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Finite element human vibration models were developed and implemented for use in human-tended spacecraft-coupled loads analysis, an analytical process used to predict low-frequency spacecraft loads which occur during dynamic phases of flight of such as launch, ascent, or ascent aborts. Human vibration may also affect stress predictions for spacecraft systems which the crew interacts with, such as crew seats and crew impact attenuation systems. These human vibration models are three-dimensional, distributed-mass representations of 1st-percentile female, 50th-percentile male, and 99th-percentile male American crew members and provide a relatively simple linear and low-load representation of the nonlinear dynamic response of a seated human. The most significant features of these finite element models are anthropometrically based geometric human mass distribution, soft tissue vibration attributes, and skeleton and joint stiffness.

This paper presents a different and more proactive approach to safety innovation based on work done in support of General Aviation (GA) safety improvement. The approach is highly transferable to other industries and environments and delivers systematic means to proactively evaluate and manage both safety innovations and the rate of creation of safety innovations to improve safety performance. Theoretical root cause models of two of the leading causes of general aviation fatalities were developed by a team of highly experienced aviation and industry experts. This approach was novel in that the selection of these two "classes of accidents" was proactive, based on historical data for the major causes of fatalities. The models therefore represent a complete and theoretical picture of fatalities causes, rather than being based on specific investigations that address individual incidents as is the norm in our industry. This approach led to several surprising and valuable findings. First, development of the models created a deep and complete picture of potential fatality causes across a class of accidents and thus created a shared and common understanding of hazards as well as common causal language transferable across the GA community and beyond. Second, the breadth and complexity of the models revealed the need to apply weighting factors to identify hazard causes to focus innovation attention and optimize innovation investments. However, since there are no shared causal models, accident investigations and data are not gathered or structured to align with these. In turn, this makes the understanding of accident causes less consistent, and probabilistic analysis of causes difficult at best. Third, with shared causal models and aligned data, systematic innovation is enabled. This "active learning cycle" creates a systematic yet open-source process that can be managed and measured proactively, focusing on the most valuable improvements to emphasize. Accountability for improvement can then be established to further drive innovation and progress. Ultimately, an active learning cycle transforms traditional, reactive accident investigations and mitigations into a systematic, purposeful, and measurable process for proactive safety performance improvement. Also, with common language, data and processes, cross industry safety challenges can be addressed cooperatively, and innovations can be leveraged.