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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Ultrastable reference frequency distribution utilizing a fiber optic link

The Frequency Standards Laboratory at the Jet Propulsion Laboratory (JPL) is responsible for the generation and distribution of ultra-stable reference frequency in NASA's Deep Space Network (DSN). Certain assemblies and components of the Radio Science and VLBI systems are located in the cones of tracking antennas hundreds of meters from the Frequency and Timing Subsystem's frequency standards. The very stringent requirements of these users challenge the performance of state-of-the-art frequency sources as well as the associated signal distribution system. The reference frequency distribution system described is designed around a low temperature coefficient of delay (TCD) optical fiber. On-site measurements of the fiber optic link alone indicate 100 MHz phase noise performance on the order of -120 dBc at 1 Hz from the carrier and Allan deviation on the order of parts in 10(exp 16) at 1000 seconds averaging time. The measured phase noise and stability of the link indicate that the performance characteristics of the hydrogen maser frequency standards are not degraded by the distribution system. Thus, optical fibers and electro-optic devices as distribution media appear to be a viable alternative to the classical coaxial cable distribution systems.

Calhoun, Malcolm↗
DOE OSTI2020

Bayesian Structural Time Series for Behind-the-Meter Photovoltaic Disaggregation: Preprint

Distributed photovoltaic (PV) generation often occurs ``behind the meter": a grid operator can only observe the net load, which is the sum of the gross load and distributed PV generation. This lack of observability poses a challenge to system operation at both bulk level and distribution level. The lack of real-time or near-future disaggregated estimates of gross load and PV generation will lead to over scheduling of energy production and regulation reserves, reliability constraints violations, wear and tear of controller devices, and potentially cascading failures of a system. In this paper we propose the use of a Bayesian Structural Time Series (BSTS) model with local solar irradiance measurements to disaggregate the summed PV generation and gross load signals at a downstream measurement site. BSTSs are a highly expressive model class that blends classic time series models with the powerful Bayesian state space estimation framework. Disaggregation is done probabilistically, which automatically quantifies the uncertainties of the estimated PV generation and gross load consumption. Depending on the data availability in real-time, it can be used to disaggragate PV and gross load at customer site, or can be used at the feeder level. In this paper, we focus on solving the problem at feeder level. We compare the performance of a BSTS model as well as a handful of state-of-the-art methods on a Pecan Street AMI dataset, using the National Solar Radiation Database (NSRDB) to estimate local irradiance.

Bayesian structural time series↗
DOE OSTI2025

Resilient Distributed Frequency Regulation of Renewable Generators under Communication Interruptions

Modern power systems (MPSs) face significant challenges due to the high penetration of renewable energy sources (RESs) and new types of loads such as electric vehicles (EVs). Traditional load frequency control (LFC) methods struggle with the intermittent, stochastic nature of RESs, the near-zero inertia of power-electronics-based generators, and the mobility of controllable loads and battery systems. This paper introduces a novel resilient distributed frequency regulation method to address these issues. The proposed method employs a state space model to represent the dynamic behavior of participating power sources while accounting for stochastic switching processes to model structural and parameter variations caused by disruptions such as generator connection/disconnection, communication interruptions, and physical faults. By integrating these dynamic and stochastic components, the method treats power grids as a comprehensive stochastic hybrid system. Our method enhances conventional frequency control by incorporating local stability control, neighborhood control decoupling, and coordination feedback. Theoretical analyses establish the stability, convergence, and resilience of the proposed method, and its effectiveness is validated through case studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2013

Statistical Analyses of Raw Material Data for MTM45-1/CF7442A-36% RW: CMH Cure Cycle

This report describes statistical characterization of physical properties of the composite material system MTM45-1/CF7442A, which has been tested and is currently being considered for use on spacecraft structures. This composite system is made of 6K plain weave graphite fibers in a highly toughened resin system. This report summarizes the distribution types and statistical details of the tests and the conditions for the experimental data generated. These distributions will be used in multivariate regression analyses to help determine material and design allowables for similar material systems and to establish a procedure for other material systems. Additionally, these distributions will be used in future probabilistic analyses of spacecraft structures. The specific properties that are characterized are the ultimate strength, modulus, and Poisson􀂶s ratio by using a commercially available statistical package. Results are displayed using graphical and semigraphical methods and are included in the accompanying appendixes.

Coroneos, Rula↗
DOE OSTI2025

Continuous-variable quantum Boltzmann machine

Here, we propose a continuous-variable quantum Boltzmann machine (CVQBM) using a powerful energy-based neural network. It can be realized experimentally on a continuous-variable (CV) photonic quantum computer. We used a CV quantum imaginary time evolution (QITE) algorithm to prepare the essential thermal state and then designed the CVQBM to proficiently generate continuous probability distributions. We applied our method to both classical and quantum data. Using real-world classical data, such as synthetic-aperture radar (SAR) images, we generated probability distributions. For quantum data, we used the output of CV quantum circuits. We obtained high fidelity and low Kullback–Leibler (KL) divergence showing that our CVQBM learns distributions from given data well and generates data sampling from that distribution efficiently. We also discussed the experimental feasibility of our proposed CVQBM. Our method can be applied to a wide range of real-world problems by choosing an appropriate target distribution (corresponding to, e.g., SAR images, medical images, and risk management in finance). Moreover, our CVQBM is versatile and could be programmed to perform tasks beyond generation, such as anomaly detection.

SAR images↗
DOE OSTI2020

Perturbation-Based Diagnosis of False Data Injection Attack Using Distributed Energy Resources

Modern smart grid relies on various sensor measurements for its operational control. In a successful false data injection attack, the attacker manipulates the measurements from the grid sensors such that undetected errors are introduced into the estimates of the system parameters leading to catastrophic situations. This paper proposes a novel perturbation based false data injection attack detection mechanism that utilizes inverter based distributed energy resources (DERs) to create low magnitude perturbation signal in the distribution system voltage that is inconsequential to the normal grid operation. Two voltage sensitivity analysis based algorithms are designed to identify the optimal set of DERs that can create the voltage perturbation signal of desired magnitude. An analytical method of voltage sensitivity analysis is used to compute the magnitude of voltage perturbation signal at each node in a computationally efficient manner. Then, a detection mechanism is developed that checks for the presence of the perturbation sequence in each sensor measurement. A sensor measurement is deemed authentic if the voltage perturbation signal is present in the data. In case of sensor malfunction or cyber-attack, the perturbation signal will not be present in the measurement data. Performance of the proposed attack detection mechanism is validated via simulation of the IEEE 69 bus test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Deep Image Prior Enabled Full Waveform Inversion (Final Technical Report)

MS Student Naveen Gupta worked on the problem of full waveform inversion (FWI) using neural networks as shown in Figure 1. Our goal was to learn a neural network to represent the subsurface velocity model, which when fed into the FWI module (implemented using a numerical forward model of wave equations) produces amplitude estimates that match with ground-truth observations of amplitude. We used neural networks to solve the inverse problem of estimating velocity distributions for a given seismic amplitude data such that, once trained, our neural network model can generate a distribution of velocity profiles for different random vectors fed as inputs to the neural network model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Deep Generative Models that Solve PDEs: Distributed Computing for Training Large Data-Free Models

Recent progress in scientific machine learning (SciML) has opened up the possibility of training novel neural network architectures that solve complex partial differential equations (PDEs). Several (nearly data free) approaches have been recently reported that successfully solve PDEs, with examples including deep feed forward networks, generative networks, and deep encoder-decoder networks. However, practical adoption of these approaches is limited by the difficulty in training these models, especially to make predictions at large output resolutions (≥1024×1024). Here we report on a software framework for data parallel distributed deep learning that resolves the twin challenges of training these large SciML models - training in reasonable time as well as distributing the storage requirements. Our framework provides several out of the box functionality including (a) loss integrity independent of number of processes, (b) synchronized batch normalization, and (c) distributed higher-order optimization methods. We show excellent scalability of this framework on both cloud as well as HPC clusters, and report on the interplay between bandwidth, network topology and bare metal vs cloud. We deploy this approach to train generative models of sizes hitherto not possible, showing that neural PDE solvers can be viably trained for practical applications. We also demonstrate that distributed higher-order optimization methods are 2-3× faster than stochastic gradient-based methods and provide minimal convergence drift with higher batch-size.

PDEs↗
NASA NTRS1981

Support systems of the orbiting quarantine facility

The physical support systems, the personnel management structure, and the contingency systems necessary to permit the Orbiting Quarantine Facility (OQF) to function as an integrated system are described. The interactions between the subsystems within the preassembled modules are illustrated. The Power Module generates and distributes electrical power throughout each of the four modules, stabilizes the OQF's attitude, and dissipates heat generated throughout the system. The Habitation Module is a multifunctional structure designed to monitor and control all aspects of the system's activities. The Logistics Module stores the supplies needed for 30 days of operation and provides storage for waste materials generated during the mission. The Laboratory Module contains the equipment necessary for executing the protocol, as well as an independent life support system.

Source record↗
NASA NTRS1992

Project APEX: Advanced Phobos Exploration. Manned mission to the Martian moon Phobos

The manned exploration of Mars is a massive undertaking which requires careful consideration. A mission to the moon of Mars called Phobos as a prelude to manned landings on the Martian surface offers some advantages. One is that the energy requirements, in terms of delta 5, is only slightly higher than going to the Moon's surface. Another is that Phobos is a potential source of water and carbon which could be extracted and processed for life support and cryogenic propellants for use in future missions; thus, Phobos might serve as a base for extended Mars exploration or for exploration of the outer planets. The design of a vehicle for such a mission is the subject of our Aerospace System Design course this year. The materials and equipment needed for the processing plant would be delivered to Phobos in a prior unmanned mission. This study focuses on what it would take to send a crew to Phobos, set up the processing plant for extraction and storage of water and hydrocarbons, conduct scientific experiments, and return safely to Earth. The size, configuration, and subsystems of the vehicle are described in some detail. The spacecraft carries a crew of five and is launched from low Earth orbit in the year 2010. The outbound trajectory to Mars uses a gravitational assisted swing by of Venus and takes eight months to complete. The stay at Phobos is 60 days at which time the crew will be engaged in setting up the processing facility. The crew will then return to Earth orbit after a total mission duration of 656 days. Both stellar and solar observations will be conducted on both legs of the mission. The design of the spacecraft addresses human factors and life science; mission analysis and control; propulsion; power generation and distribution; thermal control; structural analysis; and planetary, solar, and stellar science. A 0.5 g artificial gravity is generated during transit by spinning about the lateral body axis. Nuclear thermal rockets using hydrogen as fuel are selected to reduce total launch mass and to shorten the duration of the mission. The nuclear systems also provide the primary electrical power via dual mode operation. The overall spacecraft length is 110 meters and the total mass departing from low Earth orbit is 900 metric tons.

Source record↗
DOE OSTI2025

Distributed Coordination of Demand-side Flexible Resources in Microgrid with All-Time Feasibility

The prevalence of distributed renewable generators motivates microgrid operators to exploit demand-side flexible resources (DFRs). Due to their dispersed nature, distributed DFR coordination has been a vibrant research area, while there are several issues awaiting to be addressed. On one hand, DFR power is internally coupled through power flow, while DFR usually cannot access grid information. On the other hand, in time-restricted scenarios, solution feasibility cannot be guaranteed by conventional dual-based algorithms. To fill these gaps, we propose a distributed DFR coordination framework with all-time feasibility. The proposed framework accounts for the distinct access of microgrid entities to grid information. A distributed and all-time feasible algorithm is proposed for optimal DFR coordination, which allows DFRs to make local decisions without violating constraints throughout iterations. The effectiveness of the proposed algorithm is demonstrated through case studies. The impact of peer-to-peer communication links on algorithm convergence is also investigated, which emphasizes the balance between communication investment and algorithm performance.

Li, Hongyi [Iowa State Univ., Ames, IA (United Sta↗
DOE OSTI2021

An Analysis Framework for Distribution Network DER Integration Analysis in India: Distributed Solar in Tamil Nadu

This report is part of a two-part series that represents a year-long collaboration with the Tamil Nadu Generation and Distribution Corporation Limited (TANGEDCO) on power sector planning. The first report in this series, the Pathways for Tamil Nadu’s Electric Power Sector 2017-2030 report outlines NREL’s work with TANGEDCO's electricity sector planning department to develop a model of the State’s power system and evaluate multiple scenarios of system growth given resource constraints, costs of technologies, and power sector policies. This second report in this series focuses on the rapidly transforming distribution network in the State. The report outlines a framework developed by NREL with TANGEDCO's distribution utility to quickly and accurately analyze the impacts of integrating renewable energy, specifically rooftop solar PV onto Tamil Nadu's distribution system. Together these studies help to prepare Tamil Nadu for a rapidly transforming power system.

14 SOLAR ENERGY↗
NASA NTRS1993

Cassini Power Management and Distribution

The cassini Power Subsystem provides power generation, management and distribution functions for the Saturn Orbiter Spacecraft. Power is derived from three parallel Radioisotope Thermoelectric Generators.

Cassin↗
NASA NTRS2003

Master clock and time distribution system for the NASA Deep Space Network

NASA's Deep Space Network (DSN) consists of more than 20 antennas located at three globally spaced ground communications facilities. Local generation and distribution of precise time and frequency reference signals comprise an essential and central component of each complex. Within each complex synchronized timing references are required by approximately 100 users located at distances up to 30 kilometers from the central control center and station Master Clock. In this paper a highly modular, hot-swappable, and expandable system design for generation, delivery, and synchronization of highly precise and stable timing signals over fiber optic cables is described.

Tjoelker, R. L.↗
NASA NTRS2015

The Distribution and Annihilation of Dark Matter Around Black Holes

We use a Monte Carlo code to calculate the geodesic orbits of test particles around Kerr black holes, generating a distribution function of both bound and unbound populations of dark matter (DM) particles. From this distribution function, we calculate annihilation rates and observable gamma-ray spectra for a few simple DM models. The features of these spectra are sensitive to the black hole spin, observer inclination, and detailed properties of the DM annihilation cross-section and density profile. Confirming earlier analytic work, we find that for rapidly spinning black holes, the collisional Penrose process can reach efficiencies exceeding 600%, leading to a high-energy tail in the annihilation spectrum. The high particle density and large proper volume of the region immediately surrounding the horizon ensures that the observed flux from these extreme events is non-negligible.

astroparticle physics↗
DOE OSTI2026

Energy Adequacy Studies: Nodal vs Zonal and Ramp-Rate Representation

This work investigates limitations in current energy adequacy studies, particularly concerning the representation of grid complexities. We highlight how traditional zonal modeling approaches, which simplify large geographic areas and generator capabilities, can mask critical transmission constraints and localized resource shortfalls. By analyzing nodal versus zonal pricing differences and the empirical distribution of generator ramp-rates, we demonstrate that these simplified assumptions may misrepresent system flexibility and deliverability. Our findings underscore the need for more granular, nodal-level analyses and data-driven characterizations of generation assets to provide a more accurate and robust assessment of energy reliability and adequacy, ultimately supporting a more resilient and efficient energy infrastructure.

24 POWER TRANSMISSION AND DISTRIBUTION↗