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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 451 records · Page 25

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NASA NTRS1999

Advances in Understanding Decadal Climate Variability

Recently, a joint Brazil-France-U.S. program, known as PIRATA (Pilot Research moored Array in the Tropical Atlantic), was proposed to begin the deployment of moored measurement platforms in the tropical Atlantic in order to enhance the existing observational data base and subsequent understanding of the processes by which the ocean and atmosphere couple in key regions of the tropical Atlantic Ocean. Empirical studies have suggested that there are strong relationships between tropical Atlantic upper ocean variability, SST, ocean-atmosphere coupling and regional climate variability. During the early 1980's a coordinated set of surface wind, subsurface thermal structure, and subsurface current observations were obtained as part of the U.S.-France SEQUAL-FOCAL process experiment designed to observe the seasonal response of the tropical Atlantic Ocean to surface forcing. Since that time, however, the observational data base for the tropical Atlantic Ocean has disintegrated to a few ship-tracks measuring ocean temperatures and a small collection of tide gauge stations measuring sea level. A more comprehensive set of observations, modeling and empirical studies is now in order to make progress on understanding the regional climate variability. The proposed PIRATA program will use mooring platforms similar to the tropical Pacific Ocean TAO array to measure surface fluxes of momentum and heat and the corresponding changes in the upper ocean thermal structure. It is anticipated that the oceanic data from this monitoring array will also be used in a predictive mode for initialization studies of regional coupled climate models. Of particular interest are zonal and meridional modes of ocean-atmosphere variability within the tropical Atlantic basin that have significant impacts on the regional climate of the bordering continents.

Busalacchi, Antonio J.↗

DOE OSTI2022

A deep learning-based direct forecasting of CO 2 plume migration

Accurate and timely forecasts of CO 2 plume evolution in geological reservoirs are crucial for CO 2 migration detection, leakage risk assessment, and operation decision support. Conventional forecasting usually adopts a two-step strategy, first calibrating reservoir model parameters against observations using iterative inverse modeling (or history matching) and then applying the calibrated model for predictions. This method impedes real-time forecasts due to the heavy computational demand in inverse modeling and may suffer from poor prediction accuracy because of the limited observation data. In this work, we propose a deep learning-based latent space mapping framework to forecast CO 2 plume migration directly by avoiding the inverse modeling. We first use the convolutional autoencoder to map the high-dimensional complex plume extents onto low-dimensional latent space. Next, we use neural networks to learn the relationship between the observation variables and the prediction latent variables. And then for given observation data, we infer the prediction values directly. This one-step direct forecasting is computationally efficient which requires a few number of parallelizable reservoir simulations and it can provide accurate predictions with limited observations by learning the observation-prediction relationship in the reduced dimension. Therefore, our proposed method enables an in-time forecast of dynamic CO 2 plume distributions. In this work, we demonstrate the effectiveness and accuracy of our method in predicting the CO 2 plume migration using four metrics such as plume area, centroid movement distance, and plume spreading in the primary and secondary directions. And the spatio-temporal evolution patterns of plume migration under diverse geological complexities are also accurately quantified.

15 GEOTHERMAL ENERGY↗

DOE OSTI2015

Developing an Effective Model for Predicting Spatially and Temporally Continuous Stream Temperatures from Remotely Sensed Land Surface Temperatures

Although water temperature is important to stream biota, it is difficult to collect in a spatially and temporally continuous fashion. We used remotely-sensed Land Surface Temperature (LST) data to estimate mean daily stream temperature for every confluence-to-confluence reach in the John Day River, OR, USA for a ten year period. Models were built at three spatial scales: site-specific, subwatershed, and basin-wide. Model quality was assessed using jackknife and cross-validation. Model metrics for linear regressions of the predicted vs. observed data across all sites and years: site-specific r2 = 0.95, Root Mean Squared Error (RMSE) = 1.25 °C; subwatershed r2 = 0.88, RMSE = 2.02 °C; and basin-wide r2 = 0.87, RMSE = 2.12 °C. Similar analyses were conducted using 2012 eight-day composite LST and eight-day mean stream temperature in five watersheds in the interior Columbia River basin. Mean model metrics across all basins: r2 = 0.91, RMSE = 1.29 °C. Sensitivity analyses indicated accurate basin-wide models can be parameterized using data from as few as four temperature logger sites. This approach generates robust estimates of stream temperature through time for broad spatial regions for which there is only spatially and temporally patchy observational data, and may be useful for managers and researchers interested in stream biota.

McNyset, Kristina↗

DOE OSTI2025

Position Papers for Inverse Methods for Complex Systems under Uncertainty Workshop

The ability to solve inverse problems – inferring unknown parameters, structures, or states of a system from observed data – is essential for advancing scientific discovery and innovation capabilities for the DOE mission. Basic research needs and challenges are particularly acute in emerging areas such as the interactive, data-driven, modeling and simulation of digital twins; decision support for experiments at DOE scientific user facilities; and for other complex systems and workflows. Inverse problems are at the heart of understanding and controlling complex systems due to factors such as observational data with varying modalities and fidelities, inherent uncertainties in physical measurements and numerical models, and the computational demands of rapid and high-fidelity simulations. The convergence of recent scientific computing trends – scientific machine learning, artificial intelligence, and computing advances such as exascale computing – is creating unprecedented opportunities. These advancements offer the potential to revolutionize how we approach inverse problems to extract actionable insights with the required level of accuracy and computational efficiency. This workshop and the Call for Position Papers are vital steps in bringing together experts to collectively explore and identify the new computational and mathematical directions needed in inverse methods for complex systems under uncertainty.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1987

On the interconnection of dynamic processes in the lower thermosphere with meteorological phenomena in the tropostratosphere

Nowadays the atmosphere is considered a single dynamic system governed by inner and other factors, the separate layers (troposphere, strato-mesosphere, thermosphere) of which are in a state of interaction though they differ physically from one another in thermal stratification and energetic processes. Observational data of thermodynamic parameter connections in these layers height range are presented. The interconnection between the temperature field disturbances in the tropo-stratosphere and the dynamic regime in the lower thermosphere in winter and summer is defined as revealed by observational data.

Fakhrutdinova, A. N.↗

DOE OSTI2016

Consistency relation and non-Gaussianity in a Galileon inflation

We study a particular Galileon inflation in the light of Planck2015 observational data in order to constraint the model parameter space. We study the spectrum of the primordial modes of the density perturbations by expanding the action up to the second order in perturbations. Then we pursue by expanding the action up to the third order and find the three point correlation functions to find the amplitude of the non-Gaussianity of the primordial perturbations in this setup. We study the amplitude of the non-Gaussianity both in equilateral and orthogonal configurations and test the model with recent observational data. Our analysis shows that for some ranges of the non-minimal coupling parameter, the model is consistent with observation and it is also possible to have large non-Gaussianity which would be observable by future improvements in experiments. Moreover, we obtain the tilt of the tensor power spectrum and test the standard inflationary consistency relation ( r = −8 n {sub T} ) against the latest bounds from the Planck2015 dataset. We find a slight deviation from the standard consistency relation in this setup. Nevertheless, such a deviation seems not to be sufficiently remarkable to be detected confidently.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1973

Motion of metric type 4 radio sources and its relation to shock waves responsible for type 2 radio bursts

The relation of the motion of type 4mA sources with shock waves responsible for type 2 bursts were considered using the observed data for these two radio sources. The difference of the emission mechanism between type 2 and type 4mA bursts suggest that the moving speed of the shock waves mentioned above is not necessarily equal to the metric type 4 sources. By analyzing the observed data on the speeds for both type 2 and type 4 sources., it was found that type 4 bursts decelerate and often cease to move in the solar envelop and that type 2 sources move at higher speeds than type 4 sources.

Sakurai, K.↗

DOE OSTI2020

Arctic Tipping Points Triggering Global Change (LDRD Final Report)

The Arctic is warming and feedbacks in the coupled Earth system may be driving the Arctic to tipping events that could have critical downstream impacts for the rest of the globe. In this project we have focused on analyzing sea ice variability and loss in the coupled Earth system Summer sea ice loss is happening rapidly and although the loss may be smooth and reversible, it has significant consequences for other Arctic systems as well as geopolitical and economic implications. Accurate seasonal predictions of sea ice minimum extent and long-term estimates of timing for a seasonally ice-free Arctic depend on a better understanding of the factors influencing sea ice dynamics and variation in this strongly coupled system. Under this project we have investigated the most influential factors in accurate predictions of September Arctic sea ice extent using machine learning models trained separately on observational data and on simulation data from five E3SM historical ensembles. Monthly averaged data from June, July, and August for a selection of ice, ocean, and atmosphere variables were used to train a random forest regression model. Gini importance measures were computed for each input feature with the testing data. We found that sea ice volume is most important earlier in the season (June) and sea ice extent became a more important predictor closer to September. Results from this study provide insight into how feature importance changes with forecast length and illustrates differences between observational data and simulated Earth system data. We have additionally performed a global sensitivity analysis (GSA) using a fully coupled ultra- low resolution configuration E3SM. To our knowledge, this is the first global sensitivity analysis involving the fully-coupled E3SM Earth system model. We have found that parameter variations show significant impact on the Arctic climate state and atmospheric parameters related to cloud parameterizations are the most significant. We also find significant interactions between parameters from different components of E3SM. The results of this study provide invaluable insight into the relative importance of various parameters from the sea ice, atmosphere and ocean components of the E3SM (including cross-component parameter interactions) on various Arctic-focused quantities of interest (QOIs).

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1995

Theoretical overview and modeling of the sodium and potassium atmospheres of mercury

A general theoretical overview for the sources, sinks, gas-surface interactions, and transport dynamics of sodium and potassium in the exospheric atmsophere of Mercury is given. Information for these four factors, which control the spatial distribution of these two alkali-group gases about the planet, is incorporated in numerical models. The spatial nature and relative importance of the initial source atom atmosphere and the ambient (ballistic hopping) atom atmosphere are then examined and are shown to be controlled and coupled to a great extent by the extremely large and variable solar radiation acceleration experienced by sodium and potassium as they resonantly scatter solar photons. The lateral (antisunward) transport rate of thermally accommodated sodium and potassium ambient atoms is shown to be driven by the solar radiation acceleration and, over a significant portion of Mercury's orbit about the Sun, is sufficiently rapid to be competitive with the short photoionization lifetimes for these atoms when they are located on the summit surface near or within about 30 deg of the terminator. The lateral transport rate is characterized by a migration time determined by model calculations for an ensemble of atoms initially starting at a point source on the surface (i.e., a numerical spacetime dependent Green's function). Four animations for the spacetime evolution of the sodium (or potassium) atmosphere produced by a point source on the surface are presented on a videotape format. For extended surface sources for sodium and potassium, the local column density is determined by competition between the photoionization lifetimes and the lateral transport times of atoms originating from different surface source locations. Sodium surface source fluxes (referenced to Mercury at perihelion) that are required on the sunlit hemisphere to reproduce the typically observed several megarayleighs of D2 emission-line brightness and the inferred column densities of 1-2 x 10(exp 11) atoms per sq cm range from approximately 2-5 x 10(exp 7) atoms/sq cm/sec. The sodium model is applied to study observational data that document an anticorrelation in the average sodium column density and solar radiation acceleration. Lateral transport driven by the solar radiation acceleration is shown to produce this behavior for combinations of different sources and surface accomodation coefficients. The best fit model fits to the observational data require a significant degree of thermal accommodation of the ambient sodium atoms to the surface and a source rate that decreases as an inverse power of 1.5 to 2 in heliocentric distance.

Smyth, William H.↗

NASA NTRS1978

Luminosity and carbon enhancement in N-type carbon stars

Recent observational data indicate the likelihood of a relation between luminosity and carbon enhancement in N-type irregularly variable carbon stars in the sense that the more luminous carbon stars appear to have smaller enhancements of carbon. No relation appears between carbon enhancement and red colors. The observational data are the luminosities of Peery (1975), the colors and CN band indices of Baumert (1972), the CO indices of Fay and Ridgway (1976), and the C2 indices of Gow (1977).

Johnson, H. R.↗

NASA NTRS1983

Astrophysical consequences of a violation of the strong equivalence principle

The consequences of a supposed violation of the strong equivalence principle (SEP) for photons are analyzed theoretically and investigated using published observational data on the age of stars and globular clusters, the past temperature of the earth, the 3-K black-body radiation, and big-bang nucleosynthesis. It is shown that the photon number is unaffected by an SEP violation, which influences only massive particles. The observational data are found to be compatible with an SEP violation of the order of the Hubble constant during the matter-dominated era, while not demanding such a violation. More direct-measurement studies, based on data such as those from the Viking program, are proposed.

Canuto, V. M.↗

DOE OSTI2024

Simulation-based inference for parameter estimation of complex watershed simulators

High-resolution, spatially distributed process-based (PB) simulators are widely employed in the study of complex catchment processes and their responses to a changing climate. However, calibrating these PB simulators using observed data remains a significant challenge due to several persistent issues, including the following: (1) intractability stemming from the computational demands and complex responses of simulators, which renders infeasible calculation of the conditional probability of parameters and data, and (2) uncertainty stemming from the choice of simplified representations of complex natural hydrologic processes. Here, we demonstrate how simulation-based inference (SBI) can help address both of these challenges with respect to parameter estimation. SBI uses a learned mapping between the parameter space and observed data to estimate parameters for the generation of calibrated simulations. To demonstrate the potential of SBI in hydrologic modeling, we conduct a set of synthetic experiments to infer two common physical parameters – Manning's coefficient and hydraulic conductivity – using a representation of a snowmelt-dominated catchment in Colorado, USA. We introduce novel deep-learning (DL) components to the SBI approach, including an “emulator” as a surrogate for the PB simulator to rapidly explore parameter responses. We also employ a density-based neural network to represent the joint probability of parameters and data without strong assumptions about its functional form. While addressing intractability, we also show that, if the simulator does not represent the system under study well enough, SBI can yield unreliable parameter estimates. Approaches to adopting the SBI framework for cases in which multiple simulator(s) may be adequate are introduced using a performance-weighting approach. The synthetic experiments presented here test the performance of SBI, using the relationship between the surrogate and PB simulators as a proxy for the real case.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

The International Land Model Benchmarking (ILAMB) System: Design, Theory, and Implementation

Abstract The increasing complexity of Earth system models has inspired efforts to quantitatively assess model fidelity through rigorous comparison with best available measurements and observational data products. Earth system models exhibit a high degree of spread in predictions of land biogeochemistry, biogeophysics, and hydrology, which are sensitive to forcing from other model components. Based on insights from prior land model evaluation studies and community workshops, the authors developed an open source model benchmarking software package that generates graphical diagnostics and scores model performance in support of the International Land Model Benchmarking (ILAMB) project. Employing a suite of in situ, remote sensing, and reanalysis data sets, the ILAMB package performs comprehensive model assessment across a wide range of land variables and generates a hierarchical set of web pages containing statistical analyses and figures designed to provide the user insights into strengths and weaknesses of multiple models or model versions. Described here is the benchmarking philosophy and mathematical methodology embodied in the most recent implementation of the ILAMB package. Comparison methods unique to a few specific data sets are presented, and guidelines for configuring an ILAMB analysis and interpreting resulting model performance scores are discussed. ILAMB is being adopted by modeling teams and centers during model development and for model intercomparison projects, and community engagement is sought for extending evaluation metrics and adding new observational data sets to the benchmarking framework.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1986

Effects of salinity on the microwave emission of soils

Controlled plot experiments were conducted to collect L and C band passive microwave data concurrent with ground observations of salinity and soil moisture. Two dielectric mixing models were used with an emission model to predict the emissivity from a bare smooth uniform profile. The models produce nearly identical results when near zero salinity is involved and reproduce the observed data at L band extremely well. Discrepancies at C band are attributed to sampling depth problems. Comparisons of predicted emissivities at various salinities with observed values indicate that the dynamic range of the emissivities can be explained using either of the dielectric mixing models. Evaluation of the entire data set, which included four salinity levels, indicates that for general application the effects of soil salinity can be ignored in interpreting microwave data for estimating soil moisture under most agricultural conditions.

Jackson, T. J.↗

NASA NTRS1987

Salinity effects on the microwave emission of soils

Jackson, Thomas J.↗

DOE OSTI2026

Goal-oriented real-time Bayesian inference for linear autonomous dynamical systems with application to digital twins for tsunami early warning

We present a goal-oriented framework for constructing digital twins with the following properties: (1) they employ discretizations of high-fidelity partial differential equation (PDE) models governed by autonomous dynamical systems, leading to large-scale forward problems; (2) they solve a linear inverse problem to assimilate observational data to infer uncertain model components followed by a forward prediction of the evolving dynamics; and (3) the entire end-to-end, data-to-inference-to-prediction computation is carried out without approximation and in real time through a Bayesian framework that rigorously accounts for uncertainties. Several challenges must be overcome to realize this framework, including the large scale of the forward problem, the high dimensionality of the parameter space, and for a class of problems including those we target, the slow decay of the singular values of the parameter-to-observable map. Here we introduce a methodology to overcome these challenges by exploiting the autonomous structure of the forward model to decompose the solution of the inverse problem into a one-time-only offline phase in which the PDE model is solved a limited number of times (equal to the number of sensors), and an online phase that maps well onto GPUs and computes the parameter inference and prediction of quantities of interest in real time, given observational data. Our ultimate goal is to apply this framework to construct digital twins for subduction zones, including Cascadia, to provide early warning for tsunamis generated by megathrust earthquakes. To this end, we demonstrate how our methodology can be used to employ seafloor pressure observations, along with the coupled acoustic–gravity wave equations, to infer the earthquake-induced spatiotemporal seafloor motion (discretized with $\mathscr{O}$ (10 9 ) parameters) and forward predict the tsunami propagation. We present results of an end-to-end inference, prediction, and uncertainty quantification for a representative test problem with $\mathscr{O}$ (10 8 ) inversion parameters for which goal-oriented Bayesian inference is accomplished exactly and in real time, that is, in a matter of seconds.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Estimating heterotrophic respiration at large scales: challenges, approaches, and next steps

Heterotrophic respiration (HR), the aerobic and anaerobic processes mineralizing organic matter, is a key carbon flux but one impossible to measure at scales significantly larger than small experimental plots. This impedes our ability to understand carbon and nutrient cycles, benchmark models, or reliably upscale point measurements. Given that a new generation of highly mechanistic, genomic-specific global models is not imminent, we suggest that a useful step to improve this situation would be the development of "Decomposition Functional Types" (DFTs). Analogous to plant functional types (PFTs), DFTs would abstract and capture important differences in HR metabolism and flux dynamics, allowing models to efficiently group and vary these characteristics across space and time. We argue that DFTs should be initially informed by top-down expert opinion, but ultimately developed using bottom-up, data-driven analyses, and provide specific examples of potential dependent and independent variables that could be used. We present and discuss an example clustering analysis to show how model-produced annual HR can be broken into distinct groups associated with global variability in biotic and abiotic factors, and demonstrate that these groups are distinct from already-existing PFTs. A similar analysis, incorporating observational data, could form a basis for future DFTs. Finally, we suggest next steps and critical priorities: collection and synthesis of existing data; more in-depth analyses combining open data with high-performance computing; rigorous testing of analytical results; and planning by the global modeling community for decoupling decomposition from fixed site data. These are all critical steps to build a foundation for DFTs in global models, thus providing the ecological and climate change communities with robust, scalable estimates of HR at large scales.

carbon cycle↗

NASA NTRS1977

Low-temperature condensates in comets

Recent observational data on the volatile fraction of comets are confronted with a model based on the fractional condensation, in the 80-100 K range, of a higher-temperature equilibrium obtained from a solar mixture, more or less depleted in oxygen and in hydrogen. It is possible to almost duplicate the observational data, only by assuming that the solar ratio of C/O is at least as large as 0.66 and that the hydrogen was drastically depleted by an unknown process in the primitive solar nebula. Although none of these two assumptions is at variance with present knowledge, the latter is sufficiently exotic to propose a simpler explanation, namely that comets could be made of interstellar grains relatively unprocessed by heat.

Delsemme, A. H.↗