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The NIST Flammability Reduction Group (FRG) is conducting a series of full-scale fire experiments in collaboration with the National Fire Research Laboratory
Four members of the NIST Flammability Reduction group traveled to Stamford, Connecticut for the 29th Annual Conference on Recent Advances in Flame Retardancy of
What had been a peaceful and productive mission for the six men aboard the Russian space station Mir, including U.S. astronaut Jerry Linenger, nearly became a
The research in this program (Fire Risk Reduction in Buildings, FRRiB) will enable reductions in the two single largest components of this U.S. structure fire
Fire Research Grants and Cooperative Agreements support extramural work to reduce the total burden of fire on the U.S. economy, which is estimated as greater
Emergency response, law enforcement and military personnel must face threats and dangerous situations as part of their jobs. These threats can vary from knives
This program reduces community fire risk by 1) increasing the fire resilience of wildland-urban interface (WUI) communities and 2) enhancing the safety and
Wai Cheong Tam, Eugene Yujun Fu, Richard Peacock, Paul Reneke, Jun Wang, Grace Ngai, Hong Va Leong, Thomas Cleary
Fire fighter fatalities and injuries in the U.S. remain too high and fire fighting too hazardous. Until now, fire fighters rely only on their experience to
Jun Wang, Youwei Jia, Eugene Yujun Fu, Jiajia Li, Wai Cheong Tam
This paper aims to facilitate the use of machine learning to carry out supervised classification/regression tasks for time series data in fire research
Jun Wang, Wai Cheong Tam, Paul A. Reneke, Richard D. Peacock, Thomas G. Cleary, Eugene Yujun Fu, Grace Ngai, Hong va Leong
This paper presents a study to examine the potential use of machine learning algorithms to build a model to forecast the likelihood of flashover occurrence for
Wai Cheong Tam, Eugene Yujun Fu, Richard D. Peacock, Paul A. Reneke, Jun Wang, Jiajia Li, Thomas G. Cleary
This paper presents a learning-by-synthesis approach to facilitate the utilization of a machine learning paradigm to enhance situational awareness for fire
Fire Dynamics Simulator (FDS) is a computational fluid dynamics (CFD) model of fire-driven fluid flow. The software solves numerically a form of the Navier
The fire emulator/detector evaluator (FE/DE) is a computer-controlled flow tunnel used to re-create the environments surrounding detectors in the early stages