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NASA NTRS1996

Rayleigh Light Scattering for Concentration Measurements in Turbulent Flows

Despite intensive research over a number of years, an understanding of scalar mixing in turbulent flows remains elusive. An understanding is required because turbulent mixing has a pivotal role in a wide variety of natural and technologically important processes. As an example, the mixing and transport of pollutants in the atmosphere and in bodies of water are often dependent on turbulent mixing processes. Turbulent mixing is also central to turbulent combustion which underlies most hydrocarbon energy use in modern societies as well as in unwanted fire behavior. Development of models for combusting flows is therefore crucial, however, an understanding of scalar mixing is required before useful models of turbulent mixing and, ultimately, turbulent combustion can be developed. An important subset of turbulent flows is axisymmetric turbulent jets and plumes because they are relatively simple to generate, and because the provide an appropriate test bed for the development of general theories of turbulent mixing which can be applied to more complex geometries and flows. This paper focuses on a number of experimental techniques which have been developed at the National Institute of Standards and Development for measuring concentration in binary axisymmetric turbulent jets. In order to demonstrate the value of these diagnostics, some of the more important results from earlier and on-going investigations are summarized. Topics addressed include the similarity behavior of variable density axisymmetric jets, the behavior of absolutely unstable axisymmetric helium jets, and the role of large scale structures and scalar dissipation in these flows.

Pitts, William M.↗

NASA NTRSDate not supplied

State of Wildfires

Fire is an essential component of ecosystems and acts as key driver of biogeochemical cycling with impacts on vegetation structure and composition, soil conditions, and climate feedbacks. Fire behavior and effects vary based on the types of fuel, fuel dryness, and frequency of ignition. Due to changing climate and land use patterns, fire danger is increasing in many regions globally, and fires are having increasingly devastating impacts on human health, infrastructure, and ecosystem services. Recurrent fires help to determine the distribution of trees and grasses, and overall fuel load, which inform the behavior of future fires. Process-based models can be used to capture multi-scale impacts at the forest stand-level up to the landscape level, and across minutes to centuries, but must capture variation in fire behavior and intensity as a function of the fuel and climate, from high intensity forest crown fires within boreal regions to low intensity rapid grass fires of the tropics. Fire model development demonstrates our ability to capture large scale fire influenced biogeography and vegetation distribution at the earth system scale through vegetation traits and fire feedbacks. At the individual stand scale the importance of interactions and feedbacks between above and below ground process is essential to capturing the vegetation dynamics across boreal forest systems. Improving the mechanics of including plant physiology and specifically live fuel moisture content is the next step to advancing the capability of process based models to inform fire research. Advances in the testing and creation of a mechanistic live fuel moisture model demonstrate the foundation for future live fuel dynamics research that can be informed by field and remote sensing information. Improved remote sensing, technological and modeling capabilities support a more comprehensive and cohesive fire response that will be better equipped to overcome current barriers and anticipate the new reality of fires in a warming world.

wildfires↗

NASA NTRS2013

Unmanned Vehicle Material Flammability Test

Microgravity combustion phenomena have been an active area of research for the past 3 decades however, there have been very few experiments directly studying spacecraft fire safety under low-gravity conditions. Furthermore, none of these experiments have studied sample and environment sizes typical of those expected in a spacecraft fire. All previous experiments have been limited to samples of the order of 10 cm in length and width or smaller. Terrestrial fire safety standards for all other habitable volumes on earth, e.g. mines, buildings, airplanes, ships, etc., are based upon testing conducted with full-scale fires. Given the large differences between fire behavior in normal and reduced gravity, this lack of an experimental data base at relevant length scales forces spacecraft designers to base their designs using 1-g understanding. To address this question a large scale spacecraft fire experiment has been proposed by an international team of investigators. This poster presents the objectives, status and concept of this collaborative international project to examine spacecraft material flammability at realistic scales. The concept behind this project is to utilize an unmanned spacecraft such as Orbital Cygnus vehicle after it has completed its delivery of cargo to the ISS and it has begun its return journey to earth. This experiment will consist of a flame spread test involving a meter scale sample ignited in the pressurized volume of the spacecraft and allowed to burn to completion while measurements are made. A computer modeling effort will complement the experimental effort. Although the experiment will need to meet rigorous safety requirements to ensure the carrier vehicle does not sustain damage, the absence of a crew removes the need for strict containment of combustion products. This will facilitate the examination of fire behavior on a scale that is relevant to spacecraft fire safety and will provide unique data for fire model validation. This will be the first opportunity to examine microgravity flame behavior at scales approximating a spacecraft fire.

Combustion↗

NASA NTRS1986

Fire control method and analytical model for large liquid hydrocarbon pool fires

The dominate parameter governing the behavior of a liquid hydrocarbon (JP-5) pool fire is wind speed. The most effective method of controlling wind speed in the vicinity of a large circular (10 m dia.) pool fire is a set of concentric screens located outside the perimeter. Because detailed behavior of the pool fire structure within one pool fire diameter is unknown, an analytical model supported by careful experiments is under development. As a first step toward this development, a regional pool fire model was constructed for the no-wind condition consisting of three zones -- liquid fuel, combustion, and plume -- where the predicted variables are mass burning rate and characteristic temperatures of the combustion and plume zones. This zone pool fire model can be modified to incorporate plume bending by wind, radiation absorption by soot particles, and a different ambient air flow entrainment rate. Results from the zone model are given for a pool diameter of 1.3 m and are found to reproduce values in the literature.

Fenton, D. L.↗

NASA NTRS1992

Smoke and fire characteristics for cerrado and deforestation burns in Brazil - BASE-B experiment

Five test fires were performed during August and September 1990 in the cerrado (savannalike region) in central Brazil (three fires) and tropical moist forest (two fires) in the eastern Amazon. This paper details the gases released, the ratios of the gases to each other and to particulate matter, fuel loads, and the fraction consumed (combustion factors), and the fire behavior associated with biomass consumption. Models are presented for evaluating emission factors for CH4, CO2, CO, H2, and particles less than 2.5 micron diam (PM2.5) as a function of combustion efficiency. The ratio of carbon released as CO2 (combustion efficiency) for the cerrado fires averaged 0.94 and for the deforestation fires it decreased from 0.88 for the flaming phase to less than 0.80 during the smoldering phase of combustion. For tropical ecosystems, emissions of most products of incomplete combustion are projected to be lower than previous estimates for savanna ecosystems and somewhat higher for fires used for deforestation purposes.

Ward, D. E.↗

DOE OSTI2020

Flue Gas Desulfurization Wastewater Composition and Implications for Regulatory and Treatment Train Design

The U.S. Environmental Protection Agency is currently revising its regulations on trace element discharges from flue gas desulfurization (FGD) wastewater.<br>In this work, we expand a predictive model of trace element behavior at coal-fired power plants (CFPPs) to estimate the trace element concentration of FGD wastewater at the plant level. We demonstrate that variation in trace element concentrations in FGD wastewater can span several orders of magnitude and is a function of both coal rank and installed air pollution control devices. This conclusion suggests that the benefits and costs of FGD wastewater treatment for the median plant will poorly describe the actual benefits and costs over the full range of existing CFPPs.

Mauter, Meagan↗

DOE OSTI2023

DUET - Distribution of Understory using Elliptical Transport: A mechanistic model of leaf litter and herbaceous spatial distribution based on tree canopy structure

Heterogeneity in surface fuels produced by overstory trees and understory vegetation is a major driver of fire behavior and ecosystem dynamics. Previous attempts at predicting tree leaf and needle litter accumulation over time have been constrained in scope to probabilistic models that consider a limited number of key factors influencing tree litter dispersal patterns and decomposition processes. Here we present a mechanistic model for estimating variation in surface fuels called the Distribution of Understory using Elliptical Transport (DUET). DUET uses a pre-generated voxelated canopy array and distributes the leaf litter from the trees in an elliptical shape based on tree species characteristics, wind data, and location-specific features. DUET then calculates grass growth between trees and around the litter patterns and sets moisture levels based on relative humidity. In this study, we use DUET to simulate spatial distributions of leaf litter and illustrate model sensitivity to several parameters, providing inputs for testing fuel spatial heterogeneity impacts on fire behavior using process-based models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Maximized lateral inhibition in paired magnetic domain wall racetracks for neuromorphic computing

Lateral inhibition is an important functionality in neuromorphic computing, modeled after the biological neuron behavior that a firing neuron deactivates its neighbors belonging to the same layer and prevents them from firing. In most neuromorphic hardware platforms lateral inhibition is implemented by external circuitry, thereby decreasing the energy efficiency and increasing the area overhead of such systems. Recently, the domain wall—magnetic tunnel junction (DW-MTJ) artificial neuron is demonstrated in modeling to be intrinsically inhibitory. Without peripheral circuitry, lateral inhibition in DW-MTJ neurons results from magnetostatic interaction between neighboring neuron cells. However, the lateral inhibition mechanism in DW-MTJ neurons has not been studied thoroughly, leading to weak inhibition only in very closely-spaced devices. This work approaches these problems by modeling current- and field- driven DW motion in a pair of adjacent DW-MTJ neurons. We maximize the magnitude of lateral inhibition by tuning the magnetic interaction between the neurons. The results are explained by current-driven DW velocity characteristics in response to an external magnetic field and quantified by an analytical model. Dependence of lateral inhibition strength on device parameters is also studied. Finally, lateral inhibition behavior in an array of 1000 DW-MTJ neurons is demonstrated. Our results provide a guideline for the optimization of lateral inhibition implementation in DW-MTJ neurons. Finally, with strong lateral inhibition achieved, a path towards competitive learning algorithms such as the winner-take-all are made possible on such neuromorphic devices.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

NASA NTRS2019

Solid Fuel Ignition and Extinction (SoFIE) Project on ISS

The Solid Fuel Ignition and Extinction (SoFIE) project studies ignition and flammability of solid spacecraft materials (fuels) in practical geometries and realistic atmospheric conditions. It is an experiment insert designed for use within the existing Combustion Integrated Rack (CIR). The CIR chamber provides a level of containment and permits testing at variable oxygen concentrations and pressures representative of current and planned NASA Space Exploration Atmospheres. The applications of SoFIE include: (1) Determining safer selection of cabin materials and validating NASA materials flammability selection using 1-g test protocols for low-gravity fires, (2) Improving understanding of early fire growth behavior, (3) Validating material flammability numerical models, (4) Determining optimal suppression techniques for burning materials by diluents, flow reduction, and venting, (5) Obtaining burning behavior of actual engineering materials planned for spacecraft, (6) Developing corresponding models of microgravity flame spread, flammability, and extinction, and use the results to improve normal gravity combustion models for terrestrial applications. The hardware permits a wide range of solid-material combustion and fire suppression studies. It supports multiple investigations using common infrastructure including sample holders, flow control, test sections, external radiant heaters, igniters, and diagnostics. SoFIE has been developed to meet the requirements of five unique investigations. It is currently being built and slated to begin operations on the ISS in July 2021. Given the general capabilities of the hardware insert, it is intended to be used as a facility for future researchers who can propose to NASA for related solid combustion studies.

Ferkul, Paul↗

DOE OSTI2026

Roadmap for the future of extreme wildfire events

Background Extreme wildfire events (EWEs) represent a growing threat globally, posing substantial risks to ecosystems, human communities, and infrastructure. Despite increased recognition of their ecological, social, and economic significance, current definitions of EWEs vary widely, reflecting disciplinary biases and regional contexts. This article emerges from an interdisciplinary workshop convened to reassess and refine the definition of EWEs, examine their impacts across ecological and social dimensions, and identify critical knowledge gaps impeding our understanding of these infrequent but important events. Results Our synthesis highlights significant limitations with existing definitions, particularly their reliance on subjective thresholds and their emphasis on extreme fire behavior alone. EWEs encompass a spectrum of complex, multi-dimensional phenomena that extend beyond immediate biophysical characteristics to include cumulative social, economic, and ecological impacts. These impacts often manifest over extended timeframes and include hazardous environmental contamination, severe geomorphic disturbances, ecosystem transformations, and unintended consequences of post-fire management actions. Current wildfire modeling frameworks inadequately capture these compounding factors, particularly the interactions among social systems, ecological conditions, and extreme fire behavior. To overcome these issues, we advocate for an interdisciplinary and context-sensitive approach to defining and studying EWEs. This revised definition emphasizes wildfires exhibiting anomalies in fire behavior, ecological outcomes, or social impacts relative to historically observed baselines, accommodating variability across different geographic regions and ecological settings. Conclusions Adopting an interdisciplinary framework that integrates biophysical and social sciences will enhance the predictive capability of wildfire models and improve resilience planning and response strategies. Filling identified knowledge gaps—such as limited high-quality empirical fire behavior data and insufficient integration of social dynamics into modeling—will better prepare communities and ecosystems to cope with and adapt to EWEs. This inclusive approach underscores the necessity for collaboration across disciplines and sectors, essential to managing extreme wildfires in an era of increasing climatic and ecological uncertainty.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Sensitivity of Grass Fires Burning in Marginal Conditions to Atmospheric Turbulence

Abstract Atmospheric forcing and interactions between the fire and atmosphere are primary drivers of wildland fire behavior. The atmosphere is known to be a chaotic system that, although deterministic, is very sensitive to small perturbations to initial conditions. We assume that as a result of the tight coupling between fire and atmosphere; wildland fire behavior, in turn, should also be sensitive to perturbations in atmospheric initial conditions. Observations suggest that low intensity prescribed fire, in particular, is susceptible to small perturbations in the wind field, which can significantly alter fire spread. Here, we employ a computational fluid dynamics model of coupled fire‐atmosphere interactions to answer the question: How sensitive is fire behavior to small variations in atmospheric turbulence? We perform ensemble simulations of fires in homogenous grass fuels. The only difference between ensemble members is the state of the turbulent atmosphere provided to the model throughout the simulation. The atmospheric state is a function of the initial conditions applied at the start of the simulation and boundary conditions applied throughout the simulation. We find a wide range of outcomes, with area burned ranging from 2,212 to 11,236 m 2 (>400% change), driven primarily by sensitivity to initial conditions, with nonnegligible contributions from boundary condition variability during the initial 30 s of simulation. Our results highlight the need for ensemble simulations, especially when considering fire behavior in marginal burning conditions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Effects of slope steepness and cross-slope wind speed on fire spreading behavior for various vegetation

Wildland fire behavior is significantly influenced by environmental factors such as slope steepness, wind speed, and fuel type. Understanding these interactions is critical for improving predictive models and fire management. This study explores how slope steepness and cross-slope wind speed influence fire spread dynamics in various fuel bed types. Simulations are conducted using a physics-based wildland fire model, HIGRAD/FIRETEC, across six slope angles (0–50 %), four cross-slope wind speeds (4–10 m s –1 ), and three fuel bed types (grass, shrubland, and forest). Representative cases are additionally compared with FARSITE fireline evolution. Fire behavior is categorized into distinct propagation types based on spread characteristics and analyzed. The fire propagation angle, which indicates deviation from the wind direction, generally increases with steeper slopes and decreases with stronger cross-slope winds. Secondary upslope propagation is observed in shrubland under moderate slopes, while secondary downwind propagation occurs in all fuel beds at higher wind speeds. These findings highlight fire spread characteristics that differ from predictions by traditional models like Rothermel’s. By capturing complex propagation patterns and dynamics, this study demonstrates the value of a physics-based, atmosphere-fire coupled model for accurate wildland fire prediction. Incorporating secondary propagations and the influence of fuel bed complexities into predictive models can improve the accuracy of fire spread forecasts, enabling more effective fire management and risk mitigation efforts.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Firesense: Supporting Operational Partners Through Co-Development

Fire is a natural disturbance and fundamental to many ecosystems, but, across the United States and many areas globally, fires have become more common, larger and more likely to occur at the same time. This has the potential to challenge resource allocation and response and requires coordinated integration of technology and tools at the spatial and temporal scales required by practitioners who make wildland fire management decisions. In the US wildfire is currently managed across state, federal, and tribal agencies who leverage various datasets to guide management decisions. FireSense, a NASA Science Mission Directorate project, aims to develop and deliver trailblazing technology and tools for use before, during, and after wildland fires by working together with land managers and practitioners. FireSense is focused on turning information into solutions by expanding partnerships and collaborations with operational agencies to inform and deliver data and technology to support decisions for wildland fire management. Through implementation across the US and with campaign activities in a variety of landscapes, coordinated research and development work will build upon adaptive and use-inspired approaches that can be put into operation. By leveraging and complementing partner activities through coordinated investment, we support a more comprehensive and cohesive response that can advance fire science to better understand fire behavior and effects through integrated measurement, monitoring and modeling. We present updates from in-progress technology development and field campaigns within the US which include sampling and application across scales with information from multiple sensors collocated with field measurements and highlighting the importance of cross-scale observations during the fire lifecycle. Coordinated investment in new science and technology for the complete fire lifecycle supports a comprehensive and cohesive fire response that help to overcome barriers and anticipate and manage the new reality of extreme fires in a warming world.

Jacquelyn Shuman↗

DOE OSTI2023

HeatMap

SAND2021-14230 O HeatMap generates geospatial wildfire fuel models by applying machine learning algorithms to satellite imagery and weather station data. The code then leverages wildfire behavior software FlamMap to determine fire spread. It also uses PSLF software to model grid impacts and display data analytics for grid components that have been impacted by wildfire. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Danford, Forest↗

NASA NTRS2001

Modeling and Prediction of Wildfire Hazard in Southern California, Integration of Models with Imaging Spectrometry

Large urban wildfires throughout southern California have caused billions of dollars of damage and significant loss of life over the last few decades. Rapid urban growth along the wildland interface, high fuel loads and a potential increase in the frequency of large fires due to climatic change suggest that the problem will worsen in the future. Improved fire spread prediction and reduced uncertainty in assessing fire hazard would be significant, both economically and socially. Current problems in the modeling of fire spread include the role of plant community differences, spatial heterogeneity in fuels and spatio-temporal changes in fuels. In this research, we evaluated the potential of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Airborne Synthetic Aperture Radar (AIRSAR) data for providing improved maps of wildfire fuel properties. Analysis concentrated in two areas of Southern California, the Santa Monica Mountains and Santa Barbara Front Range. Wildfire fuel information can be divided into four basic categories: fuel type, fuel load (live green and woody biomass), fuel moisture and fuel condition (live vs senesced fuels). To map fuel type, AVIRIS data were used to map vegetation species using Multiple Endmember Spectral Mixture Analysis (MESMA) and Binary Decision Trees. Green live biomass and canopy moisture were mapped using AVIRIS through analysis of the 980 nm liquid water absorption feature and compared to alternate measures of moisture and field measurements. Woody biomass was mapped using L and P band cross polarimetric data acquired in 1998 and 1999. Fuel condition was mapped using spectral mixture analysis to map green vegetation (green leaves), nonphotosynthetic vegetation (NPV; stems, wood and litter), shade and soil. Summaries describing the potential of hyperspectral and SAR data for fuel mapping are provided by Roberts et al. and Dennison et al. To utilize remotely sensed data to assess fire hazard, fuel-type maps were translated into standard fuel models accessible to the FARSITE fire spread simulator. The FARSITE model and BEHAVE are considered industry standards for fire behavior analysis. Anderson level fuels map, generated using a binary decision tree classifier are available for multiple dates in the Santa Monica Mountains and at least one date for Santa Barbara. Fuel maps that will fill in the areas between Santa Barbara and the Santa Monica Mountains study sites are in progress, as part of a NASA Regional Earth Science Application Center, the Southern California Wildfire Hazard Center. Species-level maps, were supplied to fire managing agencies (Los Angeles County Fire, California Department of Forestry). Research results were published extensively in the refereed and non-refereed literature. Educational outreach included funding of several graduate students, undergraduate intern training and an article featured in the California Alliance for Minorities Program (CAMP) Quarterly Journal.

Roberts, Dar A.↗

NASA NTRS2013

Development and Validation of a Computational Model for Predicting the Behavior of Plumes from Large Solid Rocket Motors

Exhaust plumes from large solid rocket motors fired at ATK's Promontory test site carry particulates to high altitudes and typically produce deposits that fall on regions downwind of the test area. As populations and communities near the test facility grow, ATK has become increasingly concerned about the impact of motor testing on those surrounding communities. To assess the potential impact of motor testing on the community and to identify feasible mitigation strategies, it is essential to have a tool capable of predicting plume behavior downrange of the test stand. A software package, called PlumeTracker, has been developed and validated at ATK for this purpose. The code is a point model that offers a time-dependent, physics-based description of plume transport and precipitation. The code can utilize either measured or forecasted weather data to generate plume predictions. Next-Generation Radar (NEXRAD) data and field observations from twenty-three historical motor test fires at Promontory were collected to test the predictive capability of PlumeTracker. Model predictions for plume trajectories and deposition fields were found to correlate well with the collected dataset.

Wells, Jason E.↗

NASA NTRSDate not supplied

Overview of the NASA Earth Action Strategies Wildland Fire Initiative

As part of NASA’s new Earth Action strategy, the Wildland Fire initiative was established, which includes both the NASA Wildland Fire Program (WFP) and the FireSense project. NASA has over 50 years of experience generating data and technology to enhance fire science and operational management. The WFP’s mission is threefold: 1) assemble communities of practice through collaborative efforts with government, academia, and the private sector; 2) co-develop knowledge and applications with relevant partners and stakeholders in the wildfire community; and 3) improve wildland fire management through the transitioning of NASA data, technology, tools, and science to stakeholder organizations. The WFP is focusing on supporting proactive fire management, including situational awareness, preparedness, and risk mitigation. This will be accomplished through selected projects that identify management challenges, relevant to partners and end users, and the NASA data that will be utilized to deliver innovative solutions to enhance the management of wildland fires. Examples include: i) investigation of evaporative stress from OpenET to help predict the risk of wildfire occurrence in watersheds; ii) incorporation of space based LiDAR for the generation of 3-dimensional forest fuel metrics, used to improve wildfire risk and behavior models; iii) integration of global, multi-platform geostationary active-fire data in near-real-time into NASA’s Fire Information for Resource Management System (FIRMS); and iv) identification of post-fire ecohydrological conditions using thermal, multispectral, synthetic aperture radar (SAR), and hyperspectral remotely-sensed data to improve flood hazard forecast models. The FireSense project is a US-focused 5-year project that will focus on delivering NASA’s unique Earth science and technological capabilities to operational agencies, striving towards enhancing fire fighting and air quality management. The project will include airborne campaigns and new technology that will likely have global implications. Initial stakeholder engagement led FireSense to focus on four use-cases focused on the characterization and measurement of: (i) pre-fire fuels conditions, (ii) active fire-dynamics; (iii) post-fire impact and threats; and iv) air quality impacts and forecasting, each-developed with identified stakeholders.

Wildland Fire program↗

NASA NTRS2001

Optimal firing rate estimation

We define a measure for evaluating the quality of a predictive model of the behavior of a spiking neuron. This measure, information gain per spike (Is), indicates how much more information is provided by the model than if the prediction were made by specifying the neuron's average firing rate over the same time period. We apply a maximum Is criterion to optimize the performance of Gaussian smoothing filters for estimating neural firing rates. With data from bullfrog vestibular semicircular canal neurons and data from simulated integrate-and-fire neurons, the optimal bandwidth for firing rate estimation is typically similar to the average firing rate. Precise timing and average rate models are limiting cases that perform poorly. We estimate that bullfrog semicircular canal sensory neurons transmit in the order of 1 bit of stimulus-related information per spike.

NASA Discipline Neuroscience↗