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This paper presents progressive techniques based on the previous SAE papers [1], [2] for vibration transmission analysis (VTA) on finite element (FE) model using Transfer Path Analysis (TPA). The techniques are: 1) a contribution calculation technique for structure with manifold and continuous transfer paths: 2) a visualization technique of the influence degree for efficient derivation of measures for response reduction. In VTA, influence degree of each DOF is calculated based on TPA. In order to understand characteristics of vibration transmission (VT) easily and visually by engineers, magnitude of influence degree is expressed by replacement to magnitude of displacement in the diagram of FE vibration shape. This visualization technique is applied to an automotive body structure. The proposed techniques are applied to automotive body structure consisting of members and panels. The members are such as pillars, cross members and side members, which are the main VT paths.

The aim of this research is to develop the diesel combustion simulation (UniDES: Universal Diesel Engine Simulator) that incorporates multiple-injection strategies and in-cylinder composition changes due to exhaust gas recirculation (EGR), and that is capable of high speed calculation. The model is based on a zero-dimensional (0D) cycle simulation, and represents a multiple-injection strategy using a multi-zone model and inhomogeneity using a probability density function (PDF) model. Therefore, the 0D cycle simulation also enables both high accuracy and high speed. This research considers application to actual development. To expand the applicability of the simulation, a model that accurately estimates nozzle sac pressure with various injection quantities and common rail pressures, a model that accounts for the effects of adjacent spray interaction, and a model that considers the NOx reduction phenomenon under high load conditions were added.

This paper presents a modeling method for prediction of low-frequency road noise in a steady-state condition where rotating tires are excited by actual road profile undulation input. The proposed finite element (FE) tire model contains not only additional geometric stiffness related to inflation pressure and axle load but also Coriolis force and centrifugal force effects caused by tire rotation for precise road noise simulation. Road inputs act on the nodes of each rib in the contact patch of the stationary tire model and move along them at the driving velocity. The nodes are enforced to displace in frequency domain based on the measured road profile. Tire model accuracy was confirmed by the spindle forces on the rotating chassis drum up to 100Hz where Coriolis force effect should be considered. Full vehicle simulation results showed good agreement with the vibration measurement of front/rear suspension at two driving velocities.

There is ever increasing interest in the issues of fossil fuel depletion, global warming, due to increased atmospheric CO2, and air pollution, all of which are due in some extent to transportation, including automobiles. Hybrid Vehicles (HVs), whose performance and usage are equivalent to existing conventional vehicles, attract lots of attention and have started to come into wider use. Meanwhile, EVs have been considered by many as the best solution for the issues mentioned above. But the technical difficulty of battery energy density is an obstruction to successful implementation. Currently the Plug-in HV (PHEV), which combines the advantages of HV and EV, is being considered as one promising solution. PHEVs can be categorized into two types, according to operating modes. The first uses battery stored energy initially, only stating the internal combustion engine when the battery is depleted. This we call the All Electric Range (AER) system.

In this paper, we will introduce a stereo vision system developed as a sensor for a vehicle's front monitor. This system consists of three parts; namely, a stereo camera that collects video images of the forward view of the vehicle, a stereo ECU that processes its output image, and a near-infrared floodlight for illuminating the front at night. We were able to develop an obstacle detection function for the Pre-Crash Safety System and also a traffic lane detection function for a Lane-Keeping Assist System. Especially in regard to the obstacle detection function, we were able to achieve real-time processing of the disparity image calculations that had formerly required long processing times by using two types of recently developed LSIs.

The authors report on the design and application of a high resolution micro-electro-mechanical (MEMS) microphone array for automotive wind noise engineering. The array integrates both sensors and random access memory (RAM) chips on a flexible circuit board that eliminates high channel count wiring and allows the array to be deployed on automobile surfaces in a convenient “stick-on/peel-off” configuration. These arrays have potential application to the quantitative evaluation of interior wind noise from measurements on a clay model in the wind tunnel, when used in conjunction with a body vibro-acoustic model. The array also provides a high resolution turbulence measurement tool, suitable for validation of computation fluid dynamics (CFD) simulations for wind noise. The authors' report on the wavenumber-frequency structure of flow turbulence measured in different flow regions on a side glass and the corresponding contributions to interior wind noise.

This paper introduces reduced-order modeling techniques with Artificial Intelligence (AI) for Model-Based Development (MBD). In vehicle development, detailed physical models are replaced by reduced-order models (ROM) to expedite simulations. With recent advancements in AI-based reduced-order modeling, it is expected that modeling work will become more efficient, leading to reduced simulation times. However, the range of simulations (Model-in-the-Loop Simulation - MILS, Hardware-in-the-Loop Simulation - HILS, bench-system) compatible with ROM is limited. To overcome this limitation, this study leverages the ONNX format (Open Neural Network Exchange), a universally supported format among machine learning frameworks, and the Functional Mock-up Interface (FMI), a standard interface format for simulation tools, to enable general-purpose embedded technology with ROM. This study employs a vehicle model in engine surge simulations to validate AI-based reduced-order modeling for MBD.

Many vehicles are currently equipped with active safety systems that can detect vulnerable road users like pedestrians and bicyclists, to mitigate associated conflicts with vehicles. With the advancements in technologies and algorithms, detailed motions of these targets, especially the limb motions, are being considered for improving the efficiency and reliability of object detection. Thus, it becomes important to understand these limb motions to support the design and evaluation of many vehicular safety systems. However in current literature, there is no agreement being reached on whether or not and how often these limbs move, especially at the most critical moments for potential crashes. In this study, a total of 832 pedestrian walking or cyclist biking cases were randomly selected from one large-scale naturalistic driving database containing 480,000 video segments with a total size of 94TB, and then the 832 video clips were analyzed focusing on their limb motions.

This work proposes a new method to design crashworthiness structures that made of functionally graded cellular (porous) material. The proposed method consists of three stages: The first stage is to generate a conceptual design using a topology optimization algorithm so that a variable density is distributed within the structure minimizing its compliance. The second stage is to cluster the variable density using a machine-learning algorithm to reduce the dimension of the design space. The third stage is to maximize structural crashworthiness indicators (e.g., internal energy absorption) and minimize mass using a metamodel-based multi-objective genetic algorithm. The final structure is synthesized by optimally selecting cellular material phases from a predefined material library. In this work, the Hashin-Shtrikman bounds are derived for the two-phase cellular material, and the structure performances are compared to the optimized structures derived by our proposed framework.

Pedestrian Automatic Emergency Braking (PAEB) for helping avoiding/mitigating pedestrian crashes has been equipped on some passenger vehicles. Since approximately 70% pedestrian crashes occur in dark conditions, one of the important components in the PAEB evaluation is the development of standard testing at night. The test facility should include representative low-illuminance environment to enable the examination of the sensing and control functions of different PAEB systems. The goal of this research is to characterize and model light source distributions and variations in the low-illuminance environment and determine possible ways to reconstruct such an environment for PAEB evaluation. This paper describes a general method to collect light sources and illuminance information by processing large amount of potential collision locations at night from naturalistic driving video data.

Injuries of the human brain and spinal cord associated with the central nervous system (CNS) are seen in automotive accidents. CNS injuries are generally categorized into severe injuries (AIS 3+). However, it is not clear how the restraint conditions affect the CNS injuries. This paper presents a newly developed three-dimensional (3D) finite element head-neck model in order to investigate the biomechanical responses of the brain-spinal cord complex. The head model consists of the scalp, skull, and a detailed description of the brain including the cerebrum, cerebellum, brainstem with distinct white and gray matter, cerebral spinal fluid (CSF), sagittal sinus, dura, pia, arachnoid, meninx, falx cerebri, and tentorium. Additionally, the neck model consists of the cervical vertebral bodies, intervertebral discs, muscles, ligaments, spinal cord with white and gray matter, cervical pia, and CSF.

In order to further improve the performance of NOx storage-reduction catalysts (NSR catalysts), focus was placed on their high temperature performance deterioration via sulfur poisoning and heat deterioration. The reactions between the basicity or acidity of supports and the storage element, potassium, were analyzed. It was determined that the high temperature performance of NSR catalysts is enhanced by the interaction between potassium and zirconia, which is a basic metal oxide. Also, a new zirconia-titania complex metal oxides was developed to improve high temperature performance and to promote the desorption of sulfur from the supports after aging.

Multi-cylinder hydrogen-absorbing alloy tanks for fuel cell vehicles have 10 to 40 metallic cylinders that are bundled and filled with hydrogen-absorbing alloy. In this system, the cylinders themselves act as a heat exchanger and the working pressure is lowered to 10 to 20 MPa compared with high-pressure MH tanks. Moreover, both heat conduction and mass reduction can be achieved by reducing the wall thickness of the cylinders. A model verification experiment was conducted using a one-quarter-scale prototype of a full size tank, and a conduction simulation model verified in the experiment was used to predict the performance of the full size tank. Results showed that it is possible to fill the tank with hydrogen to 80% of its capacity in a five-minute filling time, although issues related to heat conductivity performance require improvement. Accordingly, it may be possible to adopt this tank as part of a system if the storage amount of the hydrogen-absorbing alloy can be increased.

Surface pressure measurements using microphone arrays are still challenging, especially in an automotive context with cruising speeds around Mach 0.1. The separated turbulent boundary layer excitation and the side mirror wake flow generate both acoustic and aerodynamic components, which have wavenumbers that differ by a factor of approximately 10. This calls for high spatial resolution measurements to fully resolve the wavenumber-frequency spectrum. In a previous publication [1], the authors reported a micro-electro-mechanical (MEMS) surface microphone array that successfully used wavenumber analysis to quantify acoustic versus turbulence loading. It was shown that the measured surface pressure at each microphone could be strongly influenced by self-noise induced by the microphone “packaging”, which can be attenuated with a suitable windscreen.

Low frequency interior wind noise is typically dominated by underfloor flow noise. The source mechanisms are fluctuating surface pressure loading from both flow turbulence and acoustic field levels developed in the semi-reverberant cavity between floor and road. Previous studies have used computation fluid dynamics (CFD) to estimate the aero-acoustic loading applied to a vibro-acoustic model, which is then used to predict the transmitted interior wind noise. This paper reports a new perspective in two respects. First it uses novel surface pressure microphone arrays to directly measure the underfloor aero-acoustic loading in the wind tunnel. Second, it considers two different underfloor aerodynamic configurations - with and without lightweight aero cover panels, which are installed primarily to reduce aerodynamic drag.

Breath alcohol interlock systems are used in Europe and the U.S. for drunk driving offenders, and a certain effect has been revealed in the prevention of drunk driving. Nevertheless, problems remain to be solved with commercialized detectors, i.e., a person taking the breath alcohol test must strongly expire to the alcohol detector through a mouthpiece for every test, more over the determination of the breath alcohol concentration requires more than 5 seconds. The goal of this research is to develop a device that functions suitable and unobtrusive enough as the interlock system. For this purpose, a new alcohol detector, which does not require a long and hard blowing to the detector through a mouthpiece, has been investigated. In this paper, as a tool available on board, a contact free alcohol detector for the prevention of drunk driving has been developed.

To investigate the effect of muscle activity in pre-impact on injury outcome, we developed a human arm finite element model with muscles which consisted of solid elements and truss elements that could be used for simulating muscle stiffness change for the inputted activity and 3-D geometry of each muscle. Two series of experimental tests on muscle stiffness change and arm flexion were conducted for validation of the model. Comparisons between the simulation results and test data indicated the model validity. Lateral impact simulations for a left arm demonstrated that the muscle activity in pre-impact had significant effects on the motion and stress distribution of the arm bones.

A few reports suggest differences in injury outcomes between cadaver tests and real-world accidents under almost similar conditions. This study hypothesized that muscle activity could primarily cause the differences, and then developed a human body finite element (FE) model with individual muscles. Each muscle was modeled as a hybrid model of bar elements with active properties and solid elements with passive properties. The model without muscle activation was firstly validated against five series of cadaver test data on impact responses in the anterior-posterior direction. The model with muscle activation levels estimated based on electromyography (EMG) data was secondly validated against four series of volunteer test data on bracing effects for stiffness and thickness of an upper arm muscle, and braced driver's responses under a static environment and a brake deceleration.

This paper describes a water-level prediction method for the air intake duct using multivariate analysis. When a vehicle runs on a submerged proving ground, in some cases the water level around the air intake duct rises. Although the rise in water level can be measured experimentally in actual vehicles, the design factors that determine the water level are not yet fully understood. The first step in understanding the factors for determining the water level on front-engine and front-drive (FF) -type vehicles is to establish a water level prediction technique. This is accomplished by the development of an original Computational Fluid Dynamics (CFD) analysis method capable of accurately simulating a free surface. The next step is to conduct multivariate analysis based on the results of parametric studies using this CFD analysis method that leads to the factors determining the water level at the air intake duct.

This paper gives an outline of the steering assistance system (hereinafter, SAS) and a description of its key technology: the lane recognition algorithm. To accommodate a variety of driving styles, the SAS is equipped with a lane keeping assistance mode (LKA mode) and a lane departure warning mode (LDW mode) that can be selectively set by the driver. The former mode works in combination with adaptive cruise control (ACC) and carries the advantage of relieving the driving load that is placed on the driver. The latter mode has the benefit of reducing the danger of lane departure accidents caused by the driver dozing off and taking his eyes off the road. The newly developed lane recognition ECU has a simple hardware set-up of two 32-bit microcomputers. The lane recognition algorithm was constructed on the basis of a logic process that analyzes pattern edge points and selects a set of edge points that most closely resemble lanemarks.