I had a skype teleconference with Oriol Rios last week.
Oriol is a bright MSc student at Ghent University to whom I had the pleasure to teach fire dynamics last year. He did great in my course.
He was telling me how much he is enjoying a massive online course on 'Writing in the Sciences'. This is an open and free registration course to "train scientists to become more effective, efficient, and confident writers" taught by Prof Sainani at Stanford University. I immediately agreed with the objectives of the course and praised Oriol's initiative. Hope more scientists would be taking it. Indeed, I will be taking the course myself soon, and will be strongly suggesting it to my students and postdoc. We can only improve science by learning to write better.
A bonus to our conversation was Oriol's first homework in the course, which involved writing a short summary of a 'hot paper' in each students' field of expertise. He chose one of my papers (nice!). The text is below. I was honored by his summary and understanding of what we attempted to do.
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A hot paper in a hot science field; Fire Safety
by Oriol Rios
Thinking about “hot papers” in the fire science field is inherently funny; the scientific approach to fire safety is a “hot field” –it's main journal was first published in 1977- and so is the object of study. "Forecasting fire growth using an inverse zone modelling approach" (W. Jahn , G. Rein, J.L. Torero, 2011) stands out due to its innovative and revolutionary approach to forecast building fire dynamics.
Jahn et al. explore and validate a novel forecasting technique based on data assimilation and inverse modelling. Sensor observations of an enclosure fire (e.g. fire in a bedroom) are gathered during an interval of time (assimilation windows) to estimate the invariant parameters using an inverse modelling approach; evaluating the parameters involved in an equation knowing the result in advance. These values are inputs for a two zone model -a simple model that just considers a hot upper layer of smoke and a cold lower layer of fresh air- that finally forecast the temperature of the upper layer, the heat released rate, and the smoke layer height. The forecast is delivered with positive lead time, that is before the predicted event takes place. The method was validated using a Computational Fluid Dynamic (CFD) program to prove that 30s of observation leads to a successful 100s forecast.
The ultimate aim of this new technique is to assist emergency response -particularly fire fighters crews- by giving them a schematic description of the situation and the expected fire development before they enter the scene. This paper stands out as important because it is the first to provide a method of delivering a reliable forecast with positive lead time.
Although the envisioned tool is still far from operational and more research must be conducted regarding complex fires, the authors suggest that the necessary data to run the model could be obtained just by tweaking the sensors that are already present in many new buildings -smoke detectors, temperature sensors and so on. This provides a powerful tool with a simple set up and low computational cost; A keystone of future fire safety engineering.
Note by G Rein: A minor comment is that the paper uses CFD simulations as sensor data, not for validation.
