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One of the primary issues in the interpretation of vehicle impact response data, observed from vehicle crash test events, is coping with variability. This vehicle response inconsistency generally causes test results to be unpredictable and makes CAE test validation work difficult as well. This paper, considering the uncertain characteristics of vehicle impact events, has implemented a stochastic assessment of vehicle NCAP response variation through a CAE vehicle impact model, and it has accomplished the three primary study objectives as stated follows: 1) Identify the response variation causing factors stochastically from various structural and environmental factor candidates and quantify the degree of their influences on crash response, 2) Develop a methodology for interpreting the significance of the factor effects in conjunction with vehicle impact mechanics and physics, and 3) Implement a stochastic improvement of the vehicle NCAP responses and their repeatability

As part of an ongoing technical collaboration between Ford and Rouge Steel Company, a comprehensive study of door slam event was undertaken. The experimental phase of the project involved measurements of accelerations at eight locations on the outer panel and strains on six locations of the inner panel. Although slam tests were conducted with window up and window down, results of only one test is presented in this paper. The CAE phase of the project involved the development of suitable “math” model of the door assembly and analysis methodology to capture the dynamics of the event. The predictability of the CAE method is examined through detailed comparison of accelerations and strains. While excellent agreement between CAE and test results of accelerations on the outer panel is obtained, the analysis predicts higher strains on the inner panel than the test. In addition, the tendency of outer panel to elastically buckle is examined.

Merging a CAE shape optimization system and a concept Taguchi method SN-ratio index, a robustness-focused automated shape optimization method has been developed. Applying this method to diesel intake ports, with mold position tolerance set as the error factor, SN-ratio was defined for swirl stability. As a result of the optimization provided by a multi-objective genetic algorithm, simultaneous improvement of flux, swirl rotation and SN ratio was achieved.

It is important for vehicle concept planning to estimate fuel economy and the influence of vehicle vibration using virtual engine specifications and a virtual vehicle frame. In our former study, we showed the 1D physical power plant model with electrical starter, battery that can predict combustion transient torque, combustion heat energy and fuel efficiency. The simulation result agreed with measured data. For idling stop system, the noise and vibration during start up is important factor for salability of the vehicle. In this paper, as an application of the 1D physical power plant model (engine model), we will show the result of analysis that is starter shaft resonance and the effect on the engine mount vibration of restarting from idle stop. First, an engine model for 3.5L 6cyl NA engine was developed by energy-based model using VHDL-AMS. Here, VHDL-AMS is modeling language registered in IEC international standard (IEC61691-6) to realize multi physics on 1D simulation.

The durability of fuel tank straps is essential for vehicle safety. Extensive physical tests are conducted to verify designs for durability. Due to the complexity of the loads and the fuel-to-tank interaction, computer-aided-engineering (CAE) simulation has had limited application in this area. This paper presents a CAE method for fuel tank strap durability prediction. It discusses the analytical loads, modeling of fuel-to-tank interaction, dynamic analysis methods, and fatigue analysis methods. Analysis results are compared to physical test results. This method can be used in either a fuel-tank-system model or a full vehicle model. It can give directional design guidance for fuel tank strap durability in the early stages of product development to reduce vehicle development costs.

Due to the global economic downturn and higher environmental awareness, the social demands for low cost and fuel efficient vehicles are increasing. At the same time the engine power is increasing and customer expectations of reliability and NVH levels are increasing. To meet all the requirements, engineers are challenged to design light weight parts with higher performance. However, unconsidered mass reduction carries a risk of compromised NVH, Functional Reliability, and other functional demands. In order to resolve this contradiction, it is important to establish a basic structure with minimum necessary mass at the concept design phase, when there are still many degrees of freedom in the design space. Hence, a multi-objective optimization CAE methodology applicable for designing the basic structure of the Engine system was developed and is detailed below.

Since the regulations of CO2 emissions have been tightened in each country recently, each automotive manufacturer has responded by bringing competitive technologies that maximize efficiency while promoting vehicle electrification such as xEV. Not only the efficiency, we need to meet or exceed occupant performance and comfort expectations. The climate control system expends a large amount of energy to keep a comfortable environment, having a significant impact on fuel consumption and EV driving range. Therefore, many manufacturers try to save energy and improve occupant comfort quickly by using not only the conventional convective heating by HVAC but also the conductive heating to heat the human body directly such as seat and steering wheel heater. In this study, a radiative heater, which is more efficient than a convective heating to warm anterior thigh and shin where a conductive heating cannot warm, was applied to vehicle.

Computer applications have been widely used to assist product design. The successes and sophistication of computer aided engineering (CAE) techniques are respectfully recognized in this field. CAE applications in the manufacturing area however are still developing, although the manufacturing community is increasingly starting to pay attentions to computer simulations in its daily workings. This paper will briefly introduce some of these applications and promote awareness of computer simulations in manufacturing area. It contains four main sections: finite element analysis (FEA) in machining fixture design, FEA applications in component assembly, machining process simulations and machining vibrations in the milling operation. Each section comes with a practical case study, potential benefits are identified and conclusions are presented by using an integrated design and analysis approach.

Vehicle design and packaging is a repetitive and tedious process that involves frequent engineering and design changes. To improve design efficiency, a standard naming vehicle design methodology is proposed in this paper. For the geometric or the functional object used in the vehicle context, a standard name is assigned and also used as a unique object feature through its life cycle. With the proposed standard naming design methodology, the engineering knowledge can be efficiently embedded into the CAD design, and hence, vehicle design can be executed in a more automated fashion. Work case of the standard naming design methodology is illustrated by a vehicle design and packaging application using CATIA V5.

Combustion feedback using cylinder pressure sensors, ion current sensors or alternative sensing techniques is actively under investigation by the automotive industry to meet future legislative emissions requirements. One of the drawbacks of many rapid prototyping engine management systems is their available analog interfaces, often limited to 10-12 bits with limited bandwidth, sampling rate and very simple anti-aliasing filters. Processing cylinder pressure or other combustion feedback sensors requires higher precision, wider bandwidths and more processing power than is typically available. For these reasons, Ricardo in collaboration with GM Research has developed a custom, high precision analog input subsystem for the rCube rapid prototyping control system that is specifically targeted at development of combustion feedback control systems.

Computer Aided Engineering system for automotive piston design was developed which can predict total piston performance in a short time at the planning stage of piston design. Many previous studies attempted to calculate piston performance accurately with experimental data and their main purpose was not to create a tool for piston design. The purpose of this CAE system is to provide a tool for a designer to predict total piston performance easily and rapidly without experimental data. This system has following two characteristics. Firstly, new finite element methods were developed which can predict temperature distribution without experimental data, thermal skirt expansion for a strutted piston and skirt-to-bore contact pressure under engine operating conditions. The predicted result are accurate enough to predict piston performance at the planning stage of piston design.

In recent years, improving the engine thermal efficiency is strongly required. To enhance the engine thermal efficiency, it is important to improve the engine anti-knock quality. Technologies for modifying engine cooling have been developed to improve anti-knocking quality of engines. However, excessive improvement of engine cooling leads to an increase in cooling heat loss. Therefore, it is necessary to clarify the effects of the temperature of each part of the engine such as engine head-cylinder, cylinder-liner, and piston on knocking and cooling heat loss. In this paper, computer aided engineering (CAE) is used to predict the effects of each part of the engine on engine knocking and cooling heat loss. Firstly, the amount of heat energy that air-fuel mixture receives from engine cylinder-head, cylinder-liner, and piston is calculated during the intake stroke. The result shows that the cylinder-liner contributes largest heat energy to air-fuel mixture, especially the exhaust side.

Current vehicle acoustic performance prediction methods, CAE (computer aided engineering) or physical testing, have some difficulty predicting interior sound in the mid-frequency range (300 to 1000 Hz). It is in this frequency range where the overall acoustic performance becomes sensitive to not only the contributions of structure-borne sources, which can be studied using traditional finite element analysis (FEA) methods, but also the contribution of airborne noise sources which increase proportional to frequency. It is in this higher frequency range (>1000 Hz) that physical testing and statistical CAE methods are traditionally used for performance studies. This paper will discuss a study that was undertaken to test the capability of a finite element modeling method that can accurately simulate air-borne noise phenomena in the mid-frequency range.

Knowledge of experts is necessary for judging motor current waveforms. Here, we develop an automatic judgement system for motor current waveform by establishing an AI model trained by knowledge of experts and CAE technology.

Exhaust durability is an important measure of quality, which can be predicted using CAE with accurate mount loads. This paper proposes an innovative method to calculate these loads from measured mount accelerations. A Chrysler vehicle was instrumented with accelerometers at both ends of its four exhaust mounts. The vehicle was tested at various durability routes or events at DaimlerChrysler Proving Grounds. These measured accelerations were integrated to obtain their velocities and displacements. The differences in velocities and displacements at each mount were multiplied by its damping and stiffness rates to obtain the mount load. The calculation was conducted for all three translational directions and for all events. The calculated mount loads are shown within reasonable range. Along with CAE, it is suggested to explore this method for exhaust durability development.

To adapt to Battery Electric Vehicle (BEV) integration, the significance of protective designs for battery packs against ground impact caused by road debris is very high, and there is also a keen interest in the feasibility assessment technique using Computer-Aided Engineering (CAE) tools for prototype-free evaluations. However, the challenge lies in obtaining real-world empirical data to verify the accuracy of the predictive CAE model. Collecting real-world data using actual battery pack can be time-consuming, costly, and accurately ascertaining the precise direction, magnitude, and location of the force applied from the road to the battery pack poses a challenging task. Therefore, in this study, we developed a methodology using machine learning, specifically Gaussian process regression (GPR), to perform inverse analysis of the direction, magnitude, and location of vehicle-road contact forces during rough road conditions.

Carbon neutrality has become a significant target. One essential parameter regarding energy consumption and emissions is the mass of vehicles. Lightweight design improves the result of vehicle life cycle assessment (LCA), increases efficiency, and can be a step towards sustainability and CO2 neutrality. Weight reduction through structural optimization is a challenging task. Typical design development procedures have to be overcome. Instead of just a facelift or the creation of a derivative of the predecessor design, completely alternative design creation methods have to be applied. Automated structural optimization is one tool for exploring completely new design approaches. Different methods are available and weight reduction is the focus of topology optimization. This paper describes a fatigue life homogenization method that enables the weight reduction of vehicle parts. The applied CAE process combines fatigue life prediction and topology optimization.

This paper describes a lightweight magnesium intensive automobile body structure concept developed at DaimlerChrysler to support a high fuel-efficiency vehicle project. This body structure resulted in more than 40% weight reduction over a conventional steel structure while achieving significantly improved structural performance as evaluated through CAE simulations. A business case analysis was conducted and showed promising results. One concept vehicle was built for the purpose of demonstrating concept feasibility. The paper also identifies areas for further development to enable such a vehicle to become a production reality at a later time.

In the early stages of a vehicle program, it is a common practice to set target ranges for the global body, suspension and powertrain modes. This modal management process allows engineers to avoid potential noise and vibration problems stemming from strong overlap of major global modes. Before the first prototype hardware is built, finite element models of the body, suspension and powertrain are usually exercised to compare predicted versus targeted ranges of the major system modes in the form of a modal management chart. However, uncertainty associated with the design parameters, manufacturing process and other sources can lead to a major departure from the design intent when the first hardware prototype is built. In this study, a first order reliability method is used to predict variance of the eigen values due to parameter uncertainties. This allows the CAE engineers to add a “three sigma” bound on the eigen values reported in the modal management chart.

When developing an off-road vehicle, it is essential to create excellent drivability that enables the vehicle to be driven on all surfaces while ensuring passenger comfort. Since durability is another indispensable performance aspect for these vehicles, the development method must be capable of considering a high-level combination of a wide range of performance targets. This paper proposes a method to identify the region in which each performance aspect is realized through a complex domain combination problem. The proposed method is helpful in the initial design stage when the detailed specifications of the target vehicle are not determined because it is capable of considering both the specifications and usage method of the target vehicle, such as the selection of road profiles and driving speeds as design variables. The proposed method has the advantage of enabling efficient concurrent studies to search for feasible regions.