Postdoctoral Fellow (PREP0004125) Full-time

Postdoctoral Fellow

This position is part of the National Institute of Standards (NIST) Professional Research Experience (PREP) program. NIST recognizes that its research staff may wish to collaborate with researchers at academic institutions on specific projects of mutual interest, thus requires that such institutions must be the recipient of a PREP award. The PREP program requires staff from a wide range of backgrounds to work on scientific research in many areas. Employees in this position will perform technical work that underpins the scientific research of the collaboration.

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.

The work will entail and there are three main research thrusts:

• 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.

Qualifications

– 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.