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Complete validation of any finite element (FE) model of the human brain is very difficult due to the lack of adequate experimental data. However, more animal brain injury data, especially rat data, obtained under well-defined mechanical loading conditions, are available to advance the understanding of the mechanisms of traumatic brain injury. Unfortunately, internal response of the brain in these experimental studies could not be measured. The aim of this study was to develop a detailed FE model of the rat brain for the prediction of intracranial responses due to different impact scenarios. Model results were used to elucidate possible brain injury mechanisms. An FE model, consisting of more than 250,000 hexahedral elements with a typical element size of 100 to 300 microns, was developed to represent the brain of a rat. The model was first validated locally against peak brain deformation data obtained from nine unique dynamic cortical deformation (vacuum) tests.

Dynamic data, forces, moments and displacements are widely used parameters in a simulation environment for design and testing. These results may be obtained from field tests, laboratory measurements, and numerical simulations. The correctness of the simulation results depends strongly on the models and numerical solution techniques. This paper presents a preliminary examination of the differences between results obtained from the computer code DADS (Dynamic Analysis and Design System) [1] and the field data for the response of a military tank. The differences are analyzed by standard statistical methods in the frequency domain. The statistical tests show that DADS results differ from the measured field data and that the errors are not white noise. Moreover, the principal frequencies of the differences are identified.

A high carbon, high silicon and high manganese steel containing about 1% carbon, 3.0% silicon and 2.0% manganese has been developed. This steel has been synthesized using the concepts from Austempered Ductile Cast Iron (ADI) technology. The influence of austempering process on the microstructure and the room temperature mechanical properties of this steel was investigated. The influence of microstructure on the plain strain fracture toughness of this new steel was also examined. Four batches of compact tension and cylindrical tensile samples were prepared from this steel as per ASTM standards E-399 and E-8 respectively. Two batches of specimens were processed by traditional quenching and tempering process while other two batches were austempered. The microstructures were characterized by X-ray diffraction and optical metallography.

Hat sections made of steel are frequently encountered in automotive body structural components such as front rails. These components can absorb significant amount of impact energy during collisions thereby protecting occupants of vehicles from severe injury. In the initial phase of vehicle design, it will be prudent to incorporate the sectional details of such a component based on an engineering target such as peak load, mean load, energy absorption, or total crush, or a combination of these parameters. Such a goal can be accomplished if efficient and reliable data-based models are available for predicting the performance of a section of given geometry as alternatives to time-consuming and detailed engineering analysis typically based on the explicit finite element method.

Previously, a 10-year-old (YO) pediatric thorax finite element model (FEM) was developed and verified against child chest stiffness data measured from clinical cardiopulmonary resuscitation (CPR). However, the CPR experiments were performed at relatively low speeds, with a maximum loading rate of 250 mm/s. Studies showed that the biomechanical responses of human thorax exhibited rate sensitive characteristics. As such, the studies of dynamic responses of the pediatric thorax FEM are needed. Experimental pediatric cadaver data in frontal pendulum impacts and diagonal belt dynamic loading tests were used for dynamic validation. Thoracic force-deflection curves between test and simulation were compared. Strains predicted by the FEM and the injuries observed in the cadaver tests were also compared for injury assessment and analysis. This study helped to further improve the 10 YO pediatric thorax FEM.

The present investigation details an experimental procedure for frontal impact responses of a generic steel front bumper crush can (FBCC) assembly subjected to a rigid full and 40% offset impact. There is a paucity of studies focusing on component level tests with FBCCs, and of those, speeds carried out are of slower velocities. Predominant studies in literature pertain to full vehicle testing. Component level studies have importance as vehicles aim to decrease weight. As materials, such as carbon fiber or aluminum, are applied to vehicle structures, computer aided models are required to evaluate performance. A novel component level test procedure is valuable to aid in CAE correlation. All the tests were conducted using a sled-on-sled testing method. Several high-speed cameras, an IR (Infrared) thermal camera, and a number of accelerometers were utilized to study impact performance of the FBCC samples.

This study identified the mechanical properties of ten cadaveric lumbar spines and two Hybrid III lumbar spines. Eight tests were performed on each specimen: tension, compression, anterior shear, posterior shear, left lateral shear, flexion, extension and left lateral bending. Each test was run at a displacement rate of 100 mm/sec. The maximum displacements were selected to approximate the loading range of a 50 km/h Hybrid III dummy sled test and to be non-destructive to the specimens. Load, linear displacement and angular displacement data were collected. Bending moment was calculated from force data. Each mode of loading demonstrated consistent characteristics. The load-displacement curves of the Hybrid III lumbar spine demonstrated an initial region of high stiffness followed by a region of constant stiffness.

Two-dimensional finite element models for three coronal sections of the porcine brain have been developed and the results were compared with injury data from animal experiments performed at the University of Pennsylvania (Ross et al, 1994). The models consisted of a three-layered skull, dura, CSF, white matter, gray matter and ventricles. Model I, a section at the septal nuclei and anterior commissure level, contains 490 solid elements and 108 membrane elements. Model II, a section at the rostral-thalamic level, contains 644 solid elements and 130 membrane elements. Model III, a section at the caudal hippocampal level, contains 548 solid elements and 104 membrane elements. Plane strain conditions were assumed for all models. Material properties of the brain were taken from previous human brain models, but the white matter was assumed to be about 60% stronger than the gray matter with the same Poisson's ratio.

Even under steady state operating conditions, the pressure variation in individual cylinders, and the corresponding gas-pressure torque are subjected to small random fluctuations from cycle to cycle. The gas-pressure torque of a cylinder may be expressed as a sum of harmonically variable components, each harmonic being affected by these fluctuations. A probabilistic model of the vector interpreting such a harmonic component is developed and used to determine the statistical parameters of the resultant random vector representing the corresponding harmonic order of the engine torque. At the low frequencies of the lowest harmonic orders of the engine torque the crankshaft behaves like a rigid body. This behavior permits to correlate the statistical parameters of the same harmonic components of the resultant torque and of the measured engine speed. This correlation is proved by experiments and used to identify faulty cylinders.

The influence of calcium treatment on the mechanical properties of a plain carbon steel (SAE 1050) was investigated. The mechanical properties investigated were tensile and impact strength, fatigue crack growth rate, and the fatigue threshold. Impact testing was conducted at both room temperature and at -40°C. Several heats of both calcium and non-calcium treated steel (SAE 1050) were tested in both the as hot-rolled condition and in the quenched and tempered condition (with a hardness level of HRC = 45). The results of this investigation show no significant difference in the tensile properties or room temperature impact properties between the calcium treated and the non-calcium treated steels. However, the impact strengths of calcium treated steels were slightly higher than that of non-calcium treated steels at -40°C.

Propagation-based and phase-enhanced x-ray imaging was developed as a unique metrology technique to visualize the internal structure of high-pressure fuel injection nozzles. We have visualized the microstructures inside 200-μm fuel injection nozzles in a 3-mm-thick steel housing using this novel technique. Furthermore, this new x-ray-based metrology technique has been used to directly study the highly transient needle motion in the nozzles in situ and in real-time, which is virtually impossible by any other means. The needle motion has been shown to have the most direct effect on the fuel jet structure and spray formation immediately outside of the nozzle. In addition, the spray cone-angle has been perfectly correlated with the numerically simulated fuel flow inside the nozzle due to the transient nature of the needle during the injection.

Typical automotive body structures use resistance spot welding for most joining purposes. New materials, such as Advanced High Strength Steels (AHSS) are increasingly used in the construction of automotive body structures to meet increasingly higher structural performance requirements while maintaining or reducing weight of the vehicle. One of the challenges for implementation of new AHSS materials is weldability assessment. Weld engineers and vehicle program teams spend significant efforts and resources in testing weldability of new sheet metal stack-ups. In this paper, we present a methodology to determine the weldability of sheet metal stack-ups using an Artificial Neural Network-based tool that learns from historical data. The paper concludes by reviewing weldability results predicted by using this tool and comparing with actual test results.