Postdoctoral researcher
The goal of this project is to reduce firefighter deaths and injuries due to flashover
and to enhance firefighting safety and situational awareness in commercial building environments.
Flashover is an extreme fire event. When it occurs, there is a near-simultaneous ignition of most of the
directly exposed combustible materials within a compartment. Due to the large heat release rate, gas
temperatures increase rapidly and may exceed 800 °C. Rapid fire progression, such as flashover, is the
number-two cause of firefighter deaths and injuries. Over the past 10 years, approximately 700
firefighters were killed and more than 200,000 were injured. Unfortunately, there are still no tools that
firefighters can use to detect flashover, so they rely on their past experience using so-called flashover
indicators that are difficult to recognize. For these reasons, researchers at NIST have been developing
data-driven models that can be used to help firefighters predict the potential of flashover.
Existing modeling approaches cannot be used in real-time firefighting due to two major problems. The
first problem is that the existing models are numerically inefficient for real-time applications. Even when
high performance computing is being used, a single calculation takes more than 5 minutes. The second
problem is that the fire scenarios being considered by these models are oversimplified. Sensors are
assumed to work at extremely high temperatures and the fire locations and vent opening conditions are
assumed to be well known. In real-life situations, however, sensors will fail and the inside conditions are
never known. NIST has established a smart firefighting project to enhance firefighting safety and
situational awareness by enabling real-time prediction of flashover conditions in commercial building
environments. To reach this goal, the relationships of fire data, such as temperature, smoke, and species
concentrations, and the effect of data quality, must be understood to use machine learning for effective
real-time predictions.
Data-Driven Fire Hazard Model Development in Commerical Building Environments for Smart Fire Fighting
- US Citizen Preferred
- A Ph.D. degree in Architecture and Civil Engineering, Safety Science and Engineering, Safety
Engineering, Fire Protection Engineering, or similar field. - Solid background on Fire Safety/Protection Engineering is preferred.
- Proficient in CData, CFAST, FDS.
- Proficient in Python, MATLAB, and R.
- Well-established publication record in Q1 SCI journals.
- Practical research experience in building recommendation systems and use of computer
clusters.
Key responsibilities will include but are not limited to:
- Acquire fire data, including gas temperatures, smoke concentrations, and gas species
concentrations from realistic fire scenarios in commercial building environments using CFAST
Data Generation (CData). The scenarios include various arbitrary building structures, different
fire locations, a wide range of burning items, various door and window opening conditions
simulating fire events such as glass breakage and evacuation, and operational temperature
limits for fire protection sensors to account for loss of sensor signals. The PREP researcher will
be responsible for collecting fire data from realistic fire scenarios and will lead the study of data
behavior. - Develop an accurate and numerically efficient prediction model that can correlate flashover
conditions to the corresponding temperature behaviors. The model not only needs to overcome
the limitations of available fire data, but it must be generalized so that the model can be applied
to any building structures without the need of prior knowledge of the building layout and interior conditions. The PREP researcher will be responsible for helping to develop the model and will lead in optimizing the model. - Collaborate and coordinate our research with the existing fire research community which is
focusing on fundamental studies of the onset of flashover, development of machine learning
based models, integration of fire modeling into fire protection systems, etc. The purpose of this
thrust is to use our data to reduce firefighter deaths and injuries due to flashover by supporting
the development of data-driven modeling framework to enhance firefighting safety and
situational awareness. The PREP researcher will help develop research plans and assist in the
transfer of NIST research to stakeholders by participating in meetings, giving presentations, and
preparing reports.