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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.

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At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

Buoyancy-Driven Heat Transfer During Application of a Thermal Gradient for the Study of Vapor Deposition at Low Pressure Using and Ideal Gas

A mathematical model has been developed to determine heat transfer during vapor deposition of source materials under a variety of orientations relative to gravitational accelerations. The model demonstrates that convection can occur at total pressures as low as 10-2 mm Hg. Through numerical computation, using physical material parameters of air, a series of time steps demonstrates the development of flow and temperature profiles during the course of vapor deposition. These computations show that in unit gravity vapor deposition occurs by transport through a fairly complicated circulating flow pattern when applying heat to the bottom of the vessel with parallel orientation with respect to the gravity vector. The model material parameters for air predict the effect of kinematic viscosity to be of the same order as thermal diffusivity, which is the case for Prandtl number approx. 1 fluids. Qualitative agreement between experiment and the model indicates that 6-(2-methyl-4-nitroanilino)-2,4-hexadiyn-l-ol (DAMNA) at these pressures indeed approximates an ideal gas at the experiment temperatures, and may validate the use of air physical constants. It is apparent that complicated nonuniform temperature distribution in the vapor could dramatically affect the homogeneity, orientation, and quality of deposited films. The experimental test i's a qualitative comparison of film thickness using ultraviolet-visible spectroscopy on films generated in appropriately oriented vapor deposition cells. In the case where heating of the reaction vessel occurs from the top, deposition of vapor does not normally occur by convection due to a stable stratified medium. When vapor deposition occurs in vessels heated at the bottom, but oriented relative to the gravity vector between these two extremes, horizontal thermal gradients induce a complex flow pattern. In the plane parallel to the tilt axis, the flow pattern is symmetrical and opposite in direction from that where the vessel is positioned vertically. The ground-based experiments are sufficient preliminary tests of theory and should be of significant interest regarding vapor deposited films in microgravity.

Frazier, D. O.↗
NASA NTRS2018

Evapotranspiration-Based Irrigation Scheduling in Cool-Season Vegetables

Crop evapotranspiration (ETc) is strongly linked with photosynthetically active vegetation fraction (Fc). Estimation of ETc may support efficiency gains in irrigation water management, which in turn can mitigate nitrate leaching, promote water supply sustainability, and reduce energy costs associated with water pumping or transport. The University of California Cooperative Extension operates the CropManage (CM) model as a freely-available web-application for growers and consultants to support irrigation and nitrogen scheduling decisions. CM accounts for the rapid growth and typically brief cycle of cool-season vegetables, where Fc and crop coefficient (fraction of reference ET) can change daily during canopy development. Daily weather conditions are inherently accounted for by use of grass reference ETo data imported from the California Dept. Water Resources. Crop water requirement calculations are output in terms of irrigation system runtime. Empirical equations are used to estimate daily Fc time-series for a given crop type, primarily as a function of planting date and expected harvest. An applications programming interface (API) enables CM to import satellite-based Fc observations from NASA's Satellite Irrigation Management Support, which uses Landsat imagery to monitor about eight million irrigation acres statewide. The API is intended to provide a check on internal CM predictions of Fc and to facilitate expansion of the web-app to new crops and regions. A replicated irrigation trial was performed on cauliflower during spring/summer 2018 at the USDA Agricultural Research Station in Salinas, CA. The crop was established by sprinkler irrigation, and CropManage was then used to guide a series of drip irrigation treatments at 50%, 75%, 100%, and 150% of ETc replacement levels. Results will be presented with respect to water use efficiency, nitrogen use efficiency, biomass yield, and marketable yield. Additional findings will be presented for a celery trial harvested during autumn 2018.

Johnson, Lee↗
DOE OSTI2021

Identification and Online Updating of Dynamic Models for Demand Response of an Industrial Air Separation Unit

Demand-response operation of air separation units requires frequent changes in production rate(s), and scheduling calculations must explicitly consider process dynamics to ensure feasibility of the solutions. To this end, scale-bridging models (SBMs) approximate the scheduling-relevant dynamics of a process and its controller in a low-order representation. In contrast to previous works that have employed nonlinear SBMs, this paper proposes linear SBMs, developed using time-series analysis, to facilitate online scheduling computations. Using a year-long industrial dataset, we nd that compact linear SBMs are suitable approximations over typical scheduling horizons, but that their accuracies are unpredictable over time. We introduce a strategy for online updating of the SBMs, based on Kalman ltering schemes for online parameter estimation. The approach greatly improves the accuracy of SBM predictions and will enable the use of linear SBM-based demand-response scheduling in the future.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Bayesian inference of structured latent spaces from neural population activity with the orthogonal stochastic linear mixing model

The brain produces diverse functions, from perceiving sounds to producing arm reaches, through the collective activity of populations of many neurons. Determining if and how the features of these exogenous variables (e.g., sound frequency, reach angle) are reflected in population neural activity is important for understanding how the brain operates. Often, high-dimensional neural population activity is confined to low-dimensional latent spaces. However, many current methods fail to extract latent spaces that are clearly structured by exogenous variables. This has contributed to a debate about whether or not brains should be thought of as dynamical systems or representational systems. Here, we developed a new latent process Bayesian regression framework, the orthogonal stochastic linear mixing model (OSLMM) which introduces an orthogonality constraint amongst time-varying mixture coefficients, and provide Markov chain Monte Carlo inference procedures. We demonstrate superior performance of OSLMM on latent trajectory recovery in synthetic experiments and show superior computational efficiency and prediction performance on several real-world benchmark data sets. We primarily focus on demonstrating the utility of OSLMM in two neural data sets: μ ECoG recordings from rat auditory cortex during presentation of pure tones and multi-single unit recordings form monkey motor cortex during complex arm reaching. We show that OSLMM achieves superior or comparable predictive accuracy of neural data and decoding of external variables (e.g., reach velocity). Most importantly, in both experimental contexts, we demonstrate that OSLMM latent trajectories directly reflect features of the sounds and reaches, demonstrating that neural dynamics are structured by neural representations. Together, these results demonstrate that OSLMM will be useful for the analysis of diverse, large-scale biological time-series datasets.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1984

Simulating Atmospheric Turbulence

Dimensionless time series for gusts and gradients calculated with model. Single-sided one-dimensional spectra obtained from Fourier analysis of simulated vertical gust. Each of four plots represents spectrum in one of four altitude bands ranging from 0 to 10 km. Model differs from most models; is nondimensional for greater generality and uses spectra with finite wave number limits. Developed to predict effects of atmospheric turbulence in horizontal and near-horizontal flight also applicable to commercial aviation.

Tatom, F. B.↗
NASA NTRS2002

Polar Motion Constraints on Models of the Fortnightly Tide

Estimates of the near-fortnightly Mf ocean tide from Topex/Poseidon satellite altimetry and from numerical solutions to the shallow water equations agree reasonably well, at least in their basin-scale features. For example, both show that the Pacific Ocean tide lags the Atlantic tide by roughly 30 degrees. There are hints of finer scale agreements in the elevation fields, but noise levels are high. In contrast, estimates of Mf currents are only weakly constrained by the TP data, because high-wavenumber Rossby waves (with intense currents) are associated with relatively small perturbations in surface elevation. As a result, a wide range of Mf current fields are consistent with both the TP data and the hydrodynamic equations within a priori plausible misfit bounds. We find that a useful constraint on the Mf currents is provided by independent estimates of the Earth's polar motion. At the Mf period polar motion shows a weak signal (both prograde and retrograde) which must be almost entirely caused by the ocean tide. We have estimated this signal from the SPACE2000 time series, after applying a broad-band correction for atmospheric angular momentum. Although the polar motion estimates have relatively large uncertainties, they are sufficiently precise to fix optimum data weights in a global ocean inverse model of Mf. These weights control the tradeoff between fitting a prior hydrodynamic model of Mf and fitting the relatively noisy T/P measurements of Mf. The predicted polar motion from the final inverse model agrees remarkably well with the Mf polar motion observations. The preferred model is also consistent with noise levels suggested by island gauges, and it is marginally consistent with differences observed by subsetting the altimetry (to the small extent that this is possible). In turn, this new model of the Mf ocean tide allows the ocean component to be removed from Mf estimates of length of day, thus yielding estimates of complex Love numbers less contaminated by oceanic effects than has hitherto been possible.

Ray, Richard D.↗
NASA NTRS2016

Photospheric Current Spikes as Possible Predictors of Flares

Flares involve generation of the largest current densities in the solar atmosphere. This suggests the hypothesis that prior to a large (M,X) flare there are related time dependent changes in the photospheric current distribution, and hence in the resistive heating rate in neutral line regions (NLRs). If this is true, these changes might be useful predictors of flares. Evidence supporting this hypothesis is presented. Results from a data driven, near photospheric, 3D magnetohydrodynamic type model suggest the model might be useful for predicting M and X flares several hours to several days in advance. The model takes as input the photospheric magnetic field observed by the Helioseismic & Magnetic Imager (HMI) on the Solar Dynamics Observatory (SDO) satellite. The model computes quantities in every active region (AR) pixel for 14 ARs, with spurious Doppler periods due to SDO orbital motion filtered out of the time series of the magnetic field for each pixel. Spikes in the NLR resistive heating rate Q, appearing as increases by orders of magnitude above background values in the time series of Q are found to occur, and appear to be correlated with the occurrence of M or X flares a few hours to a few days later. The subset of spikes analyzed at the pixel level are found to occur on HMI and granulation scales of 1 arcsec and 12 minutes. Spikes are found in NLRs with and without M or X flares, and outside as well as inside NLRs, but the largest spikes are localized in the NLRs of ARs with M or X flares, and associated with horizontal magnetic field strengths several hG, and vertical magnetic field strengths several orders of magnitude smaller, suggesting that the spikes are associated with current sheets.

Goodman, Michael L.↗
DOE OSTI2022

Technical note: Using long short-term memory models to fill data gaps in hydrological monitoring networks

Abstract. Quantifying the spatiotemporal dynamics in subsurface hydrological flows over a long time window usually employs a network of monitoring wells. However, such observations are often spatially sparse with potential temporal gaps due to poor quality or instrument failure. In this study, we explore the ability of recurrent neural networks to fill gaps in a spatially distributed time-series dataset. We use a well network that monitors the dynamic and heterogeneous hydrologic exchanges between the Columbia River and its adjacent groundwater aquifer at the U.S. Department of Energy's Hanford site. This 10-year-long dataset contains hourly temperature, specific conductance, and groundwater table elevation measurements from 42 wells with gaps of various lengths. We employ a long short-term memory (LSTM) model to capture the temporal variations in the observed system behaviors needed for gap filling. The performance of the LSTM-based gap-filling method was evaluated against a traditional autoregressive integrated moving average (ARIMA) method in terms of error statistics and accuracy in capturing the temporal patterns of river corridor wells with various dynamics signatures. Our study demonstrates that the ARIMA models yield better average error statistics, although they tend to have larger errors during time windows with abrupt changes or high-frequency (daily and subdaily) variations. The LSTM-based models excel in capturing both high-frequency and low-frequency (monthly and seasonal) dynamics. However, the inclusion of high-frequency fluctuations may also lead to overly dynamic predictions in time windows that lack such fluctuations. The LSTM can take advantage of the spatial information from neighboring wells to improve the gap-filling accuracy, especially for long gaps in system states that vary at subdaily scales. While LSTM models require substantial training data and have limited extrapolation power beyond the conditions represented in the training data, they afford great flexibility to account for the spatial correlations, temporal correlations, and nonlinearity in data without a priori assumptions. Thus, LSTMs provide effective alternatives to fill in data gaps in spatially distributed time-series observations characterized by multiple dominant frequencies of variability, which are essential for advancing our understanding of dynamic complex systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Application of a Chemical Index to Aerosol Mass Spectrometry: Delta Plots and Functional Group Distributions

A better understanding of the chemical properties of organic aerosol (OA) particles will improve our ability to characterize their sources and predict their lifetime. The high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) is widely used to measure OA in real time using thermal vaporization followed by electron ionization (EI). EI creates fragment ions that can be assigned to functional groups using delta analysis, a method of classifying mass spectra according to the presence of different chemically related ion series. In this study, we demonstrate the application of delta analysis to characterize molecular structures using a new visualization method. We also use delta analysis to quantify the functional group distribution with an average absolute error of ∼5–6% for individual standard molecules, comparable to the error observed for OA mixtures from biomass and coal combustion fit with Fourier transform infrared spectroscopy. Finally, we apply delta functional group analysis to AMS positive matrix factorization (PMF) factors across seven different field campaigns and find a similar composition across the more oxidized factors with about 55% acid and 26% alcohol groups. The analysis method described here can be applied to any HR-ToF-AMS data set to provide quantitative relative functional group distributions for OA mixtures.

aerosol↗
DOE OSTI2023

Fusing time-varying mosquito data and continuous mosquito population dynamics models

Climate change is arguably one of the most pressing issues affecting the world today and requires the fusion of disparate data streams to accurately model its impacts. Mosquito populations respond to temperature and precipitation in a nonlinear way, making predicting climate impacts on mosquito-borne diseases an ongoing challenge. Data-driven approaches for accurately modeling mosquito populations are needed for predicting mosquito-borne disease risk under climate change scenarios. Many current models for disease transmission are continuous and autonomous, while mosquito data is discrete and varies both within and between seasons. This study uses an optimization framework to fit a non-autonomous logistic model with periodic net growth rate and carrying capacity parameters for 15 years of daily mosquito time-series data from the Greater Toronto Area of Canada. The resulting parameters accurately capture the inter-annual and intra-seasonal variability of mosquito populations within a single geographic region, and a variance-based sensitivity analysis highlights the influence each parameter has on the peak magnitude and timing of the mosquito season. This method can easily extend to other geographic regions and be integrated into a larger disease transmission model. This method addresses the ongoing challenges of data and model fusion by serving as a link between discrete time-series data and continuous differential equations for mosquito-borne epidemiology models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Public Reference Data for Megawatt-Scale Hydrogen Electrolysis – Simulated Wind

The U.S. Department of Energy and the National Laboratory of the Rockies (NLR) demonstrate hydrogen electrolysis, hydrogen compression and storage, and variable hydrogen fuel cell power production using megawatt-scale equipment at NLR’s Flatirons Campus as part of the Advanced Research on Integrated Energy Systems (ARIES) initiative. This dataset represents part of that effort and is intended for academic, national laboratory, industrial, and other stakeholders to plan, design, and validate models of megawatt-scale hydrogen technologies and diverse energy infrastructure nationwide. These data provide a baseline for how existing hydrogen electrolysis technologies perform when coupled with various energy technologies. Future datasets will demonstrate how existing hydrogen fuel cell technologies can provide controllable, dispatchable, and variable power output for artificial intelligence (AI) data centers and other variable loads. This dataset entry describes hydrogen production using a single, simulated wind turbine. The electrolyzer is a 1.25-MW proton exchange membrane type MC250 system manufactured by Nel Hydrogen . While the unit supports up to 2.5 MW of electrolysis, NLR only has a single 1.25-MW electrolysis stack. For the simulated wind energy profiles, NLR used OpenFAST to simulate a 3.4-MW International Energy Agency (IEA) reference wind turbine. The hour-long wind energy profiles varied over wind turbulence intensity (Class A or Class C) and average wind speed (5, 7, or 9 m/s). To match the power limits of the 1.25-MW electrolyzer and 3.4-MW IEA wind turbine most effectively and to maximize the efficiency of hydrogen production at a given average wind speed, the profiles were sometimes scaled by two times. This means that, in some cases, the experimental setup assumed two 1.25-MW electrolyzers were coupled with the wind turbine, representing a total maximum electrolysis load of 2.5 MW. Finally, NLR experimented with two settings for the electrolyzer power supply minimum and maximum current ramp rates (gain and slew): 200 and 400 amperes per second. The simulated profiles were translated from power (kilowatts) to current (amperes) using a curve fit with calibration data and sent to the electrolyzer power supply at 1-Hz frequency. These datasets report relevant hydrogen balance-of-plant and system data, all captured at 1 Hz, including hydrogen mass production measured with an Emerson Coriolis flow meter. Each .zip file represents a single wind turbine electrolysis experiment and is formatted as follows: {technology}-{average wind speed}-{turbulence class}_{number of 1.25 MW electrolyzers connected}-{electrolyzer ramp rate in amperes/second} For instance, “windIEA3.4-5ms-C_2-400.zip” represents the hour-long experiment using the IEA 3.4-MW turbine, subjected to an average wind speed of 5 m/s and Class C wind turbulence, and connected to two 1.25-MW electrolyzers with the power supply set to a maximum current ramp rate (gain and slew) of 400 A/s. Each .zip folder contains the following files: A .csv file containing raw data. An .xlsx file explaining all the fields in the raw data. A .png plot showing the time series of hydrogen production in kilograms per hour, electrolysis power consumption, and input wind turbine power. An experiment labeled “characterization_200.zip” demonstrates the MC250 electrolyzer steady-state response with 30 minute load steps for a total duration of 5 hours. Finally, a .csv file is provided with all simulated wind experiments combined into one dataset labeled "combined_wind_experiments.csv". NLR also built an AI/machine-learning predictive model based on these datasets. The model ingests the electrolyzer current command in amperes, as well as various pressures and temperatures across the system, and predicts hydrogen output in kilograms per hour. The complete model can be found at https://huggingface.co/NatLabRockies/ptmelt-hydrogen-electrolysis .

08 HYDROGEN↗
NASA NTRS1974

Assembly and analysis of fragmentation data for liquid propellant vessels

Fragmentation data was assembled and analyzed for exploding liquid propellant vessels. These data were to be retrieved from reports of tests and accidents, including measurements or estimates of blast yield, etc. A significant amount of data was retrieved from a series of tests conducted for measurement of blast and fireball effects of liquid propellant explosions (Project PYRO), a few well-documented accident reports, and a series of tests to determine auto-ignition properties of mixing liquid propellants. The data were reduced and fitted to various statistical functions. Comparisons were made with methods of prediction for blast yield, initial fragment velocities, and fragment range. Reasonably good correlation was achieved. Methods presented in the report allow prediction of fragment patterns, given type and quantity of propellant, type of accident, and time of propellant mixing.

Baker, W. E.↗
NASA NTRS2010

SACCON Forced Oscillation Tests at DNW-NWB and NASA Langley 14x22-Foot Tunnel

A series of three wind tunnel static and forced oscillation tests were conducted on a generic unmanned combat air vehicle (UCAV) geometry. These tests are part of an international research effort to assess the state-of-the-art of computational fluid dynamics (CFD) methods to predict the static and dynamic stability and control characteristics. The experimental dataset includes not only force and moment time histories but surface pressure and off body particle image velocimetry measurements as well. The extent of the data precludes a full examination within the scope of this paper. This paper provides some examples of the dynamic force and moment data available as well as some of the observed trends.

Vicroy Dan D.↗
NASA NTRS2012

Community Coordinated Modeling Center: Addressing Needs of Operational Space Weather Forecasting

Models are key elements of space weather forecasting. The Community Coordinated Modeling Center (CCMC, http://ccmc.gsfc.nasa.gov) hosts a broad range of state-of-the-art space weather models and enables access to complex models through an unmatched automated web-based runs-on-request system. Model output comparisons with observational data carried out by a large number of CCMC users open an unprecedented mechanism for extensive model testing and broad community feedback on model performance. The CCMC also evaluates model's prediction ability as an unbiased broker and supports operational model selections. The CCMC is organizing and leading a series of community-wide projects aiming to evaluate the current state of space weather modeling, to address challenges of model-data comparisons, and to define metrics for various user s needs and requirements. Many of CCMC models are continuously running in real-time. Over the years the CCMC acquired the unique experience in developing and maintaining real-time systems. CCMC staff expertise and trusted relations with model owners enable to keep up to date with rapid advances in model development. The information gleaned from the real-time calculations is tailored to specific mission needs. Model forecasts combined with data streams from NASA and other missions are integrated into an innovative configurable data analysis and dissemination system (http://iswa.gsfc.nasa.gov) that is accessible world-wide. The talk will review the latest progress and discuss opportunities for addressing operational space weather needs in innovative and collaborative ways.

Kuznetsova, M.↗
NASA NTRS2016

Photospheric Current Spikes as Possible Predictors of Flares

Flares involve generation of the largest current densities in the solar atmosphere. This suggests the hypothesis that prior to a large (M,X) flare there are related time dependent changes in the photospheric current distribution, and hence in the resistive heating rate in neutral line regions (NLRs). If this is true, these changes might be useful predictors of flares. Preliminary evidence supporting this hypothesis is presented. Results from a data driven, near photospheric, 3D magnetohydrodynamic type model suggest the model might be useful for predicting M and X flares several hours to several days in advance. The model takes as input the photospheric magnetic field observed by the Helioseismic and Magnetic Imager (HMI) on the Solar Dynamics Observatory (SDO) satellite. The model computes quantities in every active region (AR) pixel for 14 ARs, with spurious Doppler periods due to SDO orbital motion filtered out of the time series of the magnetic field for each pixel. Spikes in the NLR resistive heating rate Q, appearing as increases by orders of magnitude above background values in the time series of Q are found to occur, and appear to be correlated with the occurrence of M or X flares a few hours to a few days later. The subset of spikes analyzed at the pixel level are found to occur on HMI and granulation scales of 1 arcsec and 12 minutes. Spikes are found in NLRs with and without M or X flares, and outside as well as inside NLRs, but the largest spikes are localized in the NLRs of ARs with M or X flares, and associated with horizontal magnetic field strengths approximately several hG, and vertical magnetic field strengths several orders of magnitude smaller. The spikes may be signatures of horizontal current sheets associated with emerging magnetic flux.

Goodman, Michael L.↗
DOE OSTI2020

Annual sea level variations in the Red Sea observed using GNSS

SUMMARY Annual sea level variations in the Red Sea have amplitudes of 15–20 cm as observed using various techniques such as tide gauges, satellite altimetry and recently Gravity Recovery and Climate Experiment (GRACE) satellite data. In this study, we demonstrate that Global Navigation Satellite System (GNSS) observations can also be used to measure the effect of these sea level variations. The extra water mass presses on the seafloor, which causes horizontal and vertical deformations. Using time-series from 10 coastal GNSS stations, we observe annual horizontal and vertical loading displacements with amplitudes of 2–5 mm. When correcting for atmospheric, hydrological and surface water loading and a residual geocentre motion, significant annual signals of approximately 0.5 and 2 mm are still observed for the horizontal and vertical components, respectively. In the northern Red Sea, the observed annual signals and predicted annual sea level loading show good agreement. This confirms that the signal is mostly a result of the variations in water mass and thermal expansion. Furthermore, we conclude that the uncertainties in the hydrological model over Ethiopia and Eritrea influence the loading over the southern Red Sea, which was underestimated in previous studies using GRACE data.

Alothman, A. O.↗
NASA NTRS1976

Flight test summary of modified fuel systems

Two different aircraft designs, each with two modified fuel control systems, were evaluated. Each aircraft was evaluated in a given series of defined ground and flight conditions while quantitative and qualitative observations were made. During this program, some ten flights were completed, and a total of about 13 hours of engine run time was accumulated by the two airplanes. The results of these evaluations with emphasis on the operational and safety aspects were analyzed. Ground tests of the engine alone were not able to predict acceptable limiting lean mixture settings for the flight envelopes of the Cessna Models 150 and T337.

Barrett, B. G.↗
NASA NTRS2001

Fluid Management of and Flame Spread Across Liquid Pools

The goal of our research on flame spread across pools of liquid fuel remains the quantitative identification of the mechanisms that control the rate and nature of flame spread when the initial temperature of the liquid pool is below the fuel's flash point temperature. As described in, four microgravity (mu-g) sounding rocket flights examined the effect of forced opposed airflow over a 2.5 cm deep x 2 cm wide x 30 cm long pool of 1-butanol. Among many unexpected findings, it was observed that the flame spread is much slower and steadier than in 1g where flame spread has a pulsating character. Our numerical model, restricted to two dimensions, had predicted faster, pulsating flame spread in mu-g. In a test designed to achieve a more 2-D experiment, our investigation of a shallow, wide pool (2 mm deep x 78 mm wide x 30 cm long) was unsuccessful in mu-g, due to an unexpectedly long time required to fill the tray. As such, the most recent Spread Across Liquids (SAL) sounding rocket experiment had two principal objectives: 1) determine if pulsating flame spread in deep fuel trays would occur under the conditions that a state-of-the-art computational combustion code and short-duration drop tower tests predict; and 2) determine if a long, rectangular, shallow fuel tray could achieve a visibly flat liquid surface across the whole tray without spillage in the mu-g time allotted. If the second objective was met, the shallow tray was to be ignited to determine the nature of flame spread in mu-g for this geometry. For the first time in the experiment series, two fuel trays - one deep (30 cm long x 2 cm wide x 25 mm deep) and one shallow (same length and width, but 2 mm deep)-- were flown. By doing two independent experiments in a single flight, a significant cost savings was realized. In parallel, the computational objective was to modify the code to improve agreement with earlier results. This last objective was achieved by modifying the fuel mass diffusivity and adding a parameter to correct for radiative and lateral heat loss.

Ross, H. D.↗