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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 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Optimization and stabilization of Fermilab Booster using hybrid Bayesian/RL framework

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.

Kuklev, Nikita [Fermilab]↗
DOE OSTI2024

Using automated machine learning for the upscaling of gross primary productivity

Estimating gross primary productivity (GPP) over space and time is fundamental for understanding the response of the terrestrial biosphere to climate change. Eddy covariance flux towers provide in situ estimates of GPP at the ecosystem scale, but their sparse geographical distribution limits larger-scale inference. Machine learning (ML) techniques have been used to address this problem by extrapolating local GPP measurements over space using satellite remote sensing data. However, the accuracy of the regression model can be affected by uncertainties introduced by model selection, parameterization, and choice of explanatory features, among others. Recent advances in automated ML (AutoML) provide a novel automated way to select and synthesize different ML models. In this work, we explore the potential of AutoML by training three major AutoML frameworks on eddy covariance measurements of GPP at 243 globally distributed sites. We compared their ability to predict GPP and its spatial and temporal variability based on different sets of remote sensing explanatory variables. Explanatory variables from only Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data and photosynthetically active radiation explained over 70 % of the monthly variability in GPP, while satellite-derived proxies for canopy structure, photosynthetic activity, environmental stressors, and meteorological variables from reanalysis (ERA5-Land) further improved the frameworks' predictive ability. We found that the AutoML framework Auto-sklearn consistently outperformed other AutoML frameworks as well as a classical random forest regressor in predicting GPP but with small performance differences, reaching an r 2 of up to 0.75. We deployed the best-performing framework to generate global wall-to-wall maps highlighting GPP patterns in good agreement with satellite-derived reference data. This research benchmarks the application of AutoML in GPP estimation and assesses its potential and limitations in quantifying global photosynthetic activity.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1978

The thermal structure of Saturn: Inferences from ground-based and airborne infrared observations

Spectroscopic and photometric infrared observations of Saturn are reviewed and compared to the expected flux from thermal structure models. Large uncertainties exist in the far-infrared measurements, but the available data indicate that the effective temperature of the disk of Saturn is 90 + or - 5 K. The thermal structure models proposed by Tokunaga and Cess and by Gautier et al. (model 'N') agree best with the observations. North-South limb scans of Saturn at 10 and 20 micrometers show that the temperature inversion is much stronger at the South polar region than at the equator.

Tokunaga, A.↗
NASA NTRS1987

Identification of large space structures: A state-of-practice report

An outline of this work is presented. It begins with a schematic flow diagram and a logical flow diagram of the identification process for large space structures (LSS). Next, the task is defined by a structure model definition. A matrix polynomial formulation with a node displacement equation and a state variable formulation with node displacement and velocities are outlined. Further outlined is the identification of LSS on orbit; modeling errors and uncertainties; verification and validation of model; and noise, computations, and data collection.

Source record↗
NASA NTRS1991

Dynamics of aerospace vehicles

The focus of this research was to address the modeling, including model reduction, of flexible aerospace vehicles, with special emphasis on models used in dynamic analysis and/or guidance and control system design. In the modeling, it is critical that the key aspects of the system being modeled be captured in the model. In this work, therefore, aspects of the vehicle dynamics critical to control design were important. In this regard, fundamental contributions were made in the areas of stability robustness analysis techniques, model reduction techniques, and literal approximations for key dynamic characteristics of flexible vehicles. All these areas are related. In the development of a model, approximations are always involved, so control systems designed using these models must be robust against uncertainties in these models.

Schmidt, David K.↗
NASA NTRS2005

Development of a Modeling Framework to Support Control Investigations of Sailcraft Missions A First Cut: ABLE Sailcraft Dynamics Model

Solar Sailcraft, the stuff of dreams of the H.G. Wells generation, is now a rapidly maturing reality. The promise of unlimited propulsive power by harnessing stellar radiation is close to realization. Currently, efforts are underway to build, prototype and test two configurations. These sails are designed to meet a 20m sail requirement, under guidance of the In-Space Propulsion (ISP) technology program office at MSFC. While these sails will not fly , they are the first steps in improving our understanding of the processes and phenomena at work. As part of the New Millennium Program (NMP) the ST9 technology validation mission hopes to launch and fly a solar sail by 2010 or sooner. Though the Solar Sail community has been studying and validating various concepts over two decades, it was not until recent breakthroughs in structural and material technology, has made possible to build sails that could be launched. With real sails that can be tested (albeit under earth conditions), the real task of engineering a viable spacecraft has finally commenced. Since it is not possible to accurately or practically recreate the actual operating conditions of the sailcraft (zero-G, vacuum and extremely low temperatures), much of the work has focused on developing accurate models that can be used to predict behavior in space, and for sails that are 6-10 times the size of currently existing sails. Since these models can be validated only with real test data under "earth" conditions, the process of modeling and the identification of uncertainty due to model assumptions and scope need to be closely considered. Sailcraft models that exist currently, are primarily focused on detailed physical representations at the component level, these are intended to support prototyping efforts. System level models that cut across different sail configurations and control concepts while maintaining a consistent approach are non-existent. Much effort has been focused on the areas of thrust performance, solar radiation prediction, and sail membrane behavior vis-a-vis their reflective geometry, such as wrinkling/folding/furling as it pertains to thrust prediction. A parallel effort has been conducted on developing usable models for developing attitude control systems (ACS), for different sail configurations in different regimes. There has been very little by way of a system wide exploration of the impact of the various control schemes, thrust prediction models for different sail configurations being considered.

Sarathy, Sriprakash↗
NASA NTRS2016

Model Based Mission Assurance: Emerging Opportunities for Robotic Systems

The emergence of Model Based Systems Engineering (MBSE) in a Model Based Engineering framework has created new opportunities to improve effectiveness and efficiencies across the assurance functions. The MBSE environment supports not only system architecture development, but provides for support of Systems Safety, Reliability and Risk Analysis concurrently in the same framework. Linking to detailed design will further improve assurance capabilities to support failures avoidance and mitigation in flight systems. This also is leading new assurance functions including model assurance and management of uncertainty in the modeling environment. Further, the assurance cases, a structured hierarchal argument or model, are emerging as a basis for supporting a comprehensive viewpoint in which to support Model Based Mission Assurance (MBMA).

Mission Assurance↗
NASA NTRS2017

Version 3 of the SMAP Level 4 Soil Moisture Product

The NASA Soil Moisture Active Passive (SMAP) Level 4 Soil Moisture (L4_SM) product provides 3-hourly, 9-km resolution, global estimates of surface (0-5 cm) and root zone (0-100 cm) soil moisture as well as related land surface states and fluxes from 31 March 2015 to present with a latency of 2.5 days. The ensemble-based L4_SM algorithm is a variant of the Goddard Earth Observing System version 5 (GEOS-5) land data assimilation system and ingests SMAP L-band (1.4 GHz) Level 1 brightness temperature observations into the Catchment land surface model. The soil moisture analysis is non-local (spatially distributed), performs downscaling from the 36-km resolution of the observations to that of the model, and respects the relative uncertainties of the modeled and observed brightness temperatures. Prior to assimilation, a climatological rescaling is applied to the assimilated brightness temperatures using a 6 year record of SMOS observations. A new feature in Version 3 of the L4_SM data product is the use of 2 years of SMAP observations for rescaling where SMOS observations are not available because of radio frequency interference, which expands the impact of SMAP observations on the L4_SM estimates into large regions of northern Africa and Asia. This presentation investigates the performance and data assimilation diagnostics of the Version 3 L4_SM data product. The L4_SM soil moisture estimates meet the 0.04 m3m3 (unbiased) RMSE requirement. We further demonstrate that there is little bias in the soil moisture analysis. Finally, we illustrate where the assimilation system overestimates or underestimates the actual errors in the system.

GEOS-↗
NASA NTRS2021

A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks

Retrieval of aquatic biogeochemical variables, such as the near-surface concentration of chlorophyll-a (Chla) in inland and coastal waters via remote observations, has long been regarded as a challenging task. This manuscript applies Mixture Density Networks (MDN) that use the visible spectral bands available by the Operational Land Imager (OLI) aboard Landsat-8 to estimate Chla. We utilize a database of co-located in situ radiometric and Chla measurements (N = 4,354), referred to as Type A data, to train and test an MDN model (MDN(A)). This algorithm’s performance, having been proven for other satellite missions, is further evaluated against other widely used machine learning models (e.g., support vector machines), as well as other domain-specific solutions (OC3), and shown to offer significant advancements in the field. Our performance assessment using a held-out test data set suggests that a 49% (median) accuracy with near-zero bias can be achieved via the MDN(A) model, offering improvements of 20 to 100% in retrievals with respect to other models. The sensitivity of the MDN(A) model and benchmarking methods to uncertainties from atmospheric correction (AC) methods, is further quantified through a semi-global matchup dataset (N = 3,337), referred to as Type B data. To tackle the increased uncertainties, alternative MDN models (MDN(B)) are developed through various features of the Type B data (e.g., Rayleigh-corrected reflectance spectra ρ(s)). Using held-out data, along with spatial and temporal analyses, we demonstrate that these alternative models show promise in enhancing the retrieval accuracy adversely influenced by the AC process. Results lend support for the adoption of MDN(B) models for regional and potentially global processing of OLI imagery, until a more robust AC method is developed. Index Terms—Chlorophyll-a, coastal water, inland water, Landsat-8, machine learning, ocean color, aquatic remote sensing.

Brandon Smith↗
NASA NTRS2021

Strong detection of the CMB lensing and galaxy weak lensing cross-correlation from ACT-DR4, Planck Legacy, and KiDS-1000

We measured the cross-correlation between galaxy weak lensing data from the Kilo Degree Survey (KiDS-1000, DR4) and cosmic microwave background (CMB) lensing data from the Atacama Cosmology Telescope (ACT, DR4) and the Planck Legacy survey. We used two samples of source galaxies, selected with photometric redshifts, (0.1 < z(B) < 1.2) and (1.2 < z(B) < 2), which produce a combined detection significance of the CMB lensing and weak galaxy lensing cross-spectrum of 7.7σ. With the lower redshift galaxy sample, for which the cross-correlation was detected at a significance of 5.3σ, we present joint cosmological constraints on the matter density parameter, Ω(m), and the matter fluctuation amplitude parameter, σ(8), marginalising over three nuisance parameters that model our uncertainty in the redshift and shear calibration as well as the intrinsic alignment of galaxies. We find our measurement to be consistent with the best-fitting flat ΛCDM cosmological models from both Planck and KiDS-1000. We demonstrate the capacity of CMB weak lensing cross-correlations to set constraints on either the redshift or shear calibration by analysing a previously unused high-redshift KiDS galaxy sample (1.2 < z(B) < 2), with the cross-correlation detected at a significance of 7σ. This analysis provides an independent assessment for the accuracy of redshift measurements in a regime that is challenging to calibrate directly owing to known incompleteness in spectroscopic surveys.

Naomi Clare Robertson↗
NASA NTRS2016

Lessons from Climate Modeling on the Design and Use of Ensembles for Crop Modeling

Working with ensembles of crop models is a recent but important development in crop modeling which promises to lead to better uncertainty estimates for model projections and predictions, better predictions using the ensemble mean or median, and closer collaboration within the modeling community. There are numerous open questions about the best way to create and analyze such ensembles. Much can be learned from the field of climate modeling, given its much longer experience with ensembles. We draw on that experience to identify questions and make propositions that should help make ensemble modeling with crop models more rigorous and informative. The propositions include defining criteria for acceptance of models in a crop MME, exploring criteria for evaluating the degree of relatedness of models in a MME, studying the effect of number of models in the ensemble, development of a statistical model of model sampling, creation of a repository for MME results, studies of possible differential weighting of models in an ensemble, creation of single model ensembles based on sampling from the uncertainty distribution of parameter values or inputs specifically oriented toward uncertainty estimation, the creation of super ensembles that sample more than one source of uncertainty, the analysis of super ensemble results to obtain information on total uncertainty and the separate contributions of different sources of uncertainty and finally further investigation of the use of the multi-model mean or median as a predictor.

Model ensembles↗
DOE OSTI2021

Spatial biases of information influence global estimates of soil respiration: how can we improve global predictions?

Soil respiration (Rs), the efflux of CO2 from soils to the atmosphere, is a major component of the terrestrial carbon cycle, but is poorly constrained from regional to global scales. The global soil respiration database (SRDB) is a compilation of in-situ Rs observations from around the globe that has been consistently updated with new measurements over the past decade. It is unclear whether the addition of data to new versions of the SRDB has produced better-constrained global Rs estimates. We compared two versions of the SRDB (v3.0 n=5173 and v5.0 n=10366) to determine how additional data influenced the global sum and uncertainty of Rs as predicted by a quantile regression forest model. A model parameterized using SRDBv3 yielded a global Rs sum of 88.6 Pg C yr-1, and associated uncertainty of 29.9 (mean absolute error) and 57.9 (standard deviation) Pg C yr-1, whereas parameterization using SRDBv5 yielded 96.5 Pg C yr-1 and associated uncertainty of 30.2 (mean average error) and 73.4 (standard deviation) Pg C yr-1. Empirically estimated global heterotrophic respiration (Rh) from v3 and v5 were 49.9-50.2 (mean 50.1) and 53.3-53.5 (mean 53.4) Pg C yr-1, respectively. Interestingly, SRDBv5’s inclusion of new data from underrepresented regions (e.g., Asia, Africa, South America) thus resulted in overall higher model uncertainty. The SRDBv5 is still biased towards northern latitudes and temperate zones, so we tested that an optimized global distribution of Rs measurements resulted in a global sum of 96.4 ± 21.4 Pg C yr-1 and lower model uncertainty. These results support current global estimates of Rs but highlight spatial inconsistencies that influence model parameterization and interpretation that should be addressed to improve regional- to global-scale predictions.

Stell, Emma↗
NASA NTRS2015

Uncertainty in Twenty-First-Century CMIP5 Sea Level Projections

The representative concentration pathway (RCP) simulations included in phase 5 of the Coupled Model Intercomparison Project (CMIP5) quantify the response of the climate system to different natural and anthropogenic forcing scenarios. These simulations differ because of 1) forcing, 2) the representation of the climate system in atmosphere-ocean general circulation models (AOGCMs), and 3) the presence of unforced (internal) variability. Global and local sea level rise projections derived from these simulations, and the emergence of distinct responses to the four RCPs depend on the relative magnitude of these sources of uncertainty at different lead times. Here, the uncertainty in CMIP5 projections of sea level is partitioned at global and local scales, using a 164-member ensemble of twenty-first-century simulations. Local projections at New York City (NYSL) are highlighted. The partition between model uncertainty, scenario uncertainty, and internal variability in global mean sea level (GMSL) is qualitatively consistent with that of surface air temperature, with model uncertainty dominant for most of the twenty-first century. Locally, model uncertainty is dominant through 2100, with maxima in the North Atlantic and the Arctic Ocean. The model spread is driven largely by 4 of the 16 AOGCMs in the ensemble; these models exhibit outlying behavior in all RCPs and in both GMSL and NYSL. The magnitude of internal variability varies widely by location and across models, leading to differences of several decades in the local emergence of RCPs. The AOGCM spread, and its sensitivity to model exclusion and/or weighting, has important implications for sea level assessments, especially if a local risk management approach is utilized.

Variability↗
DOE OSTI2023

Integrated Tiger Series (ITS) Code Validation and Uncertainty Quantification for Electron Number Albedo in Various Materials

In the 1970’s and 1980’s, researchers at Sandia National Laboratories produced electron albedo data for a range of materials. Since that time, the electron albedo data has been used for a wide variety of purposes including the validation of Monte Carlo electron transport codes. This report was compiled to examine the electron albedo experiment results in the context of Integrated Tiger Series (ITS) validation. The report presents tables and figures that could provide insight into the underlying model form uncertainty present in the ITS code. Additionally, the report provides data on potential means to reduce these model form errors by highlighting potential refinements in the cross-section generation process.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Evaluation of Leading Modes of Climate Variability in the CMIP Archives

The adequate simulation of internal climate variability is key for our understanding of climate as it underpins efforts to attribute historical events, predict on seasonal and decadal time scales, and isolate the effects of climate change. Here the skill of models in reproducing observed modes of climate variability is assessed, both across and within the CMIP3, CMIP5, and CMIP6 archives, in order to document model capabilities, progress across ensembles, and persisting biases. A focus is given to the well-observed tropical and extratropical modes that exhibit small intrinsic variability relative to model structural uncertainty. These include El Niño–Southern Oscillation (ENSO), the Pacific decadal oscillation (PDO), the North Atlantic Oscillation (NAO), and the northern and southern annular modes (NAM and SAM). Significant improvements are identified in models’ representation of many modes. Canonical biases, which involve both amplitudes and patterns, are generally reduced across model generations. For example, biases in ENSO-related equatorial Pacific sea surface temperature, which extend too far westward, and associated atmospheric teleconnections, which are too weak, are reduced. Stronger tropical expression of the PDO in successive CMIP generations has characterized their improvement, with some CMIP6 models generating patterns that lie within the range of observed estimates. For the NAO, NAM, and SAM, pattern correlations with observations are generally higher than for other modes and slight improvements are identified across successive model generations. Finally, for ENSO and PDO spectra and extratropical modes, changes are small compared to internal variability, precluding definitive statements regarding improvement.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2016

Aerodynamic Models for the Low Density Supersonic Decelerator (LDSD) Test Vehicles

An overview of aerodynamic models for the Low Density Supersonic Decelerator (LDSD) Supersonic Flight Dynamics Test (SFDT) campaign test vehicle is presented, with comparisons to reconstructed flight data and discussion of model updates. The SFDT campaign objective is to test Supersonic Inflatable Aerodynamic Decelerator (SIAD) and large supersonic parachute technologies at high altitude Earth conditions relevant to entry, descent, and landing (EDL) at Mars. Nominal SIAD test conditions are attained by lifting a test vehicle (TV) to 36 km altitude with a helium balloon, then accelerating the TV to Mach 4 and 53 km altitude with a solid rocket motor. Test flights conducted in June of 2014 (SFDT-1) and 2015 (SFDT-2) each successfully delivered a 6 meter diameter decelerator (SIAD-R) to test conditions and several seconds of flight, and were successful in demonstrating the SFDT flight system concept and SIAD-R technology. Aerodynamic models and uncertainties developed for the SFDT campaign are presented, including the methods used to generate them and their implementation within an aerodynamic database (ADB) routine for flight simulations. Pre- and post-flight aerodynamic models are compared against reconstructed flight data and model changes based upon knowledge gained from the flights are discussed. The pre-flight powered phase model is shown to have a significant contribution to off-nominal SFDT trajectory lofting, while coast and SIAD phase models behaved much as predicted.

Van Norman, John W.↗
NASA NTRS1981

The two-dimensional diagnostic model for tropospheric OH - An uncertainty analysis

Tropospheric OH is believed to play a major role in atmospheric photochemistry, and, via its chemical interactions OH ultimately may affect the climate, stratospheric ozone levels, and the pH of rain. A valuable tool for predicting the global OH abundance as a function of latitude and altitude is the two-dimensional diagnostic model for tropospheric OH. This model can also be applied to budget studies of species which interact with OH, such as CH4, CO, and O3. In addition to laboratory-obtained kinetic data, important variables include the rate constants for several reactions, the rate of heterogeneous removal of soluble species, and the global abundance and spatial variability in H2O, O3, CO, and NOx. The uncertainties in these parameters imply over a factor of 3 variability in the calculated global OH concentration as well as significant uncertainties in the latitudinal and vertical OH distribution.

Chameides, W. L.↗
NASA NTRS1988

Precise orbit determination for NASA's Earth Observing System using GPS

A precision orbit determination technique for NASA's Earth Observing System (EOS) is discussed. The technique exploits the redundant geometric information from GPS carrier phase and P-code pseudorange measurements and minimizes the requirements for precision dynamical modeling. Performance estimates for the application of the technique to the orbit geometry planned for the EOS platforms show that decimeter accuracies of orbit position may be obtained. The sensitivity of the predicted orbit uncertainties to model errors for station locations, nongravitational platform accelerations, and earth gravity are presented.

Williams, Bobby G.↗