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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Physics-Informed Learning for Predictive Multi-Scale Modeling of Water Cycle and Extreme Events

Climate and earth system models have limited skill in predicting water cycle and associated extremes such as intense rain- and wind-producing storms and resulting hazards across all timescales. Water cycle predictions are inherently limited by the predictability of the chaotic system, but large potential for improving predictions remains untapped due to limited use of data-model integration to improve predictive modeling. This white paper proposes a hybrid data-physics paradigm based on physics-informed neural networks (PINNs) to improve the Energy Exascale Earth System Model (E3SM). In particular, we aim to predict and quantify uncertainties of water cycle processes and low probability high impact events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Evaluation of Commercially Available Precursors for Obscuring Wet Industrial Irradiator Facility Pools to Delay Adversarial Source Removal (Part 2)

The behavior of commercially available potential obscurants for cobalt-60 ( 60 Co) wet-source storage industrial irradiator facilities (IRFs) were further evaluated for corrosive behavior of Nordion C-188 pencil stubs and obscurant properties under radiation exposure ( 60 Co). The potential obscurants studied included: titania aqueous dispersions (TAD - water soluble white paint), Chlorazol Black (CBOD - Chlorazol Black organic dye), powdered milk (COW - calcium obscurant in water), diatomaceous earth (DEA - diatomaceous earth additive), and rhodamine 6G (R6G). For corrosion efforts, stubs from an inert C-188 pencil-source rod were soaked in obscurant solutions and visually inspected. For radiation stability, obscurant samples were exposed to 60 Co radiation sources at 207 rad/s. The results from these studies reveal: the obscurants had no impact on the surrogate samples and may assist in terms of corrosion resistance; materials that did not rely on organic compounds to provide obscurance performed the best, as the organic compounds decomposed too rapidly in the high radiation environment, whereas the materials survived.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2021

Hybridizing Machine Learning and Physically-based Earth System Models to Improve Prediction of Multivariate Extreme Events (AI Exploration of Wildland Fire Prediction)

Focal Areas: This project responds to two focal areas identified in the DOE Call for AI4ESP White Papers: 1) Predictive modeling through the use of artificial intelligence (AI) techniques, and 2) insights gleaned from complex data using explainable AI and big data analytics. Science Challenge: Large wildland fires (hereafter wildfires) appearing as high-impact compound climate extreme events are closely related to hydroclimate and water cycle extremes that modulate surface fuel supply and combustibility. These compound events have multivariate climatic features (e.g., temperature, precipitation, relative humidity, wind, lightning) and societal drivers (e.g., forest management, land use change, human caused ignitions). Meanwhile, they induce strong feedbacks to the coupled atmosphere, biosphere, and hydrosphere by perturbing regional and global radiation budget as well as ecological, biogeochemical, and water cycles across multiple spatiotemporal scales. The nonlinear interactions between these natural and anthropogenic components of the Earth system are too complex to be completely and adequately represented in today’s Earth system models (ESMs). The inherent stochastic nature of fire activity at all scales further increases the difficulty of its prediction using ESMs that are usually developed from deterministic equations and parameterizations. Besides, concurrence of long-term (decadal to interdecadal) global climate change and fire regime shifts overlapping with short-term (intraseasonal to interannual) variations of regional fire weather and burning activity confound predictability of these compound extreme events. We propose to address the above scientific challenges by using machine learning (ML)-based data-driven modeling techniques to integrate observations and physically-based ESMs’ simulations in a computationally efficient hybrid prediction system. This prediction system is supposed to characterize the wildfire’s sensitivity to climate and exogenous drivers at high resolution (~ 0.25°) on subseasonal to seasonal (S2S) timescales providing improved predictability and explainability. We will use the system to help identify: (1) What are the computational elements of a hybrid system needed to predict compound climate extreme events such as global wildfires? (2) What are the key drivers (either natural or anthropogenic) that modulate short-term variations of multivariate fire weather and burning activity over different regions? How can one take advantage of those driver-response relationships to improve the predictability of large wildfires on S2S time scales? (3) What are the underlying physical mechanisms and sources of improved predictability? Which ML techniques are optimal in revealing and adapting these mechanisms?

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Towards green synthesis of Mn 4+ -doped fluoride phosphors: a review

Mn 4+ -doped fluoride phosphors are attractive for application in white solid-state lighting devices because their distinct red emission can improve the color rendering index, while the earth-abundance of Mn 4+ ions can reduce the production cost. However, highly hazardous HF is used as the fluoride source in the synthesis processes, which requires extreme caution. In this review, we introduce various methods for synthesizing Mn 4+ -doped fluoride phosphors, categorized according to the presence or absence of HF in the synthesis. The underlying optical properties of the phosphors fabricated using the various methods are discussed. In addition, in the discussion of each method, we emphasize on the key factors and strategies for developing phosphors. Finally, this review highlights perspectives on and future directions for the safe synthesis of Mn 4+ -doped fluoride phosphors to achieve solid-state lighting devices with superior optical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Highlight of IC project: w25_dreamxd

(left) The Earth’s radiation belts are donut-shaped regions containing MeV electrons (color contour for density) trapped by the magnetic field (white curves). We use DREAMxD code to model their dynamics. (right) We develop a new method to accelerate a key piece of the code – diffusion coefficient calculation. Comparing the compute time in node hours required by the standard approach to the compute time required for the fast method, it shows that the new method can be 100x faster for a large problem size.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

WD1032 + 011, an inflated brown dwarf in an old eclipsing binary with a white dwarf

We present the discovery of only the third brown dwarf known to eclipse a non-accreting white dwarf. Gaia parallax information and multicolour photometry confirm that the white dwarf is cool (9950 ± 150 K) and has a low mass (0.45 ± 0.05 M ⊙ ), and spectra and light curves suggest the brown dwarf has a mass of 0.067 ± 0.006 M ⊙ (70M Jup ) and a spectral type of L5 ± 1. The kinematics of the system show that the binary is likely to be a member of the thick disc and therefore at least 5-Gyr old. In conclusion, the high-cadence light curves show that the brown dwarf is inflated, making it the first brown dwarf in an eclipsing white dwarf-brown dwarf binary to be so.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

How Gaps in Time-Series Data Affect Asteroseismic Interpretation

Most pulsating white dwarf stars pulsate with many periods, each of which is a probe of their interior, which has made asteroseismolgy of these stars an active field. However, disentangling the multiple periodicities requires long, uninterrupted strings of data. We briefly describe the history of multi-site observing campaigns that culminated in the development of the Whole Earth Telescope in the late 1980s that still functions today. Through examples from the May 1990 campaign on GD 358, we show how critical it is to eliminate periodic gaps in data to greatly reduce aliasing in Fourier Transforms normally used to analyze the frequency content of pulsating white dwarfs. We close with a brief description of space satellite-based data, along with the advantages and disadvantages of these data compared to ground-based data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Community Geothermal: Final Thermal Conductivity Test Report and Data Logs - Carbondale, CO

Provided here are a final thermal conductivity test report, a drilling log, and a heat rejection log from Carbondale, CO. Also attached is a report made before drilling, which contains predictions on the hydrogeologic conditions of the drill site. The forty-eight (48.9) hour in-situ thermal conductivity test was performed on the 28th through the 30th, of November 2023. The test was performed at the borehole drilled on November 16th through the 20th, at the 3rd Street Center at 520 S. 3rd, Street in Carbondale, Colorado. Testing was done with a certified Ewbank portable test unit.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Leveraging ARM Data to Improve Models for Predictive Understanding of Energy and Security Challenges

Extreme weather and natural hazards can disrupt the energy sector, affecting demand, generation, transmission, distribution, consumption and operational planning at regional and national scales. These disruptions stem from a broad range of atmospheric phenomena, including winter storms, freezing rain, wet snow loading, severe convection, flooding and landslides, wildfires, prolonged heat, and drought. Many of these same phenomena can also affect national security through impacts to transportation and infrastructure. To support the U.S. Department of Energy (DOE) focus on energy resilience and national security, the Atmospheric Radiation Measurement (ARM) User Facility is uniquely positioned to contribute measurement data, analyses, and modeling frameworks that can significantly improve predictive understanding of these hazards to mitigate their effects. To explore this opportunity, ARM convened a two-part virtual workshop in November 2025. The workshop engaged interdisciplinary experts in atmospheric science, energy systems, modeling, and operations. The goal of the meeting was to engage with these interdisciplinary experts to address three questions: • What are examples of atmospheric processes that represent significant risks to energy security or national security and where are those risks greatest? • What measurements or measurement strategies would improve ARM’s capacity to address these issues? • How can ARM and users of the ARM facility better work with the Energy Exascale Earth System Model (E3SM) and multi-sector modeling communities to apply ARM data to improving E3SM simulations of these phenomena? Participants were asked to submit white papers ahead of the meeting to initiate thinking on these themes and to help organize discussions. Workshop sessions were then organized around themes identified in the white papers. First from the white papers and then through subsequent discussions, workshop participants identified many examples that address the three questions listed above. Participants called out energy system vulnerabilities to weather phenomena such as the impact of freezing rain, strong winds, and excessive heat on power grids. They also noted the effects that weather phenomena could have on energy demand or supply (e.g., through effects of extreme temperatures). They called out security vulnerabilities such as impacts to crops from aerosol-borne pathogens and risks to industry due to melting permafrost in the Arctic. In all, over a dozen meteorological phenomena were linked to energy or security vulnerabilities. For many of the identified phenomena, participants pointed out where ARM was well poised to address issues (e.g., through measurements of cloud microphysics to inform studies of freezing rain) but also noted needs for additional measurements or modified measurement strategies. For example, adaptive scanning of severe weather would be valuable for probing winter storms or severe convection. Participants pointed out the value in integrating external observations with ARM measurements and with applying artificial intelligence (AI) to ARM observation analysis and they advocated for using model simulations to help optimize measurement strategies through Observing System Simulation Experiments (OSSEs). It was clear from the workshop that there are many ways that ARM observations can be used to mitigate energy and security concerns, but meeting participants were also asked to identify what they considered to be the greatest opportunities by ranking issues pertaining to the three workshop questions. This was accomplished through a survey administered to participants between the two virtual sessions. The highest-priority phenomena identified were winter storms, severe convection, and arctic processes. Discussion in the second session, therefore, focused primarily on these three areas, which were most fully developed in exploring ARM opportunities. Nevertheless, it was also clear that ARM has opportunities to contribute to all the identified topics. This report describes the workshop, including input from discussion and white papers (Sections 2 and 3) and a list of priority recommendations (section 4). Many other ideas for ARM contributions are discussed in individual white papers (Appendix D).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Integration of AI/ML with Data Assimilation for Earth System Prediction

Focal Area(s): Development of dynamically consistent AI/ML methods that can integrate with the data assimilation cycle to improve the efficiency and effectiveness of state estimation and prediction at short to medium range forecast horizons. Science Challenge: This white paper addresses a first step toward complete integration of AI/ML and DA. Here, we address model emulation and its application to simple coupled atmosphere-ocean dynamics for use in data assimilation applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Where Are White Roofs More Effective in Cooling the Surface?

Solar reflective roofs (also called white or cool roofs) are a common heat mitigation strategy. By reflecting more sunlight than regular roofs, cool roofs experience lower surface temperatures and produce less heating of the surrounding air. The effectiveness of cool roofs, quantified by the surface temperature difference between cool roofs and regular roofs ??????, is known to vary spatially. A common perception is that ?????? is controlled by solar radiation that reaches the roof surface. Here we use an Earth System Model and a surface energy balance model to show that the spatial variability of ??????, when normalized by the albedo difference between cool roofs and regular roofs (????), is in fact more controlled by an energy distribution factor that encodes the efficiencies of surface energy balance components in dissipating heat. Our results suggest that painting the roof white is more effective in reducing the roof surface temperature when the roof has less water holding capacity and smaller thermal admittance and is located in places with more solar radiation, less precipitation, and lower wind speed.

Wang, Linying↗
DOE OSTI2022

Understanding the Drivers of Atlantic Multidecadal Variability using a Stochastic Model Hierarchy

The relative importance of ocean and atmospheric dynamics in generating Atlantic Multidecadal Variability (AMV) remains an open question. Comparisons between climate models with SLAB and fully-dynamic (FULL) ocean components are often used to explore this question, but cannot reveal how individual ocean processes generate these differences. We build a hierarchy of physically interpretable stochastic models to investigate the contribution of two upper-ocean processes to AMV: the role of seasonal variation and mixed-layer entrainment. This interpretability arises from the stochastic model’s simplified representation of sea surface temperature (SST), considering only the local upper ocean response to white-noise atmospheric forcing and its impact on surface heat exchange. We focus on understanding differences between SLAB and FULL non-eddy resolving pre-industrial control simulations of the Community Earth System Model 1 (CESM), and estimate the stochastic model parameters from each respective simulation. Despite its simplicity, the stochastic model reproduces temporal characteristics of SST variability in the SPG, including reemergence, seasonal-to-interannual persistence and power spectra. Furthermore, unrealistically persistent SST of the CESM-SLAB ocean simulation is reproduced in the equivalent stochastic model configuration where the mixed-layer depth (MLD) is constant. The stochastic model also reveals that vertical entrainment primarily damps SST variability, thus explaining why SLAB exhibits larger SST variance than FULL. Here, the stochastic model driven by temporally stochastic, spatially coherent forcing patterns reproduces the canonical AMV pattern. However, the amplitude of low-frequency variability remains underestimated, suggesting a role for ocean dynamics beyond entrainment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Site‐selective fluorescence and spectroscopic properties of Yb‐doped lanthanum titanate glasses

Abstract Ytterbium‐doped lanthanum titanate glasses were prepared by levitation melting for the detailed characterization of the spectroscopic properties in the rare‐earth titanate glass host. Low‐temperature fluorescence spectroscopy reveals distinct site‐selectivity in both static and lifetime fluorescence measurements suggesting an absence of clustering as well as significant variation of local ytterbium environments. Typical site‐selectivity behavior of a shrinking Stark manifold with lower excitation energy is observed. At 77 K, both the mean emission frequency and the fluorescence lifetime initially increase as the excitation energy decreases from about 11100 to 10750 and then slightly decrease at lower excitation energy. Temperature‐dependent lifetime measurements between 77 and 420 K show a decreasing lifetime with increasing temperature and are well described by a two‐level thermal activation model. The temperature‐dependent fluorescence spectroscopy coupled with a room temperature white light absorption measurement allow the determination of the Stark energy levels of in lanthanum titanate glass as well as the calculation of the laser cross‐sections.

Materials Science↗
DOE OSTI2022

Luminescent MOFs (LMOFs): recent advancement towards a greener WLED technology

The replacement of traditional incandescent, halogen and fluorescent lamps by white light emitting diodes (WLEDs) is expected to reduce the global electricity consumption by one-third by 2030, according to the US Department of Energy. The current WLED technology uses rare-earth element (REE) based phosphor materials, which, not only is cost-intensive but also constitutes an environmental concern. Hence, researchers are in a quest for a new-generation of opto-electronic materials that can replace the conventional phosphors in WLEDs and thus aim towards a cleaner and more energy efficient lighting technology for the future. Luminescent metal–organic frameworks (LMOFs) have recently emerged as a new sub-class of MOFs which have demonstrated enormous potential for applications in sensing, imaging, optoelectronics and in solid-state lighting (SSL) technology. LMOFs could be game changers as lighting phosphors due to advantages such as high luminescence quantum yield, tunable excitation and emission which can be achieved by rational design and optimization of metal centers, linkers, and the guest molecules, facile fabrication into devices, and structural robustness. These clear advantageous features of LMOFs make them score over other contemporary materials, and enable them to be futuristic phosphor materials for WLED technology. Here, in this feature article, we will provide an overview of the most recent developments of LMOF-based phosphor materials for SSL with a special focus on WLED technology. The emphasis will be centered around REE-free LMOFs, as the aim is to direct the attention of the readers towards a more viable and greener lighting technology.

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

Data-Driven Exploration of Climate Attractor Manifolds For Long-Term Predictability

Focal Area: This white paper responds to Focal Area 3. We seek to gain insight into decadal-scale climate predictability by applying novel manifold-finding probabilistic AI techniques to the complex data produced by Earth System models (ESMs) such as E3SM. The associated portfolio of research activities leverages DOE’s asset mix of HPC platforms, climate expertise, climate simulation codes, and AI expertise. Science Challenge: Climate and climate models are dynamical systems exhibiting properties that are interpretable through chaos theory. The theory contains an important concept that is relevant to multi-decade-scale climate prediction: a chaotic attractor. While the space containing all the possible states of the Earth’s atmosphere and ocean, the possible weather, is large, the realized states tend to stay near the smaller-dimensioned attractor. This behavior is responsible for the “order behind the irregularity” [1] of climate phenomena. Climate change can be thought of as a change in the properties of the attractor, and predicting the climate over years to decades is equivalent to predicting how those properties will change. To date, the attractor has been a useful conceptual tool, but has not been amenable to direct characterization. A new development is the advent of efficient high-dimensional manifold-finding probabilistic AI techniques, which permit a data-driven characterization of the ESM attractor and its probability distribution over weather states. Such a characterization would result in a natural dimensional reduction — a “non-linear Principal Components Analysis (PCA) adapted to climate simulation data” — leading to important advances in scenario-based long-term climate prediction, long-term prediction of water cycle extremes, ESM verification, inter-model comparison, and process model development.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Evaporation Dataset from a Temperate Closed-basin Lake in Minnesota 2014-2016

This data set contains observed and modeled evaporation data, eddy covariance data, and supporting information measured at White Bear Lake in the Twin Cities Metropolitan Area near St. Paul, Minnesota, USA. The data package includes csv files containing half-hourly latent heat flux, sensible heat flux, and meteorological data from 2014 to 2016. It also includes csv files containing half-hourly observed evaporation measurements, along with supporting data for validation and retrospective modeling of evaporation. This data was collected to examine current, past, and future lake evaporation to better understand how climate will impact the water balance of lakes within this region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Strong Red Noise Ocean Forcing on Atlantic Multidecadal Variability Assessed from Surface Heat Flux: Theory and Application

The role of ocean forcing on Atlantic multidecadal variability (AMV) is assessed from the (downward) heat flux–SST relation in the framework of a new stochastic climate theory forced by red noise ocean forcing. Previous studies suggested that atmospheric forcing drives SST variability from monthly to interannual time scales, with a positive heat flux–SST correlation, while heat flux induced by ocean processes can drive SST variability at decadal and longer time scales, with a negative heat flux–SST correlation. Here, first, we develop a theory to show how the sign of heat flux–SST correlation is affected by atmospheric and oceanic forcing with time scale. In particular, a red noise ocean forcing is necessary for the sign reversal of heat flux–SST correlation. Furthermore, this sign reversal can be detected equivalently in three approaches: the low-pass correlation at lag zero, the unfiltered correlation at long (heat flux) lead, and the real part of the heat flux–SST coherence. Second, we develop a new scheme in combination with the theory to assess the magnitude and time scale of the red noise ocean forcing for AMV in the GFDL SPEAR model (Seamless System for Prediction and Earth System Research) and observations. In both the model and observations, the ocean forcing on AMV is in general comparable with the atmospheric forcing, with a 90% probability greater than the atmospheric forcing in observations. In contrast to the white noise atmospheric forcing, the ocean forcing has a persistence time comparable or longer than a year, much longer than the SST persistence of ~3 months. This slow ocean forcing is associated implicitly with slow subsurface ocean dynamics.

54 ENVIRONMENTAL SCIENCES↗