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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS1978

Helios mission support

Activities of the Deep Space Network Operations organization in support of the Helios Project from 15 October 1977 through 15 December 1977 are described. Topics covered include: (1) Mark 3 data subsystem testing at the conjoint Deep Space Stations (DSS) 42/43 (Canberra, Australia); (2) MDS implementation at DSS 61/63 (Madrid, Spain); (3) Radio Science update, and (4) other mission-related activities.

Goodwin, P. S.↗
DOE OSTI2024

Toward a Machine Learning Approach to Interpreting X-ray Spectra of Trace Impurities by Converting XANES to EXAFS

The fact that the photoabsorption spectrum of a material contains information about the atomic structure, commonly understood in terms of multiple scattering theory, is the basis of the popular extended X-ray absorption spectroscopy (EXAFS) technique. How much of the same structural information is present in other complementary spectroscopic signals is not obvious. Here we use a machine learning approach to demonstrate that within theoretical models that accurately predict the EXAFS signal, the extended near-edge region does indeed contain the EXAFS-accessible structural information. We do this by exhibiting deep operator neural networks (DeepONets) that have learned the relationship between the extended and near edge portions of the X-ray absorption spectrum to predict the former from the latter. We find that we can accurately predict the EXAFS spectrum between 6 and 14 Å –1 from the first 6 Å –1 (≈100 eV) of the absorption spectrum of Cu 2 + substitutional defects in the Fe 3+ mineral hematite (α-Fe 2 O 3 ). This surprising finding implies that theoretical analyses of X-ray absorption spectra could be implemented that extract the same conclusions as high-quality EXAFS studies from spectra collected over a much smaller range of photon energies. This relaxes a host of experimental limitations related to the X-ray source and measurement sample, including collection time, minimum dopant concentration, source brilliance, and energy range. We describe the theoretical data sets and DeepONet construction and show that the resulting DeepONets produce EXAFS that recovers linear combination fits to experimental data with accuracy approaching the original ab initio calculations. We discuss the implications of our findings for minor constituent characterization and for understanding the information content of spectroscopic data more broadly, including how this approach might be applied to measured experimental spectra. In conclusion, to encourage similar efforts, the simulated X-ray spectra, machine learning, and fitting code are publicly available.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1977

VLBI Validation Project

Two training and three operational Deep Space Network experiments conducted in support of the very long baseline interferometry (VLBI) validation project are reported. Vector measurements using common benchmark locations at continental distances (Goldstone, Haystack, and the McDonald Observatory) with an accuracy goal on the order of 5 cm. were obtained and compared with laser ranging systems data.

Ross, W. J.↗
NASA NTRS1980

Block 4 receiver tracking loop performance in the presence of a CW RFI

A model that allows one to predict the tracking performance of the Block 4 receiver in the presence of a continuous wave radio frequency interference is discussed. Experimental and analytical results are provided for a typical Deep Space Network operational mode. Simulation and experimental results show good agreement with theoretical prediction for the static phase error and out-of-lock values. Predicted phase jitter is consistently lower than the experimental and simulated results by a factor of one-half for small interference to signal ratio (ISR) when the offset frequency is small. For large ISR, good agreement is observed. The analytical model assumes a noiseless condition, which is valid only when the loop is operated at strong signal levels. Experimental data indicate, however, that even at the minimum operating signal level of 10-dB carrier margin, reasonably good prediction can still be obtained. A curve of protection criteria that extends the current recommendation is also presented.

Sue, M. K.↗
NASA NTRS1981

The telecommunications and data acquisition report

Deep Space Network operations, engineering, and implementation are reported. Developments in Earth-based radiotechnology as applied to other research programs in the fields of Geodynamics, Astrophysics, and programs related to radio searchers (instrumentation and methods) in extraterrestrial areas in the microwave region of the electromagnetic spectrum are also presented.

Renzetti, N. A.↗
NASA NTRS1982

Networks consolidation program

Progress in the networks consolidations program (NCP) to combine the resources of the two NASA ground spacecraft tracking networks (the Deep Space Network, operated by JPL, and the ground spaceflight tracking and data network, operated by Goddard) into one consolidated network is reported. Management, design, and implementation activities occurring between August 1981 and April 1982 are addressed, with special emphasis on planning and budgeting activities.

Yeater, M. L.↗
NASA NTRS1988

The Deep Space Network: An instrument for radio astronomy research

The NASA Deep Space Network operates and maintains the Earth-based two-way communications link for unmanned spacecraft exploring the solar system. It is NASA's policy to also make the Network's facilities available for radio astronomy observations. The Network's microwave communication systems and facilities are being continually upgraded. This revised document, first published in 1982, describes the Network's current radio astronomy capabilities and future capabilities that will be made available by the ongoing Network upgrade. The Bibliography, which includes published papers and articles resulting from radio astronomy observations conducted with Network facilities, has been updated to include papers to May 1987.

Renzetti, N. A.↗
NASA NTRS1994

(abstract) Deep Space Network Radiometric Remote Sensing Program

Planetary spacecraft are viewed through a troposphere that absorbs and delays radio signals propagating through it. Tropospheric water, in the form of vapor, cloud liquid,and precipitation , emits radio noise which limits satellite telemetry communication link performance. Even at X-band, rain storms have severely affected several satellite experiments including a planetary encounter. The problem will worsen with DSN implementation of Ka-band becausecommunication link budgets will be dominated by tropospheric conditions. Troposphere-induced propagation delays currently limit VLBI accuracy and are significant sources of error for Doppler tracking. Additionally, the success of radio science programs such as satellite gravity wave experiments and atmospheric occultation experiments depends on minimizing the effect of watervapor-induced prop agation delays. In order to overcome limitations imposed by the troposphere, the Deep Space Network has supported a program of radiometric remote sensing. Currently, water vapor radiometers (WVRs) and microwave temperature profilers (MTPs) support many aspects of the Deep Space Network operations and research and development programs. Their capability to sense atmospheric water, microwave sky brightness, and atmospheric temperature is critical to development of Ka-band telemetry systems, communication link models, VLBI, satellite gravity waveexperiments, and r adio science missions. During 1993, WVRs provided data for propagation mode development, supp orted planetary missions, and demonstrated advanced tracking capability. Collection of atmospheric statistics is necessary to model and predict performance of Ka-band telemetry links, antenna arrays, and radio science experiments. Since the spectrum of weather variations has power at very long time scales, atmospheric measurements have been requested for periods ranging from one year to a decade at each DSN site. The resulting database would provide reliable statistics on daily, monthly, and seasonal variations. Only long-term monitoring will prevent biases from being introduced by an exceptionally wet or dry year. Support for planetary missions included tropospheric calibration for the recent Mars Observer gravity wave experiments and Ka-band link experiment (KaBLE). Additionally, several proposed radio science experiments such as profiling planetary atmospheres using satellite occultations and Ka-band gravitational wave searches require advanced radiometer technology development. Finally, there has been a consistent advanced technology program to advance satellite navigational and tracking capabilities. This year that included an experiment with radiometer based tropospheric calibration for a series of VLBI catalog measurements.

microwave propagation Ka-band weather variations r↗
NASA NTRS2005

Impact of interference on the receiving systems of the Deep-Space Network (DSN) Earth stations operated by NASA due to adjacent band emissions from Earth exploration satellites operating in the 8025-

This paper will first describe the characteristics of interference from a typical EESS satellite, including the intensity, frequency and duration of such interference. The paper will then discuss the DSN interference susceptibility, including the various components in the receiving systems that are susceptible to interference and the recovery time after a strong interference. Finally, the paper will discuss the impact of interference on science data and missions operations.

space research↗
NASA NTRS1994

Deep Space Network Radiometric Remote Sensing Program

Planetary spacecraft are viewed through a troposphere that absorbs and delays radio signals propagating through it. Tropospheric water, in the form of vapor, cloud liquid, and precipitation, emits radio noise which limits satellite telemetry communication link performance. Even at X-band, rain storms have severely affected several satellite experiments including a planetary encounter. The problem will worsen with DSN implementation of Ka-band because communication link budgets will be dominated by tropospheric conditions. Troposphere-induced propagation delays currently limit VLBI accuracy and are significant sources of error for Doppler tracking. Additionally, the success of radio science programs such as satellite gravity wave experiments and atmospheric occultation experiments depends on minimizing the effect of water vapor-induced propagation delays. In order to overcome limitations imposed by the troposphere, the Deep Space Network has supported a program of radiometric remote sensing. Currently, water vapor radiometers (WVRs) and microwave temperature profilers (MTPs) support many aspects of the Deep Space Network operations and research and development programs. Their capability to sense atmospheric water, microwave sky brightness, and atmospheric temperature is critical to development of Ka-band telemetry systems, communication link models, VLBI, satellite gravity wave experiments, and radio science missions. During 1993, WVRs provided data for propagation model development, supported planetary missions, and demonstrated advanced tracking capability. Collection of atmospheric statistics is necessary to model and predict performance of Ka-band telemetry links, antenna arrays, and radio science experiments. Since the spectrum of weather variations has power at very long time scales, atmospheric measurements have been requested for periods ranging from one year to a decade at each DSN site. The resulting database would provide reliable statistics on daily, monthly, and seasonal variations. Only long-term monitoring will prevent biases from being introduced by an exceptionally wet or dry year. Support for planetary missions included tropospheric calibration for the recent Mars Observer gravity wave experiments and Ka-band link experiment (KaBLE). Additionally, several proposed radio science experiments such as profiling planetary atmospheres using satellite occultations and Ka-band gravitational wave searches require advanced radiometer technology development. Finally, there has been a consistent advanced technology program to advance satellite navigational and tracking capabilities. This year that included an experiment with radiometer based tropospheric calibration for a series of VLBI catalog measurements.

tropospheric calibration atmospheric water radio s↗
NASA NTRS2016

Achieving Fast Operational Intelligence in NASA's Deep Space Network Through Complex Event Processing

NASA’s Deep Space Network (DSN) is a complex, global project, in which the expertise of human operators remain crucial for its successful operation. To find ways to save costs in operations and to improve its services, a number of modernization efforts are underway in the DSN. One such effort is a research and technology development task at the Jet Propulsion Laboratory that is investigating the use of complex event processing (CEP) for intelligent assessment of situations, trend analysis, and advanced automation. The technology leverages the significant business intelligence (BI) and data science advancements made in the enterprise industries over the last several years. The open source big data processing engine Apache SparkTM and the high-throughput, distributed messaging system Apache Kafka form the core of the DSN Complex Event Processing (DCEP) framework. This paper discusses the system engineering perspective of why achieving efficient, lower-cost operations in the DSN is a challenging problem, how the DCEP system handles the use cases that help realize intelligent operations, and how this solution fits into the overall model of the planned DSN Follow-the- Sun Operations (FtSO).

Choi, Joshua S.↗
DOE OSTI2026

AnisONet: A deep neural operator-based anisotropic permeability upscaler from pore to Darcy scale

Directional permeability variations, which govern directional fluid flow in porous media with anisotropy, are important to accurately predict flow behavior, reactive transport, and fluid–solid interactions for various processes such as enhanced geothermal systems, energy storage devices, and biological systems. However, the intricate architecture of porous media makes it difficult to predict directional permeabilities. In this work, we present a novel machine learning (ML) framework, AnisONet, built upon an integration of a convolutional neural network, Swin transformer, and the deep operator network architecture, designed to predict anisotropic permeability and upscale predictions to larger spatial domains. First, AnisONet was evaluated with three classes of two-dimensional (2D) porous media, including synthetic circular and elliptical grains and natural sandstone grains from micro-computed tomography images. A lattice Boltzmann model (LBM) was used to calculate directional permeabilities at every 10° angle, producing 19 data points per image of porous media. AnisONet is then trained to predict permeability as a function of rotation angle. AnisONet showed strong predictive capability of directional permeability. Second, we tested our model for five upscaling cases with a large image size in the finite-element method (FEM) for 2D Darcy flow with various permeability tensor construction methods. Overall, upscaled permeability tensors in FEM simulations produce a reasonably good match with LBM results, highlighting the importance of selecting appropriate tensor formation strategies for accurate permeability upscaling. AnisONet, as a directional permeability estimator, could be further developed for more complex geometries, with the potential to develop a foundational ML model for various applications in porous media.

42 ENGINEERING↗