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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

ICE-RASSOR: Intelligent Capabilities Enhanced

NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for In-Situ Resource Utilization (ISRU) processing. RAS-SOR’s design enables it to efficiently collect and deposit regolith, return collected material for processing, and myriad related ISRU activities. To reliably perform these operations on the lunar sur-face, RASSOR software and sensory systems need to be robust and maximize the information extracted from on-board sensing. Herein, we present preliminary findings from the Intelligent Capabilities Enhanced RASSOR project. We apply supervised learning using real data to estimate the soil mass collected without the need for mass flow rate monitors or other explicate sensing techniques. We also create a reduced-order simulation environment to develop autonomous trenching controllers via reinforcement learning and proto-type state estimation architectures. Our initial results suggest that excavated regolith mass can be inferred within 2.9% RMS error of full scale, and reinforcement learning for autonomous operations has learned viable trenching strategies and helped identify desirable sensing capabilities, arrangements, and considerations. Future work includes regolith mass estimation during dynamic operation, expanding our simulation to more complex environments, and transfer learning from simulation to hardware.

machine learning↗
NASA NTRSDate not supplied

ICE-RASSOR: Intelligent Capabilities Enhanced Regolith Advanced Surface Systems Operations Robot

NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for In-Situ Resource Utilization (ISRU) processing. RASSOR’s design enables it to efficiently collect and deposit regolith, return collected material for processing, and myriad related ISRU activities. To reliably perform these operations on the lunar surface, RASSOR software and sensory systems need to be robust and maximize the information extracted from on-board sensory. Herein, we present preliminary findings from the Intelligent Capabilities Enhanced RASSOR project. We apply supervised learning using real data to estimate the soil mass collected without the need for mass flow rate monitors or other explicate sensing techniques. We also create a reduced-order simulation environment to develop autonomous trenching controllers via reinforcement learning and proto-type state estimation architectures. Our initial results suggest that excavated regolith mass can be inferred within 2.9% RMS error of full scale, and reinforcement learning for autonomous operations has learned viable trenching strategies and helped identify desirable sensing capabilities, arrangements, and considerations. Future work includes regolith mass estimation during dynamic operation, expanding our simulation to more complex environments, and transfer learning from simulation to hardware.

machine learning↗
DOE OSTI2022

AXEAP : a software package for X-ray emission data analysis using unsupervised machine learning

The Argonne X-ray Emission Analysis Package ( AXEAP ) has been developed to calibrate and process X-ray emission spectroscopy (XES) data collected with a two-dimensional (2D) position-sensitive detector. AXEAP is designed to convert a 2D XES image into an XES spectrum in real time using both calculations and unsupervised machine learning. AXEAP is capable of making this transformation at a rate similar to data collection, allowing real-time comparisons during data collection, reducing the amount of data stored from gigabyte-sized image files to kilobyte-sized text files. With a user-friendly interface, AXEAP includes data processing for non-resonant and resonant XES images from multiple edges and elements. AXEAP is written in MATLAB and can run on common operating systems, including Linux, Windows, and MacOS.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Machine learning and TDDFT software for stopping power computation

(SF-24-012) Stopping power describes the rate that a material slows radiation particles passing through it and is useful in designing many technologies. Few organizations can perform new measurements, which require significant resources and rare equipment, and all others rely on coarse approximations rendered from pre-existing data. Methods for computing stopping power in new materials, such as time-dependent density functional theory (TD-DFT), have only recently (circa-2015) become available but are too computationally costly to use frequently enough to have a pronounced impact. We have created a method that opens a pathway to computing stopping power without any need for experimental data by combining electronic structure computations and machine learning.

Ward, Logan↗
DOE OSTI2020

Frequency Disturbance Event Detection Based on Synchrophasors and Deep Learning

Power system frequency disturbances are caused by various generation and transmission events including generator trips, load disconnections, line trips, etc. Accurate detections of the events are crucial to bulk power system situation awareness and event investigation. This paper utilizes the recent advances of deep learning to build a convolutional neural network model to detect events in an accurate yet straightforward manner. Herein, the rate of change of frequency and the relative angle shift are converted to images as the inputs of the proposed model. Finally, this paper uses two convolutional neural networks and classifier fusion to achieve the detection result. Compared with the conventional event detection algorithm and the frequency only deep learning model, the proposed model improves the detection accuracy by over 48%. As a promising tool for bulk power system situation awareness, the proposed model requires a short decision time, which is suitable for practical scenarios.

42 ENGINEERING↗
DOE OSTI2021

AADL: Anderson Accelerated Deep Learning

We propose a stable, distributed approach to perform AA that accelerates the convergence rate of stochastic first-order optimizers to train neural networks. Differently from previous works, we do not alter neither the scheme to perform AA nor the loss function minimized during the training. To improve robustness against stagnation, we customize general guidelines that suggest to relax the frequency of AA corrections by performing AA only at the end of an entire training epoch. To improve robustness of AA against the stochastic oscillations of first-order optimizers, we average the gradients computed on consecutive stochastic optimization updates. The improved regularity of the converging sequence and the reduced amplitude of stochastic oscillations across consecutive optimization steps allows AA to efficiently extrapolate an improved converging sequence, thereby overcoming limitations of existing approaches to perform AA on stochastic optimization.

Lupo Pasini, Massimiliano [Oak Ridge National Lab.↗
NASA NTRS2005

Stable Isotope Characteristics of Jarosite: The Acidic Aqueous History of Mars

The Mars Rover Opportunity found jarosite (Na(+) or K(+))Fe3SO4(OH)6 at the Meridiani Planum site. This mineral forms from the evaporation of an aqueous acidic sulfate brine. Oxygen isotope compositions may characterize formation conditions but subsequent isotope exchange may have occurred between the sulfate and hydroxide of jarosite and water. The rate of oxygen isotope exchange depends on the acidity and temperature of the brine, but it has not been investigated in detail. We performed laboratory experiments to determine the rate of oxygen isotope exchange under varying acidities and temperatures to learn more about this process. Barium sulfate samples were precipitated weekly from acidic sodium sulfate brines. The oxygen isotope composition of the precipitated sulfate was obtained using a Finnigan MAT253 Isotope Ratio Mass-Spectrometer. The results show that water was trapped in barium sulfate during precipitation. Trapped water may exchange with sulfate when exposed to high temperatures, thus changing the isotope composition of sulfate and the observed fractionation factor of oxygen isotope exchange between sulfate and water. The results of our research will contribute to the understanding of oxygen isotope exchange rates between water and sulfate under acidic conditions and provide experimental knowledge for the dehydration of barium sulfate samples.

Earl, Lyndsey D.↗
DOE OSTI2026

Diagnostics, Prognostics, and Optimization for Lithium-Ion Battery Systems

Health management of lithium-ion battery systems presents a host of challenges due to their complex physics, large numbers of components, and a wide variety of degradation behaviors across different battery types. Dr. Paul Gasper will present on research from the Electrochemical Energy Storage Group on Lithium-ion battery diagnostics, prognostics, and optimization. Diagnostics research, including state-estimation via machine-learning from electrochemical impedance spectroscopy and DC pulses as well as continuous state-estimation via Kalman filters, will highlight the ongoing challenges for accurately measuring the state of batteries without performing time-consuming characterization tests. NLR's industry-recognized battery prognostics work, which predicts real-world battery degradation by identifying degradation rate models from accelerated aging data using statistical modeling and machine-learning, will be used to demonstrate the critical impact of battery controls, thermal management, and operating strategy on durability and lifetime. Finally, the use of prognostic models for financial or lifetime optimization will be discussed.

25 ENERGY STORAGE↗
DOE OSTI2024

Prognostic analysis of high-flow nasal cannula therapy and non-invasive ventilation in mild to moderate hypoxemia patients and construction of a machine learning model for 48-h intubation prediction—a retrospective analysis of the MIMIC database

Background This study aims to investigate the clinical outcome between high-flow nasal cannula (HFNC) and non-invasive ventilation (NIV) therapy in mild to moderate hypoxemic patients on the first ICU day and to develop a predictive model of 48-h intubation. Methods The study included adult patients from the MIMIC III and IV databases who first initiated HFNC or NIV therapy due to mild to moderate hypoxemia (100 < PaO2/FiO2 ≤ 300). The 48-h and 30-day intubation rates were compared using cross-sectional and survival analysis. Nine machine learning and six ensemble algorithms were deployed to construct the 48-h intubation predictive models, of which the optimal model was determined by its prediction accuracy. The top 10 risk and protective factors were identified using the Shapley interpretation algorithm. Result A total of 123,042 patients were screened, of which, 673 were from the MIMIC IV database for ventilation therapy comparison (HFNC n = 363, NIV n = 310) and 48-h intubation predictive model construction (training dataset n = 471, internal validation set n = 202) and 408 were from the MIMIC III database for external validation. The NIV group had a lower intubation rate (23.1% vs. 16.1%, p = 0.001), ICU 28-day mortality (18.5% vs. 11.6%, p = 0.014), and in-hospital mortality (19.6% vs. 11.9%, p = 0.007) compared to the HFNC group. Survival analysis showed that the total and 48-h intubation rates were not significantly different. The ensemble AdaBoost decision tree model (internal and external validation set AUROC 0.878, 0.726) had the best predictive accuracy performance. The model Shapley algorithm showed Sequential Organ Failure Assessment (SOFA), acute physiology scores (APSIII), the minimum and maximum lactate value as risk factors for early failure and age, the maximum PaCO 2 and PH value, Glasgow Coma Scale (GCS), the minimum PaO 2 /FiO 2 ratio, and PaO 2 value as protective factors. Conclusion NIV was associated with lower intubation rate and ICU 28-day and in-hospital mortality. Further survival analysis reinforced that the effect of NIV on the intubation rate might partly be attributed to the other impact factors. The ensemble AdaBoost decision tree model may assist clinicians in making clinical decisions, and early organ function support to improve patients’ SOFA, APSIII, GCS, PaCO 2 , PaO 2 , PH, PaO 2 /FiO 2 ratio, and lactate values can reduce the early failure rate and improve patient prognosis.

Fu, Wei↗
NASA NTRS2011

Orion Entry Handling Qualities Assessments

The Orion Command Module (CM) is a capsule designed to bring crew back from the International Space Station (ISS), the moon and beyond. The atmospheric entry portion of the flight is deigned to be flown in autopilot mode for nominal situations. However, there exists the possibility for the crew to take over manual control in off-nominal situations. In these instances, the spacecraft must meet specific handling qualities criteria. To address these criteria two separate assessments of the Orion CM s entry Handling Qualities (HQ) were conducted at NASA s Johnson Space Center (JSC) using the Cooper-Harper scale (Cooper & Harper, 1969). These assessments were conducted in the summers of 2008 and 2010 using the Advanced NASA Technology Architecture for Exploration Studies (ANTARES) six degree of freedom, high fidelity Guidance, Navigation, and Control (GN&C) simulation. This paper will address the specifics of the handling qualities criteria, the vehicle configuration, the scenarios flown, the simulation background and setup, crew interfaces and displays, piloting techniques, ratings and crew comments, pre- and post-fight briefings, lessons learned and changes made to improve the overall system performance. The data collection tools, methods, data reduction and output reports will also be discussed. The objective of the 2008 entry HQ assessment was to evaluate the handling qualities of the CM during a lunar skip return. A lunar skip entry case was selected because it was considered the most demanding of all bank control scenarios. Even though skip entry is not planned to be flown manually, it was hypothesized that if a pilot could fly the harder skip entry case, then they could also fly a simpler loads managed or ballistic (constant bank rate command) entry scenario. In addition, with the evaluation set-up of multiple tasks within the entry case, handling qualities ratings collected in the evaluation could be used to assess other scenarios such as the constant bank angle maintenance case. The 2008 entry assessment was divided into two sections (see Figure 1). Entry I was the first, high speed portion of a lunar return and Entry II was the second, lower speed portion of a lunar return, which is similar (but not identical) to a typical ISS return.

Bihari, B.↗
DOE OSTI2021

Smart Methane Emission Detection System Development Final Report

Working with the Department of Energy's National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real- time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI's Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators.

03 NATURAL GAS↗
DOE OSTI2025

Search for solar axions produced through the axion-electron coupling gae using a new GridPix detector at CAST

We present a search for solar axions produced through the axion-electron coupling (g ae ) using data from a novel 7-GridPix detector installed at the CERN Axion Solar Telescope (CAST). The detector, featuring ultra-thin silicon nitride windows and multiple veto systems, collected approximately 160 hours of solar tracking data between 2017–2018. Using machine learning techniques and the veto systems, we achieved a background rate of 1.06 × 10 −5 keV −1 cm −2 s −1 at a signal efficiency of about 80% in the 0.2 to 8 keV range. Analysis of the data yielded no significant excess above background, allowing us to set a new upper limit on the product of the axion-electron and axion-photon couplings of g ae · g aγ < 7.35 × 10 −23 GeV −1 at 95% confidence level for axion masses below 10 meV. This result improves upon the previous best helioscope limit and demonstrates the potential of GridPix technology for rare event searches. Additionally, we derived a limit on the axion-photon coupling of g aγ < 9.0 × 10 −11 GeV −1 at 95% CL, which, while not surpassing CAST’s best limit, provides complementary constraints on axion models.

Beyond Standard Model↗
DOE OSTI2022

Development, characterization, and modeling of a high-performance Ru/B2CA catalyst for ammonia synthesis

This paper documents the development and performance of a nano-phase Ru catalyst on a (BaO) x (CaO) y (Al 2 O 3 ) support. Extensive screening of the support’s ternary composition shows the best stoichiometry is (BaO) 2 (CaO)(Al 2 O 3 ), denoted B2CA. The paper first describes catalyst preparation and characterization. The paper reports a detailed 12-step reaction mechanism that represents ammonia synthesis over wide ranges of temperature, pressure, space velocity, and feed composition. Additionally, the mechanism is developed and validated using results of packed-bed experiments. The elementary reaction pathways consider surface adsorbates, including catalyst-poisoning behaviors. The rate expressions include important coverage-dependent activation barriers. Machine learning models assist interpretation of the catalyst-support interactions. The detailed chemistry is much more predictive than is possible with global representations (N 2 + 3H 2 ⇌ 2NH 3 ). The validated models can be applied to assist optimizing reactor design and operating conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

On Alfvénic turbulence of solar wind streams observed by Solar Orbiter during March 2022 perihelion and their source regions

It has been recently accepted that the standard classification of the solar wind solely according to flow speed is outdated, and particular interest has been devoted to the study of the origin and evolution of so-called Alfvénic slow solar wind streams and to what extent such streams resemble or differ from fast wind. In March 2022, Solar Orbiter completed its first nominal phase perihelion passage. During this interval, it observed several Alfvénic streams, allowing for characterization of fluctuations in three slow wind intervals (AS1-AS3) and comparison with a fast wind stream (F) at almost the same heliocentric distance. This work makes use of Solar Orbiter plasma parameters from the Solar Wind Analyzer (SWA) and magnetic field measurements from the magnetometer (MAG). The magnetic connectivity to the solar sources of selected solar wind intervals was reconstructed using a ballistic extrapolation based on measured solar wind speed down to the (spherical) source surface at 2.5 R s below which a potential field extrapolation was used to map back to the Sun. The source regions were identified using SDO/AIA observations. A spectral analysis of in situ measured magnetic field and velocity fluctuations was performed to characterize correlations, Alfvénicity, normalized cross-helicity, and residual energy in the frequency domain as well as intermittency of the fluctuations and spectral energy transfer rate estimated via mixed third-order moments. A machine learning technique was used to separate proton core, proton beam, and alpha particles and to study v − b correlations for the different ion populations in order to evaluate the role played by each population in determining the Alfvénic content of solar wind fluctuations. The comparison between fast wind and Alfvénic slow wind intervals highlights the differences between the two solar wind regimes: The fast wind is characterized by larger amplitude fluctuations, and magnetic and velocity fluctuations are closer to equipartition of energy. In fact the Alfvénic slow wind streams appear to be on a spectrum of wind types, with AS1, originating from open field lines neighboring active regions and displaying similarities with the fast wind in terms of fluctuation amplitude and turbulence characteristics, but not with respect to the alpha particles and proton beams. The other two slow streams differed both in their sources as well as plasma characteristics, with AS2 coming from the expansion of a narrow coronal hole corridor and AS3 from a region straddling a pseudostreamer. The latter displayed the coldest and highest density but the slowest stream with the smallest fluctuation amplitude and greatest magnetic energy excess. It also showed the largest scatter in proton beam speeds and the greatest difference in speed between proton beam and alpha particles. This study shows how the old fast–slow solar wind dichotomy, already called into question by the observations of slower Alfvénic solar wind streams, should further be refined, as the Alfvénic slow wind, originating in different solar wind regions, show significant differences in density, temperature, and proton and alpha-particle properties in the inner heliosphere. The observations presented here provide the starting point for a better understanding of the origin and evolution of different solar wind streams as well as the evolving turbulence contained within.

magnetohydrodynamics (MHD)↗
DOE OSTI2024

Advanced Signal Decomposition Analysis and Anomaly Detection in Photovoltaic Systems

With the rapid expansion of large-scale photovoltaic (PV) plants, it is paramount for solar stakeholders to understand the reliability and efficiency of their plants to inform maintenance decisions, increase production, and understand the design factors that impact performance. Diagnosing underperformance in PV plants is challenging due to the relatively few monitoring points with respect to the large geographic footprint of the plant. This work introduces a cutting-edge method that transforms the analysis and management of key factors influencing PV plant performance, including performance loss rate (PLR), recoverable soiling, and major system changes. Identifying these factors is critical for deriving actionable insights. Leveraging advanced analytical techniques such as wavelet transformation, robust regression, and extreme point analysis, this approach provides a nuanced understanding of these factors. This method has been tested across two synthetic datasets and one real dataset, consistently surpassing existing benchmarks by achieving a lower median mean absolute error and reduced error variability across all comparable components.

14 SOLAR ENERGY↗
DOE OSTI2021

OPERATING EXPERIENCE AND ITS RELATIONSHIP TO EMBEDDED DIGITAL DEVICES IN SAFETY SYSTEMS

Interest in embedded digital devices (EDDs) for monitoring and providing control of components is increasing and the added functionality digital brings such as diagnostics, displays, control functions is welcomed in both the nuclear and other industries. As a result, pure analog-based components continue to disappear from the industrial I&C marketplace. Equipment with EDDs, also known by related terms such as smart devices, intelligent devices, and digital devices of limited functionality, can include sensors, breakers, priority logic modules, time delay relays, pumps, valve actuators, motor control centers, and uninterruptible power supplies. Not only is equipment consisting of analog and older digital technology being replaced with commercial grade products containing EDDs, but EDDs can also be retrofitted to work with existing components. EDDs may be installed physically within a component, such as in a relay. There are also external devices that some would not consider an EDD, but that operate in almost all technical ways identically to one internal to a component such as in a valve controller. In fact, the digital portions may be located hundreds of feet from the actual component.Due to the potential of being used in an identical manner and without alteration from their application in other industries, operating experience (OpE) for EDDs may be much more applicable than in the case of other more system level devices. Clear separation and independence of functionality provided with EDDs may also allow greater applicability of existing OpE from industry for changes that can be shown not to affect its safety function. However, OpE also demonstrates issues with different conditions between other industries and nuclear power plants, such as issues with undeclared changes or undeclared digital devices and the new failure modes they may introduce, issues with the reporting of OpE in industry, and a lack of transparency into OpE for EDDs.Understanding and addressing these issues will be key in the future use of EDDs in nuclear power plants, but failure rate data is difficult to obtain for EDDs, and lessons learned from the use of EDDs are difficult to collect and identify.Key words: embedded digital device, smart device, operating experience, failure modes

Muhlheim, Michael↗
DOE OSTI2025

OLCF’s Advanced Computing Ecosystem (ACE): FY25 Update for Ongoing Efforts

The advent of widespread use of artificial intelligence (AI) and machine learning (ML) models in science, coupled with fast data production rates of scientific instruments strain the traditional batch-oriented high-performance computing (HPC) environment. As scientific exploration continues to require more data and faster processing and analysis, new emerging technologies and capabilities to enable cross-facility and time-sensitive workflows are required for seamless integration of HPC and experimental facilities. The Advanced Computing Ecosystem (ACE) is a strategic initiative within the Oak Ridge Leadership Computing Facility (OLCF) established in 2024 to support the development of cutting-edge technologies to advance computational research and infrastructure at OLCF and across the Department of Energy (DOE). Several DOE initiatives are spearheading the evolution of the scientific landscape by blurring facility boundaries and connecting the user facilities to advance scientific capabilities and ensure energy dominance. The DOE Integrated Research Infrastructure (IRI) program is one example that is laying a foundation to support complex cross-facility workflows. The IRI program aims to integrate diverse computational resources, data infrastructures, and scientific instruments to facilitate collaboration and accelerate scientific discovery. The Interconnected Science Ecosystem (INTERSECT) initiative at Oak Ridge National Laboratory (ORNL) is another example that aims to revolutionize scientific research through AI-driven, interconnected autonomous laboratories and research facilities. Finally, the American Science Cloud (AmSC), recently announced in the “One Big Beautiful Bill”, aims to leverage prior infrastructure efforts of the IRI and automation and AI efforts of INTERSECT (and others) to build a federated, AI-augmented AmSC platform to unify the DOE’s computing, experimental, and data resources to catalyze scientific innovation.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2020

ISEE System Technical Report

The Immersive Simulations and Engineering Environment (ISEE) Laboratory at Kennedy Space Center is a tool used to assist with operation simulation, for Human Factors operation activities, assisting future missions. The simulations created are important because they ensure that simulated operations are performed for customers, to assist with requirements. Simultaneously, each simulation will allow stakeholders to determine any and all risk associated with the Human Factors involved in a specific assignment. My internship consists of multiple project elements. The first of which, was to assist the Integrations branch chief in developing a growth rate algorithm proof. The integral part of this project, involved learning to powerup and perform the operational procedures of the ISEE lab, capturing and creating characters used in the CAD model virtual environment. In doing so, this led to use the software to model and 3D print screw cap structures for mock-up. The structures will be used to manipulate weight change out used in mock-up design and construction. These structures will also be used in future mock-up models constructed in the lab.

Darryl A J Minor↗