BRRC developed a machine learning model to predict the average ambient sound level anywhere on Earth. The model was trained to identify relationships between more than 1.5 million hours of ambient sound level measurements and environmental variables such as the population density, land cover, and climate. These environmental variables are used to predict the sound levels produced by humans, animals, water, and weather at locations where no measurements were taken. BRRC applied the machine learning model to create the first-ever global map of the median ambient sound level. Learn more…

BRRC developed Cursor-on-Target Flight Path Guidance as a near real-time mission planning tool for maximum “show-of-force” effect through exploitation of the focus of sonic booms for the B-1B aircraft. Current military scenarios call for show-of-force operations, and this tool increases the effectiveness of focused sonic boom operations by providing flight path guidance to the pilot.

BRRC has developed a prototype flight profile optimization system that will provide the DoD with a tool to better balance the needs of reducing community noise impacts while maximizing the ability and effectiveness of training operations. Our optimization system provides the most cost effective near-term solution for jet noise reduction that can be applied to any military aircraft at any airfield for relatively small incremental costs. In addition, BRRC is developing advanced acoustic propagation and detection algorithms for use with Unmanned Aircraft Systems. Learn more...