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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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Class B Materials Code Case to include: FY25 - High temperature design methodology, overview of ASME Section III (Division 5), Design for elevated temperature service, Goal to address current gaps, evaluation of proposed rules, allowable stress development, Class A materials (existing ASME approach), variable confidence index, data extrapolation, bounding value for SEE, and next steps.
As energy system design moves to more complex methods of optimization including machine learning there is a significant need for more weather data than is available. One method to solve this is using synthetic data models such as the auto-regressive moving-average (ARMA) model which has been frequently utilized to create such data. This paper looks at extending the ARMA algorithm to generate solar components through the use of clearsky detrending, maintaining vector relationships and by leveraging physical relationships. The method for the creation of entirely synthetic weather data files including key weather variables for energy system analysis is presented. Furthermore, a detailed comparison of energy system simulations utilizing both real and synthetic data is made using NREL’s System Advisor Model. Whilst good agreement is made for the solar variables, and other weather variables, ARMA methods often fail to capture the standard deviation and skew of annual weather distributions. Vector-ARMA is shown to maintain correlations between variables and thus generate data sets that perform similarly in energy system design. Here, it is finally shown that the ARMA method fails to preserve day-today correlations in weather variables and thus over-predicts optimal energy storage by 21% for a residential solar application.
The United States (U.S.) Department of Energy (DOE) developed the Billion-Ton Vision to enable production of one-billion tons of sustainable, reliable biomass for the bioenergy industry by 2030 (Perlack et al., 2005). The Sun Grant Regional Feedstock Partnership (RFP) was organized to fill information gaps and validate biomass yield assumptions related to the Billion-Ton Study (Owens, 2018; Owens, Karlen, and Lacey, 2016). Along with the more than 130 scientific publications generated from these studies, yield and sustainability data from the RFP field trials not only validated the Billion-Ton estimates, but were critical in developing both the U.S. Billion-Ton Update report in 2011 and the 2016 Billion-Ton Report (DOE, 2011; 2016). The intention of this biomass quality assessment report is to build on these initial successes from the RFP field trials by focusing on variability in biomass quality data necessary to evaluate conversion performance. This report contains a summary of chemical quality results from samples collected as part of the RFP field trials. This report focuses on assessment of the impact of experimental agronomic designs on biomass properties followed by analyses of the impact of environmental and production variables on biomass properties. Datasets include species and other genetic variables, fertilizer treatments, harvest information, and yield, as well as other publicly available data such as precipitation, temperature, soil properties, and drought. The key outcomes from this chemical quality focused assessment have included: • Complete evaluation of the impacts of agronomic designs, genetics, and environmental conditions on chemical properties for Miscanthus, switchgrass, sorghum, energycane, mixed perennial grasses, and shrub willow short-rotation feedstocks • Over 30 peer review publications and technical reports focused on variability in quality data • Development of spatial and temporal environmental quality prediction maps for Miscanthus and switchgrass feedstocks allowing for comprehensive evaluation of variability in feedstock chemical quality across U.S. regions and over multiple harvest years
Data from the Earth Radiation Budget Experiment scanning radiometer aboard the NOAA-9 operational meteorological satellite are used to investigate the spatial variability of Outgoing Longwave Radiation (OLR). Daily and monthly radiation maps at 2.5 deg latitude-longitude scale are used as a basis for the study. The regions of greatest variability are in the tropics and subtropics. Storm tracks such as the South Pacific convergence zone appear as regions of high OLR variability. Spatial spectra in longitude show two regimes of OLR. At large scales (wavenumbers less than 6), the spatial spectrum is flat. For wavenumbers greater than 10, the spectra decrease as wavenumber to the -3 power. The spatial spectrum of daily anomalies from the mean is a strong function of latitude and season, with interesting features. Correlations of daily anomalies from the monthly mean decrease exponentially in latitude but have a damped-wave structure in longitude. The spatial variability of the daily maps, as measured by degree variance, have 10 times the power at degree 24 than the monthly maps, but at scales between 1 and 10, the degree variance is practically the same for daily as for monthly.
With the development of new technologies, data mining has become increasingly popular. However, caution should be exercised in choosing the variables to include in data mining. A series of regression trees was created to demonstrate the change in the selection by the program of significant predictors based on the nature of variables.
Manual and automatic methods and devices using a ACOMP system for active control of polymerization reaction processes. An ideal desired trajectory of one or more reaction and polymer characteristics can be established to produce a desired final polymer product with specified characteristics from a polymerization reaction process. A current reaction trajectory of a polymerization reaction process can be driven to an ideal or desired reaction trajectory. In a manual embodiment an operator can use ACOMP data to adjust process variables in order to drive the current reaction trajectory toward the ideal or desired reaction trajectory. In an automated mode a control program can use ACOMP data to make adjustments to process variables to drive the polymerization reaction process toward the desired trajectory as closely as possible either empirically or by solving the governing equations for the polymerization reaction process.
An analysis of FGGE dropwindsonde data using conserved thermodynamic variables shows mixing line structures for the convective boundary layer over the equatorial Pacific. Deeper boundary layers show a double structure. Reversals of the gradients of mixing ratio and equivalent potential temperature above the boundary-layer top are present in all the averages and suggest that the origin of the air sinking into the boundary layer needs further study.
Terrestrial hydrological variables are important in global hydrology, climate, and carbon cycle studies. Generating global fields of these variables, however, is still a challenge. The goal of a land data assimilation system (LDAS)is to ingest satellite-and ground-based observational data products, using advanced land surface modeling and data assimilation techniques, in order to generate optimal fields of land surface states and fluxes data and, thereby, facilitate hydrology and climate modeling, research, and forecast.
The goals of this research effort have been to use MF radar and UARS/HRDI wind measurements for correlative studies of large-scale atmospheric dynamics, focusing specifically on the tidal and various planetary wave structures occurring in the middle atmosphere. We believed that the two data sets together would provide the potential for much more comprehensive studies than either by itself, since they jointly would allow the removal of ambiguities in wave structure that are difficult to resolve with either data set alone. The joint data were to be used for studies of wave structure, variability, and the coupling of these motions to mean and higher-frequency motions.
Two inlet performance tests and one inlet/airframe drag test were conducted in 1969 at the NASA-Ames Research Center. The basic inlet system was two-dimensional, three ramp (overhead), external compression, with variable capture area. The data from these tests were analyzed to show the effects of selected design variables on the performance of this type of inlet system. The inlet design variables investigated include inlet bleed, bypass, operating mass flow ratio, inlet geometry, and variable capture area.
Problems encountered during attempts to calibrate SAR imagery, recent successful experiments conducted with SEASAT SAR data, and a proposed program for the calibration and validation of the radar imagery from the forthcoming SIR-B SAR are discussed. The SEASAT SAR data for 10 passes over Death valley, California, were processed with a modified digital correlator. The procedure included a preliminary screening of the data to check for raw data saturation, compensation of waveforms and estimation of the amplitude of the pilot tone. All data was normalized to this pilot tone signal to reduce the effects of variable gains in the data links and ground receivers. The digital correlation algorithm generated image data. Evaluation of 6 passes results in a maximum pass to pass gain variation of only 1.1 dB and a standard deviation amongst the passes of 0.35 dB.
Problems encountered during attempts to calibrate SAR imagery, recent successful experiments conducted with SEASAT SAR data, and a proposed program for the calibration and validation of the radar imagery from the forthcoming SIR-B SAR are discussed. The SEASAT SAR data for 10 passes over Death Valley, California, were processed with a modified digital correlator. The procedure included a preliminary screening of the data to check for raw data saturation, compensation of waveforms and estimation of the amplitude of the pilot tone. All data was normalized to this pilot tone signal to reduce the effects of variable gains in the data links and ground receivers. The digital correlation algorithm generated image data. Evaluation of 6 passes results in a maximum pass to pass gain variation of only 1.1 dB and a standard deviation amongst the passes of 0.35 dB. previously announced in STAR as N83-26215
Along the Colorado Front Range, forest management has gained significant attention due to uncharacteristically large fires that burned late in 2020. The Cameron Peak Fire (largest in Colorado recorded history) and the CalWood Fire collectively burned an estimated 219,019 acres from August through December of 2020. Project partners at the Coalition for the Poudre River Watershed, Colorado State Forest Service, Ben Delatour Scout Ranch, The Nature Conservancy, Colorado Forest Restoration Institute, and Colorado State University were interested in understanding the effectiveness of previous forest treatments in reducing burn severity within the Cameron Peak Fire and the CalWood Fire. We first collated a forest treatment dataset from pre-existing datasets by reclassifying over 29,000 treatments, which occurred across the Northern Colorado Front Range between 1970-2020. Secondly, we mapped three burn severity indices using Landsat 8 OLI and Sentinel-2 MSI Earth observations and compared them to soil burn severity field data. Thirdly, a total of 35 topographic, disturbance, forest structure, and treatment predictor variables were generated across the fires. Finally, we assessed relationships between these predictor variables and burn severity using the random forest algorithm. Model results indicate that the primary drivers of burn severity were elevation and distance to treatment edge for the Cameron Peak Fire and fire area and forest canopy cover for the CalWood Fire. Further analysis of these variables paired with field data is necessary to understand the relationship between burn severity and treatments to guide future restoration efforts, improve forest resiliency, and mitigate fire risks.
Distributionally robust optimization (DRO) is a powerful tool for decision making under uncertainty. It is particularly appealing because of its ability to leverage existing data. However, many practical problems call for decision-making with some auxiliary information, and DRO in the context of conditional distributions is not straightforward. We propose a conditional kernel distributionally robust optimization (CKDRO) method that enables robust decision making under conditional distributions through kernel DRO and the conditional mean operator in the reproducing kernel Hilbert space (RKHS). In particular, we consider problems where there is a correlation between the unknown variable y and an auxiliary observable variable x. Given past data of the two variables and a queried auxiliary variable, CKDRO represents the conditional distribution P(y|x) as the conditional mean operator in the RKHS space and quantifies the ambiguity set in the RKHS as well, which depends on the size of the dataset as well as the query point. To justify the use of RKHS, we demonstrate that the ambiguity set defined in RKHS can be viewed as a ball under a metric that is similar to the Wasserstein metric. The DRO is then dualized and solved via a finite dimensional convex program. The proposed CKDRO approach is applied to a generation scheduling problem and shows that the result of CKDRO is superior to common benchmarks in terms of quality and robustness.
Two major criticisms of using Shuttle hand held photography as an Earth science sensor are that it is nondigital, nonquantitative and that it has inconsistent platform characteristics, e.g., variable look angles, especially as compared to remote sensing satellites such as LANDSAT and SPOT. However, these criticisms are assumptions and have not been systematically investigated. The spectral effects of off-nadir views of hand held photography from the Shuttle and their role in interpretation of lava flow morphology on the island of Hawaii are studied. Digitization of photography at JSC and use of LIPS image analysis software in obtaining data is discussed. Preliminary interpretative results of one flow are given. Most of the time was spent in developing procedures and overcoming equipment problems. Preliminary data are satisfactory for detailed analysis.
The first 22 months of ozone data from the Global Atmospheric Sampling Program are summarized. Variations in space and time were examined at nearly all scales permitted by the data. Case studies in the tropics suggest that local ozone maxima may be found in or near clouds. Preliminary seasonal mean maps of ozone during spring are presented for the Northern Hemisphere. In the troposphere over the United States during summer there is a distinct midcontinental ozone maximum. There is a diurnal variation in ozone in the upper troposphere and the daily range is about 5 ppbv. Correlations between ozone and other variables are given for the synoptic-scale and on a hemispheric scale. The possible bearing of these results on ozone transport computations is discussed.
Geochemical systems are known to exhibit highly variable spatiotemporal behavior. This may be observed both in non-smooth concentration curves in space for a single sampling time and also in variability between samples taken from the same location at different times. However, most models that are designed to simulate these systems provide only single-solution smooth curves and fail to capture the noise and variability seen in the data. We apply a recently developed reactive particle-tracking method to a system that displays highly complex geochemical behavior. When the method is made to most closely resemble a corresponding Eulerian method, in its unperturbed form, we see near-exact match between solutions of the two models. More importantly, we consider two approaches for perturbing the model and find that the spatially-perturbed condition is able to capture a greater degree of the variability present in the data. This method of perturbation is a task to which particle methods are uniquely suited and Eulerian models are not well-suited. Additionally, because of the nature of the algorithm, noisy spatial gradients can be highly resolved by a large number of mobile particles, and this incurs negligible computational cost, as compared to expensive chemistry calculations.
This paper presents a multifidelity uncertainty quantification framework called MFNets. We seek to address three existing challenges that arise when experimental and simulation data from different sources are used to enhance statistical estimation and prediction with quantified uncertainty. Specifically, we demonstrate that MFNets can (1) fuse heterogeneous data sources arising from simulations with different parameterizations, e.g simulation models with different uncertain parameters or data sets collected under different environmental conditions; (2) encode known relationships among data sources to reduce data requirements; and (3) improve the robustness of existing multi-fidelity approaches to corrupted data. MFNets construct a network of latent variables (LVs) to facilitate the fusion of data from an ensemble of sources of varying credibility and cost. These LVs are posited as explanatory variables that provide the source of correlation in the observed data. Furthermore, MFNets provide a way to encode prior physical knowledge to enable efficient estimation of statistics and/or construction of surrogates via conditional independence relations on the LVs. We highlight the utility of our framework with a number of theoretical results which assess the quality of the posterior mean as a frequentist estimator and compare it to standard sampling approaches that use single fidelity, multilevel, and control variate Monte Carlo estimators. We also use the proposed framework to derive the Monte Carlo-based control variate estimator entirely from the use of Bayes rule and linear-Gaussian models -- to our knowledge the first such derivation. Finally, we demonstrate the ability to work with different uncertain parameters across different models.