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Sandia National Laboratory conducted fire tests on ten 3013 containers with varying amounts of water. The containers plastically deformed during the fire tests. The plastic deformation increased the free-gas volume within the containers. The amount of plastic deformation is measured and post-test free gas volumes at 25°C is reported.
The documented safety analysis for the Savannah River National Laboratory (SRNL) evaluates the consequences of a postulated 1273 K fire in a glovebox. The radiological dose consequences for a pressurized release of plutonium oxide powder during such a fire depend on the maximum pressure that is attained inside the oxide storage containers. The oxide storage configuration selected for analysis is can/bag/can, comprised of oxide powder inside an 8.38 × 10 -6 m 3 stainless steel B vial inside 0.006 kg of polyethylene bagging inside a one-quart screw top utility can of the type commonly used to package solvents or rubber cements. To enable evaluation of the dose consequences, temperature and pressure transients have been calculated for exposure of a typical set of storage containers to the fire. The pressurization analysis credits venting to and from the B vial but does not credit venting or leakage from the can. Due to the low rate of venting from the B vial into the can gas space, the can pressure is nearly independent of the B vial pressure. Calculated maximum pressures are compared to the utility can burst pressure. In lieu of a structural analysis of the utility cans, burst pressures and leakage rates were measured using compressed nitrogen gas. The measured gauge burst pressure was 0.250 ± 0.043 MPa. The measured burst pressures are lower than the calculated maximum pressure due to fire exposure, indicating that the utility cans could burst during exposure to a 1273 K fire.
This report evaluates leakage behavior from tritium containment vessels under thermal abuse and combined thermal-mechanical abuse conditions to better understand safety implications for releases occurring in a fire scenario. Surrogate gases were used for all tests in this report. Leakage through the valves from thermal pressurization was observed when heating rates >0.8°C/s were sustained to >300°C. Gas plumes were visualized from vessels that were heated above 260°C and then dropped.
Normalized difference vegetation index (NDVI) composite image data, produced from AVHRR data collected in 1990, were evaluated for locating and mapping the areal extent of wildfires in the boreal forests of Alaska during that year. A technique was developed to map forest fire boundaries by subtracting a late-summer AVHRR NDVI image from an early summer scene. The locations and boundaries of wildfires within the interior region of Alaska were obtained from the Alaska Fire Service, and compared to the AVHRR-derived fire-boundary map. It was found that AVHRR detected 89.5 percent of all fires with sizes greater than 2000 ha with no false alarms and that, for most cases, the general shape of the fire boundary detected by AVHRR matched those mapped by field observers. However, the total area contained within the fire boundaries mapped by AVHRR were only 61 percent of those mapped by the field observers. However, the AVHRR data used in this study did not span the entire time period during which fires occurred, and it is believed the areal estimates could be improved significantly if an expanded AVHRR data set were used.
This research effort was commissioned to examine factors related to loss of containment due to fire within a glovebox (GB). Tests consisted of multiple trials with varying initial conditions. Com bustible levels and types were chosen to reflect realistic operation conditions (including worst-case scenarios), involving flammable liquids, GB tools, and other combustible items commonly found in GBs at Los Alamos National Labs (LANL).
Program objectives are outlined as follows: (1) examine the thermal and environmental characteristics of three types of fuels burned in two quantities contained within a metal lavatory; (2) determine the hazard experienced in opening the door of a lavatory containing a developed fire; (3) select the most severe source fuel for use in a baseline test; and (4) evaluate the effect of the most severe source upon a lavatory constructed of contemporary materials. All test were conducted in the Douglas Cabin Fire Simulator.
In the first year of the project, we developed a fire event tracking system using active fire detections recorded by the Visible Infrared Imaging Radiometer Suite (VIIRS). We employed this algorithm to track all large fires that occurred in California from 2013 to 2020, and created a retrospective Fire Event Data Suite (FEDS) that contains the vector shapes of fire perimeters, active fire fronts, and the locations and fire radiative powers for each fire pixel at 12-hour temporal resolution for each fire event. Using the 2020 Creek Fire as a test bed, we also delineated hourly fire perimeters by combining the FEDS data with the hourly rates of spread derived from Geostationary Operational Environmental Satellite (GOES) Advanced Baseline Imager (ABI) fire data. This dataset will form the basis of the near-real-time hourly fire emissions product that we plan to develop at years 2 and 3 of the project.
Report contains evaluations of flammability and physical properties of twenty-four upholstery and furnishing materials, such as those used in commercial aircraft. Data presented include degradation upon heating, limited oxygen indices, smoke generation, flammability afterglow, and char length.
This report contains the results of three projects conducted by undergraduate students from Worcester Polytechnic Institute at the NASA's Lewis Research Center under a NASA Award NCC3-312. The students involved in these projects spent part of the summer of 1993 at the Lewis Research Center (LeRC) under the direction of Dr. Howard Ross, head of the Combustion group and other NASA engineers and scientists. The Principal Investigator at Worcester Polytechnic Institute was Professor Vahid Motevalli. Professor Motevalli served as the principal project advisor for two of the three projects which were in Mechanical Engineering. The third project was advised by Professor Duckworth of Electrical and Computer Engineering, while Professor Motevalli acted as the co-advisor. These projects provided an excellent opportunity for the students to participate in the cutting edge research and engineering design, interact with NASA engineers and gain valuable exposure to a real working environment. Furthermore, the combustion group at LeRC was able to forward their goals by employing students to work on topics of immediate use and interest such as experimental research projects planned for the space shuttle, the future space station, or to develop demonstration tools to educate the public about LeRC activities.
This report contains the results of three projects conducted by undergraduate students from Worcester Polytechnical Institute at the NASA's Lewis Research Center under a NASA Award NCC3-312. The students involved in these projects spent part of the summer of 1993 at the Lewis Research Center (LeRC). The Principal Investigator at Worcester Polytechnic Institute was Professor Vahid Motevalli. Professor Motevalli served as the principal project advisor for two of the three projects which were in Mechanical Engineering. The third project was advised by Professor Duckworth of Electrical and Computer Engineering, while Professor Motevalli acted as the co-advisor. These projects provided an excellent opportunity for the students to participate in the cutting edge research and engineering design, interact with NASA engineers and gain valuable exposure to a real working environment. This report has been divided to three sections, representing the outcome of each of the separate projects. The three reports which have been written by the students under the supervision of their advisors have been compiled into a combined report by Dr. Motevalli. Each project report is presented here as a section which is essentially self-contained. Each section contains chapters introducing the problem, solution approach, description of the experiments, results and analysis, conclusions and appendixes as appropriate.
The LOX/Hydrocarbon Combustion Instability Investigation Program was structured to determine if the use of light hydrocarbon combustion fuels with liquid oxygen (LOX) produces combustion performance and stability behavior similar to the LOX/hydrogen propellant combination. In particular methane was investigated to determine if that fuel can be rated for combustion instability using the same techniques as previously used for LOX/hydrogen. These techniques included fuel temperature ramping and stability bomb tests. The hot fire program probed the combustion behavior of methane from ambient to subambient temperatures. Very interesting results were obtained from this program that have potential importance to future LOX/methane development programs. A very thorough and carefully reasoned documentation of the experimental data obtained is contained. The hot fire test logic and the associated tests are discussed. Subscale performance and stability rating testing was accomplished using 40,000 lb. thrust class hardware. Stability rating tests used both bombs and fuel temperature ramping techniques. The test program was successful in generating data for the evaluation of the methane stability characteristics relative to hydrogen and to anchor stability models. Data correlations, performance analysis, stability analyses, and key stability margin enhancement parameters are discussed.
The increasing incidence and severity of wildfires underscores the necessity of accurately predicting their behavior. While high-fidelity models derived from first principles offer physical accuracy, they are too computationally expensive for use in real-time fire response. Low-fidelity models sacrifice some physical accuracy and generalizability via the integration of empirical measurements, but enable real-time simulations for operational use in fire response. Machine learning techniques have demonstrated the ability to bridge these objectives by learning first-principles physics while achieving computational speedups. While deep learning approaches have demonstrated the ability to predict wildfire propagation over large time periods, time-resolved fire-spread predictions are needed for active fire management. Here, in this work, we evaluate the ability of deep learning approaches in accurately modeling the time-resolved dynamics of wildfires. We use an autoregressive process in which a convolutional recurrent deep learning model makes predictions that propagate a wildfire over 15 min increments. We apply the model to four simulated datasets of increasing complexity, containing both field fires with homogeneous fuel distribution as well as real-world topologies sampled from the California region of the United States. We show that even after 100 autoregressive predictions representing more than 24 h of simulated fire spread, the resulting models generate stable and realistic propagation dynamics, achieving a Jaccard score between 0.89 and 0.94 when predicting the resulting fire scar. The inference time of the deep learning models are examined and compared, and directions for future work are discussed.
High-intensity wildfires alter the chemical composition of organic matter, which is expected to be distinctly different from low-intensity prescribed fires. In this study, we used pyrolysis gas chromatography/mass spectrometry (Py-GC/MS), in conjunction with solid-state 13 C nuclear magnetic resonance (NMR) and Fourier transform infrared (FT-IR) spectroscopy, to assess chemical alterations from three wildfires and a long-term frequent prescribed fire site. Our results showed that black ash formed under moderate intensity burns contained less aromatic (ArH), polyaromatic hydrocarbon (PAH), and nitrogen-containing compounds (Ntg) but more lignin (LgC) and phenol compounds (PhC), compared to white ash formed under high intensity burns. Both 13 C NMR and FT-IR confirmed a higher relative percentage of carboxyl carbon in white ash, indicating the potential for higher water solubility and more mobile carbon, relative to black ash. Compared to wildfires, ash from low-intensity prescribed fire contained less ArH, PAH, and Ntg and more LgC and PhC. Controlled laboratory burning trials indicated that organic matter alteration was sensitive to the burn temperature, but not related to the fuel type (pine vs fir) nor oxygen absence/presence at high burn temperatures. This study concludes that higher burn temperatures resulted in higher (poly)aromatic carbon/nitrogen and lower lignin/phenol compounds.
This report presents the results of a study, funded by the Federal Aviation Administration (FAA), of transport airplane crash-resistant fuel system (CRFS). The report covers the historical studies related to aircraft crash fires and fuel containment concepts undertaken by the FAA, NASA, and the U.S. Army, which ultimately led to the current state of the art in CRFS technology. It describes the basic research, testing, field investigations and production efforts which have led to the highly successful military CRFS, which has saved many lives and reduced costs of accidents. Current CRFS technology used in transport category airplanes is defined and compared to the available state-of-the-art technology. The report provides information to the FAA and other government organizations which can help them plan their efforts to improve the state of crash fire protection in the transport airplane fleet. The report provides guidance to designers looking for information about CRFS design problems, analysis tools to use for product improvement, and a summary of current and proposed regulations for transport category airplane fuel systems.
In the United States, many impoundments at coal-fired power plants contain elevated contaminants like arsenic, boron, barium, and selenium. Zeolites synthesized from fly ash show promise as sorbents for these contaminants. However, optimizing sorption capacity is challenging due to numerous possible topologies, silicon to aluminum (Si/Al) ratios, and cation types. In this study, molecular simulations are used to design cationic zeolites for boric acid adsorption. Force field models based on quantum mechanical calculations (PBE + D2) for Na-, Ca-, Mn-, and Fe-exchanged chabazite and LTA are presented. The new D2FF force fields reproduce DFT energies with about half the error of UFF. Zeolite performance depends on Si/Al ratio and cation type, with low Si/Al ratio chabazite (CHA) and phillipsite (PHI) zeolite frameworks exchanged with Ca 2+ or Na + /Ca 2+ mixtures showing the highest adsorption. In conclusion, these findings suggest tailored fly ash-derived zeolites could provide effective boron removal from leachate ponds.
Flue gas desulfurization (FGD) wastewater generated from coal-fired power plants contain potentially harmful heavy metal pollutants that pose a threat to public health and clean water. In this work, we present a water stable polyethylenimine-n,n’-methylenebisacrylamide (PEI-MBAA) functionalized SiO 2 solid sorbent material (PMS-1.2/1/4) and investigate its metal adsorption kinetics, selectivity, regenerability, and space velocity. The kinetic studies of six of the toxic heavy metals (As, Cd, Cr, Pb, Se, and Hg) prepared with single elements in Milli-Q water showed the effect of chemical bonding and intraparticle mass transfer resistance on the sorption process. The selectivity studies demonstrated the significant adsorption efficiency toward trace-level heavy metals (Se, Cd, U, Al, etc.) from authentic industrial FGD wastewater. Through five consecutive adsorption–desorption cycles with the FGD (uptake)-citrate (release)-based buffer pair, the sorbent showed high heavy metal removal ability and good reusability. The maximum flow rate for the removal of Se from industrial FGD wastewater was determined to be as high as 8 bed volumes/minute of the sorbent bed. Finally, the results demonstrate the PMS-1.2/1.4 sorbent is a promising candidate for the removal of heavy metals from practical aqueous solutions.
This dataset is a compilation of tropical root traits data in response to different global changes in tropical sites, considering 23.50N and S as latitudinal boundaries. The global changes considered are warming, drought, flooding, cyclones, nitrogen addition, CO2 fertilization, and fire. This dataset contains 266 root trait observations from 93 studies across 24 tropical countries. The full citation from where the data was taken from is provided in the dataset, as well as the global change, the ecosystem type, location, coordinates, the root traits measured, and the direction of their response after the global change. Additional information such as the duration of the experiment, the intensity of the global change, the soil layers from where the roots were collected, the root orders, and the type of experiment are also shown. We obtained this dataset by performing a systematic literature review on Web of Science using standardized keywords in English, Spanish, and Portuguese (Yaffar, Lugli et al. in press).
Large scale biologically-realistic computational models are key to investigating the interplay between structure and function in nervous systems, thus paving the way to new clinical methods and neuro-inspired computing solutions. This project focuses on the hippocampus, in particular the CA3-CA1 regions, due to their role in associative learning and memory, pattern separation and completion, and spatial navigation. Investigations into the neuronal organization and learning rule(s) of this circuit can shed light into how declarative memories are formed, stored, recalled and forgotten and inform computational, experimental and clinical neuroscience work. Our project aims at developing a novel data-driven methodology supported by a broad heterogeneous base of neuroscience experimental knowledge and inspired from advances in computer science and engineering. Specifically, this work will benchmark existing and new learning rules within a full-scale spiking neural network simulation of the CA3-CA1 region. The model will be based on an open-source repository, called the Hippocampome, which contains neuronal morphologies, firing patterns, synapse probabilities, and most other required parameters for all known neuron types in the rodent hippocampal formation. The model will be first trained in a supervised fashion for associative memory tasks using backpropagation through time traditionally used in computer science, enhanced with a new technique called the surrogate gradient method. This optimization method will be used to obtain a global loss minimization, but it is not biologically inspired as it assumes the use of data not locally available to the synapses. However, we propose its use as a benchmarking tool, to compare the training performance of local biologically plausible and hardware-mappable learning rules at scale. New rules or combinations will be proposed and tested as needed, based on the obtained results. Progress in this area will also drive the development of novel hardware-mappable algorithms for continual lifelong learning and categorization of new events from few presented examples. This project goes beyond the existing state-of-the-art by looking at large scale realistic neuronal circuits as networks trainable via global optimization methods such as surrogate gradient descent. The objective function of the brain that supports learning is largely unknown, but it is likely that it operates through local learning rules. Studying network trajectories around local minima as proposed in this work represents a useful strategy for understanding whether a network is training by using a specific (set of) learning rule(s). Starting from a completely untrained network is a challenging test since it is difficult to determine how the learning rule affects the trajectory of the network. This interdisciplinary project will help understand what rule governs learning in these regions or if multiple learning rules are involved. The work will develop a robust methodology to measure if the network is converging to the target solution, oscillating around it, or diverging away.