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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS1984

On the generation and evolution of internal gravity waves

The tidal generation and evolution of internal gravity waves is investigated experimentally and theoretically using a two-dimensional two-layer model. Time-dependent flow is created by moving a profile of maximum submerged depth 7.7 cm through a total stroke of 29 cm in water above a freon-kerosene mixture in an 8.6-m-long 30-cm-deep 20-cm-wide transparent channel, and the deformation of the fluid interface is recorded photographically. A theoretical model of the interface as a set of discrete vortices is constructed numerically; the rigid structures are represented by a source distribution; governing equations in Lagrangian form are obtained; and two integrodifferential equations relating baroclinic vorticity generation and source-density generation are derived. The experimental and computed results are shown in photographs and graphs, respectively, and found to be in good agreement at small Froude numbers. The reasons for small discrepancies in the position of the maximum interface displacement at large Froude numbers are examined.

Lansing, F. S.↗
NASA NTRS2018

Cryogenic Selective Surfaces

There are many challenges involved in deep-space exploration, but several of these can be mitigated, or even solved, by the development of a coating that reflects most of the Sun’s energy, yet still provides far-infrared heat emission. Such a coating would allow non-heat-generating objects in space to reach cryogenic temperatures without using an active cooling system. This would benefit deep-space sensors that require low temperatures, such as the James Webb Telescope focal plane array. It would also allow the use of superconductors in deep space, which could lead to magnetic energy storage rings, lossless power delivery, or perhaps a large-volume magnetic shield against galactic cosmic radiation. However, perhaps the most significant enablement achieved from such a coating would be the long-term, deep space storage of cryogenic liquids, such as liquid oxygen (LOX). In our Phase I NIAC study, we realized that a combination of scattering particles and a silver backing could yield a highly effective, very broadband, reflector that could potentially reflect more than 99.9% of the Sun’s irradiant power. We developed a sophisticated model of this reflector and theoretically showed that cryogenic temperatures could be achieved in deep space at one astronomical unit (1 AU) from the Sun. We showed how this new reflector could minimize heat conduction into the cryogenic tanks by coating the tank support struts. We then modelled a strawman architecture for a mission to Mars, using a coated LOX tank, coated struts, and infrared shields, to show that with our new coating it would be possible to maintain liquid oxygen passively. As a result of this work a patent application was generated and a paper published in Optics Letters. Our Phase II NIAC study had two primary goals, to develop a rigid version of the cryogenic selective surface proposed in Phase I and to test its performance in a simulated deep space environment. During the first year of the project the work concentrated on developing rigid tiles of BaF2, leading to tiles as large as 4 inches in diameter that transmitted very little visible light. In addition, during the first year a simulated deep space environment was created using a vacuum chamber and cryocooler. Using this facility, we showed that our BaF2 tiles absorbed less than ¼% of 375 nm radiation, a significant milestone for the work. During the second year of the project, we continued to develop the BaF2 tiles and we put significant effort into the construction of a deep space environment where we could project simulated solar radiation onto a sample. In the spring of 2018, we conducted our first solar simulator test with BaF2 and saw about 3.6% absorption. This is better than the state-of-the-art, but disappointing since predictions were for much lower absorption. We, erroneously, attributed this absorption to water retention by the BaF2, and decided to change materials. We considered several oxides and settled on yttrium oxide (Y2O3) for further development, because it is broadband, lightweight, has high index, and is hydrophobic. In July 2018 we conducted our first test of a rigid tile of Y2O3 in the simulated deep space environment and saw significant absorption again. We then realized that the issue was not water, but mid-wave radiation passing through the tile and being absorbed by the temperature sensor and the varnish used to hold it in place. We wrapped the sensor in silver foil, re-ran the test, and saw much lower absorption; only 1.1%. We then re-ran the BaF2 tile and saw 1.4% absorption. These values are almost adequate to maintain LOX in deep space, but we suspect that there are still issues in our test apparatus; we suspect thermocouple wires may be absorbing radiation. Further, post-NIAC, testing will better determine the performance of our new solar reflector. In order to restrict the size of this report, we will only briefly describe topics that we have previously published, allowing us to devote more time to new material. So minimal material will be devoted to modeling the material and deep space cryogenic storage, while longer sections will cover our material development, simulated deep space testing, and new applications. The Launch Service Program (LSP) requested that we explore ways to use this new coating to maintain LOX in low Earth Orbit and that work is described. In addition, the Nuclear Thermal Propulsion (NTP) Program asked us to explore ways to reduce the heat load for liquid hydrogen, resulting in the development of a spray-on version of the coating that should significantly improve in-space multi-layer insulation performance.

Robert C. Youngquist,↗
DOE OSTI2026

Expert evaluation of LLM world models: A high-T c superconductivity case study

Large Language Models (LLMs) show great promise as a powerful tool for scientific literature exploration. However, their effectiveness in providing scientifically accurate and comprehensive answers to complex questions within specialized domains remains an active area of research. Using the field of high-temperature cuprates as an exemplar, we evaluate the ability of LLM systems to understand the literature at the level of an expert. We construct an expert-curated database of 1,726 scientific papers that covers the history of the field, and a set of 67 expert-formulated questions that probe deep understanding of the literature. We then evaluate six different LLM-based systems for answering these questions, including both commercially available closed models and a custom retrieval-augmented generation (RAG) system capable of retrieving images alongside text. Experts then evaluate the answers of these systems against a rubric that assesses balanced perspectives, factual comprehensiveness, succinctness, and evidentiary support. Among the six systems, two using RAG on curated literature outperformed existing closed models across key metrics, particularly in providing comprehensive and well-supported answers. We discuss promising aspects of LLM performances as well as critical short-comings of all the models. The set of expert-formulated questions and the rubric will be valuable for assessing expert level performance of LLM based reasoning systems.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Measurement of exclusive 𝜋 + -argon interactions using ProtoDUNE-SP

We present the measurement of 𝜋 + -argon inelastic cross sections using the ProtoDUNE single-phase liquid argon time projection chamber in the incident 𝜋 + kinetic energy range of 500–800 MeV in multiple exclusive channels (absorption, charge exchange, and the remaining inelastic interactions). The results of this analysis are important inputs to simulations of liquid argon neutrino experiments such as the Deep Underground Neutrino Experiment and the Short Baseline Neutrino program at Fermi National Accelerator Laboratory. They will be employed to improve the modeling of final state interactions within neutrino event generators used by these experiments, as well as the modeling of 𝜋 + -argon secondary interactions within the liquid argon. This is the first measurement of 𝜋 + -argon absorption at this kinetic energy range as well as the first ever measurement of 𝜋 + -argon charge exchange.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Adaptive optical third-harmonic generation microscopy for in vivo imaging of tissues

Third-harmonic generation microscopy is a powerful label-free nonlinear imaging technique, providing essential information about structural characteristics of cells and tissues without requiring external labelling agents. In this work, we integrated a recently developed compact adaptive optics module into a third-harmonic generation microscope, to measure and correct for optical aberrations in complex tissues. Taking advantage of the high sensitivity of the third-harmonic generation process to material interfaces and thin membranes, along with the 1,300-nm excitation wavelength used here, our adaptive optical third-harmonic generation microscope enabled high-resolution in vivo imaging within highly scattering biological model systems. Examples include imaging of myelinated axons and vascular structures within the mouse spinal cord and deep cortical layers of the mouse brain, along with imaging of key anatomical features in the roots of the model plant Brachypodium distachyon. In all instances, aberration correction led to enhancements in image quality.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1981

An optimization model for energy generation and distribution in a dynamic facility

An analytical model is described using linear programming for the optimum generation and distribution of energy demands among competing energy resources and different economic criteria. The model, which will be used as a general engineering tool in the analysis of the Deep Space Network ground facility, considers several essential decisions for better design and operation. The decisions sought for the particular energy application include: the optimum time to build an assembly of elements, inclusion of a storage medium of some type, and the size or capacity of the elements that will minimize the total life-cycle cost over a given number of years. The model, which is structured in multiple time divisions, employ the decomposition principle for large-size matrices, the branch-and-bound method in mixed-integer programming, and the revised simplex technique for efficient and economic computer use.

Lansing, F. L.↗
DOE OSTI2025

Deep Reinforcement Learning for Distribution System Operations: A Tutorial and Survey

Here, the rapid evolution of modern electric power distribution systems into complex networks of interconnected active devices, distributed generation (DG), and storage poses increasing difficulties for system operators. The large-scale integration of distributed energy resources (DERs) and the rapid exchange of measurement data via communication networks present major opportunities for advancing grid operations but also introduce greater uncertainty, higher data dimensionality, more complex network and device models, and challenging control and optimization problems. Deep reinforcement learning (DRL) algorithms are promising in addressing these challenges. However, they have not been effectively adapted for power systems applications, requiring extensive customization for implementation and evaluation. This has resulted in reproducibility challenges and a steep learning curve for researchers new to applying DRL algorithms to the power systems domain. To bridge these gaps, this tutorial aims to serve as a valuable resource for researchers interested in exploring learning-based algorithms to operate active power distribution networks. Specifically, this work presents a generalized process for translating sequential decision-making problems in power distribution systems into Markov decision process (MDP) formulations, illustrated through concrete grid service examples. Additionally, we introduce a simple environment design strategy to develop and evaluate example DRL algorithms for distribution system applications, complete with an included code repository to guide users through environment construction.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2008

The NASA Evolutionary Xenon Thruster (NEXT): NASA's Next Step for U.S. Deep Space Propulsion

NASA s Evolutionary Xenon Thruster (NEXT) project is developing next generation ion propulsion technologies to enhance the performance and lower the costs of future NASA space science missions. This is being accomplished by producing Engineering Model (EM) and Prototype Model (PM) components, validating these via qualification-level and integrated system testing, and preparing the transition of NEXT technologies to flight system development. The project is currently completing one of the final milestones of the effort, that is operation of an integrated NEXT Ion Propulsion System (IPS) in a simulated space environment. This test will advance the NEXT system to a NASA Technology Readiness Level (TRL) of 6 (i.e., operation of a prototypical system in a representative environment), and will confirm its readiness for flight. Besides its promise for upcoming NASA science missions, NEXT may have excellent potential for future commercial and international spacecraft applications.

Schmidt, George R.↗
DOE OSTI2024

Time Series Foundation Models and Deep Learning Architectures for Earthquake Temporal and Spatial Nowcasting

Advancing the capabilities of earthquake nowcasting, the real-time forecasting of seismic activities, remains crucial for reducing casualties. This multifaceted challenge has recently gained attention within the deep learning domain, facilitated by the availability of extensive earthquake datasets. Despite significant advancements, the existing literature on earthquake nowcasting lacks comprehensive evaluations of pre-trained foundation models and modern deep learning architectures; each focuses on a different aspect of data, such as spatial relationships, temporal patterns, and multi-scale dependencies. This paper addresses the mentioned gap by analyzing different architectures and introducing two innovative approaches called Multi Foundation Quake and GNNCoder. We formulate earthquake nowcasting as a time series forecasting problem for the next 14 days within 0.1-degree spatial bins in Southern California. Earthquake time series are generated using the logarithm energy released by quakes, spanning 1986 to 2024. Our comprehensive evaluations demonstrate that our introduced models outperform other custom architectures by effectively capturing temporal-spatial relationships inherent in seismic data. The performance of existing foundation models varies significantly based on the pre-training datasets, emphasizing the need for careful dataset selection. However, we introduce a novel method, Multi Foundation Quake, that achieves the best overall performance by combining a bespoke pattern with Foundation model results handled as auxiliary streams.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Super Resolution for Renewable Energy Resource Data With Wind From Reanalysis Data (Sup3rWind) and Application to Ukraine [Slides]

In this work we present a novel deep learning-based downscaling method, using generative adversarial networks (GANs), for generating high-resolution wind resource data from ECMWF Reanalysis v5 data (ERA5). We show that by training a GAN model on ERA5, as opposed to coarsened high-resolution data, we achieve results that are competitive with conventional dynamical downscaling. This GAN-based downscaling method additionally reduces computational costs over dynamical downscaling by two orders of magnitude. All GANs are trained on data sampled from CONUS, selected to provide a diverse sampling of terrain conditions, and validated on observational data along with data held out from training. This cross-validation shows low error and high correlations with observations and excellent agreement with hold out data across physical distributions. Our approach is finally used to downscale 30km hourly ERA5 to 2-km 5-minute wind data, for January 2000 through December 2023, at multiple hub heights, over Ukraine, Moldova, and part of Romania. Comparisons against observational data from Meteorological Assimilation Data Ingest System (MADIS) and multiple wind farms show the same level of performance as for CONUS validation. This 24 year data record is the first member of the "super resolution for renewable energy resource data with wind from reanalysis data" dataset (Sup3rWind).

17 WIND ENERGY↗
NASA NTRS1981

Further SEASAT SAR coastal ocean wave analysis

Analysis techniques used to exploit SEASAT synthetic aperture radar (SAR) data of gravity waves are discussed and the SEASAT SAR's ability to monitor large scale variations in gravity wave fields in both deep and shallow water is evaluated. The SAR analysis techniques investigated included motion compensation adjustments and the semicausal model for spectral analysis of SAR wave data. It was determined that spectra generated from fast Fourier transform analysis (FFT) of SAR wave data were not significantly altered when either range telerotation adjustments or azimuth focus shifts were used during processing of the SAR signal histories, indicating that SEASAT imagery of gravity waves is not significantly improved or degraded by motion compensation adjustments. Evaluation of the semicausal (SC) model using SEASAT SAR data from Rev. 974 indicates that the SC spectral estimates were not significantly better than the FFT results.

Kasischke, E. S.↗
NASA NTRS2013

Integration of Mirror Design with Suspension System using NASA's New Mirror Modeling Software

Advances in mirror fabrication is making very large space based telescopes possible. In the many applications, only monolithic mirrors meet the performance requirements. The existing and near-term planned heavy launch vehicles place a premium on lowest possible mass. Again, available and planned payload shroud size limits near term designs to 4 meter class mirror. Practical 8 meter and beyond designs could encourage planners to include larger shrouds if it can be proven that such mirrors can be manufactured. These two factors lower mass and larger mirrors, presents the classic optimization problem. There is a practical upper limit to how large a mirror can be supported by a purely kinematic mount system and be launched. This paper shows how the design of the suspension system and mirror blank needs to be designed simultaneously. We will also explore the concepts of auxiliary support systems, which act only during launch and disengage on orbit. We will define required characteristics of these systems and show how they can substantially reduce the mirror mass. The AMTD project is developing and maturing the processes for future replacements for HUBBLE, creating the design tools, validating the methods and techniques necessary to manufacture, test and launch extremely large optical missions. This paper will use the AMTD 4 meter "design point" as an illustration of the typical use of the modeler in generating the multiple models of mirror and suspension systems used during the conceptual design phase of most projects. The influence of Hexapod geometry, mirror depth, cell size and construction techniques (Exelsis Deep Core Low Temperature Fusion (c) versus Corning Frit Bonded (c) versus Schott Pocket Milled Zerodur (c) in this particular study) are being evaluated. Due to space and time consideration we will only be able to present snippets of the study in this paper. The advances in manufacturing techniques for lightweight mirrors, such as EXELSIS deep core low temperature fusion, Corning's continued improvements in the Frit bonding process and the ability to cast large complex designs, combined with water-jet and conventional diamond.

Arnold, William↗
NASA NTRS2022

Bayesian Deep Learning for Segmentation for Autonomous Safe Planetary Landing

Hazard detection is critical for enabling autonomous landing on planetary surfaces. Current state-of-the-art methods leverage traditional computer vision approaches to automate the identification of safe terrain from input digital elevation models (DEMs). However, performance for these methods can degrade for input DEMs with increased sensor noise. In the last decade, deep learning techniques have been developed for various applications. Nevertheless, their applicability to safety-critical space missions has often been limited due to concerns regarding their outputs’ reliability. In response to these limitations, this paper proposes an application of the Bayesian deep learning segmentation method for hazard detection. The developed approach enables reliable, safe landing site detection by i) generating simultaneously a safety prediction map and its uncertainty map via Bayesian deep learning and semantic segmentation, and ii) using the uncertainty map to filter out the uncertain pixels in the prediction map so that the safe site identification is performed only based on the certain pixels (i.e., pixels for which the model is certain about its safety prediction). Experiments are presented with simulated data based on a Mars HiRISE digital terrain model by varying uncertainty threshold and noise levels to demonstrate the performance of the proposed approach.

Kento Tomita↗
DOE OSTI2024

Implementation of Detailed Polyethylene Pyrolysis Kinetics into CFD Simulations using Machine Learning

Municipal solid waste (MSW) and waste plastics have received significant attention due to the issues of waste generation and storage, as well as their potential as an energy resource. High-density polyethylene (HDPE) makes up a large portion of plastic waste and has been the subject of several conversion studies. However, the mechanisms associated with converting HDPE through pyrolysis and gasification are extensive and complex making them difficult to implement into high-fidelity computational fluid dynamic (CFD) simulations. For this project, a primary pyrolysis mechanism containing 42 unique species and 737 heterogeneous reactions was used to generate kinetic data over a range of operating conditions. A machine learning (ML) model was developed to replicate the results of the detailed pyrolysis mechanism while significantly increasing the computational efficiency. A deep operator network (DeepONet) architecture was adopted to train the model using time steps relevant to CFD simulations. The ML used physics-based loss functions to ensure mass conservation. The ML model has been deployed in simple MFiX CFD simulations, single particle, and an experimental drop tube reactor, and has shown promising performance compared to the original scheme.

Houston, Ross↗
NASA NTRS1987

A new linear quadratic optimal controller for the 34-meter high efficiency antenna position loop

The design of a new position loop controller for the 34-meter High Efficiency Deep Space antennas using linear quadratic (LQ) optimal control techniques is discussed. The LQ optimal control theory is reviewed, and model development and verification are discussed. Families of optimal gain vectors are generated by varying weight parameters. Performance specifications were used to select a final gain vector. Estimator dynamics were selected and the corresponding gain vectors were computed. Final estimator selection was based on position, commanded rate, and estimator error responses.

Nickerson, J. A.↗
NASA NTRS2005

A Hierarchical Modeling Study of the Interactions Among Turbulence, Cloud Microphysics, and Radiative Transfer in the Evolution of Cirrus Clouds

This project used a hierarchy of cloud resolving models to address the following science issues of relevance to CRYSTAL-FACE: What ice crystal nucleation mechanisms are active in the different types of cirrus clouds in the Florida area and how do these different nucleation processes influence the evolution of the cloud system and the upper tropospheric humidity? How does the feedback between supersaturation and nucleation impact the evolution of the cloud? What is the relative importance of the large-scale vertical motion and the turbulent motions in the evolution of the crystal size spectra? How does the size spectra impact the life-cycle of the cloud, stratospheric dehydration, and cloud radiative forcing? What is the nature of the turbulence and waves in the upper troposphere generated by precipitating deep convective cloud systems? How do cirrus microphysical and optical properties vary with the small-scale dynamics? How do turbulence and waves in the upper troposphere influence the cross-tropopause mixing and stratospheric and upper tropospheric humidity? The models used in this study were: 2-D hydrostatic model with explicit microphysics that can account for 30 size bins for both the droplet and crystal size spectra. Notably, a new ice crystal nucleation scheme has been incorporated into the model. Parcel model with explicit microphysics, for developing and evaluating microphysical parameterizations. Single column model for testing bulk microphysics parameterizations

Curry, Judith↗
DOE OSTI2024

A Lie algebraic theory of barren plateaus for deep parameterized quantum circuits

Variational quantum computing schemes train a loss function by sending an initial state through a parametrized quantum circuit, and measuring the expectation value of some operator. Despite their promise, the trainability of these algorithms is hindered by barren plateaus (BPs) induced by the expressiveness of the circuit, the entanglement of the input data, the locality of the observable, or the presence of noise. Up to this point, these sources of BPs have been regarded as independent. In this work, we present a general Lie algebraic theory that provides an exact expression for the variance of the loss function of sufficiently deep parametrized quantum circuits, even in the presence of certain noise models. Our results allow us to understand under one framework all aforementioned sources of BPs. This theoretical leap resolves a standing conjecture about a connection between loss concentration and the dimension of the Lie algebra of the circuit’s generators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Risk-Aware Measurement Synchronization and Recovery for DSSE With Heterogeneous Data Sources

Power distribution systems are increasingly integrating heterogeneous sensors with varying data reporting rates and types, which pose challenges to achieving observability at the desired temporal resolution of distribution system state estimation (DSSE). Multisensor failures caused by extreme events exacerbate these issues, introducing substantial uncertainties into DSSE. This article proposes a novel solution to these challenges by ensuring high-resolution system observability despite heterogeneous data sources and multisensor failures. First, a deep learning architecture combining long short-term memory (LSTM) and graph convolutional network (GCN) is employed to synchronize meters with different reporting rates, aiming to achieve system observability. A random-walk-model-based approach is introduced to generate pseudo-measurements while properly characterizing their uncertainties under multisensor failures. Finally, a disaster-risk-informed observability metric (RiOM) is defined to quantify the uncertainty associated with state estimation results. The proposed framework offers deeper insights into the system observability on the fly compared with conventional analysis. The effectiveness of the framework is demonstrated on an IEEE standard test case and a large-scale real-world distribution feeder in mid-Minnesota in the U.S.

97 MATHEMATICS AND COMPUTING↗