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Natural fiber-reinforced composites are currently gaining increasing attention as potential substitutes to pervasive synthetic fiber-reinforced composites, particularly glass fiber-reinforced plastics (GFRP). The advantages of the former category of composites include (a) being conducive to occupational health and safety during fabrication of parts as well as handling as compared to GFRP, (b) economy especially when compared to carbon fiber-reinforced composites (CFRC), (c) biodegradability of fibers, and (d) aesthetic appeal. Jute fibers are especially relevant in this context as jute fabric has a consistent supply base with reliable mechanical properties. Recent studies have shown that components such as tubes and plates made of jute-polyester (JP) composites can have competitive performance under impact loading when compared with similar GFRP-based structures.

Multidisciplinary Design Optimization (MDO) is of great significance in the lean design of vehicles. The present work is concerned with the objective of cross-functional optimization (i.e. MDO) of automotive body. For simplicity, the main goal adopted here is minimizing the weight of the body meeting NVH and crash safety targets. The stated goal can be achieved following either of two different ways: classic response surface method (RSM) and practical MDO methodology espoused recently. Even though RSM seems to be able to find a design point which satisfies the constraints, the problem is with the time associated with running such CAE algorithms that can provide a single optimal solution for multi-disciplinary areas such as NVH and crash safety.

This paper describes the development of a three-dimensional lumped-mass structure and dummy model to study barrier-to-car side impacts. The test procedures utilized to develop model input data are also described. The model results are compared to crash test results from a series of six barrier-to-car crash tests. Sensitivity analysis using the validated model show the necessity to account for dynamic structural rate effects when using quasi-statically measured vehicle crush data.

A computational model of a mid-size sport utility vehicle was developed using MADYMO. The model includes a detailed description of the suspension system and tire characteristics that incorporated the Delft-Tyre magic formula description. The model was correlated by simulating a vehicle suspension kinematics and compliance test. The correlated model was then used to simulate a J-turn vehicle dynamics test maneuver, a roll and non-roll ditch test, corkscrew ramp and a lateral trip test, the results of which are presented in this paper. The results indicate that MADYMO is able to reasonably predict the vehicle and occupant responses in these types of applications and is potentially suited as a tool to help setup a suite of vehicle configurations and test conditions for rollover sensor testing. A suspension system sensitivity study is presented for the laterally tripped non-roll event.

An assessment tool was developed for rollover CAE signals evaluation to assess primarily the qualities of CAE generated sensor waveforms. This is a key tool to be used to assess CAE results as to whether they can be used for algorithm calibration and identify areas for further improvement of sensor. Currently, the method is developed using error estimates on mean, peak and standard deviation. More metrics, if necessary, can be added to the assessment tool in the future. This method has been applied to various simulated signals for laboratory-based rollover test modes with rigid-body-based MADYDO models.

Explicit method is commonly used in crashworthiness analysis due to its capability to solve highly non-linear problems without numerous iterations and convergence problems. However, the time step for explicit methods is limited by the time that the physical wave crosses the element. Therefore, to avoid large amount of CPU time, the explicit method is usually used for non-linear dynamic problems with a short period of simulation duration. For problems under quasi-static loading conditions at pre-crash and post-crash, implicit method could be more efficient than explicit methods because the required computation time is much shorter. Due to the recent advance of crash codes, which allows both implicit and explicit computations to be performed in the same code, crash engineers are able to use explicit computation for crash simulation as well as implicit computation for some of the pre-crash quasi-static loading or post-crash spring back simulations.

There are a wide variety of approaches to model the automotive seat and occupant interaction. This paper traces the studies conducted for simulating the occupant to seat interaction in frontal and/or rear crash events. Starting with an initial MADYMO model, a MADYMO-LS/DYNA coupled model was developed. Subsequently, a full Finite Element Analysis model using LS/DYNA was studied. The main objective of the studies was to improve the accuracy and efficiency of CAE models for predicting the dummy kinematics and structural deformations at the restraint attachment locations in laboratory tests. The occupant and seat interaction was identified as one of the important factors that needed to be accurately simulated. Quasi-static and dynamic component tests were conducted to obtain the foam properties that were input into the model. Foam specimens and the test setup are discussed. Different material models in LS/DYNA were evaluated for simulating automotive seat foam.

This paper describes the development of a simulation methodology to predict body mount functional stiffness. The method provides some improvement over the one previously developed by Cheng and Le. Advantages of this method are 1) user's expertise is not a requirement, 2) elimination of trial-and-error runs for tuning the stiffness characteristics and 3) a predictive tool applicable prior to crash tests. Verification of the method is carried out by comparing the numerical results with those obtained from trial-and-error method and tests. The method leads to promising results in full vehicle crash simulations.

The effect of epoxy-based structural foam on strength, stiffness, and energy absorption of foam filled structural components is investigated and implemented to formulate design guide-lines that can be used in enhancing weight reduction and engineering functions of systems. An experimental approach is first utilized to identify design variables such as foam density, gage, and foam layer thickness, that are needed to enhance the weight/ performance ratio of structural hat-section components. A CAE approach using non-linear, large deformation finite element analysis is used to model the hat-section components. An acceptance level of confidence in the CAE analytical tools is then established based on comparisons of results between the two approaches. Upon that, the CAE analytical tools are deployed in a sensitivity study to quantify the crush/crash characteristics of foam-filled hat-section components with respect to the changes in the afore mentioned design variables.

For a body-on-frame (BOF) vehicle, the frame is the major structural subsystem to absorb the impact energy in a frontal vehicle impact. It is also a major contributor to energy absorption in rear impact events as well. Thus, the accuracy of the finite element frame model has significant influence on the quality of the BOF vehicle impact predictability. This study presents the latest development of the frame modeling methodology on the simulation of BOF vehicle impact performance. The development is divided into subsystem (frame sled test) and full system (full vehicle test). This paper presents the first phase, subsystem testing and modeling, of the frame modeling development. Based on the major deformation modes in frontal impact, the frame is cut into several sections and put on the sled to conduct various tests. The success of the sled test highly depends on whether the sled results can replicate the deformation modes in the full vehicle.

This study focuses on the modeling of a frame in a body-on-frame (BOF) vehicle to improve the prediction of vehicle response in crashes. The study is divided into three phases - component (frame material modeling), subsystem (frame sled test) and full system (full vehicle test). In the component level, we investigate the available strain rate data, the performance of various material models in crash codes and the effect of the strain rate in crash simulation. In the subsystem phase, we incorporate the strain rate modeling and expand the scope to include both the forming and the welding effects in the subsystem CAE model to improve the correlation between CAE and test. Finally the improved frame modeling methodology with strain rate, forming and welding effects is adopted in full vehicle model. It is found that the proposed frame modeling methodology is crucial to improve the pulse prediction of a full vehicle in crashes.

The current paper presents improvements of a previous single-degree-of-freedom lumped parameter model with a nonlinear spring that could be used for preliminary design of headform impact safety countermeasures for normal impact with negligible headform rotation. The unloading taking place along the elastic path has been dispensed with and a parabolic unloading path may yield more realistic force-deformation and deceleration-time behaviors when compared with test results. The effects of the modified unloading behavior on HIC(d) are illustrated with examples. Additionally, a new velocity-dependent yield force criterion is adopted for the spring element to represent strain rate sensitive countermeasures. It is observed that inclusion of strain rate effect can either increase or decrease predicted HIC(d) when compared with using only quasi-static yield force.

This paper presents the design of a crash detection system based on the principles of continuous-mode Hidden Markov Models (HMM) with real-valued emission parameters. Our design utilizes log-likelihood for optimizing HMM parameters including the number of states in the model and the accelerometer crash-pulse buffer size resulting in lower costs and complexity of the crash detection system. Cross validation technique based on Jackknifing is utilized to estimate the crash pulse detection rate for a variety of crash events involving rigid as well as offset deformable barriers with head-on and oblique angle impacts. The system is simulated using Matlab and Simulink, and the proposed model is able to accurately classify crash-events within 10 ms from the time of the impact.

Development of frontal impact airbag sensor algorithms/calibrations requires crash signals, which can be obtained from vehicle crash testing and/or CAE simulations. This paper presents the development of finite element sensor models to generate CAE simulated crash pulses/signals at the sensing location during frontal impacts. These signals will be evaluated for potential used in the airbag sensor algorithm/calibration.

CAE has played a key role in development of the rollover safety technology by reducing the required number of prototypes. CAE-led Design Of Experiments (DOE) studies have helped in developing the process to minimize the number of CAE runs and to optimize use of the prototypes. This paper demonstrates the use of CAE/DOE for the design and optimization of rollover vehicle prototypes and also investigates effects of various factors in the selection of vehicle configuration for rollover sensor calibration testing. The process described herein has been successfully applied to vehicle programs. Modeling and analysis guidelines are also presented for CAE engineers to help in optimizing vehicle prototypes at program level.

State of the art electronic restraint systems rely on the acceleration measured during a vehicle crash for deployment decisions. The acceleration signal is analyzed with different criteria, among which the velocity change is a dominant criterion in almost any existing crash detection algorithm. Sensors in the front crush zone have recently been added to help develop restraint systems that comply with the new FMVSS208 and EuroNCAP regulations. Front crash sensors are usually evaluated for their velocity change during a crash and typically play a key role in the deployment decision. CAE based FEA analysis has recently been used to generate signals at the sensor module locations in crash simulations to provide supplemental information for crash sensing algorithm development and calibration. This paper presents an initial effort in developing a velocity-based crash detection algorithm, that allows broad use of CAE generated velocity time histories for system calibration.

This paper describes (1) the development of a component test methodology for testing a cooling module including radiator, condenser and trans. cooler, and (2) the associated CAE model development. A series of light truck/SUV cooling module component tests were conducted to obtain their characteristics as inputs for frontal impact and sensor modeling development. First, the cooling module component CAE sub-model was developed using soft springs along with fine-mesh sheet metal shell elements. Second, simulated sub-model results were correlated fairly well with the test data. Third, this component CAE sub-model was then incorporated into a full vehicle CAE model that was used for frontal impact (NCAP) and sensor development. Results indicated that the proposed test method for cooling module components provided consistent data and the results from cooling module sub-model can be incorporated into the full vehicle CAE model for improving the quality and accuracy of CAE models.

The present work is concerned with the objective of design optimization of an automotive front end structure meeting both occupant and pedestrian safety requirements. The main goal adopted here is minimizing the mass of the front end structure meeting the safety requirements without sacrificing the performance targets. The front end structure should be sufficiently stiff to protect the occupant by absorbing the impact energy generated during a high speed frontal collision and at the same time it should not induce unduly high impact loads during a low speed pedestrian collision. These two requirements are potentially in conflict with each other; however, there may exist an optimum design solution, in terms of mass of front end structure, that meets both the requirements.

Periprosthetic fractures refer to the fractures that occur in the vicinity of the implants of joint replacement arthroplasty. Most of the fractures during an automotive frontal collision involve the long bones of the lower limbs (femur and tibia). Since the prevalence of persons living with lower limb joint prostheses is increasing, periprosthetic fractures that occur during vehicular accidents are likely to become a considerable burden on health care systems. It is estimated that approximately 4.0 million adults in the U.S. currently live with Total Knee Replacement (TKR) implants. Therefore, it is essential to study the injury patterns that occur in the long bone of a lower limb containing a total knee prosthesis. The aim of the present study is to develop an advanced finite element model that simulates the possible fracture patterns that are likely during vehicular accidents involving occupants who have knee joint prostheses in situ.

This paper represents the development of a new design methodology based on data mining theory for decision making in vehicle crashworthy components (or parts) development. The new methodology allows exploring the big crash simulation dataset to discover the underlying complicated relationships between vehicle crash responses and design variables at multi-levels, and deriving design rules based on the whole vehicle safety requirements to make decisions towards the component and sub-component level design. The method to be developed will resolve the issue of existing design approaches for vehicle crashworthiness, i.e. limited information exploring capability from big datasets, which may hamper the decision making and lead to a nonoptimal design. A preliminary design case study is presented to demonstrate the performance of the new method. This method will have direct impacts on improving vehicle safety design and can readily be applied to other complex systems.