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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

2005 Tahoe Regional Household Travel Survey

The 2005 Tahoe Regional Household Travel Survey is a comprehensive survey of the travel patterns of households in the Tahoe Basin region of California and Nevada. The survey was conducted for the Tahoe Regional Planning Agency over a four-month period, from June through September 2005. This collection contains two sets of travel data: 1) a survey of 1,220 permanent households within the Tahoe Basin region and 2) a subset survey of 125 seasonal households within the same area. The permanent resident dataset contains information about 2,732 household members, 2,471 vehicles, and 11,048 trips made during a 24-hour period. The seasonal resident subset features 726 trip observations and information about 229 household members and 185 vehicles. The survey relied on the willingness of study area residents to 1) provide demographic information about their household, its members, and its vehicles and 2) document all travel for each household member, regardless of age, over an assigned 24-hour period. During the recruitment phase, respondents were asked to provide detailed household information, including permanent or seasonal resident status, number of household occupants, type of dwelling unit, number of vehicles available to household members, as well as the year, body type, and fuel type of each vehicle. Survey participants received mailed travel logs to aid in documenting travel information for the 24-hour assessment period. Respondents were instructed to record their mode of transportation, trip purpose, number of passengers, departure and arrival times, activities at each trip destination, ride fare, and parking costs. Demographic information for this study includes age, gender, income, education, and employment status.

1Hz data↗
DOE OSTI2023

Reliable extrapolation of deep neural operators informed by physics or sparse observations

Deep neural operators can learn nonlinear mappings between infinite-dimensional function spaces via deep neural networks. As promising surrogate solvers of partial differential equations (PDEs) for real-time prediction, deep neural operators such as deep operator networks (DeepONets) provide a new simulation paradigm in science and engineering. Pure data-driven neural operators and deep learning models, in general, are usually limited to interpolation scenarios, where new predictions utilize inputs within the support of the training set. However, in the inference stage of real-world applications, the input may lie outside the support, i.e., extrapolation is required, which may result to large errors and unavoidable failure of deep learning models. Here, we address this challenge of extrapolation for deep neural operators. First, we systematically investigate the extrapolation behavior of DeepONets by quantifying the extrapolation complexity, via the 2-Wasserstein distance between two function spaces and propose a new strategy of bias–variance trade-off for extrapolation with respect to model capacity. Subsequently, we develop a complete workflow, including extrapolation determination, and we propose five reliable learning methods that guarantee a safe prediction under extrapolation by requiring additional information—the governing PDEs of the system or sparse new observations. The proposed methods are based on either fine-tuning a pre-trained DeepONet or multifidelity learning. We demonstrate the effectiveness of the proposed framework for various types of parametric PDEs. Furthermore, our systematic comparisons provide practical guidelines for selecting a proper extrapolation method depending on the available information, desired accuracy, and required inference speed.

42 ENGINEERING↗
DOE OSTI2025

Generalized geometric speed limits for quantum observables

Leveraging quantum information geometry, we derive generalized quantum speed limits on the rate of change of the expectation values of observables. These bounds subsume and, for Hilbert space dimension ≥3, tighten existing bounds—in some cases by an arbitrarily large multiplicative constant. Our theoretical results are supported by illustrative examples and an experimental demonstration using a superconducting qutrit. We also derive two upper bounds on the generalized quantum Fisher information in terms of the condition number of the density matrix. One of these bounds applies only to coherent dynamics and depends also on the variance of the Hamiltonian. The other bound depends also on the so-called Wigner-Yanase skew information. These bounds generalize well-known bounds on the symmetric logarithmic derivative quantum Fisher information and are tighter than the existing bounds for sufficiently mixed states (e.g., for sufficiently high temperature thermal states).

open quantum systems & decoherence↗
DOE OSTI2022

Generative Ensemble Regression: Learning Particle Dynamics from Observations of Ensembles with Physics-informed Deep Generative Models

Here, we propose a new method for inferring the governing stochastic ordinary differential equations (SODEs) by observing particle ensembles at discrete and sparse time instants, i.e., multiple “snapshots.” Particle coordinates at a single time instant, possibly noisy or truncated, are recorded in each snapshot but are unpaired across the snapshots. By training a physics-informed generative model that generates “fake” sample paths, we aim to fit the observed particle ensemble distributions with a curve in the probability measure space, which is induced from the inferred particle dynamics. We employ different metrics to quantify the differences between distributions, e.g., the sliced Wasserstein distances and the adversarial losses in generative adversarial networks. We refer to this method as generative “ensemble-regression” (GER), in analogy to the classic “point-regression,” where we infer the dynamics by performing regression in the Euclidean space. We illustrate the GER by learning the drift and diffusion terms of particle ensembles governed by SODEs with Brownian motions and Lévy processes up to 100 dimensions. We also discuss how to treat cases with noisy or truncated observations. Apart from systems consisting of independent particles, we also tackle nonlocal interacting particle systems with unknown interaction potential parameters by constructing a physics-informed loss function. Finally, we investigate scenarios of paired observations and discuss how to reduce the dimensionality in such cases by proving a convergence theorem that provides theoretical support.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Propagating information content: an example with advection

The mathematical algorithm to derive geophysical information from remote sensing observations is called a retrieval. The mathematics of many retrieval problems are ill-posed, and thus a priori information is used to help constrain the derived geophysical variable to realistic values. One quantity of interest, therefore, is the information content of the observation. Perfect information content in the observation would be achieved if the retrieval were able to capture any perturbation in the desired geophysical variable with the proper magnitude. Many new data products can be derived by combining geophysical variables retrieved from multiple different remote sensors. This paper explores, for the first time, how to derive the information content of these derived products. The approach uses traditional error propagation techniques to derive the uncertainty of the derived field twice, both when the observations are used in the retrieval and also when only the a priori information from each remote sensor is propagated. These two uncertainties are then used to provide an estimate of the information content of the derived geophysical variable. This study demonstrates how to propagate the uncertainties from six different instruments to provide the information content for water vapor and temperature advection. A multi-month analysis demonstrates that, in a mean sense, the information content for temperature advection is nearly unity for all heights below 700 m while, the information content for water vapor advection is somewhat more variable but still larger than 0.6 in the convective boundary layer.

Turner, David D. [National Oceanic and Atmospheric↗
DOE OSTI2025

Hydropower Fish Passage Webmap

The National Fish Passage Webmap application provides an environment that allows users to visualize information information on fish passage facility existence, type, and direction at hydropower developments across the conterminous United States. It was developed through collaborative partnerships with fish passage engineers and biologists at both the US Fish and Wildlife Service (USFWS) and the National Marine Fisheries Service (NMFS), and hydropower experts at the Low Impact Hydropower Institute (LIHI). Data on fish passage facilities at hydropower features were compiled from numerous sources including published and non-published datasets, published reports, email communications with federal and state resource managers and hydropower operators, and by extracting information from regulatory documents within the FERC eLibrary. The number of sources for a given feature varied, which occasionally resulted in conflicting information regarding the existence of fish passage facilities or in the type or sub-type of passage technologies. Such discrepancies were reviewed and resolved individually, based on the weight of evidence or, when available, on direct observations from information providers or aerial imagery.

13 HYDRO ENERGY↗
DOE OSTI2022

Deep learning-based spatio-temporal estimate of greenhouse gas emissions using satellite data

Accurate estimation of greenhouse gases (GHGs) emissions is very important for developing mitigation strategies to climate change by controlling and reducing GHG emissions. This project aims to develop multiple deep learning approaches to estimate anthropogenic greenhouse gas emissions using multiple types of satellite data. NO2 concentration is chosen as an example of GHGs to evaluate the proposed approach. Two sentinel satellites (sentinel-2 and sentinel-5P) provide multiscale observations of GHGs from 10-60m resolution (sentinel-2) to ~kilometer scale resolution (sentinel-5P). Among multiple deep learning (DL) architectures evaluated, two best DL models demonstrate that key features of spatio-temporal satellite data and additional information (e.g., observation times and/or coordinates of ground stations) can be extracted using convolutional neural networks and feed forward neural networks, respectively. In particular, irregular time series data from different NO 2 observation stations limit the flexibility of long short-term memory architecture, requiring zero-padding to fill in missing data. However, deep neural operator (DNO) architecture can stack time-series data as input, providing the flexibility of input structure without zero-padding. As a result, the DNO outperformed other deep learning architectures to account for time-varying features. Overall, temporal patterns with smooth seasonal variations were predicted very well, while frequent fluctuation patterns were not predicted well. In addition, uncertainty quantification using conformal inference method is performed to account for prediction ranges. Overall, this research will lead to a new groundwork for estimating greenhouse gas concentrations using multiple satellite data to enhance our capability of tracking the cause of climate change and developing mitigation strategies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Study of the agreement between binary neutron star ejecta models derived from numerical relativity simulations

Neutron star mergers have recently become a tool to study extreme gravity, nucleosynthesis, and the chemical composition of the Universe. To date, there has been one joint gravitational and electromagnetic observation of a binary neutron star merger, GW170817, as well as a solely gravitational observation, GW190425. In order to accurately identify and interpret electromagnetic signals of neutron star mergers, better models of the matter outflows generated by these mergers are required. We compare a series of ejecta models to see where they provide strong constraints on the amount of ejected mass expected from a system, and where systematic uncertainties in current models prevent us from reliably extracting information from observed events. We also examine 2396 neutron star equations of state compatible with GW170817 to see whether a given ejecta mass could be reasonably produced with a neutron star of said equation of state, and whether different ejecta models provide consistent predictions. We find that the difference between models is often comparable to or larger than the error generally assumed for these models, implying better constraints on the models are needed. In conclusion, we also note that the extrapolation of outflow models outside of their calibration window, while commonly needed to analyze gravitational wave events, is extremely unreliable and occasionally leads to completely unphysical results.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Cluster Structures with Machine Learning Support in Neutron Star M-R relations

Neutron stars (NS) are compact objects with strong gravitational fields, and a matter composition subject to extreme physical conditions. The properties of strongly interacting matter at ultra-high densities and temperatures impose a big challenge to our understanding and modelling tools. Some difficulties are critical, since one cannot reproduce such conditions in our laboratories or assess them purely from astronomical observations. The information we have about neutron star interiors are often extracted indirectly, e.g., from the star mass-radius relation. The mass and radius are global quantities and still have a significant uncertainty, which leads to great variability in studying the micro-physics of the neutron star interior. This leaves open many questions in nuclear astrophysics and the suitable equation of state (EoS) of NS. Recently, new observations appear to constrain the mass-radius and consequently has helped to close some open questions. In this work, utilizing modern machine learning techniques, we analyze the NS mass-radius (M-R) relationship for a set of EoS containing a variety of physical models. Our objective is to determine patterns through the M-R data analysis and develop tools to understand the EoS of neutron stars in forthcoming works.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Assessing Cloud and Precipitation Properties on Temporal and Spatial Scales Using LASSO Simulations over ENA

Low clouds and precipitation representation remain a major source of uncertainty in Earth System Models (ESMs), particularly due to challenges in representing their sub-grid variability and scale-dependent sampling. This study evaluates the performance of preliminary simulations from the Large-Eddy Simulation (LES) ARM Symbiotic Simulation and Observation (LASSO) project over the Eastern North Atlantic (ENA), with a focus on liquid water path (LWP), ice water path (IWP), cloud fraction (CF), and surface precipitation simulated across closed-cell, open-cell, and transitional cloud regimes. Using LES (100 m horizontal grid spacing) driven by ERA5 and MERRA-2 reanalyses, we assess the representativeness of ground-based point observations by analyzing their correspondence to model-resolved spatial and temporal means. Results suggest that observational sampling of at least 6 hours is required to achieve consistency with domain-scale averages, in particular for observations that exhibit pronounced sub-grid heterogeneity, such as precipitation. ERA5-forced simulations exhibit improved spatial coherence and agreement with domain-averaged quantities when compared to MERRA-2 runs, with performance discrepancies largest for convective cloud conditions due to differences in forcing fidelity and temporal resolution. These findings highlight the importance of regime-aware model evaluation strategies and potentially demonstrate how LES can inform observation-model comparison practices and the development of cloud and precipitation parameterizations in ESMs.

Liang, Jiakun [University of Hawai'i at Manoa] (OR↗
DOE OSTI2023

SPRUCE Manual Phenology Observations and Photographs Beginning in 2011

This dataset contains periodic (most often weekly during the active season) observations of the phenology of vegetation bud swell, leaf out, leaf off, flowering, fruiting and the nature of snow cover and ice presence from the 2011-2023 growing seasons. These observations are supplemented by the collection of images from periodic photographs of the experimental plots (e.g., panoramic photographs) or images within plots (e.g., pictures of vegetation development or community status within defined ground footprints such as flux collars). A summary of vegetation phenology and snow and ice presence form 2011-2016 is also provided. Related data sets are also available that summarize site phenology (see Related Datasets). This dataset contains 336 phenology notes files in *.pdf format, 2284 phenology photos in *.jpg format, and three companion files containing phenology summaries, methodological, other contextual information. Early observations were conducted throughout the S1-Bog with an emphasis on the southern end of the bog. Observations collected since the spring of 2013 have focused on the southern end of the S1-Bog and the plots 2 through 21 of the SPRUCE experiment. Beginning in 2015 those observations included assessments within the enclosure space surrounding plots 4, 6, 8, 10, 11, 13, 16, 17, 19 and 20. These observations are ongoing, and this dataset will be appended annually.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Micro Baselines for Operational Technology Environments

Critical infrastructure stakeholders need to baseline their networks to understand expected communications. Top-down approaches to baselining rely on observables that are generally available but lack properties upon which traditional statistical tools depend. We propose to construct micro-baselines: signatures within operational networks based on observables associated with specific events. Such observables are informed by precursor analysis reports of historical cyber attacks on operational environments developed by Cybersecurity for Operational Technology Environments (CyOTE). Baseline measurements depend upon context beyond the cyber domain. An energy plant's baseline running in the summer may statistically differ from a similar facility in a colder region. Domain knowledge must be integrated to apply general micro-baselining algorithms to a facility-specific context. Therefore, we propose to explore the feasibility of transferring micro baselining algorithms across different facilities. Facilities that implement the same processes in different geographic locations will be compared relative to observable measurements used in micro-baselining for comparable events. One evaluation approach would condition or augment dynamic observables measured within a facility network testbed with additional observables derived from geographic context or infrastructure dependencies such as those provided by the All-Hazards Analysis tool.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Modeling Profiles of Micrometeorological Variables in a Tropical Premontane Rainforest Using Multi-Layered CLM (CLM-ML)

This study updates the multi-layered Community Land Model (CLM-ml) for hillslopes and compares predictions from against observations collected in tropical montane rainforest, Costa Rica. Modifications are made in order to capture a wider array of vertical leaf area distributions, predict CO 2 profiles, account for soil respiration, and adjust wind forcings for difficult topographic settings. Test results indicate that the modified multi-layer CLM model can successfully replicate the shape of various micrometeorological profiles (humidity, CO 2 , temperature, and wind speed) under the canopy. In the single-layer models (CLM4.5 and CLM5), excessive day-to-night differences in leaf temperature and leaf wetness were originally noted, but CLM-ml significantly improved these issues, decreasing the amplitudes of diurnal cycles by 67% and 47%. Sub-canopy considerations, such as canopy shapes and turbulent transfer parameters, also played a significant role in model performance. More importantly, unlike single layer models, the results that CLM-ml produces can be compared to variables measured within the canopy to provide far more detailed diagnostic information. Further observations and model developments, aimed at reflecting surface heterogeneity, will be necessary to adequately capture the complexity and the features of the tropical montane rainforest.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Wind Energy Forecasting with the Weather Research and Forecasting Model

This was a collaborative effort between Lawrence Livermore National Security, LLC as manager and operator of Lawrence Livermore National Laboratory (LLNL) and Siemens Energy, Inc. (Siemens) to develop a wind resource forecasting tool. LLNL was to develop an independent high-resolution mesoscale modeling capability forecasting tool that could be implemented in conjunction with existing wind farm control and monitoring software to provide forecasting of wind resources using local observations of winds and temperature. Research with LLNL’s state-of-the-art large-eddy simulation meteorological prediction model, based on the community WRF model and innovative turbulence parameterizations, would improve that model’s applicability to large wind farms offshore and in complex terrain. The modeling capability would include uncertainty quantification. Finally, the application of the modeling tool and existing global climate change predictions would enable the delineation of the likely effects of climate change on wind resources. Siemens was to provide high time resolution hub-height wind speed and other meteorological data streams, including temperature profiles from wind farms, for LLNL to incorporate into the modeling system, to validate and tune this forecasting model for their locations of interest. These data streams would also be used for longer-term studies of correlations of wind resources to climate oscillations to indicate how long-term climate change trends may affect the available wind resource. Siemens would also provide information and observations of turbine wakes for incorporation into the modeling tool. By implementing state-of-the-art turbulence parameterizations into a simulation model and/or ensembles of simulation models, and by integrating real-time hub height wind speed and other meteorological datastreams from wind farms into that model or ensemble of models, LLNL would develop a forecasting tool that could be implemented by Siemens as an add-on to existing wind farm control and monitoring software to provide owners with useful resource forecasting. The desired outcome was that the accuracy level of the output would be sufficient to substantiate power output commitments. The final deliverable for this work would consist of a document outlining the algorithms and software tools that could be integrated into Siemens Wind Park Supervisor.

17 WIND ENERGY↗
DOE OSTI2025

Approximately 15% of Miscanthus yield is lost at current commercial cutting heights in Iowa

Various works have quantitatively characterized the effects of environmental and management factors on Miscanthus x giganteus Greef et Deu (mxg) yield and, therefore, anticipated land requirement per unit production. However, little work has addressed the effects of cutting height, which may significantly contribute to the difference between the standing aboveground biomass at harvest (i.e., biological yield) and harvested yield. This study quantitatively characterized the effect of cutting height using a replicated nitrogen trial of a 5-year-old mxg stand in southeast Iowa and related this information to observations of cutting height in nearby commercial fields. Nitrogen fertilizer did not significantly change the relationship of the stem segment mass to length, and overall, a 1-cm stem segment contributes 0.5% of the total stem biomass within the bottom 44 cm of the stem. This results in an average harvest loss of 15% of the aboveground standing biomass when cutting at 30 cm, typically seen in commercial mxg fields in eastern Iowa. Cutting height should be considered when accurately predicting commercial mxg harvest yields and changes in soil organic carbon in a commercial mxg agroecosystem.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Search for dark matter produced in association with a dark Higgs boson decaying into $W$ + $W$ - in the one-lepton final state at $\sqrt{s}$ = 13 TeV using 139 fb -1 of $pp$ collisions recorded with the ATLAS detector

Several extensions of the Standard Model predict the production of dark matter particles at the LHC. A search for dark matter particles produced in association with a dark Higgs boson decaying into $W$ + $W$ in the $\ell$ ± $vq$$\overline{q}$ $'$ final states with $\ell$ = $e, μ$ is presented. This analysis uses 139 fb-1 of pp collisions recorded by the ATLAS detector at a centre-of-mass energy of 13 TeV. The $W$ ± → $q$$\overline{q}$ $'$ decays are reconstructed from pairs of calorimeter-measured jets or from track-assisted reclustered jets, a technique aimed at resolving the dense topology from a pair of boosted quarks using jets in the calorimeter and tracking information. The observed data are found to agree with Standard Model predictions. Scenarios with dark Higgs boson masses ranging between 140 and 390 GeV are excluded.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Estimating the Contribution of the Nickel to Protium Loading of Full-Length Getters

The full-length getter (FLG) is a critical component of the tritium producing burnable absorber rod (TPBAR), designed to capture tritium produced within the lithium aluminate pellets. However, due to the chemical similarity between protium (¹H) and tritium (³H), the FLG readily absorbs protium, reducing its capacity to absorb tritium and increasing the risk of tritium permeation into the reactor coolant. This study evaluates protium produced from neutron irradiation of nickel plating on the FLG through 5?Ni(n,p) reactions, quantifies its contribution to the total protium observed, and informs models of tritium and hydrogen transport within TPBARs. During irradiation, neutron capture by 58Ni results in the formation of 5?Ni, which undergoes neutron bombardment to produce 4He via 5?Ni(n,a) reactions and protium via 5?Ni(n,p) reactions. The measured helium content post-irradiation provides insight into the neutron capture processes within the getter. The 4He measured within the getter may be useful in determining hydrogen produced by the nickel plating on FLG because 59Ni also produces protium in a 59Ni(n, p) reaction. The contribution of 1H from the nickel in FLG contributed less than 1% of the measured H2 gas in PIE, ranging from 0.005% to 0.614%. These findings refine current knowledge of the protium-tritium interplay in TPBARs and support the development of improved transport models for tritium and hydrogen, ultimately aiding in the optimization of TPBAR design and reactor operations.

Arbova, Dana L.↗
DOE OSTI2021

Evaluating the benefits of kinetic Monte Carlo and microkinetic modeling for catalyst design studies in the presence of lateral interactions

Popular computational catalyst design strategies rely on the identification of reactivity descriptors, which can be used along with Brønsted–Evans–Polanyi (BEP) and scaling relations as input to a microkinetic model (MKM) to make predictions for activity or selectivity trends. The main benefit of this approach is related to the inherent dimensionality reduction of the large material space to just a few catalyst descriptors. Conversely, it is well documented that a small set of descriptors is insufficient to capture the intricacies and complexities of a real catalytic system. The inclusion of coverage effects through lateral adsorbate-adsorbate interactions can narrow the gap between simplified descriptor predictions and real systems, but mean-field MKMs cannot properly account for local coverage effects. This shortcoming of the mean-field approximation can be rectified by switching to a lattice-based kinetic Monte Carlo (kMC) method using cluster expansion representation of adsorbate–adsorbate lateral interactions. Using the prototypical CO oxidation reaction as an example, we critically evaluate the benefits of kMC over MKM in terms of trend predictions and computational cost when using only a small set of input parameters. After confirming that in the absence of lateral interactions the kMC and MKM approaches yield identical trends and mechanistic information, we observed substantial differences between the two kinetic models when lateral interactions were introduced. Here, the mean-field implementation applies coverage corrections directly to the descriptors, causing an artificial overprediction of the activity of strongly binding metals. In contrast, the cluster expansion in kMC implementation can differentiate among the highly active metals but it is very sensitive to the set of included interaction parameters. Considering that computational screening relies on a minimal set of descriptors, for which MKM makes reasonable trend predictions at a ca. three orders of magnitude lower computational cost than kMC, the MKM approach does provide a better entry point for computational catalyst design.

36 MATERIALS SCIENCE↗