Spacecraft Occupant Protection
Spacecraft occupant protection entails computational analysis using finite element models of anthropomorphic test dummies or human body models integrated into aerospace seats and restraint systems. These models are an essential component of the suite of analytical methods that can be used to predict crew injury risk due to spacecraft dynamic loadings. Simulations of dynamic spacecraft acceleration events, such as landings, are performed with these numerical model to predict occupant injuries. Methods to reduce the transmission of injurious loads to the crew are explored in order to increase the safety of future spacecraft systems.
Causes of General Aviation Accidents
The Experimental Aircraft Association (EAA) sponsors an annual innovation competition aimed at engaging the General Aviation (GA) community to create novel solutions to long-standing safety challenges. For the past 5 years, the Founders Innovation Prize (FIP) has focused on Loss of Control (LOC) accidents – the leading cause of GA fatalities.
In support of that thrust, and now addressing the next leading cause of GA fatalities – Loss of Power (LOP), a group of volunteers has taken a step back to create from first principles detailed causal models of the reasons why these fatalities occur. The aim of these modeling efforts is to deliver a complete, logic-based picture of all of the potential causes in the chain of events that lead to fatalities. The EAA published and presented in 2019 the findings from the Loss of Control modeling effort, delivering to the GA innovation community a broad picture of where “innovation space” exists – causes where there is not much innovation focus, but where potential solutions can alter the fatality rate significantly.
Important to note is that the LOC and LOP models are theoretical, where all causes are equally weighted. This equality of weighting is in reality not accurate, since some causes are more likely to be true in actual GA accidents. However, data that allows for this weighting consideration is not readily available. Indeed, overall accident investigation data is rarely if ever gathered to the same level of granularity as the causal models (an area for innovation of its own).
Nonetheless, the Aviation Safety Reporting System (ASRS), sponsored by NASA, is a large repository of pilot-reported information related to near misses, and offers a useful source of potential insights into predominant accident causes at a much greater level of granularity than accident reports.
In an effort to address harness the ASRS data and thus add insights into the causal models, this TAMU project aims to harvest the ASRS data to add real-world experiences and thus a view of the relative weights of the individual fatalities causes. These insights are then to be overlaid onto the LOC and LOP causal models, providing a more accurate and weighted definition of the remaining innovation space.
In turn, this will serve to focus FIP efforts and increase the likelihood that meaningful innovations make their way into the GA community, and leading to a reduction in fatalities.