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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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DOE OSTI2020

Dynamic Topology Reconfiguration of Boltzmann Machines on Quantum Annealers

Boltzmann machines have useful roles in deep learning applications, such as generative data modeling, initializing weights for other types of networks, or extracting efficient representations from high-dimensional data. Most Boltzmann machines use restricted topologies that exclude looping connectivity, as such connectivity creates complex distributions that are difficult to sample. We have used an open-system quantum annealer to sample from complex distributions and implement Boltzmann machines with looping connectivity. Further, we have created policies mapping Boltzmann machine variables to the quantum bits of an annealer. These policies, based on correlation and entropy metrics, dynamically reconfigure the topology of Boltzmann machines during training and improve performance.

Liu, Jeremy (ORCID:0000000172299417)↗

DOE OSTI2023

PV inverter experimental data

The increase in power electronic based generation sources require accurate modeling of inverters. Accurate modeling requires experimental data over wider operation range. We used 20 kW off-the-shelf grid following PV inverter in the experiments. We used controllable AC supply and controllable DC supply to emulate AC and DC side characteristics. The experiments were performed at NREL's Energy Systems Integration Facility. Due to the limitations of the DC supply used, inverter is tested under 75%, 50%, 25% load conditions (This dataset does not contain 100% load condition). In the first dataset, for each operating condition, controllable AC source voltage is varied from 0.88 to 1.09 per unit (p.u) with a step value of 0.025 p.u while keeping the frequency at 60 Hz. In the second dataset, under similar load conditions (75%, 50%, 25% ), the frequency of the controllable AC source voltage was varied from 59.4 Hz to 60.45 Hz with a step value of 0.2 Hz. Voltage and frequency range is chosen based on inverter protection. Voltages and currents on DC and AC side are included in the dataset.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1990

Statistical methods and neural network approaches for classification of data from multiple sources

Statistical methods for classification of data from multiple data sources are investigated and compared to neural network models. A problem with using conventional multivariate statistical approaches for classification of data of multiple types is in general that a multivariate distribution cannot be assumed for the classes in the data sources. Another common problem with statistical classification methods is that the data sources are not equally reliable. This means that the data sources need to be weighted according to their reliability but most statistical classification methods do not have a mechanism for this. This research focuses on statistical methods which can overcome these problems: a method of statistical multisource analysis and consensus theory. Reliability measures for weighting the data sources in these methods are suggested and investigated. Secondly, this research focuses on neural network models. The neural networks are distribution free since no prior knowledge of the statistical distribution of the data is needed. This is an obvious advantage over most statistical classification methods. The neural networks also automatically take care of the problem involving how much weight each data source should have. On the other hand, their training process is iterative and can take a very long time. Methods to speed up the training procedure are introduced and investigated. Experimental results of classification using both neural network models and statistical methods are given, and the approaches are compared based on these results.

Benediktsson, Jon Atli↗

NASA NTRS2012

A Probabilistic Mass Estimation Algorithm for a Novel 7- Channel Capacitive Sample Verification Sensor

A document describes an algorithm created to estimate the mass placed on a sample verification sensor (SVS) designed for lunar or planetary robotic sample return missions. A novel SVS measures the capacitance between a rigid bottom plate and an elastic top membrane in seven locations. As additional sample material (soil and/or small rocks) is placed on the top membrane, the deformation of the membrane increases the capacitance. The mass estimation algorithm addresses both the calibration of each SVS channel, and also addresses how to combine the capacitances read from each of the seven channels into a single mass estimate. The probabilistic approach combines the channels according to the variance observed during the training phase, and provides not only the mass estimate, but also a value for the certainty of the estimate. SVS capacitance data is collected for known masses under a wide variety of possible loading scenarios, though in all cases, the distribution of sample within the canister is expected to be approximately uniform. A capacitance-vs-mass curve is fitted to this data, and is subsequently used to determine the mass estimate for the single channel s capacitance reading during the measurement phase. This results in seven different mass estimates, one for each SVS channel. Moreover, the variance of the calibration data is used to place a Gaussian probability distribution function (pdf) around this mass estimate. To blend these seven estimates, the seven pdfs are combined into a single Gaussian distribution function, providing the final mean and variance of the estimate. This blending technique essentially takes the final estimate as an average of the estimates of the seven channels, weighted by the inverse of the channel s variance.

Wolf, Michael↗

DOE OSTI2020

ASGS - An Innovative Approach for Waste Characterization - From the First Study to Experimental Validation - 20260

For disposal of nuclear waste a clear identification of the nuclear inventory is mandatory. Since this waste is usually filled in drums, nuclear specific characterization methods using HPGe detectors usually require the understanding of the limitations of the method assumptions (such as the homogeneity criteria), and often results in applying high uncertainties to the result in waste that is not well known a priori. There are several methods and approaches such as Far-Field, Segmented Gamma Scans (SGS) or Tomographic Gamma Scans (TGS) available. The SGS allows measuring the waste drum in different segments (or 'layers') which allows getting more detailed information about the activity distribution inside a waste drum. In addition, with the transmission source used in tomographic scans, it is possible to also determine the material (and its homogeneity) composition inside a drum. Mirion Technologies developed in cooperation with Aachen Institute for Nuclear Training (AiNT) an innovative measurement approach which allows more accurate results and therefore applying lower uncertainties. This new measurement approach extends the concept of SGS by partitioning the drum into single drum sectors instead of segments. The goal is to perform measurements with high throughput and an accurate analysis based on the reconstruction of activities for each sector. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRSDate not supplied

Simulation of the Aerosol Size Distribution Using a Neural Network Surrogate for the Modal Aerosol Module (MAM7)

One objective of atmospheric simulations is to quantify the distribution of aerosols and their properties. Accurate parameterizations of the processes governing aerosol mass, particle number, and particle size distribution are important for predicting the Earth’s net radiative balance and aerosol-cloud interactions. The Modal Aerosol Module (MAM7) is a two-moment aerosol model that simulates mass, number, and size distribution of seven modes comprised of internally mixed aerosol species. The two-moment scheme adds significant computational expense but allows for the prediction of varying particle size distribution relative to the bulk method which predicts only total mass. In this work, we developed a neural network surrogate model for MAM7 (MAMnet) to predict the aerosol number concentration in NASA’s Global Earth Observing System (GEOS) without adding prohibitive computational expense. MAMnet, can be driven by output from a single moment, mass-based, aerosol scheme (Goddard Chemistry Aerosol and Radiation model (GOCART)) or from reanalysis products (Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2)). MAMnet was trained using number concentrations from a 5-year GEOS/MAM7 simulation at 1-degree horizontal resolution and using the total mass calculated across modes as inputs, as well as temperature and air density. The model architecture for MAMnet was based on AlexNet, the 2012 winner of the ImageNet Large Scale Visual Recognition Challenge. While some modifications were necessary to accommodate our problem, important aspects of the network were preserved. MAMnet was able to reproduce zonal dynamics and spatial distributions of the aerosol number concentration however predictability in the upper troposphere was poor.

Katherine H Breen↗

DOE OSTI2024

Hydropower Black Start: A Guidebook for Retrofitting Grid Dependent Hydropower

Not all United States (US) hydropower plants were designed to provide black start, but they are increasingly needed to uphold resilience in the evolving electric grid. This guidance is designed to help understand the minimal retrofits required for grid dependent hydropower (GDH) plants behind the point of interconnection (POI). For distribution connected hydropower plants or those with dedicated cranking paths, such upgrades can be sufficient for the plant to provide black start. For others, more coordination with the transmission system operator will be needed. This guidebook answers a number of questions relevant to retrofitting hydropower plants with black start capabilities. For example, the guidebook answers: • How flexible do the wicket gate controls need to be? • Who needs to do hydro governor model validation, why, and how? • How robust and flexible do the excitation and AVR controls need to be? • What protection settings need to be adjusted? • What relay(s) will need to be bypassed or overridden and at what risk? • What is the electrical energy demand of the station load or auxiliary power systems? • What should the strategy to energize transformer(s) along cranking path to address inrush currents be? • How should the critical load restoration be sequenced? In addition to outlining the specifications that hydropower plants need to meet for each component to be able to perform black start, this guidebook provides a set of case studies for specific upgrades needed at actual plants. Between the case studies of plants that have already performed black start retrofits and the examples of how this guidebook can be applied to scope future retrofits, five key themes have been identified for retrofit needs. 1. Protection needs “black start” mode: hydropower plants that are not designed with black start capabilities will have protections that prevent them from interconnecting to a “dead bus.” These protections will need to be overridden in every retrofit case and a separate black start mode should be established so that operators can safely switch between black start and grid connected modes, minimizing the risk to the plant. 2. Wicket gates need modern controls: digital governors accelerate the parameter tuning process and gate position sensors improve controllability, so plants with mechanical governors should be upgraded. Furthermore, a black start and islanding mode should be established for controls to maximize plant performance. 3. Robust excitation support: the DC system or excitation generator needs to be reliable enough to form and sustain the rotor electromagnetic field. These systems are typically undersized in plants that were not designed for black start, so they will need to be upgraded. 4. Turbine-governor model validation and operator training: validation of a standard hydro governor model is needed to characterize the dynamic response (i.e., inertial and primary frequency response) of the GDH. This is required for control development and old hydropower plants often have outdated or incorrect models. Operator training is also typically required to ensure the hardware retrofits are utilized correctly during the black start process. 5. Transformer and cranking path energization: any upgradation and control adjustment in front of the POI will depend upon the existing interconnection. Coordination with the transmission or distribution operator may be required.

13 HYDRO ENERGY↗

DOE OSTI2025

Accelerating Hamiltonian Monte Carlo for Bayesian inference in neural networks and neural operators

Hamiltonian Monte Carlo (HMC) is a powerful and accurate method to sample from the posterior distribution in Bayesian inference. However, HMC techniques are computationally demanding for Bayesian neural networks due to the high dimensionality of the network’s parameter space and the non-convexity of their posterior distributions. Therefore, various approximation techniques, such as variational inference (VI) or stochastic gradient MCMC, are often employed to infer the posterior distribution of the network parameters. Such approximations introduce inaccuracies in the inferred distributions, resulting in unreliable uncertainty estimates. In this work, we propose a hybrid approach that combines inexpensive VI and accurate HMC methods to efficiently and accurately quantify uncertainties in neural networks and neural operators. The proposed approach leverages an initial VI training on the full network. We examine the influence of individual parameters on the prediction uncertainty, which shows that a large proportion of the parameters do not contribute substantially to uncertainty in the network predictions. This information is then used to significantly reduce the dimension of the parameter space, and HMC is performed only for the subset of network parameters that strongly influence prediction uncertainties. This yields a framework for accelerating the full batch HMC for posterior inference in neural networks. We demonstrate the efficiency and accuracy of the proposed framework on deep neural networks and operator networks, showing that inference can be performed for large networks with tens to hundreds of thousands of parameters. Finally, we show that this method can effectively learn surrogates for complex physical systems by modeling the operator that maps from upstream conditions to wall-pressure data on a cone in hypersonic flow.

Bayesian inference↗

DOE OSTI2023

Machine learning for interpreting coherent X-ray speckle patterns

Speckle patterns produced by coherent X-ray have a close relationship with the internal structure of materials but quantitative inversion of the relationship to determine structure from speckle patterns is challenging. Here, in this work, we investigate the link between coherent X-ray speckle patterns and sample structures using a model 2D disk system and explore the ability of machine learning to learn aspects of the relationship. Specifically, we train a deep neural network to classify the coherent X-ray speckle patterns according to the disk number density in the corresponding structure. It is demonstrated that the classification system is accurate for both non-disperse and disperse size distributions.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Z-Contrast Enhancement in Au–Pt Nanocatalysts by Correlative X-ray Absorption Spectroscopy and Electron Microscopy: Implications for Composition Determination

The properties of bimetallic nanoparticles (BNPs) vary widely as a function of their composition and size distributions. X-ray absorption fine structure analysis is commonly used to characterize their structure, but its application to elements that are close to each other in the periodic table is hampered by poor Z-contrast. For this study, we trained an artificial neural network to recognize the partial coordination numbers in AuPt NPs synthesized via peptide templating using their X-ray absorption near-edge structure spectra. This approach, combined with scanning transmission electron microscopy analysis, revealed unique details of this prototype catalytic system that has different forms of heterogeneities at both the intra- and inter-particle levels. Our method based on the enhancement of Z-contrast of metal species will have implications for compositional studies of BNPs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Hybrid Entanglement Distribution between Remote Microwave Quantum Computers Empowered by Machine Learning

Superconducting microwave circuits with Josephson junctions, the major platform for quantum computing, can only reach the full capability when connected. This requires an efficient protocol to distribute microwave entanglement. While quantum computers typically use discrete-variable (DV) methods for information encoding, the entire continuous-variable (CV) degree of freedom in electromagnetic fields must be utilized to achieve the highest entanglement distribution rate. Here, we propose a hybrid protocol to resolve the incompatibility between DV microwave quantum computers and CV quantum communications. CV microwave entanglement is distributed using optical swapping of optical-microwave entanglement pairs. To interface with DV microwave quantum computers, we further design a hybrid circuit to simultaneously convert and distill high-quality DV entanglement from noisy CV entanglement. The hybrid circuit is trained with machine-learning algorithms, ensuring high entanglement fidelity and generation rate. In conclusion, our work not only provides a practical method to realize efficient quantum links for superconducting microwave quantum computers, but also opens avenues to bridge the gap between DV and CV quantum systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Electrical Load Forecasting Over Multihop Smart Metering Networks With Federated Learning

Electric load forecasting is essential for power management and stability in smart grids. This is mainly achieved via advanced metering infrastructure, where smart meters (SMs) record household energy data. Traditional machine learning (ML) methods are often employed for load forecasting, but require data sharing, which raises data privacy concerns. Federated learning (FL) can address this issue by running distributed ML models at local SMs without data exchange. However, current FL-based approaches struggle to achieve efficient load forecasting due to imbalanced data distribution across heterogeneous SMs. Here, this article presents a novel personalized FL (PFL) method for high-quality load forecasting in metering networks. A meta-learning-based strategy is developed to address data heterogeneity at local SMs in the collaborative training of local load forecasting models. Moreover, to minimize the load forecasting delays in our PFL model, we study a new latency optimization problem based on optimal resource allocation at SMs. A theoretical convergence analysis is also conducted to provide insights into FL design for federated load forecasting. Extensive simulations from real-world datasets show that our method outperforms existing approaches regarding better load forecasting and reduced operational latency costs.

Rahman, Ratun [Univ. of Alabama, Huntsville, AL (U↗

DOE OSTI2021

Diverse Super-Resolution (diversity_SR) [SWR-21-60]

Deep learning tools for enhancing the spatial resolution of wind data. The software is developed in Python using the TensorFlow deep learning package. Models for diversity super-resolution is provided. Included in the package are pretrained models with example code/data to perform the super-resolution as well as tools of training models for different enhancement- or data-types. The super-resolution is an inherently ill-conditioned problem, with multiple high-resolution fields plausibly mapping to the same coarse field. Considitional GANs provide a framework for generating a distribution of high-resolution realizations from a given low-resolution input. Stochastic estimation is used to inform the network of the expected degree and location of sub-grid diversity. The package includes a pretrained network to generate distributions of 10x-enhanced fields of wind data.

Glaws, Andrew↗

DOE OSTI2024

Hybrid Storage Solution

With the rise of artificial intelligence and machine learning, data sets used to train models have become increasingly large. The availability, accessibility and integrity of large data sets has become important to the research conducted at Los Alamos National Laboratory. Ceph is a storage solution suitable for use with critical data because of its distributed nature and ability to keep multiple copies of a file in different locations. The amount of data means that bandwidth, latency, and cost are important factors and the reason most storage solutions are on-premises. However, there are distinct advantages to hosting services in the cloud, namely scalability and ease-of-use. In this paper, we explore the possibility of provisioning a hybrid Ceph cluster that leverages the benefits of both cloud architectures and on-premise performance.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1993

Denver TMA assessment

This report describes the assessment of the Traffic Management Advisor (TMA) conducted at the Denver Center and TRACON, January 11-February 5, 1993. The assessment addressed the effectiveness of TMA for supporting various traffic management activities. At the Center, traffic management coordinators (TMC's) shadowed traffic operations, using TMA to make metering time and internal release decisions. At the TRACON, TMC's accessed TMA in an advisory mode for different planning activities such as staffing, distributing the traffic load, and changing the airport acceptance rate. These different opportunities for assessing TMA provide insight into TMA as a potential communication aid and planning tool. Findings from this assessment indicate that TMA can be used to support traffic management planning and decisions. TMC's at the center were able to use information provided by TMA to determine metering times as well as internal release times. At the TRACON, TMA supported decisions on airport configuration, airport acceptance rate, load distribution, proactive coordination with the center, and staffing. While findings of TMA use are generally positive, it must be kept in mind that this assessment is a snapshot in time. Not all TMA capabilities were assessed, nor were TMC's fully trained and knowledgeable on all features. Feature use will continue to evolve and strategies emerge as the TMC's gain experience with TMA over a variety of traffic situations and their understanding of TMA capabilities broadens with continued training.

Harwood, Kelly↗

NASA NTRS1996

Experimental and computational investigation of lift-enhancing tabs on a multi-element airfoil

An experimental and computational investigation of the effect of lift enhancing tabs on a two-element airfoil was conducted. The objective of the study was to develop an understanding of the flow physics associated with lift enhancing tabs on a multi-element airfoil. A NACA 63(sub 2)-215 ModB airfoil with a 30 percent chord Fowler flap was tested in the NASA Ames 7 by 10 foot wind tunnel. Lift enhancing tabs of various heights were tested on both the main element and the flap for a variety of flap riggings. Computations of the flow over the two-element airfoil were performed using the two-dimensional incompressible Navier-Stokes code INS2D-UP. The computer results predict all of the trends in the experimental data quite well. When the flow over the flap upper surface is attached, tabs mounted at the main element trailing edge (cove tabs) produce very little change in lift. At high flap deflections. however, the flow over the flap is separated and cove tabs produce large increases in lift and corresponding reductions in drag by eliminating the separated flow. Cove tabs permit high flap deflection angles to be achieved and reduce the sensitivity of the airfoil lift to the size of the flap gap. Tabs attached to the flap training edge (flap tabs) are effective at increasing lift without significantly increasing drag. A combination of a cove tab and a flap tab increased the airfoil lift coefficient by 11 percent relative to the highest lift tab coefficient achieved by any baseline configuration at an angle of attack of zero percent and the maximum lift coefficient was increased by more than 3 percent. A simple analytic model based on potential flow was developed to provide a more detailed understanding of how lift enhancing tabs work. The tabs were modeled by a point vortex at the training edge. Sensitivity relationships were derived which provide a mathematical basis for explaining the effects of lift enhancing tabs on a multi-element airfoil. Results of the modeling effort indicate that the dominant effects of the tabs on the pressure distribution of each element of the airfoil can be captured with a potential flow model for cases with no flow separation.

Ashby, Dale↗

NASA NTRS2014

Autonomous Mission Operations Roadmap

As light time delays increase, the number of such situations in which crew autonomy is the best way to conduct the mission is expected to increase. However, there are significant open questions regarding which functions to allocate to ground and crew as the time delays increase. In situations where the ideal solution is to allocate responsibility to the crew and the vehicle, a second question arises: should the activity be the responsibility of the crew or an automated vehicle function? More specifically, we must answer the following questions: What aspects of mission operation responsibilities (Plan, Train, Fly) should be allocated to ground based or vehicle based planning, monitoring, and control in the presence of significant light-time delay between the vehicle and the Earth?How should the allocated ground based planning, monitoring, and control be distributed across the flight control team and ground system automation? How should the allocated vehicle based planning, monitoring, and control be distributed between the flight crew and onboard system automation?When during the mission should responsibility shift from flight control team to crew or from crew to vehicle, and what should the process of shifting responsibility be as the mission progresses? NASA is developing a roadmap of capabilities for Autonomous Mission Operations for human spaceflight. This presentation will describe the current state of development of this roadmap, with specific attention to in-space inspection tasks that crews might perform with minimum assistance from the ground.

Mission Operations↗

NASA NTRSDate not supplied

A Decade of NASA Strategic Astrophysics Technology Investments: Technology Maturation, Infusion, and Other Benefits

NASA’s Astrophysics Division (APD) funds development of cutting-edge technology to enable its missions to achieve ambitious and groundbreaking science goals. These technology development efforts are managed by the Physics of the Cosmos (PCOS), Cosmic Origins (COR), and Exoplanet Exploration (ExE) Programs. The NASA Strategic Astrophysics Technology (SAT) Program was established in 2009 as a new technology maturation program to fill the gap in the Technology Readiness Level (TRL) range from 3 to 6. Since program inception, 100 SAT grants have been openly competed and awarded, along with dozens of direct-funded projects, leading to a host of technologies advancing their TRLs and/or being infused into space and suborbital missions and ground-based projects. We present the portfolio distribution in terms of specific technology areas addressed, including optics, detectors, coatings, coronagraphs, starshades, lasers, electronics, cooling systems, and micro-thruster subsystems. We show an analysis of the rate of TRL advances, infusion success stories, and other benefits such as training the future astrophysics workforce, including students and postdoctoral fellows hired by projects. Finally, we present APD’s current strategic technology maturation priorities for investment, enabling a range of future strategic astrophysics missions

NASA, astrophysics, technology development, optics↗