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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 127 records · Page 7

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NASA NTRSDate not supplied

Air Quality Forecasting at Sub-City Scale by Combining Models, Satellites, and Surface Measures

While there are a variety of sources for air quality information, no one source simultaneously allows for high accuracy, low bias, fine spatial resolution, wide spatial coverage, high temporal frequency, and the capability for near-term forecasting of air quality. Global models, like the NASA’s Goddard Earth Observing System -Composition Forecasting (GEOS-CF) model, provide global coverage and forecasting capabilities, but operate at relatively coarse spatial resolution and require ground-truthing with in-situ data. Polar-orbiting satellite data products, like those of the ESA TROPOspheric Monitoring Instrument (TROPOMI), provide higher-spatial-resolution remote sensing of atmospheric composition, but are limited by cloud cover and overpass times and report column-integrated quantities. Surface measurements, both from regulatory-grade monitors and low-cost networks, measure “nose-level” air quality, but may not represent concentration variability across large spatial domains, and (in the case of low-cost sensors) are subject to interference and biases. There exists a great potential to combine these diverse data sources together, using the strengths of some to offset the weaknesses of others to build a more comprehensive picture of air quality. This presentation will summarize results from ongoing efforts to produce such a combined forecast, with application case studies for surface-level Nitrogen Dioxide forecasting in several major US cities. Furthermore, we will examine the relative impacts and benefits of different data sources on the forecasting accuracy at different spatial and temporal scales. Finally, we will examine the potential for integrating low-cost sensors into such a system, both in terms of using these integrated air quality estimates as a baseline from which to calibrate networks of low-cost sensors in the field, and in terms of using dense networks of low-cost sensors to refine the spatial resolution of integrated air quality forecasts.

Air Quality↗

NASA NTRS1990

Estimability of geodetic parameters from space VLBI observables

The feasibility of space very long base interferometry (VLBI) observables for geodesy and geodynamics is investigated. A brief review of space VLBI systems from the point of view of potential geodetic application is given. A selected notational convention is used to jointly treat the VLBI observables of different types of baselines within a combined ground/space VLBI network. The basic equations of the space VLBI observables appropriate for convariance analysis are derived and included. The corresponding equations for the ground-to-ground baseline VLBI observables are also given for a comparison. The simplified expression of the mathematical models for both space VLBI observables (time delay and delay rate) include the ground station coordinates, the satellite orbital elements, the earth rotation parameters, the radio source coordinates, and clock parameters. The observation equations with these parameters were examined in order to determine which of them are separable or nonseparable. Singularity problems arising from coordinate system definition and critical configuration are studied. Linear dependencies between partials are analytically derived. The mathematical models for ground-space baseline VLBI observables were tested with simulation data in the frame of some numerical experiments. Singularity due to datum defect is confirmed.

Adam, Jozsef↗

DOE OSTI2022

Adaptive time scaling for accelerating explicit finite element analysis

A method for accelerating an explicit finite element analysis (FEA) simulation of a modeled system or process includes performing an initial iteration of the FEA simulation according to a baseline time interval via an FEA computing network, and calculating a criteria ratio of a predetermined set of scaling criteria for the modeled system or process. The method includes determining a time-scaling factor using the criteria ratio via the FEA computing network as a function of the criteria ratio, and then applying the time-scaling factor to the baseline time interval to generate a scaled time interval. The scaled time interval accelerates simulation time of the FEA simulation. The method includes performing a subsequent iteration of the explicit FEA simulation at the scaled time interval using the FEA computing network. The process continues for subsequent iterations, with the time-scaling factor adapting with each iteration.

Chen, Jian↗

DOE OSTI2024

Open Call LDRD: Physically Informed Autoencoders for Galactic Redshift Regression

Physical constraints have been suggested to make neural network models more generalizable, act scientifically plausible, and be more data-efficient over unconstrained baselines. In this report, we present preliminary work on evaluating the effects of adding soft physical constraints to computer vision neural networks trained to estimate the conditional density of redshift on input galaxy images for the Sloan Digital Sky Survey. We introduce physically motivated soft constraint terms that are not implemented with differential or integral operators. We frame this work as a simple ablation study where the effect of including soft physical constraints is compared to an unconstrained baseline. We compare networks using standard point estimate metrics for photometric redshift estimation, as well as metrics to evaluate how faithful our conditional density estimate represents the probability over the ensemble of our test dataset. We find no evidence that the implemented soft physical constraints are more effective regularizers than augmentation.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1990

Information content of a single pass of phase-delay data from a short baseline connected element interferometer

An analytic development of the information array obtained with a single tracking pass of phase-delay measurements made from a short baseline interferometer is presented. Phase-delay observations can be made with great precision from two antennas using a single, common distributed frequency standard, hence the name connected element. With the information array, closed-form expressions are developed for the error covariance in declination and right ascension. These equations serve as useful tools for analyzing the relative merits of candidate station locations for connected element interferometry (CEI). The navigation performance of a short baseline interferometer located at the Deep Space Network's (DSN's) Goldstone intracomplex is compared with that which is presently achievable using Very Long Baseline Interferometry (VLBI) over intercontinental baselines. The performance of an intracomplex pair of short baselines formed by three stations is also investigated, along with the use of a single baseline in conjunction with conventional two-way Doppler data. The phase-delay measurement accuracy and data rate used in the analysis are based on the expected performance of an experimental connected element system presently under construction at Goldstone. The results indicate that the VLBI system that will be used during the Galileo mission can determine the declination and right ascension of a distant spacecraft to an accuracy of 20 to 25 nrad, while the CEI triad system and the combination of CEI-Doppler system are both capable of 30 to 70 nrad performance.

Thurman, S. W.↗

NASA NTRS1990

Mars global network mission

The status of a program aimed at placing a global network of small surface landers on Mars for direct surface and subsurface measurements is reviewed. A possible baseline mission concept to establish a global network is discussed with particular reference to mission and system requirements, lander entry design considerations, and orbit design. The principal lander subsystems are described, including attitude control, power, command and data handling, telecommunications, and thermal subsystems.

Mostert, R. N.↗

DOE OSTI2023

Design and Development of a High Fidelity Cyber-Physical Testbed

In order to ensure that future critical infrastructure systems are resilient to various types of such advanced and persistent threats, it is important to develop and integrate tailored solutions that holistically address cyber-attack detection and mitigation in a timely manner such that adverse system impacts that impact a large population are avoided. Further, it is essential to create environments that allow control, protection and communication to exist within a realistic environment to analyze the effects of adverse conditions and system operating modes. This project aims to establish a high-fidelity testbed environment for modeling and simulating a single microgrid all the way up to a network of microgrids along with baseline controls, protection, and associated cyber communication. This is an important activity because accurately modeling and simulating the various power-electronics-based DERs and loads in a microgrid is critical to adequately capturing their behaviors over a wide range of off-normal conditions, as well as to evaluate the resilience of the system using the developed controls. The work presented in this report focuses on the process of building this high-fidelity testbed and the associated experimentation it enables. The model enables the creation of high-fidelity use cases and associated datasets that have been used extensively within the initiative to study resilience and support novel control development and prototyping. The work heavily leverages existing capability that is part of the high-fidelity cyber-physical system experimentation lab to create a power hardware-in-the-loop setup. The report also details the creation of an automated model building platform that can enable high-fidelity real-time models to be built without much effort allowing existing low-fidelity models to be analyzed in higher fidelity. Lastly, the report also discusses efforts center around scaling to large complex power system models to make the experimentation more effective.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1983

Radio stars - A possible link between the Hipparcos optical reference frame and an extra-galactic very long baseline interferometry reference frame

The concept of typing the Hipparcos optical and the JPL VLBI frames of reference by means of VLBI measurements of the positions and proper motions of the radio components of some bright stars is considered. The properties of the thermal and non-thermal radio-stars are discussed and 22 candidate stars are selected to achieve this tie. A description is given of the first VLBI attempt to detect these stars on the intercontinental baselines of the Deep Space Network with the Mark II recording system.

Lestrade, J.-F.↗

NASA NTRS2001

The Use of Eccentric and Circular Orbits in the Design of a Mars Network Constellation

This study examined different constellation configurations to determine their suitability for the Mars Network. Some variations on the baseline case of four circular orbits were initially studied. Eccentric orbits were then used to determine their effects on several figures of merit that were selected as representative of the design goals. It was eventually found that the use of eccentric orbits in combination with circular orbits can improve aspects of some navigation and communication figures of merit. The ability to use orbits at different inclinations helps smooth coverage over the middle and upper latitudes for these figures of merit. This configuration has more variability than one consisting of circular orbits, so the occurrence of unfavorable arrangements also results in degradation of some figures of merit. Some cases were used which improved different figures of merit, so a solution could be chosen depending on specified requirements.

Anderson, Rodney↗

NASA NTRS2011

The Catalog of Positions of Optically Bright Extragalactic Radio Sources OBRS-1

It is expected that the European Space Agency mission Gaia will make it possible to determine coordinates in the optical domain of more than 500,000 quasars. In 2006, a radio astrometry project was launched with the overall goal of making comparisons between coordinate systems derived from future space-born astrometry instruments and the coordinate system constructed from analysis of global very long baseline interferometry (VLBI) more robust. Investigation of the rotation, zonal errors, and non-alignment of the radio and optical positions caused by both radio and optical structures is needed to validate both techniques. In order to support these studies, the densification of the list of compact extragalactic objects that are bright in both radio and optical ranges is desirable. A set of 105 objects from the list of 398 compact extragalactic radio sources with decl. > -10deg was observed with the Very Long Baseline Array and European VLBI Network (EVN) with the primary goal of producing images with milliarcsecond resolution. These sources are brighter than 18 mag in the V band, and they were previously detected by the EVN. In this paper, coordinates of observed sources have been derived with milliarcsecond accuracies from analysis of these VLBI observations using an absolute astrometry method. The catalog of positions for 105 target sources is presented. The accuracies of source coordinates are in the range of 0.3.7 mas, with a median of 1.1 mas.

Petrov, L.↗

NASA NTRSDate not supplied

Optimizing Altitude Sampling and Sensitivity with the Goldstone Orbital Debris Radar

The NASA Orbital Debris Program Office (ODPO) has used the Goldstone Orbital Debris Radar (Goldstone) since 1993 to characterize orbital debris (OD) in low Earth orbit too small to be tracked by the U.S. Space Surveillance Network. Operated by NASA’s Jet Propulsion Laboratory, Goldstone can measure OD as small as 3 mm at 1000 km altitude and lower. Goldstone is a bistatic radar that for 25 years used Deep Space Station (DSS)-14 as a transmitter and DSS-15 as a receiver. In early 2018, DSS-15 was decommissioned and replaced with DSS-25 (and occasionally DSS-26) of the Deep Space Network Apollo Cluster. The increased baseline between DSS-14 and DSS-25 significantly reduced the instantaneous altitude coverage of the bistatic beam overlap. Initial measurements in 2018 were focused around 800 km, which has approximately the highest flux of sub-centimeter debris. In 2019, DSS-14 was offline for maintenance, and the ODPO designed an annual survey observation plan to efficiently sample altitudes from 700 km to 1000 km, since many NASA satellites fly in this range. This paper discusses the observation plan, including the development of the pointings, a refinement of the altitudes of interest, and an analysis of the effects of random pointing errors on beam overlap. Additionally, results from measurements taken in 2020 and 2021 are presented, showing that not only is the observation plan effective at sampling 700 km to 1000 km altitude, but it is also producing the most sensitive terrestrial radar measurements at these altitudes to date.

James Murray↗

NASA NTRS2023

Optimizing Altitude Sampling and Sensitivity with the Goldstone Orbital Debris Radar

James Murray↗

NASA NTRS2023

Baseline Vector Repeatability at the Sub-Millimeter Level Enabled by Radio Interferometer Phase Delays of Intra-Site Baselines

We report the results of position ties for short baselines at eight geodetic sites based on phase delays that are extracted from global geodetic very-long-baseline interferometry (VLBI) observations rather than dedicated short-baseline experiments. An analysis of phase delay observables at X band from two antennas at the Geodetic Observatory Wettzell, Germany, extracted from 107 global 24-hr VLBI sessions since 2019 yields weighted root-mean-square scatters about the mean baseline vector of 0.3, 0.3, and 0.8 mm in the east, north, and up directions, respectively. Position ties are also obtained for other short baselines between legacy antennas and nearby, newly built antennas. They are critical for maintaining a consistent continuation of the realization of the terrestrial reference frame, especially when including the new VGOS network. The phase delays of the baseline WETTZ13N–WETTZELL enable an investigation of sources of error at the sub-millimeter level. We found that a systematic variation of larger than 1 mm can be introduced to the Up estimates of this baseline vector when atmospheric delays were estimated. Although the sub-millimeter repeatability has been achieved for the baseline vector WETTZ13N–WETTZELL, we conclude that long term monitoring should be conducted for more short baselines to assess the instrumental effects, in particular the systematic differences between phase delays and group delays, and to find common solutions for reducing them. This will be an important step toward the goal of global geodesy at the 1 mm level.

geodetic VLBI↗

DOE OSTI2020

Constrained Deep Reinforcement Learning for Energy Sustainable Multi-UAV Based Random Access IoT Networks With NOMA

In this paper, we apply the Non-Orthogonal Multiple Access (NOMA) technique to improve the massive channel access of a wireless IoT network where solar-powered Unmanned Aerial Vehicles (UAVs) relay data from IoT devices to remote servers. Specifically, IoT devices contend for accessing the shared wireless channel using an adaptive p-persistent slotted Aloha protocol; and the solar-powered UAVs adopt Successive Interference Cancellation (SIC) to decode multiple received data from IoT devices to improve access efficiency. To enable an energy-sustainable capacity-optimal network, we study the joint problem of dynamic multi-UAV altitude control and multi-cell wireless channel access management of IoT devices as a stochastic control problem with multiple energy constraints. We first formulate this problem as a Constrained Markov Decision Process (CMDP), and propose an online model-free Constrained Deep Reinforcement Learning (CDRL) algorithm based on Lagrangian primal-dual policy optimization to solve the CMDP. Extensive simulations demonstrate that our proposed algorithm learns a cooperative policy in which the altitude of UAVs and channel access probability of IoT devices are dynamically controlled to attain the maximal long-term network capacity while ensuring energy sustainability of UAVs, outperforming baseline schemes. The proposed CDRL agent can be trained on a small network, yet the learned policy can efficiently manage networks with a massive number of IoT devices and varying initial states, which can amortize the cost of training the CDRL agent.

42 ENGINEERING↗

DOE OSTI2023

Load Profile Inpainting for Missing Load Data Restoration and Baseline Estimation

This paper introduces a Generative Adversarial Nets (GAN) based, Load Profile Inpainting Network (Load-PIN) for restoring missing load data segments and estimating the baseline for a demand response event. The inputs are time series load data before and after the inpainting period together with explanatory variables (e.g., weather data). Here, we propose a Generator structure consisting of a coarse network and a fine-tuning network. The coarse network provides an initial estimation of the data segment in the inpainting period. The fine-tuning network consists of self-attention blocks and gated convolution layers for adjusting the initial estimations. Loss functions are specially designed for the fine-tuning and the discriminator networks to enhance both the point-to-point accuracy and realisticness of the results. We test the Load-PIN on three real-world data sets for two applications: patching missing data and deriving baselines of conservation voltage reduction (CVR) events. We benchmark the performance of Load-PIN with five existing deep-learning methods. Our simulation results show that, compared with the state-of-the-art methods, Load-PIN can handle varying-length missing data events and achieve 15-30% accuracy improvement.

14 SOLAR ENERGY↗

DOE OSTI2021

Power System Event Identification Based on Deep Neural Network With Information Loading

Online power system event identification and classification are crucial to enhancing the reliability of transmission systems. In this study, we develop a deep neural network (DNN) based approach to identify and classify power system events by leveraging real-world measurements from hundreds of phasor measurement units (PMUs) and labels from thousands of events. Two innovative designs are embedded into the baseline model built on convolutional neural networks (CNNs) to improve the event classification accuracy. First, we propose a graph signal processing based PMU sorting algorithm to improve the learning efficiency of CNNs. Second, we deploy information loading based regularization to strike the right balance between memorization and generalization for the DNN. Numerical results based on real-world dataset from the Eastern Interconnection of the U.S power transmission grid show that the combination of PMU based sorting and the information loading based regularization techniques help the proposed DNN approach achieve highly accurate event identification and classification results.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Reinforcement Learning via Gaussian Processes with Neural Network Dual Kernels

While deep neural networks (DNNs) and Gaussian Processes (GPs) are both popularly utilized to solve problems in reinforcement learning, both approaches feature undesirable drawbacks for challenging problems. DNNs learn complex non-linear embeddings, but do not naturally quantify uncertainty and are often data-inefficient to train. GPs infer posterior distributions over functions, but popular kernels exhibit limited expressivity on complex and high-dimensional data. Fortunately, recently discovered conjugate and neural tangent kernel functions encode the behavior of overparameterized neural networks in the kernel domain. We demonstrate that these kernels can be efficiently applied to regression and reinforcement learning problems by analyzing a baseline case study.We apply GPs with neural network dual kernels to solve reinforcement learning tasks for the first time. We demonstrate, using the well understood mountain-car problem, that GPs empowered with dual kernels perform at least as well as those using the conventional radial basis function kernel. Finally, we conjecture that by inheriting the probabilistic rigor of GPs and the powerful embedding properties of DNNs, GPs using NN dual kernels will empower future reinforcement learning models on difficult domains.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1984

A comparison between Lageos laser ranging and VLBI determined baselines

Two independent measurement techniques, Lageos satellite laser ranging (SLR), and very long baseline interferometry (VLBI) are compared in the measurement of distances (or baselines) between several locations in the continental U.S. The results of this analysis is summarized where both the SLR and VLBI baseline lengths and their differences (SLR minus VLBI) are presented. A comparison of the 22 baselines shows a mean difference of 1.0 + or - 1.1 cm with a scatter about zero of 5.2 cm. No apparent systematic scale difference between the networks is evident. A map of the baselines is included and indicates their differences, SLR minus VLBI, in centimeters.

Kolenkiewicz, R.↗