ABSTRACT
Introduction
The Office of Naval Research sponsored the Blast Load Assessment-Sense and Test program to develop a rapid, in-field solution that could be used by team leaders, commanders, and medical personnel to make science-based stand-down decisions for service members exposed to blast overpressure. However, a critical challenge to this goal was the reliable interpretation of surface pressure data collected by body-worn blast sensors in both combat and combat training scenarios. Without an appropriate standardized metric, exposures from different blast events cannot be compared and accumulated in a service member’s unique blast exposure profile. In response to these challenges, we developed the Fast Automated Signal Transformation, or FAST, algorithm to automate the processing of large amounts of pressure–time data collected by blast sensors and provide a rapid, reliable approximation of the incident blast parameters without user intervention. This paper describes the performance of the FAST algorithms developed to approximate incident blast metrics from high-explosive sources using only data from body-mounted blast sensors.
Methods and Materials
Incident pressure was chosen as the standardized output metric because it provides a physiologically relevant estimate of the exposure to blast that can be compared across multiple events. In addition, incident pressure serves as an ideal metric because it is not directionally dependent or affected by the orientation of the operator. The FAST algorithms also preprocess data and automatically flag “not real” traces that might not be from blasts events (false positives). Elimination of any “not real” blast waveforms is essential to avoid skewing the results of subsequent analyses. To evaluate the performance of the FAST algorithms, the FAST results were compared to (1) experimentally measured pressures and (2) results from high-fidelity numerical simulations for three representative real-world events.
Results
The FAST results were in good agreement with both experimental data and high-fidelity simulations for the three case studies analyzed. The first case study evaluated the performance of FAST with respect to body shielding. The predicted incident pressure by FAST for a surrogate facing the charge, side on to charge, and facing away from the charge was examined. The second case study evaluated the performance of FAST with respect to an irregular charge compared to both pressure probes and results from high-fidelity simulations. The third case study demonstrated the utility of FAST for detonations inside structures where reflections from nearby surfaces can significantly alter the incident pressure. Overall, FAST predictions accounted for the reflections, providing a pressure estimate typically within 20% of the anticipated value.
Conclusions
This paper presents a standardized approach—the FAST algorithms—to analyze body-mounted blast sensor data. FAST algorithms account for the effects of shock interactions with the body to produce an estimate of incident blast conditions, allowing for direct comparison of individual exposure from different blast events. The continuing development of FAST algorithms will include heavy weapons, providing a singular capability to rapidly interpret body-worn sensor data, and provide standard output for analysis of an individual’s unique blast exposure profile.
Guidelines to inform the generation of clinically relevant and realistic blast loading conditions for primary blast injury research