RxCADRE 2011 and 2012: Ignition data

Hudak, Andrew T.; Bright, Benjamin C.; Williams, Brett; Hiers, J. Kevin

doi:10.2737/rds-2017-0065

Cited by 4 publications

(5 citation statements)

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“…FIRETEC inputs included fuel configuration and atmospheric condition before ignition, as well as upwind atmospheric conditions for the duration of the fire. The input data were derived from published and archived sources from RxCADRE [4,28,[36][37][38][39].…”

Section: Experimental Datamentioning

confidence: 99%

Modeling Low Intensity Fires: Lessons Learned from 2012 RxCADRE

et al. 2021

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Coupled fire-atmosphere models are increasingly being used to study low-intensity fires, such as those that are used in prescribed fire applications. Thus, the need arises to evaluate these models for their ability to accurately represent fire spread in marginal burning conditions. In this study, wind and fuel data collected during the Prescribed Fire Combustion and Atmospheric Dynamics Research Experiments (RxCADRE) fire campaign were used to generate initial and boundary conditions for coupled fire-atmosphere simulations. We present a novel method to obtain fuels representation at the model grid scale using a combination of imagery, machine learning, and field sampling. Several methods to generate wind input conditions for the model from eight different anemometer measurements are explored. We find a strong sensitivity of fire outcomes to wind inputs. This result highlights the critical need to include variable wind fields as inputs in modeling marginal fire conditions. This work highlights the complexities of comparing physics-based model results against observations, which are more acute in marginal burning conditions, where stronger sensitivities to local variability in wind and fuels drive fire outcomes.

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Section: Experimental Datamentioning

confidence: 99%

Modeling Low Intensity Fires: Lessons Learned from 2012 RxCADRE

et al. 2021

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“…Due to the small quantity of the available material, no crack growth data for the two base metals, SAE 1010 and SAE 950X steels, were generated in this study. Thus, the constants C, m and c, were chosen from established data for similar ferrite-pearlite steels [35,36]. Values for the constant C were chosen so that they made upper and lower bounds on established data within the Paris regime of fatigue crack growth, and were C=7×10−9 and 2×10−9, respectively.…”

Section: Data Interpretationmentioning

confidence: 99%

“…The chosen value of m is typical for ferrite-pearlite steels [35], and c is estimated from the limited R-ratio data on a steel similar to SAE 1010, specifically ASTM A36 [S. J. Hudak (1979) unpublished data]. The constants used were m=3 and c=0.75.…”

Section: Data Interpretationmentioning

confidence: 99%

A Crack Growth Approach to Life Prediction of Spot‐welded Lap Joints

Newman

Dowling

1998

Fatigue Fract Eng Mat Struct

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Fatigue behaviour of spot‐welded lap joints is modelled as a single‐degree‐of‐freedom crack growth problem. Although such a model involves simplification of a complicated problem, predictions are in good agreement with experimental results. The model developed here allows a design engineer to analyse the fatigue behaviour of spot‐welded steel sheets, which are commonly used in structures, without knowledge of metallurgical and fine geometric details of spot‐welds. Only fatigue properties of the sheet metal are needed, so no laboratory facilities are required to generate fatigue data specific to spot‐welds or weld metal.

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“…Near-surface wind and temperature sonic anemometer time series for in-situ and background locations are available from Seto and Clements [13,14]. Ignitions timing and locations were obtained from field-grade GPS units, mounted on-board firing vehicles [15]. Fuel data used for this evaluation study included photographs of pre-burn samples, as well as measurements of size, loading and moisture content of species groups.…”

Section: Observational Datamentioning

confidence: 99%

“…During the campaign, the prescribed burn was ignited with drip torches attached to moving all-terrain vehicles (ATVs). Using GPS data from these vehicles (available from [15]), we extracted the locations of start and end points of the four fire lines, as well as their individual start and end ignition times. While the real-life ignition process was not perfectly uniform in time, the modeled fire lines were approximated as being ignited at a constant speed, such that the time and location of the start and end points matched those of the real burn (see Animation S1 in the Supplementary Materials).…”

Section: Numerical Setupmentioning

confidence: 99%

Capturing Plume Rise and Dispersion with a Coupled Large-Eddy Simulation: Case Study of a Prescribed Burn

Moisseeva

Stull

2019

Atmosphere

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Current understanding of the buoyant rise and subsequent dispersion of smoke due to wildfires has been limited by the complexity of interactions between fire behavior and atmospheric conditions, as well as the uncertainty in model evaluation data. To assess the feasibility of using numerical models to address this knowledge gap, we designed a large-eddy simulation of a real-life prescribed burn using a coupled semi-emperical fire–atmosphere model. We used observational data to evaluate the simulated smoke plume, as well as to identify sources of model biases. The results suggest that the rise and dispersion of fire emissions are reasonably captured by the model, subject to accurate surface thermal forcing and relatively steady atmospheric conditions. Overall, encouraging model performance and the high level of detail offered by simulated data may help inform future smoke plume modeling work, plume-rise parameterizations and field experiment designs.

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RxCADRE 2011 and 2012: Ignition data

Cited by 4 publications

References 0 publications

Modeling Low Intensity Fires: Lessons Learned from 2012 RxCADRE

Modeling Low Intensity Fires: Lessons Learned from 2012 RxCADRE

A Crack Growth Approach to Life Prediction of Spot‐welded Lap Joints

Capturing Plume Rise and Dispersion with a Coupled Large-Eddy Simulation: Case Study of a Prescribed Burn

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