Journal Article Reports on New Engineering Approach to Modeling Pyrolysis
The peer-reviewed journal article, “An Approach for Flux and Thickness Scaling of Cone Calorimeter Data for Predicting the Pyrolysis of Materials,” has recently been published in Fire Technology. Key findings from this study support the “Fire Modeling Development and Validation” project, led by the Fire Safety Research Institute, part of UL Research Institutes. Jason Floyd, principal research engineer, co-authored this paper with Jonathan Hodges from Jensen Hughes.
Modeling Fire Growth
When research engineers model the impact of a fire, inputting the size and location of the fire over time is critical to creating the most accurate model. This can be done as a prescribed or predicted fire. For a prescribed fire, the spatial and temporal details of the fire are specified by the modeler. The model inputs are the heat release rate over time and the location and surface area of the fire. For predicted fires, the modeler instead provides inputs that allow the model to predict the fire size, surface area, and location over time. Three general approaches to predicting fire growth and spread have been used in fire safety science:
1. Defining an ignitable object in the model
This approach is the simplest—it involves monitoring the object's heat flux or surface temperature. When a predefined ignition point is reached, the object ignites and burns following a predefined heat release rate for the entire object. While this approach is simple to implement, a major challenge is locating burning rate data for the exact object being modeled. A significant limitation of this method is that single-object data is often obtained by burning the object in the open under a hood calorimeter. Heat feedback enhancement or reduction due to an enclosure cannot be accounted for, nor can the effect of changes in the physical location and method of ignition. In addition, while these experiments provide valuable data points, the specific fuel package in a design basis often varies from available experiments. In a design application, engineering judgment must be used to assess the applicability of these individual tests and how to scale measured data to the specific application.
2. Replacing full object data with the time-dependent pyrolysis rate of a material based on bench-scale test data
With this approach, heat transfer to an object made of the material is modeled. Different regions of the object are allowed to ignite when reaching a predefined ignition temperature. At ignition, the region then follows the measured pyrolysis rate per unit area from the bench-scale test. With this approach, fire can spread across an object rather than having the entire object ignite and burn. An advantage of this approach is that bench-scale testing is relatively inexpensive compared to full object testing. A limitation is that while fire can spread over an object with this method, each region still burns based on the bench-scale data, without changes in the burning rate if heat feedback differs from the exposure used in testing. Since this approach requires predicting surface temperature, it requires material properties for heat transfer. However, since it applies bench-scale data post-ignition, the burning rate is not coupled with the surface temperature post-ignition.
3. Describing the small-scale processes and transport phenomena occurring within the fuel
This is typically based on an Arrhenius kinetics formulation:
Where ri is the reaction rate, Ai and Ei are the Arrhenius pre-exponential factor and activation energy of the i-th reaction, T is the temperature, R is the molar gas constant, and j is the impact of the j-th material component’s concentration on ri. A key advantage of this approach is that the material decomposition rate will change based on the predicted thermal exposure to the material. The challenge is determining the number of Arrhenius reactions and their parameters, along with the properties required to determine the in-depth temperature and errors in material properties, Arrhenius reaction data, and the predicted heat feedback can combine multiplicatively. While optimization approaches exist to determine these properties, the overall effort needed to quantify these properties may not be feasible for specific fire safety applications. This approach can be challenging to apply outside of research applications of fire models.
Unfortunately, the third approach proves to be too complex, and the second approach is too rigid. However, there is great appeal in using cone calorimeter data. It is relatively quick and inexpensive to run a small number of cone tests for a material. These observations led to the development of an enhanced second approach called the scaling-based pyrolysis model (Spyro). This approach was developed to bridge the gap between the second and third modeling approaches.
While this first version of the Spyro showed promise, there were several limitations in its initial development:
- The original formulation was not able to take full advantage of the full set of data collected when material testing in a cone calorimeter is done at multiple exposures;
- The original model used a fixed reference heat flux in scaling, which assumed the flame heat flux occurring during the experiment was constant;
- The model was limited to materials of the same thickness as those tested in the cone calorimeter experiment.
To address these limitations, this study develops an enhanced Spyro model that can use cone data sets for a material consisting of different material thicknesses and/or multiple exposures.
Developing an Enhanced Spyro Model
The method developed in this study adapts the original Spyro model to incorporate multiple cone calorimeter exposures plus the effects of varying initial thickness on the predicted Heat Release Rate (HRR) of the material. This method is based on interpolation and scaling of the burning rate curves based on non-dimensional analysis of heat transfer during cone calorimeter testing. Additionally, an empirical formulation for the time-varying reference heat flux is developed based on CFD predicted values and integrated into the method.
The FDS Spyro method of predicting burning rate was enhanced to utilize data from multiple cone calorimeter exposures and/or sample thicknesses. Utilizing cone data for predicting burning rate is attractive, given modest material quantity needs for a cone calorimeter test, the availability of commercial testing labs, and the time and sample preparation required to perform a cone calorimeter test. Scaling approaches for flux and thickness were developed from dimensionless quantities for heat transfer and the energy balance for a quasi-steady burning surface.
Validation of this approach was performed at multiple scales, including:
- Large series of 1D simulations for 141 materials
- A cone calorimeter geometry for PMMA, a single burning item geometry for PMMA
- A stack of wood pallets in a corner
- A room corner test at three length scales using plywood or fiber-reinforced polymer
“Development of detailed methods for modeling pyrolysis is still an area of active research without a clear consensus or guidance for practitioners looking to make predictions now with the tools we have. The Spyro approach shows great promise as an engineering method for predicting fire growth that uses readily available fire test methods for input development.”
– Jason Floyd, principal research engineer, Fire Safety Research Institute
For additional details on this study, including the methodology, verification, and validation, read the full article below.
