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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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For this presentation, we looked into using uncertainty quantification to understand the uncertainty in model parameters in predicting voltage and time response of a lithium-air battery.
This study examines aerosol properties in the vicinity of clouds by analyzing high-resolution atmospheric correction parameters provided in the MODIS (Moderate Resolution Imaging Spectroradiometer) ocean color product. The study analyzes data from a 2 week long period of September in 10 years, covering a large area in the northeast Atlantic Ocean. The results indicate that on the one hand, the Quality Assessment (QA) flags of the ocean color product successfully eliminate cloud-related uncertainties in ocean parameters such as chlorophyll content, but on the other hand, using the flags introduces a sampling bias in atmospheric products such as aerosol optical thickness (AOT) and Angstrom exponent. Therefore, researchers need to select QA flags by balancing the risks of increased retrieval uncertainties and sampling biases. Using an optimal set of QA flags, the results reveal substantial increases in optical thickness near clouds-on average the increase is 50% for the roughly half of pixels within 5 km from clouds and is accompanied by a roughly matching increase in particle size. Theoretical simulations show that the 50% increase in 550nm AOT changes instantaneous direct aerosol radiative forcing by up to 8W/m2 and that the radiative impact is significantly larger if observed near-cloud changes are attributed to aerosol particles as opposed to undetected cloud particles. These results underline that accounting for near-cloud areas and understanding the causes of near-cloud particle changes are critical for accurate calculations of direct aerosol radiative forcing.
One of the goals of the First ISCCP Regional Experiment (FIRE) is the quantification of the uncertainties in the cloud parameter products derived by the International Satellite Cloud Climatology Project (ISCCP). This validation effort has many facets including sensitivity analyses and comparisons to similar data or theoretical results with known accuracies. The FIRE provides cloud-truth data at particular points or along particular lines from surface and aircraft measurement systems. Relating these data to the larger, area-averaged ISCCP results requires intermediate steps using higher resolution satellite data analyses. Errors in the cloud products derived with a particular method can be determined by performing analyses of high resolution satellite data over the area surrounding the point or line measurement. This same analysis technique may then be used to derive cloud parameters over a larger area containing similar cloud fields. It is assumed that the uncertainties found for the small scale analyses are the same for the large scale so that the method has been calibrated for the particular cloud type; i.e., its accuracy is known. Differences between the large scale results using the ISCCP technique and the calibrated method can be computed and used to determine if any significant biases or rms errors occur in the ISCCP results. Selected ISCCP results are compared to cloud parameters derived using the hybrid bispectral threshold method over the FIRE IFO and extended observation areas.
Modeling and monitoring of earth’s processes through physical models and satellite observations at high resolutions is crucial for ensuring society’s ability to adapt to climate change. Deep learning (DL) has been shown to be a valuable tool for generating high resolution data, emulating physical models, and detecting weather patterns which can then be used to inform stakeholders and decision makers. However, both the data and model parameters contain substantial uncertainties that may alter users’ decisions. In this work we present two DL applications on high-resolution climate and satellite datasets using Bayesian neural networks to generate well calibrated uncertainty estimates.
Here, we present a comprehensive evaluation of the nuclear structure properties of 19 Ne using a novel and rigorous Bayesian statistical framework. Precise characterization of 19 Ne resonance parameters is critical for accurately determining reaction rates of the astrophysically significant 15 O (𝛼, 𝛾) 19 Ne and 18 F (𝑝, 𝛼) 15 O reactions, which govern breakout from the hot CNO cycle in x-ray bursts and influence 𝛾-ray emission in novae, respectively. By reconstructing likelihood functions from published experimental data—including asymmetric uncertainties and upper or lower limits—we derive posterior distributions for resonance energies, decay widths, and branching ratios. Our Bayesian approach systematically incorporates previously reported discrepancies among measurements, providing a statistically robust and consistent treatment of these uncertainties. The evaluated resonance parameters and associated uncertainties provide crucial input for stellar nucleosynthesis modeling, contributing to a refined understanding of explosive astrophysical phenomena.
The simultaneous measurements of atomic O, O2, O3, and N2 between 90 and 110 km obtained by a rocket-borne mass spectrometer flown during the Aladdin 74 program provide an opportunity to test directly the simple atmosphperic chemical model first proposed more than 50 years ago by S. Chapman, which is today the core of the complex chemistry controlling stratospheric O3. The Aladdin 74 O3 results are found to be significantly in excess of model abundances computed using the most current values of the key chemical parameters. The calculations may possibly be in error due to uncertainties in these key parameters and/or in the measurement of the related species. However, the extra O3 may be a consequence of reactions between ground state O2 and electronically excited O2 resulting from the recombination of atomic O.
A model developed specifically for accretional heating of small icy satellites of massive planets was used to investigate the effects of various parameters on the accretional heating of the Saturn and Uranus satellites. It was shown that accretional heating is completely dominated by cisplanetary impactors, with substantial deposition of heat occurring well below a satellite's surface. Three parameters that have large uncertainties were found to affect accretional temperature profile: the Safronov parameter for cisplanetary impactors, the fraction of impactor energy deposited below the surface, and the exponent in the impactor size-distribution equation. For most satellites, the depth of maximum heating and the maximum temperature reached have smooth positive dependences on satellite size. Accretional heating of these satellites is insufficient to melt H2O ice, but, if NH3 or CH4 are present, accretional heating may produce a warm buoyant mobile zone tens of kilometers below the surface.
A short-pulse CO2 Doppler lidar with 150-m range resolution measured vertical profiles of turbulence and momentum flux. Example measurements are reported of a daytime mixed layer with strong mechanical mixing caused by a wind speed of 15 m/sec, which exceeded the speed above the capping inversion. The lidar adapted an azimuth scanning technique previously demonstrated by radar. Scans alternating between two elevation angles allow determination of mean U-squared, V-squared, and W-squared. Expressions were derived to estimate the uncertainty in the turbulence parameters. A new processing method, partial Fourier decomposition, has less uncertainty than the filtering used earlier.
Great effort has been devoted towards validating geophysical parameters retrieved from ultraspectral infrared radiances obtained from satellite remote sensors. An error consistency analysis scheme (ECAS), utilizing fast radiative transfer model (RTM) forward and inverse calculations, has been developed to estimate the error budget in terms of mean difference and standard deviation of error in both spectral radiance and retrieval domains. The retrieval error is assessed through ECAS without relying on other independent measurements such as radiosonde data. ECAS establishes a link between the accuracies of radiances and retrieved geophysical parameters. ECAS can be applied to measurements from any ultraspectral instrument and any retrieval scheme with its associated RTM. In this manuscript, ECAS is described and demonstrated with measurements from the MetOp-A satellite Infrared Atmospheric Sounding Interferometer (IASI). This scheme can be used together with other validation methodologies to give a more definitive characterization of the error and/or uncertainty of geophysical parameters retrieved from ultraspectral radiances observed from current and future satellite remote sensors such as IASI, the Atmospheric Infrared Sounder (AIRS), and the Cross-track Infrared Sounder (CrIS).
The problem considered in this paper deals with the control of linear discrete-time stochastic systems with unknown (possibly time-varying and random) gain parameters. The philosophy of control is based on the use of an open-loop feedback optimal (OLFO) control using a quadratic index of performance. It is shown that the OLFO system consists of (1) an identifier that estimates the system state variables and gain parameters and (2) a controller described by an 'adaptive' gain and correction term. Several qualitative properties and asymptotic properties of the OLFO adaptive system are discussed. Simulation results dealing with the control of stable and unstable third-order plants are presented. The key quantitative result is the precise variation of the control system adaptive gains as a function of the future expected uncertainty of the parameters; thus, in this problem the ordinary 'separation theorem' does not hold.
This paper presents a comprehensive approach to filter and dynamics modeling for attitude determination error analysis. The discussion includes models for both batch least-squares and sequential estimators, a specific dynamic model for attitude determination error analysis of a three-axis stabilized spacecraft equipped with strapdown gyros, and the incorporation of general attitude sensor observations. An analyst using this approach to perform an error analysis chooses a subset of the spacecraft parameters to be 'solve-for' parameters, which are to be estimated, and another subset to be 'consider' parameters, which are assumed to have errors but not to be estimated. The result of the error analysis is an indication of overall uncertainties in the 'solve-for' parameters, as well as the contributions of the various error sources to these uncertainties, including those of errors in the a priori 'solve-for' estimates, of measurement noise, of dynamic noise (also known as process noise or plant noise), and of 'consider' parameter uncertainties. The analysis of attitude, star tracker alignment, and gyro bias uncertainties for the Gamma Ray Observatory spacecraft provide a specific example of the use of a general-purpose software package incorporating these models.
Applied research and technology development (R&TD) is often characterized by uncertainty, risk, and significant delays before tangible returns are obtained. Given the increased awareness of limitations in resources, effective R&TD today needs a method for up-front assessment of competing technologies to help guide technology investment decisions. Such an assessment approach must account for uncertainties in system performance parameters, mission requirements and architectures, and internal and external events influencing a development program. The methodology known as decision analysis has the potential to address these issues. It was evaluated by performing a case study assessment of alternative carbon dioxide removal technologies for NASA's proposed First Lunar Outpost program. An approach was developed that accounts for the uncertainties in each technology's cost and performance parameters as well as programmatic uncertainties such as mission architecture. Life cycle cost savings relative to a baseline, adjusted for the cost of money, was used as a figure of merit to evaluate each of the alternative carbon dioxide removal technology candidates. The methodology was found to provide a consistent decision-making strategy for development of new life support technology. The case study results provided insight that was not possible from more traditional analysis approaches.
Applied research and technology development (R&TD) is often characterized by uncertainty, risk, and significant delays before tangible returns are obtained. Given the increased awareness of limitations in resources, effective R&TD today needs a method for up-front assessment of competing technologies to help guide technology investment decisions. Such an assessment approach must account for uncertainties in system performance parameters, mission requirements and architectures, and internal and external events influencing a development program. The methodology known as decision analysis has the potential to address these issues. It was evaluated by performing a case study assessment of alternative carbon dioxide removal technologies for NASA"s proposed First Lunar Outpost program. An approach was developed that accounts for the uncertainties in each technology's cost and performance parameters as well as programmatic uncertainties such as mission architecture. Life cycle cost savings relative to a baseline, adjusted for the cost of money, was used as a figure of merit to evaluate each of the alternative carbon dioxide removal technology candidates. The methodology was found to provide a consistent decision-making strategy for the develpoment of new life support technology. The case study results provided insight that was not possible from more traditional analysis approaches.
Experimental aerodynamic researchers require estimated precision and bias uncertainties of measured physical quantities, typically at 95 percent confidence levels. Uncertainties of final computed aerodynamic parameters are obtained by propagation of individual measurement uncertainties through the defining functional expressions. In this paper, rigorous mathematical techniques are extended to determine precision and bias uncertainties of any instrument-sensor system. Through this analysis, instrument uncertainties determined through calibration are now expressed as functions of the corresponding measurement for linear and nonlinear univariate and multivariate processes. Treatment of correlated measurement precision error is developed. During laboratory calibration, calibration standard uncertainties are assumed to be an order of magnitude less than those of the instrument being calibrated. Often calibration standards do not satisfy this assumption. This paper applies rigorous statistical methods for inclusion of calibration standard uncertainty and covariance due to the order of their application. The effects of mathematical modeling error on calibration bias uncertainty are quantified. The effects of experimental design on uncertainty are analyzed. The importance of replication is emphasized, techniques for estimation of both bias and precision uncertainties using replication are developed. Statistical tests for stationarity of calibration parameters over time are obtained.
Abstract This study investigates uncertainties in greenhouse gas (GHG) emission factors related to switchgrass‐based biofuel production in Michigan. Using three life cycle assessment (LCA) databases—US lifecycle inventory (USLCI) database, GREET, and Ecoinvent—each with multiple versions, we recalculated the global warming intensity (GWI) and GHG mitigation potential in a static calculation. Employing Monte Carlo simulations along with local and global sensitivity analyses, we assess uncertainties and pinpoint key parameters influencing GWI. The convergence of results across our previous study, static calculations, and Monte Carlo simulations enhances the credibility of estimated GWI values. Static calculations, validated by Monte Carlo simulations, offer reasonable central tendencies, providing a robust foundation for policy considerations. However, the wider range observed in Monte Carlo simulations underscores the importance of potential variations and uncertainties in real‐world applications. Sensitivity analyses identify biofuel yield, GHG emissions of electricity, and soil organic carbon (SOC) change as pivotal parameters influencing GWI. Decreasing uncertainties in GWI may be achieved by making greater efforts to acquire more precise data on these parameters. Our study emphasizes the significance of considering diverse GHG factors and databases in GWI assessments and stresses the need for accurate electricity fuel mixes, crucial information for refining GWI assessments and informing strategies for sustainable biofuel production.
With the availability of modern flight computers, realtime neighboring extremal guidance seems feasible. To overcome sensitivity to unknown system parameters and environmental uncertainties, a robust neighboring extremal guidance scheme is proposed. About the optimal trajectory, the accessory problem in the calculus of variations is formed, generating a quadratic cost criterion in the perturbed states and controls. By formulating a disturbance attenuation problem based upon the second variation cost criterion, a differential game is formulated. The game theoretic cost criterion is minimized with respect to the perturbed control but maximized with respect to the unknown parameters in the linearized dynamics. The resulting differential game problem gives rise to a two-point boundary-value problem solved using the sweep method. The sweep method solution provides a linear robust neighboring extremal guidance scheme that is applied to the Advanced Launch System.
Due to remaining uncertainties in model parameters and assumptions in the calculation of model ages, it is not yet possible to resolve the precise dating and duration of lunar crust-mantle differentiation phenomena. Norites 78236 and 73255,27,45 were examined utilizing the Sm-Nd radiometric system. The systematics of 78236 show post-crystallization disturbance but indicate a crystallization date 4.34 AE ago. An isochron is defined by the 73255,27,45 data, with a crystallization date of 4.23 AE. The initial Nd isotopic composition of both norites is within uncertainty limits for a 'chondritic' reference reservoir at the time of their respective crystallizations. The implications for lunar crustal formation persisting over a time span of nearly 350 million years are discussed.
We develop numerical models of accretions disks in cataclysmic variables (CVs), including and emphasizing the boundary layer region where the accretion disk meets the accreting white dwarf. We confine ourselves to solutions where the boundary layer region is vertically optically thick, and find that these solutions share several common features. The angular and radial velocities of the accreting material drop rapidly in a dynamical boundary layer, which has a radial width approximately 1%-3% of the white dwarf radius. The energy dissipated in this region diffuses through the inner part of the disk and is radiated from the disk surface in a thermal boundary layer, which has a radial width comparable to the disk thickness, approximately 5%-15% of the white dwarf radius. We examine the dependence of the boundary layer structure on the mass accretion rate, the white dwarf mass and rotation rate, and the viscosity parameter alpha. We delineate the boundary between optically thick and optically thin boundary layer solutions as a function of these parameters and suggest that by means of a careful comparison with observations it may be possible to estimate alpha in CVs. We derive an expression for the total boundary layer luminosities as a function of the parameters and show that it agrees well with the luminosites of our numerical solutions. Finally, we calcuate simple blackbody continuum spectra of the boundary layer and disk emission for our solutions and compare these to soft X-ray, EUV, and He II emission-line observations of CVs. We show that, through such comparisons, it may be possible to determine the rotation rates of the accreting stars in CVs, and perhaps also the white dwarf masses and the accretion rates. The spectra are quite insensitive to alpha, so the uncertainty in this parameter does not affect such comparisons.