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This study applies a detailed finite element model of the human body to simulate occupant knee impacts experienced in vehicular frontal crashes. The human body model includes detailed anatomical features of the head, neck, chest, thoracic and lumbar spine, abdomen, and lower and upper extremities. The material properties used in the model for each anatomic part of the human body were obtained from test data reported in the literature. The total human body model used in the current study has been previously validated in frontal and side impacts. Several cadaver knee impact tests representing occupants in a frontal impact condition were simulated using the previously validated human body model. Model impact responses in terms of force-time and acceleration-time histories were compared with test results. In addition, stress distributions of the patella, femur, and pelvis were reported for the simulated test conditions.

Human abdominal response and injury in blunt impacts was investigated through finite element simulations of cadaver tests using a full human body model of an average-sized adult male. The model was validated at various impact speeds by comparing model responses with available experimental cadaver test data in pendulum side impacts and frontal rigid bar impacts from various sources. Results of various abdominal impact simulations are presented in this paper. Model-predicted abdominal dynamic responses such as force-time and force-deflection characteristics, and injury severities, measured by organ pressures, for the simulated impact conditions are presented. Quantitative results such as impact forces, abdominal deflections, internal organ stresses have shown that the abdomen responded differently to left and right side impacts, especially in low speed impact.

A provisional, age-dependent thoracic risk equation (or, “risk curve”) was derived to estimate moderate-to-fatal injury potential (AIS2+), pertaining to men with responses gaged by the advanced mid-sized male test dummy (THOR50). The derivation involved two distinct data sources: cases from real-world crashes (e.g., the National Automotive Sampling System, NASS) and cases involving post-mortem human subjects (PMHS). The derivation was therefore more comprehensive, as NASS datasets generally skew towards younger occupants, and PMHS datasets generally skew towards older occupants. However, known deficiencies had to be addressed (e.g., the NASS cases had unknown stimuli, and the PMHS tests required transformation of known stimuli into THOR50 stimuli).