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Digital engineering has been utilized in product development to improve the quality. The actual object was measured and digitized into the three-dimensional (3-D) data, and the requirement of evaluating and analyzing the CAD data has been increased in these activities. So, we developed the technology that measures the actual object and obtains the 3-D model data for general automotive parts. The features of this new system are high-speed and high-accuracy by using high energy X-ray CT technology and 3-D model data technology. 3-D model data can be obtained for about 5 hours in case of the engine block and the error is 0.1mm or less. We also show the examples of the new automotive parts development using this technology.

In an evaluation of crashworthiness for the cars made of aluminum alloys, the evaluation considering fracture phenomenon comes to be needed because conventional aluminum alloys have low fracture strain (10-20%). In case of the development of a B-Pillar made by die cast, if crack occurrence, furthermore, separation of a part can be estimated by using CAE in crashworthiness evaluations, we can reduce the number of prototype makings and the cost of development using expensive dies. Therefore, we performed crashworthiness evaluations by CAE using some sort of a damage & fracture material model. It is known as “Orthotropic damage & fracture model”.

Cutting development times in car manufacturing means bringing forward the knowledge processes. Simulations based directly on CAD data reduce or replace time-consuming hardware loops significantly and therefore make a significant contribution to this. Ergonomic product design is an area that is challenged as far as the further development of virtual methods is concerned. Simulation of the static and quasi-static positions of passengers inside the car is the current state of the art in ergonomic product design. For this reason, interest is strongly focused on the simulation of complex movement processes within the context of enhancing simulation tools. For the car manufacturer, the manner in which people enter and leave the car is of particular interest. Getting into the car is the customers' first actual contact with it. It may also develop into a serious problem for car drivers, as they get older.

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.

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.

Nowadays there is an increasing need to streamline CAE processes. One such process consists of translating a Multibody Dynamics System (MBS) model into an equivalent Finite Element Analysis (FEA) model. Typically, users start with the creation of a MBS model which is set at a desired operating point by means of running simulations in the MBS domain (e.g. dynamics, statics.) The MBS model is then further translated into an equivalent FEA model which is used to perform simulations in the FEA domain (e.g. passive safety/crash, noise vibration harshness/NVH.) Currently, the translation of the MBS model into a FEA model is done either manually or by means of using a user-written script. This paper shows that a user-written script that translates a MBS model into a FEA model can not provide a high fidelity translation. In general, it is found that eigenvalues computed by the FEA code would not match eigenvalues computed by the MBS code.

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.

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.

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.

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.

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.

The trend in the previous years showed that an ideal product is not obtained as a sum of development results of several separated disciplines but rather as a result of a holistic multidisciplinary CAE approach. In the course of the whole component development process it is necessary to consider all functions of an individual component equivalent to their importance in the system as a whole, in order to achieve both a technical and a financial optimum. The predictability and the accuracy of an individual computational method have to be regarded against the background of the entire simulation process. A continuative CAE-standard and a harmonious interaction between the different computational disciplines promise more success than focusing specifically on individual topics and thereby neglecting the “bigger picture”. This awareness provided the basis for a decision to change the entire crash simulation software to ABAQUS.

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.

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.

Sport Activity Vehicle (SAV) share a growing market of an entirely new class of vehicles. Outstanding comfort in traditional on-road and also off-road terrain combined with leading edge technology are basic features of this concept. But in addition to that, the SAV has to offer the same overall safety features, expected by e.g. a luxury-segment sedan. A way to ensure the BMW X5 becoming one of the safest cars was the consequent use of simulation techniques in passive safety development. This paper deals with introduction of a CAE driven development process for passive safety in the BMW X5 project, focusing on examples in front and side impact.

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 importance of the automotive interior as a characteristic feature in the competition for the goodwill of the customer has increased significantly in recent years. Whilst there are established, more or less efficient CAE processes for the solution of problems in the areas of occupant safety and service strength, until now the implementation of CAE in themes such as dimensional stability, warpage and corrugation1 of plastic parts has been little investigated. The developmental support in this field is predominantly carried out by means of hardware tests. Real plastic components alter their form as a result of internal forces often during the first weeks following production. The process, known as “creep”, can continue over an extended period of time and is exacerbated by high ambient temperatures and additional external loads stemming from installation and post assembly position.

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.

Comfort and well-being have always been connected with a flawless interior acoustic, free of any background noise or BSR, (buzz, squeak and rattle). BSR noises dominate the interior acoustic and represent one of the main sources for discomfort often causing considerable warranty costs. Traditionally BSR issues have been identified and rectified through extensive hardware testing, which by its nature intensifies toward the end of the car development process. In the following paper the integration of a virtual BSR validation technique in a modern development process by the use of appropriate CAE methods is presented. The goal is to shift, in compliance with the front loading concept, the development activities into the early phase. The approach is illustrated through the example of an instrument panel, from the early concept draft for single components to an assessment of the complete assembly.

Over the past 30 years, the computer-aided engineering (CAE) tools have been applied extensively in the automotive industry. In order to accelerate time-to-market while coping with legal limits that have become increasingly restrictive over the last decades, CAE has become an indispensable tool covering all major fields in a modern automotive product design process. However, when tackling complex real-life engineering problems, the computational models might become rather involved and thus less efficient. Therefore, the overall trend in the automotive industry is currently heading towards combined approaches, which allow the best of the both worlds, namely the experimental measurement and numerical simulation, to be merged into one integrated scheme. In this paper, the so-called patch transfer function (PTF) approach is adopted to solve coupled vibro-acoustic problems. In the PTF scheme, the interfaces between fluid and structure are discretised in terms of patches.