Search NASA⌕ Search

SEARCH · Search NASA

Results for “simulated fire”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

A neural network simulation package in CLIPS

The intrinsic similarity between the firing of a rule and the firing of a neuron has been captured in this research to provide a neural network development system within an existing production system (CLIPS). A very important by-product of this research has been the emergence of an integrated technique of using rule based systems in conjunction with the neural networks to solve complex problems. The systems provides a tool kit for an integrated use of the two techniques and is also extendible to accommodate other AI techniques like the semantic networks, connectionist networks, and even the petri nets. This integrated technique can be very useful in solving complex AI problems.

Bhatnagar, Himanshu↗
NASA NTRS1989

Expert systems applied to spacecraft fire safety

Expert systems are problem-solving programs that combine a knowledge base and a reasoning mechanism to simulate a human expert. The development of an expert system to manage fire safety in spacecraft, in particular the NASA Space Station Freedom, is difficult but clearly advantageous in the long-term. Some needs in low-gravity flammability characteristics, ventilating-flow effects, fire detection, fire extinguishment, and decision models, all necessary to establish the knowledge base for an expert system, are discussed.

Smith, Richard L.↗
DOE OSTI2020

Simulations of Laser Experiments to Study the Origin of Cosmic Magnetic Fields

Magnetic fields are ubiquitous in the universe; however, their origin is not fully understood. Cosmologists and astrophysicists have proposed a variety of ways in which small seed magnetic fields could be created. It is widely thought that the much larger values of the cosmic magnetic fields that we observe are a result of the amplification of these seed fields by the nonlinear turbulent dynamo mechanism. Such a mechanism had not yet been demonstrated in a controlled laboratory environment. We conceived experiments designed to demonstrate and study the nonlinear turbulent dynamo mechanism in the laboratory. These experiments characterize the distribution of turbulent energy among the velocity, magnetic field, and density fluctuations, providing a comprehensive picture of the energy cascade in a magnetized, turbulent plasma. The experiments build on the pathfinder experiments we have conducted on the Vulcan laser at the Rutherford-Appleton Laboratory in the UK and the OMEGA laser at Laboratory for Laser Energetics at Rochester. They utilize the high-intensity lasers at the Omega Laser Facility, the National Ignition Facility (NIF) at LLNL and the Laser Megajoule (LMJ) Facility in France – the largest laser facilities in the world. The goal of this project was to design and model the highly demanding experiments through simulation campaigns using FLASH, a highly capable radiation-MHD code we have developed, and large-scale 3D simulations on the Mira supercomputer at ANL. The simulations were vital to ensuring the experiments achieve the strong turbulence and large magnetic Reynolds numbers required for the nonlinear turbulent dynamo mechanism to operate. The simulations were also critical to determining when to fire the diagnostics, since the experiments last tens of nanoseconds but the signals of the strongly amplified magnetic fields last only a few nanoseconds. Finally, high-fidelity, validated FLASH simulations were crucial to interpreting the results of the experiments. By combining theory, experiments, and simulations, we were able to succeed in demonstrating and characterizing the nonlinear turbulent dynamo mechanism. The effort leveraged (1) the emergence of the Flash Center for Computational Science as a leader in numerical modeling of academic High Energy Density Physics (HEDP) experiments; (2) the FLASH code, which has been developed by the Flash Center and is a leadership-class, multi- physics, publicly available community code that has been applied successfully in many laser-driven experiments; (3) the expertise acquired through pathfinder experiments we performed on the Vulcan laser at the Rutherford-Appleton Laboratory in the UK and on the Omega laser facility; (4) the experience we have gained in designing and interpreting these experiments using validated simulations done with FLASH; and (5) the close collaborations we have established with outstanding experimental groups in the US and abroad. The broader impacts of the project are two-fold: (1) the work furthered the transformation of the academic community’s ability to design and analyze HEDP experiments at large laser facilities that is happening through the availability of FLASH and its widespread adoption by the academic community – a community that previously had had limited access to open and validated hydro/MHD simulation tools for HEDP experiments; and (2) the work trained young scientists to design and interpret HEDP experiments using validated simulations – a critical national need.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1994

Long Term Dynamic Stream Nitrate and Phosphate Changes Following Watershed Wildfires

During and following the 1988 Yellowstone National Park wildfires, airborne remotely sensed data were collected in order to characterize various vegetative components, fire front movements and bum intensities. ER-2 derived Thematic Mapper Simulator (TMS) data were used in conjunction with water sampling and chemistry analysis to determine fire intensities in various watersheds and aquatic system condition changes. The airborne Daedalus multispectral TMS data allowed the characterization of various bum intensities in watersheds. Stream sampling was then conducted in those various burned watersheds to determine nitrate and phosphate concentration changes. Six stream watersheds were monitored for five years (1989-1993) during non-snow periods (May/June through September): Cache Creek (intensely burned), Blacktail Deer Creek (intensely burned), Snake River (moderately burned), Lamar River (mixed burning), Soda Butte Creek (lightly burned), and Amphitheatre Creek (unburned). One litre samples were collected from those streams with ISCO water samplers every 12 hours. The samples were removed every 14 days .(28 Samples), and water chemistry analysis was performed. Chemistry analysis indicated that nitrate and phosphate concentrations were elevated in moderately burned watersheds and significantly elevated in severely burned watersheds. The results during the five year study indicate that bum intensities regulate stream water nitrate and phosphate concentrations, and that remotely sensed data can be used effectively to predict watershed chemical changes which will affect aquatic conditions.

Ambrosia, Vincent G.↗
DOE OSTI2016

Performance of a Surface Barrier for Waste Isolation and Flux Reduction at the Hanford Site - 16099

Based on the knowledge gained from a decade of laboratory, field, and numerical studies, the Prototype Hanford Barrier (PHB) was designed and constructed between late 1993 and late 1994 over the 216-B-57 Crib in the 200-BP-1 Operable Unit at the Hanford Site. The PHB has been monitored since 1994 to evaluate the physical, hydrologic, and ecological performance. Two stress tests were carried out in the past: 1) an enhanced (about 3 times the multi-year average of 160 mm/year) precipitation test from water year (WY) 1995 to WY1997, which included simulated 1000-year return, 24-hour rainstorms in March of each year, and 2) a controlled fire test in 2008. The purpose of this article is to present the main findings of the PHB demonstration since 1994. From 1994 to 2013-during which time the barrier experienced 3 years of enhanced precipitation, three 1000-year return, 24-hour simulated rainstorms, and a controlled fire-the PHB limited drainage to well below the 0.5 mm yr{sup -1} design criterion and had minimal erosion. Although the test period represents only 2% of the design life, the observations suggest the PHB is robust enough to control drainage and isolate subsurface contaminants. Future barrier performance will depend on barrier stability and hydrology. Given the 19-year record of successful performance and considering all processes and mechanisms that could degrade barrier stability and hydrology in the future, the results suggest that the PHB is very likely to perform for at least the remainder of its 1000-year design life. This conclusion is based on two assumptions: 1) the exposed sub-grade receives protection against erosion and 2) institutional controls prevent inadvertent human activity on the barrier. The findings at the PHB are useful for the design and monitoring of future surface barriers at Hanford and elsewhere. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Physical Review Journal Club: Structural uncertainty assessment for fire-engulfed objects in crosswind: Establishing credibility for a multiphysics wall-modeled large-eddy simulation paradigm

This is an invited presentation for APS's monthly Journal Club where important papers that have recently been accepted for publication are highlighted in a seminar-like setting. The presentation will be "live" (May 1, 2025) and streamed across the world (including an archived recording).

Domino, Stefan Paul [Sandia National Laboratories ↗
DOE OSTI2024

Quantifying wildfire drivers and predictability in boreal peatlands using a two-step error-correcting machine learning framework in TeFire v1.0

Abstract. Wildfires are becoming an increasing challenge to the sustainability of boreal peatland (BP) ecosystems and can alter the stability of boreal carbon storage. However, predicting the occurrence of rare and extreme BP fires proves to be challenging, and gaining a quantitative understanding of the factors, both natural and anthropogenic, inducing BP fires remains elusive. Here, we quantified the predictability of BP fires and their primary controlling factors from 1997 to 2015 using a two-step correcting machine learning (ML) framework that combines multiple ML classifiers, regression models, and an error-correcting technique. We found that (1) the adopted oversampling algorithm effectively addressed the unbalanced data and improved the recall rate by 26.88 %–48.62 % when using multiple datasets, and the error-correcting technique tackled the overestimation of fire sizes during fire seasons; (2) nonparametric models outperformed parametric models in predicting fire occurrences, and the random forest machine learning model performed the best, with the area under the receiver operating characteristic curve ranging from 0.83 to 0.93 across multiple fire datasets; and (3) four sets of factor-control simulations consistently indicated the dominant role of temperature, air dryness, and climate extreme (i.e., frost) for boreal peatland fires, overriding the effects of precipitation, wind speed, and human activities. Our findings demonstrate the efficiency and accuracy of ML techniques in predicting rare and extreme fire events and disentangle the primary factors determining BP fires, which are critical for predicting future fire risks under climate change.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2017

DSMC simulations of the Shuttle Plume Impingement Flight EXperiment(SPIFEX)

During orbital maneuvers and proximity operations, a spacecraft fires its thrusters inducing plume impingement loads, heating and contamination to itself and to any other nearby spacecraft. These thruster firings are generally modeled using a combination of Computational Fluid Dynamics (CFD) and DSMC simulations. The Shuttle Plume Impingement Flight EXperiment(SPIFEX) produced data that can be compared to a high fidelity simulation. Due to the size of the Shuttle thrusters this problem was too resource intensive to be solved with DSMC when the experiment flew in 1994.

Stewart, Benedicte↗
DOE OSTI2021

North American boreal forests are a large carbon source due to wildfires from 1986 to 2016

Wildfires are a major disturbance to forest carbon (C) balance through both immediate combustion emissions and post-fire ecosystem dynamics. Here we used a process-based biogeochemistry model, the Terrestrial Ecosystem Model (TEM), to simulate C budget in Alaska and Canada during 1986–2016, as impacted by fire disturbances. We extracted the data of difference Normalized Burn Ratio (dNBR) for fires from Landsat TM/ETM imagery and estimated the proportion of vegetation and soil C combustion. We observed that the region was a C source of 2.74 Pg C during the 31-year period. The observed C loss, 57.1 Tg C year -1 , was attributed to fire emissions, overwhelming the net ecosystem production (1.9 Tg C year -1 ) in the region. Our simulated direct emissions for Alaska and Canada are within the range of field measurements and other model estimates. As burn severity increased, combustion emission tended to switch from vegetation origin towards soil origin. When dNBR is below 300, fires increase soil temperature and decrease soil moisture and thus, enhance soil respiration. However, the post-fire soil respiration decreases for moderate or high burn severity. The proportion of post-fire soil emission in total emissions increased with burn severity. Net nitrogen mineralization gradually recovered after fire, enhancing net primary production. Net ecosystem production recovered fast under higher burn severities. The impact of fire disturbance on the C balance of northern ecosystems and the associated uncertainties can be better characterized with long-term, prior-, during- and post-disturbance data across the geospatial spectrum. Our findings suggest that the regional source of carbon to the atmosphere will persist if the observed forest wildfire occurrence and severity continues into the future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The Latent Dirichlet Allocation model applied to airborne LiDAR data: A case study on mapping forest degradation associated with fragmentation and fire in the Amazon region

1. LiDAR data are being increasingly used to provide a detailed characterization of the vertical profile of forests. This characterization enables the generation of new insights on the influence of environmental drivers and anthropogenic disturbances on forest structure as well as on how forest structure influences important ecosystem functions and services. Unfortunately, extracting information from LiDAR data in a way that enables the spatial visualization of forest structure, as well as its temporal changes, is challenging due to the high dimensionality of these data. 2. In this study, we show how the Latent Dirichlet Allocation model applied to LiDAR data (LidarLDA) can be used to identify forest structural types and how the relative abundance of these forest types changes throughout the landscape. The code to fit this model is made available through the open-source r package LidarLDA in github. We illustrate the use of LidarLDA both with simulated data and data from a large-scale fire experiment in the Brazilian Amazon region. 3. Using simulated data, we demonstrate that LidarLDA accurately identifies the number of forest types as well as their spatial distribution and absorptance probabilities. For the empirical data, we found that LidarLDA detects both landscape-level patterns in forest structure as well as the strong interacting effect of fire and forest fragmentation on forest structure based on the experimental fire plots. More specifically, LidarLDA reveals that proximity to forest edge exacerbates the impact of fires, and that burned forests remain structurally different from unburned areas for at least 7 years, even when burned only once. Importantly, LidarLDA generates insights on the 3D structure of forest that cannot be obtained using more standard approaches that just focus on top-of-the-canopy information (e.g. canopy height models based on LiDAR data). 4. By enabling the mapping of forest structure and its temporal changes, we believe that LidarLDA will be of broad utility to the ecological research community.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2020

How emissions uncertainty influences the distribution and radiative impacts of smoke from fires in North America

Fires and the aerosols that they emit impact air quality, health, and climate, but the abundance and properties of carbonaceous aerosol (both black carbon and organic carbon) from biomass burning (BB) remain uncertain and poorly constrained. We aim to explore the uncertainties associated with fire emissions and their air quality and radiative impacts from underlying dry matter consumed and emissions factors. To investigate this, we compare model simulations from a global chemical transport model, GEOS-Chem, driven by a variety of fire emission inventories with surface and airborne observations of black carbon (BC) and organic aerosol (OA) concentrations and satellite-derived aerosol optical depth (AOD). We focus on two fire-detection-based and/or burned-area-based (FD-BA) inventories using burned area and active fire counts, respectively, i.e., the Global Fire Emissions Database version 4 (GFED4s) with small fires and the Fire INventory from NCAR version 1.5 (FINN1.5), and two fire radiative power (FRP)-based approaches, i.e., the Quick Fire Emission Dataset version 2.4 (QFED2.4) and the Global Fire Assimilation System version 1.2 (GFAS1.2). We show that, across the inventories, emissions of BB aerosol (BBA) differ by a factor of 4 to 7 over North America and that dry matter differences, not emissions factors, drive this spread. We find that simulations driven by QFED2.4 generally overestimate BC and, to a lesser extent, OA concentrations observations from two fire-influenced aircraft campaigns in North America (ARCTAS and DC3) and from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network, while simulations driven by FINN1.5 substantially underestimate concentrations. The GFED4s and GFAS1.2-driven simulations provide the best agreement with OA and BC mass concentrations at the surface (IMPROVE), BC observed aloft (DC3 and ARCTAS), and AOD observed by MODIS over North America. We also show that a sensitivity simulation including an enhanced source of secondary organic aerosol (SOA) from fires, based on the NOAA Fire Lab 2016 experiments, produces substantial additional OA; however, the spread in the primary emissions estimates implies that this magnitude of SOA can be neither confirmed nor ruled out when comparing the simulations against the observations explored here. Given the substantial uncertainty in fire emissions, as represented by these four emission inventories, we find a sizeable range in 2012 annual BBA PM2.5 population-weighted exposure over Canada and the contiguous US (0.5 to 1.6 µg/cu. m). We also show that the range in the estimated global direct radiative effect of carbonaceous aerosol from fires (−0.11 to −0.048 W/sq. m) is large and comparable to the direct radiative forcing of OA (−0.09 W/sq. m) estimated in the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC). Our analysis suggests that fire emissions uncertainty challenges our ability to accurately characterize the impact of smoke on air quality and climate.

North America↗
DOE OSTI2023

Performance of Fire Danger Indices and Their Utility in Predicting Future Wildfire Danger Over the Conterminous United States

Abstract Predicting current and future wildfire frequency and size is central to wildfire control and management. Multiple fire danger indices (FDIs) that incorporate weather and fuel conditions have been developed and utilized to support wildfire predictions and risk assessment. However, the scale‐dependent performance of individual FDIs remains poorly understood, which leads to large uncertainty in the estimated fire sizes under climate change. Here, we calculate four commonly used FDIs over the conterminous United States using high‐resolution (4 km) climate and fuel data sets for the 1984–2019 period. The relationships of these four FDIs to the observed wildfire sizes show that higher values of FDIs correlate to larger total fire sizes; this correlation is more robust at larger spatial scales. Sensitivity analysis indicates that the daily minimum relative humidity and precipitation are the most important drivers of the annual mean fire danger. In the instances of extreme fire danger, wind speed becomes a critical factor and should be considered in the calculation of the FDI. To assess the impact of climate change on future fire size, we calculate the present‐day and end‐of‐century FDIs using the 12 km regional climate model simulations. The four FDIs generally predict consistent changes in future fire potential, suggesting an overall higher fire potential in conjunction with a prolonged wildfire season in future climate. Regionally, the four FDIs also reveal similar seasonal patterns as the enhancement arises mostly in spring and summer over the southwest US while in summer and fall over the northern and eastern US.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1951

Investigation of Altitude Starting and Acceleration Characteristics of J47 Turbojet Engine

An investigation was conducted on an axial-flow-compressor type turbojet engine in the NACA Lewis altitude wind tunnel to determine the operational characteristics of several ignition systems, cross-fire tube configurations and fuel systems over a range of simulated flight conditions. The opposite-polarity-type spark plug provided the most satisfactory ignition. Increasing the cross-fire-tube diameter improved intercombustor flame propagation. At high windmilling speeds, accelerations to approximately 6200 rpm could be made at a preset constant throttle position. The use of a variable-area nozzle reduced acceleration time.

IGNITION SYSTEMS↗
DOE OSTI2018

An Integrated Supercritical System for Efficient Produced Water Treatment and Power Generation

The main goal of this project was to evaluate the feasibility of an integrated supercritical (SC) water desalination and power generation system for treatment of produced waters from CO 2 sequestration, oil production, and coal-bed methane recovery. This system may offer a transformative approach to generating power from coal or natural gas (NG) and purifying saline water or produced water with high total dissolved solids (TDS) in a zero-liquid discharge plant. The closed-loop steam cycle of conventional power plants was replaced with an open-loop steam cycle that heated the pretreated produced water above the SC point of water (374 °C and 221 bar) to precipitate and separate the salts dissolved in the water. After separation of the precipitated salt, the SC steam required further treatment to reduce the salt content from ~100 mg/L (ppm) to <1 ppm to make it suitable for use in turbines for power generation. A SC membrane distillation unit was used to polish the SC steam, which could be sent through a series of turbines for power generation, and the steam exiting the low-pressure turbine could be condensed by cooling water to produce pure water. Project tasks included a process simulation and a techno-economic evaluation of the integrated system; design and assembly of SC salt precipitation and membrane distillation systems; development and characterization of advanced carbon membranes for SC membrane distillation; and pretreatment, desalination, and purification of different produced water samples with salt concentrations of 30,000–200,000 ppm to a high-purity (<1 ppm) level. Process simulations were performed for 550 MW SC coal-fired or NG baseline cases and various scenarios for the proposed integrated SC system using produced water as the water source and coal or NG as the fuel source. Simulation results indicated that the net plant efficiency loss of the proposed SC cogeneration process would be in the range of <1% to ~4%, in which NG and lower TDS produced water cases would have higher efficiencies. The proposed process for coal-fired cases could provide all water needs for the power generation process and generate additional pure water for beneficial use. A techno-economic analysis was performed to estimate the cost of produced water treatment (dollars /kgal of treated water) by conventional treatment processes compared with the proposed cogeneration process. The cost estimation for the proposed cogeneration system included the cost of pretreatment, the cost of power loss to integrate the water desalination process into the power generation system, and the cost of plant modification or retrofitting. The cost of produced water treatment was calculated by assuming three different scenarios in which converting a baseline power plant to the proposed cogeneration plant would increase the cost of the boiler and feed water system by 10%, 50%, and 100%. Results indicated that the cost of produced water treatment, even with a 100% cost increase, was in the range of $8.6–11.6 dollars per kgal, which is about half of the treatment cost by conventional methods. The cost of produced water treatment by the proposed cogeneration system could be further reduced or a considerable income could be generated if the values of saltwater and freshwater products are considered. Overall, the techno-economic analysis results indicated that the proposed integrated SC system could potentially treat produced water at a cost significantly lower than the cost of produced water treatment by conventional technologies. To evaluate the feasibility of the proposed treatment system at a laboratory scale, a SC salt precipitation and membrane distillation system was designed and fabricated to treat a simulated brine or pretreated produced water by precipitating salts and further polishing the SC steam containing ~100 ppm salts to a high-purity steam. Through baseline tests with NaCl solutions, with concentrations ranging from 30,000 to 200,000 ppm, it was demonstrated that concentrated feed solutions could be treated by precipitating salt at SC conditions to a TDS level of 130 ppm. Produced water samples were collected from a potential CO 2 sequestration site, a coal-bed methane site, and an oilfield in Illinois. Representative samples were characterized for various water quality parameters, including pH; turbidity; conductivity; and concentrations of selected anions, cations, alkalinity, dissolved organic carbon, total suspended solids, TDS, and total petroleum hydrocarbons. The produced water samples were first treated by conventional pretreatment methods to remove residual oil, dissolved organic matter, and scale-forming species. Pretreatment methods included coagulation, sedimentation, filtration, activated carbon adsorption, and ion-exchange adsorption. Pretreated produced waters were treated at SC conditions in which salts were precipitated. Analysis of treated samples revealed that the concentration of Na (the main cation) in produced waters was reduced dramatically from 14,000–47,400 ppm in the feed to 21–80 ppm in treated waters. Concentrations of other main cations (Ca, Mg, Sr, K) also decreased significantly from initial values of ~1,000–22,000 ppm to ~1 ppm or less. However, a significant increase in the concentrations of Fe, Ni, Cu, and Co was observed, mainly caused by leaching of these elements from the system (vessels, filters, tubing), which is manufactured from alloys containing Fe, Ni, Cu, and Co. To polish the SC steam containing a salt content of ~100 ppm to a high-purity level of <1 ppm, a SC membrane distillation treatment using carbon membranes was employed. Different types of self-standing or supported carbon membranes were developed from graphite, graphene, carbon nanotubes, carbon nanofibers, and pyrolytic carbon. Screening tests revealed that only a limited number of developed carbon membranes were suitable for the purification of steam at SC conditions. Membranes that were either fragile and lacked mechanical strength or had large pores and showed limited salt rejection (<20%) were found to be unsuitable for the purification of SC steam. Promising carbon membranes that were successfully tested for purifying SC steam included two graphite membranes (i.e., AF-5, a porous graphite material, and FGS, a flexible graphite sheet) and a carbon nanotube membrane grown on a Hastelloy substrate and then further modified using a proprietary method (i.e., membrane C). The AF-5 membrane exhibited a stable water flux of ~50 kg/m 2 ∙h and ~40% salt rejection. The FGS membrane had a high salt rejection of 95%–98% but a lower water flux of ~15 kg/m 2 ∙h. Membrane C showed an extremely high flux of 200-800 kg/m 2 ∙h, which is one order of magnitude higher than the water flux of conventional membranes. Membrane C also exhibited 87%–93% salt rejection. Overall, a two-stage or multistage polishing treatment by these membranes could lower the salt content of the SC steam from initial values of ~100–200 ppm to the steam quality of <1 ppm or to parts per billion levels required for the power cycle.

20 FOSSIL-FUELED POWER PLANTS↗
NASA NTRS1993

A systems approach to a DSMC calculation of a control jet interaction experiment

This study deals with the development of a methodology for numerically simulating the interaction of a reaction control system (RCS) jet with a low-density external flow. A European Space Agency (ESA) experiment was chosen as a test case since it provided experimental data that could validate some of the numerical results. The initial approach was to focus on several subproblems having direct relevance to the full interaction problem. This enabled different numerical methods to be separately investigated and validated for each part of the interaction problem. In this manner, the best methodology for solving the full interaction problem was developed. The subproblems considered in this study included typical RCS nozzle and plume flows, a flat plate at zero incidence, and the flow past the experimental test model without the control jet firing. Once these calculations were completed, a simulation was performed with a control jet operating at a relatively low density. The results from this latter simulation provided qualitative insight into the complex interaction process.

Tartabini, Paul V.↗
DOE OSTI2022

Industry Level Feasibility of LiDAR Data into Fire Modeling Using Fire Risk Investigation in 3D (FRI3D)

Many evaluation, assessment, and modeling tasks at nuclear power plants require spatial information this often requires physical visits to locations within the facility because the 2D or 3D schematics and current models do not contain enough detail or do not capture as-built and real-world conditions. These visits require extensive manual labor for not only the requesting party, but support groups such as security. LIDAR mapping is trying to solve that problem by providing very detailed 3D models for low costs. However, the use of these models can be very limited because either component reference information is missing and too costly to add, or there is no way to extract specific spatial data needed for other tools. This report presents Idaho National Lab's work with Environmental Intellect (Ei) covering two main efforts. First, to reduce the effort of "tagging" data in large 3D models. By using both existing plant database information, and artificial intelligence (AI) to find and read equipment labels. This research explores the ability of to provide a simple way for the user to tag items and verify plant data, capturing both the speed of AI and human verification. The second part of the work is the development of an interface for importing pieces needed for Modeling & Simulation. Analysis work such as that for fire, flood, or physical security all require spatial or 3D models in various levels of detail. This interface will allow for the retrieval of item location or boundaries, enabling the auto generation of models for varying tools. The application program interface (API) of the fire risk investigation in 3D (FRI3D) was used to test feasibility of exporting the LiDAR tagged spatial information. Outcomes from this work provide preliminary data to determine if the tools and methods could provide substantial industry benefit if fully matured.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Validation Assessment of Turbulent Reacting Flow Model Using The Area-Validation Metric on Medium-Scale Methanol Pool Fire Results

Accident analysis and ensuring power plant safety are pivotal in the nuclear energy sector. Significant strides have been achieved over the past few decades regarding fire protection and safety, primarily centered on design and regulatory compliance. Yet, after the Fukushima accident a decade ago, the imperative to enhance measures against fire, internal flooding, and power loss has intensified. Hence, a comprehensive, multilayered protection strategy against severe accidents is needed. Consequently, gaining a deeper insight into pool fires and their behavior through extensive validated data can greatly aid in improving these measures using advanced validation techniques. A model validation study was performed at Sandia National Laboratories in which a 30-cm diameter methanol pool fire was modeled using the SIERRA/Fuego turbulent reacting flow code. This validation study used a standard validation experiment to compare model results against, and conclusions have been published. The fire was modeled with a Large Eddy Simulation (LES) turbulence model with subgrid turbulent kinetic energy closure. Combustion was modeled using a strained laminar flamelet library approach. Radiative heat transfer was accounted for with a model utilizing the gray-gas approximation. In the present study, additional validation analysis is performed using the area validation metric (AVM). These activities are done on multiple datasets involving different variables and temporal/spatial ranges and intervals. In conclusion, the results provide insight into the use of the area validation metric on such temporally varying datasets and the importance of physics-aware use of the metric for proper analysis.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
NASA NTRS2005

Applications of Principled Search Methods in Climate Influences and Mechanisms

Forest and grass fires cause economic losses in the billions of dollars in the U.S. alone. In addition, boreal forests constitute a large carbon store; it has been estimated that, were no burning to occur, an additional 7 gigatons of carbon would be sequestered in boreal soils each century. Effective wildfire suppression requires anticipation of locales and times for which wildfire is most probable, preferably with a two to four week forecast, so that limited resources can be efficiently deployed. The United States Forest Service (USFS), and other experts and agencies have developed several measures of fire risk combining physical principles and expert judgment, and have used them in automated procedures for forecasting fire risk. Forecasting accuracies for some fire risk indices in combination with climate and other variables have been estimated for specific locations, with the value of fire risk index variables assessed by their statistical significance in regressions. In other cases, the MAPSS forecasts [23, 241 for example, forecasting accuracy has been estimated only by simulated data. We describe alternative forecasting methods that predict fire probability by locale and time using statistical or machine learning procedures trained on historical data, and we give comparative assessments of their forecasting accuracy for one fire season year, April- October, 2003, for all U.S. Forest Service lands. Aside from providing an accuracy baseline for other forecasting methods, the results illustrate the interdependence between the statistical significance of prediction variables and the forecasting method used.

Glymour, Clark↗