The majority of car crash deaths occur in the front seats because the majority of occupants sit in the front seats. Traditionally, the rear seats were safer than the front seats because a front seated occupant would be closer to rigid structures such as the steering wheel, and they would be closer to the location of the impact. Therefore, government crash test regulations as well as academic and industry testing up to this point have principally focused on the front seats. Since the beginning of efforts to make cars safer, innovations were applied to the front seats first. Only some of these safety innovations have transitioned into the rear seats. Over the years, the front seats have gotten much safer due to advanced seatbelts with pretentioners and load limiters, airbags surrounding the driver, and structural changes to the vehicle frame to prevent intrusion into the occupant compartment. At the same time, occupant safety in the rear seats has also improved, however at only a fraction of the improvement of the front seats. With modern vehicles, the front seats have actually become safer than the rear seats for certain occupants and specific crash types (e.g., adult occupants in frontal crash). The lagging performance of the rear seats represents a problem because thousands of rear-seated occupants are injured or killed each year. With the rise in autonomous driving systems, the amount of occupants sitting in the rear seats, and therefore sustaining injury, could increase dramatically. In this dissertation, rear seats of a range of current vehicles were reconstructed to examine injury risk with the finite element models of two anthropomorphic test devices. These models showed a wide range of injury risks in the reconstructed seats. They were also able to show results similar to sled impact tests with the same vehicles. Knowledge gained from these reconstructions was then used to perform parametric studies on key variables that influence injury risk in the rear seats. From the parametric studies, it was found that the seat back angle, the width of the seatbelt anchors, and the presence of a seatbelt pretensioner had the largest influences on the injury risk. One of the injury mechanisms prevalent in the rear seats is submarining. Submarining likelihood and injury probability is difficult to predict with anthropomorphic test devices; however, human body models can help to improve injury prediction in these cases. To improve the injury prediction capability of human body models, several additions to the models are necessary. This dissertation outlines the investigation of spleen and kidney shapes through statistical shape analysis. This type of analysis allows more customizable human body models which could better capture the injury probability to these organs for a wider range of the population. Finally, subject-specific models of ribs were created to investigate factors affecting the predictive capability of finite element models. The findings and methodology from this body of work have the ability to add critical contributions to the understanding of injury risk and injury mechanisms in the rear seats. / Doctor of Philosophy / The majority of car crash deaths occur in the front seats because the majority of occupants sit in the front seats. Traditionally, the rear seats were safer than the front seats because a front seated occupant would be closer to hard objects such as the steering wheel, and they would be closer to the location of the impact. Therefore, government crash test regulations as well as academic and industry testing up to this point have principally focused on the front seats. Since the beginning of efforts to make cars safer, technology such as seatbelts and airbags were applied to the front seats first. Only some of this technology has been added into the rear seats. Over the years, the front seats have gotten much safer due to all the work focused on the front seats. At the same time, the rear seats have also improved, however at only a fraction of the improvement of the front seats. With modern vehicles, the front seats have actually become safer than the rear seats in some cases. The lagging performance of the rear seats represents a problem because thousands of rear-seated occupants are injured or killed each year. With the rise in self driving cars, the amount of occupants sitting in the rear seats, and therefore sustaining injury, could increase dramatically. In this dissertation, rear seats of a range of current vehicles were reconstructed to examine injury risk with the models of two crash test dummies. These models showed a wide range of injury risks in the reconstructed seats. They were also able to show results similar to physical tests with the same vehicles. Knowledge gained from this work was then used to help look at key variables that influence injury risk in the rear seats. It was found that the angle of the seat back, the width of the seatbelt anchors, and the presence of advanced seatbelts had the largest influences on the injury risk. One of the injury mechanisms prevalent in the rear seats is submarining, where the seatbelt slides up off the hips. Submarining likelihood and injury probability is difficult to predict with crash test dummies; however, human body models can help to improve injury prediction in these cases. To improve the injury prediction capability of human body models, several additions to the models are necessary. This dissertation outlines the investigation of spleen and kidney shapes to allow more customizable human body models which could better capture the injury probability to these organs for a wider range of the population. Finally, subject-specific models of ribs were created to investigate factors affecting the predictive capability of rib models. The findings and methodology from this body of work have the ability to add critical contributions to the understanding of injury risk and injury mechanisms in the rear seats.

A procedure for generating a customized weight function for wing bending material weight of a High Speed Civil Transport (HSCT) is described. The weight function is based on HSCT configuration parameters. A response surface methodology is used to fit a quadratic polynomial to data gathered from a large number of structural optimizations. To reduce the time of performing a large number of structural optimizations, coarse-grained parallelization with a master-slave processor assignment on an Intel Paragon computer is used. The results of the structural optimization are noisy. Noise reduction in the structural optimization results is discussed. It is shown that the response surface filters out this noise. A statistical design of experiments technique is used to minimize the number of required structural optimizations and to maintain accuracy. Simple analysis techniques are used to find regions of the design space where reasonable HSCT designs could occur, thus customizing the weight function to the design requirements of the HSCT, while the response surface itself is created employing detailed analysis methods. Analysis of variance is used to reduce the number of polynomial terms in the response surface model function. Linear and constant corrections based on a small number of high fidelity results are employed to improve the accuracy of the response surface model. Configuration optimization of the HSCT employing a customized weight function is compared to the configuration optimization of the HSCT with a general weight function. / Ph. D.