from both kinematics and compliance standpoints have been applied in analyzing sensitivities of the vehicle systems to BTV. This paper presents a recently developed analytical approach that identifies parameters of steering and suspension systems for achieving optimal settings that desensitize the vehicle response to BTV. The analytical steps of this integrated approach started with creating a lumped mass noise-vibration-harshness (NVH) control model and a separate multi-body dynamics (MBD) suspension model. Then, both models were linked to run in a sequence through optimization software so the results from the MBD model were used as quasi-static inputs to the lumped mass NVH model.

The benefits of utilizing virtual engineering include not only shortened product development time and reduced reliance on expensive physical testing, but also the opportunities for greater standardization to support higher product quality. This paper describes a project for building a smart meshing template with a CAD/CAE link. The objective of the project is to optimize the utilization of CAD software and CAE preprocessing software capabilities. The deliverable of the project is a cylinder head mesh template which meets all the cylinder head durability simulation meshing requirements, and which links to CAD/CAE software. Special surface areas identified are built into the cylinder head CAD model design. By using one of the features in CAD software, all the special surfaces can be automatically updated throughout the design process.

Robustness/Reliability Assessment and Optimization (RRAO) is often computationally expensive because obtaining accurate Uncertainty Quantification (UQ) may require a large number of design samples. This is especially true where computationally expensive high fidelity CAE simulations are involved. Approximation methods such as the Polynomial Chaos Expansion (PCE) and other Response Surface Methods (RSM) have been used to reduce the number of time-consuming design samples needed. However, for certain types of problems require the RRAO, one of the first question to consider is which method can provide an accurate and affordable UQ for a given problem. To answer the question, this paper tests the PCE, RSM and pure sampling based approaches on each of the three selected test problems: the Ursem Waves mathematical function, an automotive muffler optimization problem, and a vehicle restraint system optimization problem.

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.