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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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Code for plots, models, data generation and other utilities relating to the paper "wa-hls4ml: A Benchmark and Surrogate Models for hls4ml Resource and Latency Estimation" https://arxiv.org/abs/2511.05615 [FERMILAB-PUB-25-0359-CSAID]
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 existence, evolution, and interaction of vertical circulations associated with upper-level jet streaks during a moderate snowfall event are diagnosed using a nested grid model (NGM). The event itself is overviewed, and the diagnosis of transverse circulations utilizing NGM output is discussed. Focus is placed on the identification of the horizontal and vertical components of the circulation, the temporal evolution of the circulation, and the comparison of the circulation from successive model forecasts. A preliminary analysis of the model data indicates that the horizontal and vertical branches of the circulations can be diagnosed from the model output, and the horizontal and vertical components of the circulations may be identified and followed during an individual forecast cycle.
It is proposed to conduct research and development on a system of expert systems for intelligent data management (SEIDAM). CCRS has much expertise in developing systems for integrating geographic information with space and aircraft remote sensing data and in managing large archives of remotely sensed data. SEIDAM will be composed of expert systems grouped in three levels. At the lowest level, the expert systems will manage and integrate data from diverse sources, taking account of symbolic representation differences and varying accuracies. Existing software can be controlled by these expert systems, without rewriting existing software into an Artificial Intelligence (AI) language. At the second level, SEIDAM will take the interpreted data (symbolic and numerical) and combine these with data models. At the top level, SEIDAM will respond to user goals for predictive outcomes given existing data. The SEIDAM Project will address the research areas of expert systems, data management, storage and retrieval, and user access and interfaces.
A proposal to conduct research and development on a system of expert systems for intelligent data management (SEIDAM) is being developed. CCRS has much expertise in developing systems for integrating geographic information with space and aircraft remote sensing data and in managing large archives of remotely sensed data. SEIDAM will be composed of expert systems grouped in three levels. At the lowest level, the expert systems will manage and integrate data from diverse sources, taking account of symbolic representation differences and varying accuracies. Existing software can be controlled by these expert systems, without rewriting existing software into an Artificial Intelligence (AI) language. At the second level, SEIDAM will take the interpreted data (symbolic and numerical) and combine these with data models. at the top level, SEIDAM will respond to user goals for predictive outcomes given existing data. The SEIDAM Project will address the research areas of expert systems, data management, storage and retrieval, and user access and interfaces.
LLEGO is a model for understanding recurring launch and landing operations costs at Kennedy Space Center for human space flight. Launch and landing operations are often referred to as ground processing, or ground operations. Currently, this function is specific to the ground operations for the Space Shuttle Space Transportation System within the Space Shuttle Program. The Constellation system to follow the Space Shuttle consists of the crewed Orion spacecraft atop an Ares I launch vehicle and the uncrewed Ares V cargo launch vehicle. The Constellation flight and ground systems build upon many elements of the existing Shuttle flight and ground hardware, as well as upon existing organizations and processes. In turn, the LLEGO model builds upon past ground operations research, modeling, data, and experience in estimating for future programs. Rather than to simply provide estimates, the LLEGO model s main purpose is to improve expenses by relating complex relationships among functions (ground operations contractor, subcontractors, civil service technical, center management, operations, etc.) to tangible drivers. Drivers include flight system complexity and reliability, as well as operations and supply chain management processes and technology. Together these factors define the operability and potential improvements for any future system, from the most direct to the least direct expenses.
Meteorologists from the 45th Weather Squadron (45 WS) and National Weather Service Spaceflight Meteorology Group (SMG) have identified anvil forecasting as one of their most challenging tasks when predicting the probability of violations of the Lightning Launch Commit Criteria and Space Shuttle Flight Rules. As a result, the Applied Meteorology Unit (AMU) was tasked to create a graphical overlay tool for the Meteorological Interactive Data Display System (MIDDS) that indicates the threat of thunderstorm anvil clouds, using either observed or model forecast winds as input. The tool creates a graphic depicting the potential location of thunderstorm anvils one, two, and three hours into the future. The locations are based on the average of the upper level observed or forecasted winds. The graphic includes 10 and 20 n mi standoff circles centered at the location of interest, as well as one-, two-, and three-hour arcs in the upwind direction. The arcs extend outward across a 30 sector width based on a previous AMU study that determined thunderstorm anvils move in a direction plus or minus 15 of the upper-level wind direction. The AMU was then tasked to transition the tool to the Advanced Weather Interactive Processing System (AWIPS). SMG later requested the tool be updated to provide more flexibility and quicker access to model data. This presentation describes the work performed by the AMU to transition the tool into AWIPS, as well as the subsequent improvements made to the tool.
Classification, decomposition and modeling of polarimetric SAR data has received a great deal of attention in the recent literature. The objective behind these efforts is to better understand the scattering mechanisms which give rise to the polarimetric signatures seen in SAR image data. In this Paper an approach is described, which involves the fit of a combination of two simple scattering mechanisms to polarimetric SAR observations. The mechanisms am canopy scatter from a cloud of randomly oriented oblate spheroids, and a ground scatter term, which can represent double-bounce scatter from a pair of orthogonal surfaces with different dielectric constants or Bragg scatter from a moderately rough surface, seen through a layer of vertically oriented scatterers. An advantage of this model fit approach is that the scattering contributions from the two basic scattering mechanisms can be estimated for clusters of pixels in polarimetric SAR images. The solution involves the estimation of four parameters from four separate equations. The model fit can be applied to polarimetric AIRSAR data at C-, L- and P-Band.
This slide presentation reviews the Ares 1X test flight and compares the resultant flight data with the results of modeled data from siumulations of the flight. It includes: (1) Ares 1X Flight Summary, (2) Ares 1X Data Summary (3) Model Descriptions (4) Model Comparisons to Flight Data in three areas: (a) Liftoff, (b) Transonic and (c) Roll Control Firings (RCS) Firings.
Although significant progress has been made in recent years, estimating volcanic ash concentration for the full extent of the airspace affected by volcanic ash remains a challenge. No single satellite, airborne or ground observing system currently exists which can sufficiently inform dispersion models to provide the degree of accuracy required to use them with a high degree of confidence for routing aircraft in and near volcanic ash. Toward this end, the detection and characterization of volcanic ash in the atmosphere may be substantially improved by integrating a wider array of observing systems and advancements in trajectory and dispersion modeling to help solve this problem. The qualitative aspect of this effort has advanced significantly in the past decade due to the increase of highly complementary observational and model data currently available. Satellite observations, especially when coupled with trajectory and dispersion models can provide a very accurate picture of the 3-dimensional location of ash clouds. The accurate estimate of the mass loading at various locations throughout the entire plume, however improving, remains elusive. This paper examines the capabilities of various satellite observation systems and postulates that model-based volcanic ash concentration maps and forecasts might be significantly improved if the various extant satellite capabilities are used together with independent, accurate mass loading data from other observing systems available to calibrate (tune) ash concentration retrievals from the satellite systems.
Environmental observation networks, such as AmeriFlux, are foundational for monitoring ecosystem response to climate change, management practices, and natural disturbances; however, their effectiveness depends on their representativeness for the regions or continents. We proposed an empirical, time series approach to quantify the similarity of ecosystem fluxes across AmeriFlux sites. We extracted the diel and seasonal characteristics (i.e., amplitudes, phases) from carbon dioxide, water vapor, energy, and momentum fluxes, which reflect the effects of climate, plant phenology, and ecophysiology on the observations, and explored the potential aggregations of AmeriFlux sites through hierarchical clustering. While net radiation and temperature showed latitudinal clustering as expected, flux variables revealed a more uneven clustering with many small (number of sites < 5), unique groups and a few large (> 100) to intermediate (15–70) groups, highlighting the significant ecological regulations of ecosystem fluxes. Many identified unique groups were from under-sampled ecoregions and biome types of the International Geosphere-Biosphere Programme (IGBP), with distinct flux dynamics compared to the rest of the network. At the finer spatial scale, local topography, disturbance, management, edaphic, and hydrological regimes further enlarge the difference in flux dynamics within the groups. Nonetheless, our clustering approach is a data-driven method to interpret the AmeriFlux network, informing future cross-site syntheses, upscaling, and model-data benchmarking research. Finally, we highlighted the unique and underrepresented sites in the AmeriFlux network, which were found mainly in Hawaii and Latin America, mountains, and at under-sampled IGBP types (e.g., urban, open water), motivating the incorporation of new/unregistered sites from these groups.
Graphite under high temperatures and irradiation is central to advanced reactors. We develop a Bayesian calibration framework for graphite property models that explicitly represents model-data mismatch via a Gaussian-process discrepancy. The approach propagates uncertainty from parameters, experimental noise, and model form, with a hierarchical variance structure to capture group and cross-group noise. Using two predictive models across five grades (IG-110, NBG-18, PCEA, NBG-17, 2114) and four properties-irradiation-induced dimension change, creep, Young’s modulus change ratio, and coefficient of thermal expansion change ratio-we obtain average predictive-error reductions of 54%, 65%, 17%, and 17% when discrepancy is included. We illustrate engineering impact with a multiphysics model of a very-high-temperature reactor prismatic reflector brick, analyzing stresses under high fluence and temperature. Accounting for model discrepancy markedly improves predictive accuracy and provides a robust basis for reliable graphite component design in advanced reactors.
PIPII project will raise Fermilab Booster intensity and ramp rate. Beam losses will limit average power and are hard to simulate. Presently, Booster uses operator-guided empirical tuning. This task is challenging due to high dimensionality, multiple objectives, critical safety constraints, and drifts. We developed a synergistic suite of Bayesian optimization (BO) and reinforcement learning (RL) tools to optimize and stabilize beam losses. First, active learning was used to build a rough model. Data was collected parasitically using two novel safety constraint types – nonlinear input space restrictions (based on optics model), and uncertainty constraints (to stop bad steps/beam aborts). We then applied online multi-objective BO with scalarized objectives and fitting to improve/rebalance losses, increasing safety margins by 25%. Using BO model as a safety veto, we tried several on/off-policy RL agents for long term stabilization; SAC had best performance. We found that adding contextual (state) information further improved performance, eventually integrating key knobs like linac phase and temperature into the parameter space. Long term testing is ongoing to enable operational use.
Small-scale-model data have shown large static loads on the flap system behind the engines. The large-scale-model tests confirmed the magnitude of these loads and indicated that the relative loading of each flap element depends on the engine-wing-flap geometry. Flap response measurements indicated that the unsteady pressure loading excited the natural vibration modes of the flap system on this model. Since this was a boilerplate model, the only conclusion that can be drawn is that the possibility of large vibration loads must be considered for a flight-weight structure. The similarity of the unsteady pressure and flap response spectra for the wind-off and wind-on cases indicated that it may be possible to realistically test flight-weight flap structures on a static test stand rather than endure the extra costs and scheduling problems associated with large-scale wind-tunnel tests. There is a potential flap-temperature problem which if not resolved might preclude the use of materials such as aluminum and the composites in the flap structure.
Current/PAD research in the area of distributed data base management considers facilities for supporting CAD/CAM data management in a heterogeneous network of computers encompassing multiple data base managers supporting a variety of data models. These facilities include coordinated execution of multiple DBMSs to provide for administration of and access to data distributed across them.
At the NASA Goddard Earth Sciences Data and Information Services (GES DISC), we strive to simplify data discovery and data access to our wide range of global climate data, concentrated primarily in the areas of atmospheric composition, atmospheric dynamics, global precipitation, solar irradiance, and several modeling data sets related to land surface hydrology. To help meet user needs, we will demonstrate how you can use the GES DISC knowledge-base resources (HowTo's) and we also encourage community contributions.
The purpose of this paper is discuss the concept of fractal dimension; multifractal statistics as an extension of this; the use of simple multifractal statistics (power spectrum, structure function) to characterize cloud liquid water data; and to understand the use of multifractal cloud liquid water models based on real data as input to Monte Carlo radiation models of shortwave radiation transfer in 3D clouds, and the consequences of this in two areas: the design of aircraft field programs to measure cloud absorptance; and the explanation of the famous "Landsat scale break" in measured radiance.
A comparison of the diurnal component of the daily variation in the neutral composition data from the San Marco 3 Nace (Neutral Atmospheric Composition Experiment) mass spectrometer at 220, 250, and 280 km, the OGO-6 model data at 450 km, rocket measurements of nitrogen between 140 and 300 km, and the two-constituent theoretical model of Mayr and Volland (1973) gives good agreement for molecular nitrogen and atomic oxygen. The Nace and rocket measurements overlap in altitude, and there is good agreement in the amplitude and phase of the diurnal and semidiurnal components for molecular nitrogen. The helium diurnal amplitude and phase of the theoretical model are in fair agreement with the Nace results, while the amplitude from the OGO-6 model at 450 km is much larger than the model predicts, a result suggesting that the OGO-6 model diurnal amplitude may be overestimated as the result of an incomplete separation of local-time and seasonal effects.