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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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With operational spacecraft in excess of 10 astronomical units from Earth, NASA's Deep Space Network requires sensitive receivers for detection of weak spacecraft signals.
NASA’s Space Communications and Navigation (SCaN) program office builds, operates, and maintains NASA’s two primary communications networks – the Near Space Network and the Deep Space Network. These networks allow spacecraft near and far to send critical tracking, telemetry, command, and science data to Earth. SCaN also investigates new communications and navigation technologies so that these networks can support advanced missions. SCaN’s Commercialization, Innovation, and Synergies (CIS) office is dedicated to creating robust partnerships with industry, academia, and other government agencies to advance communications and navigation capabilities.
Nearly all data acquired by vehicles on the surface of Mars are returned to Earth via Mars orbiters – more than 1.7 terabits so far. Successful communication between the various spacecraft is achieved via the careful implementation of internationally recognized CCSDS telecommunications protocols and the use of planning and coordination services provided by NASA’s Mars Program Office and the Multimission Ground Systems and Services (MGSS) Program at the Jet Propulsion Laboratory in Pasadena, CA. This modern Mars relay network has evolved since its inception in 2004 with the addition and loss of several missions, but it has fundamentally remained unchanged. Ground interfaces between the various spacecrafts’ mission operations centers on Earth remain largely unique for each participant, each mission maintains its own interfaces with deep space communications networks (e.g. DSN, ESTRACK), which are similar but still unique; and relay sessions at Mars require careful ground planning, coordination, and implementation. This paper will discuss the existing architecture and consider how several technologies may be applied to the next generation of relay services at Mars. Ultimately, these are expected to lead to the implementation of a delay and disruption tolerant network at Mars, a precursor to becoming a major element in an emerging Solar System Internet.
The retrieval of aerosol properties from spaceborne sensors requires highly accurate and precise radiometric measurements, thus placing stringent requirements on sensor calibration and characterization. For the Terra/Moderate Resolution Imaging Spedroradiometer (MODIS), the characteristics of the detectors of certain bands, particularly band 8 [(B8); 412 nm], have changed significantly over time, leading to increased calibration uncertainty. In this paper, we explore a possibility of utilizing a cross-calibration method developed for characterizing the Terral MODIS detectors in the ocean bands by the National Aeronautics and Space Administration Ocean Biology Processing Group to improve aerosol retrieval over bright land surfaces. We found that the Terra/MODIS B8 reflectance corrected using the cross calibration method resulted in significant improvements for the retrieved aerosol optical thickness when compared with that from the Multi-angle Imaging Spectroradiometer, Aqua/MODIS, and the Aerosol Robotic Network. The method reported in this paper is implemented for the operational processing of the Terra/MODIS Deep Blue aerosol products.
The precise knowledge regarding the state of the power grid is important in order to ensure optimal and reliable grid operation. Specifically, knowing the state of the distribution grid becomes increasingly important as more renewable energy sources are connected directly into the distribution network, increasing the fluctuations of the injected power. In this paper, we consider the case when the distribution grid becomes partially observable, due to for example cyber attacks, and the state estimation problem is under-determined. We present a new methodology that leverages a deep neural network (DNN) to estimate the grid state. The standard DNN training method is modified to explicitly incorporate the physical information of the grid topology and line/shunt admittance. We show that our method leads to a superior accuracy of the estimation when compared to the case when no physical information is provided. Finally, we compare the performance of our method to the standard state estimation approach, which is based on the weighted least squares with pseudo-measurements, and show that our method performs significantly better with respect to the estimation accuracy.
The Goldstone Deep Space Communications Complex (GDSCC), located in the MoJave Desert, is part of the National Aeronautics and Space Administration's (NASA's) Deep Space Network (DSN), the world's largest and most sensitive scientific telecommunications and radio navigation network. The Goldstone Complex is operated for NASA by the Jet Propulsion Laboratory. At present, activities at the GDSCC support the operation of nine parabolic dish antennas situated at five separate locations known as 'sites.' Each of the five sites at the GDSCC has one or more antennas, called 'Deep Space Stations' (DSS's). In the course of operation of these DSS's, various hazardous and non-hazardous wastes are generated. In 1992, JPL retained Kleinfelder, Inc., San Diego, California, to quantify the various streams of hazardous and non-hazardous wastes generated at the GDSCC. In June 1992, Kleinfelder, Inc., submitted a report to JPL entitled 'Waste Minimization Assessment.' This present volume is a JPL-expanded version of the Kleinfelder, Inc. report. The 'Waste Minimization Assessment' report did not find any deficiencies in the various waste-management programs now practiced at the GDSCC, and it found that these programs are being carried out in accordance with environmental rules and regulations.
The Technical Facilities Controller is a microprocessor-based energy management system that is to be implemented in the Deep Space Network facilities. This system is used in conjunction with facilities equipment at each of the complexes in the operation and maintenance of air-conditioning equipment, power generation equipment, power distribution equipment, and other primary facilities equipment. The implementation of the Technical Facilities Controller was completed at the Goldstone Deep Space Communications Complex and is now operational. The installation completed at the Goldstone Complex is described and the utilization of the Technical Facilities Controller is evaluated. The findings will be used in the decision to implement a similar system at the overseas complexes at Canberra, Australia, and Madrid, Spain.
For the first time since the Apollo era, NASA is planning on sending astronauts on flights beyond Low-Earth Orbit (LEO). The Human Space Flight (HSF) program started with a successful initial flight in Earth orbit, in December 2014. The program will continue with two Exploration Missions (EM) to Lunar orbit: EM-1 will be unmanned and EM-2, carrying astronauts, will follow. NASA established a multi-center team to address the communications, and related navigation, needs. This paper will focus on the lessons learned in the team, planning for the missions' parts that are beyond Earth orbit. Many of these lessons had to be re-learned, as the HSF program after operated for many years in Earth orbit. Fortunately, the experience base from tracking robotic missions in deep space by the Deep Space Network (DSN) and close interaction with the HSF community to understand the unique needs (e.g. 2-way voice) resulted in a ConOps that leverages of both the deep space robotic and the Human LEO experiences. Several examples will be used to highlight the unique operational needs for HSF missions beyond Earth Orbit, including: - Navigation. At LEO, HSF missions can rely on Global Positioning System (GPS) devices for orbit determination. For Lunar-and-beyond HSF missions, techniques such as precision 2-way and 3-way Doppler and ranging, Delta-Difference-of-range, and eventually on-board navigation will be used. - Impact of latency - the delay associated with Round-Trip-Light-Time (RTLT). Imagine trying to have a 2-way discussion (audio or video) with an astronaut, with a 2-3 sec delay inserted (for Lunar distances) or 20 minutes delay (for Mars distances). - Balanced communications link. For robotic missions, there has been a heavy emphasis on the downlink data rates, bringing back science data from the instruments on-board the spacecraft. Uplink data rates were of secondary importance, used to send commands to the spacecraft. The ratio of downlink-to-uplink data rates was often 10:1 or more. For HSF, rates for uplink and downlink, at least for high-quality video, need to be similar.
The Flight Dynamics Division (FDD) of the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GFSC) provides extensive support and products for Space Shuttle missions, expendable launch vehicle launches, and routine on-orbit operations for a variety of spacecraft. A major challenge in providing support for these missions is defining and generating the products required for mission support and developing the method by which these products are exchanged between supporting agencies. As interagency and international cooperation has increased in the space community, the FDD customer base has grown and with it the number and variety of external interfaces and product definitions. Currently, the FDD has working interfaces with the NASA Space and Ground Networks, the Johnson Space Center, the White Sands Complex, the Jet propulsion Laboratory (including the Deep Space Network), the United States Air Force, the Centre National d'Etudes Spatiales, the German Spaceflight Operations Center, the European Space Agency, and the National Space Development Agency of Japan. With the increasing spectrum of possible data product definitions and delivery methods, the FDD is using its extensive interagency experience to improve its support of established customers and to provide leadership in adapting/developing new interfaces. This paper describes the evolution of the interfaces between the FDD and its customers, discusses many of the joint activities ith these customers, and summarizes key lessons learned that can be applied to current and future support.
NASA’s Deep Space Network (DSN) serves as a critical element in the exploration of deep space, typically supporting 30-40 operational missions at any given time, each with unique characteristics and telecommunications requirements. In order to meet the needs of its diverse customer base, DSN relies on the Mission Support Definition and Commitments Office to interface with its customers and to develop, negotiate, and document the appropriate service commitments to meet each mission’s needs. Members of this office provide the needed support throughout each mission’s lifetime. While DSN’s primary focus in recent years has been supporting scientific missions by robotic spacecraft deployed across the solar system, the network is ramping-up to provide support for human spaceflight (HSF) endeavours to the moon and beyond, starting with the Artemis missions which aim to land humans on the lunar surface in the 2020’s. These HSF missions pose unique challenges for the DSN, including technical, operational, and programmatic concerns. Examples of challenges in each of these areas are provided, along with descriptions of how they are being addressed and open issues remaining.
Alternating current optimal power flow (OPF) analysis is critical for efficient and reliable operation of power systems. For large systems or repetitive computations, the traditional methods such as the direct and gradient methods, or non-traditional methods, such as the genetic algorithm and simulating annealing, are time-consuming and unsuitable for real-time computing. The work in this paper proposes a novel framework to obtain the optimal solution of power flow in real-time using a combination of convolutional neural networks and a self-attention mechanism. All parameters of the power networks are rearranged in an image-like shape of a multi-channel image where each channel is a two-dimensional matrix. The proposed approach is adaptive with every input size of power systems as well as frequent variations of network topologies without intervention to the framework core. The encompassment of all power system contexts in which all parameters of internal elements, generation costs, and topology information are included, contributes to the higher accuracy of inference compared to other current machine-learning-based OPF-solving methods. Besides, the proposed framework established on ubiquitous platforms is effortlessly integrated into current infrastructures of power systems, and the great efficiency along with the computation speed may serve as a critical point for practical implications, such as enabling faster decision-making during real-time operations, predicting system contingencies, and remedial actions based on an offline pre-trained model. Furthermore, this supervised learning process is applied to the dataset of four case studies of meshed power systems: the IEEE 5-bus system (IEEE-5), the IEEE 30-bus system (IEEE-30), the IEEE 39-bus system (IEEE-39), and the IEEE 57-bus system (IEEE-57) to prove the efficacy of the proposed method.
Deep-learning inversion has recently drawn attention in geological carbon storage research due to its potential of imaging and monitoring carbon storage in real time, significantly improving efficiency and safety of carbon storage operations. We present a deep-learning full waveform inversion method that after the neural network has been trained can image CO 2 saturation and its uncertainty in real time. Our deep-learning inversion method is based on the U-Net architecture with the neural network trained on pairs of synthetic seismic data and CO 2 saturation models. Accordingly, our training establishes a mapping relationship between seismic data and CO 2 saturation models and once fully trained directly estimates CO 2 saturation as a function of subsurface location. We further quantify uncertainties of CO 2 saturation estimates using the Monte Carlo dropout method and a bootstrap aggregating method. For this proof-of-concept study, the CO 2 training models and data are derived from the Kimberlina 1.2 model, a hypothetical 3D geological carbon storage model that is constructed based on various geological and hydrological data from the Southern San Joaquin Basin, California. We perform deep-learning inversion experiments using noise-free and noisy training and test data sets and compare the results. Our modelling experiments show that (1) the deep-learning inversion can estimate 2D distributions of CO 2 fairly well even in the presence of Gaussian random noise and (2) both CO 2 saturation imaging and uncertainty quantification can be done in real time. Our results suggest that the deep-learning inversion method can serve as a robust real-time monitoring tool for geological carbon storage and/or other time-varying reservoir/aquifer properties that result from injection, extraction, and/or other subsurface transport phenomena.
Deep Space Network report, including Pioneer, Mariner Mars 1971, and Viking mission support, advanced engineering, operations, and development
Progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations is reported.
Progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations is reported.
Progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations are reported.
Progress on the Deep Space Network (DSN) supporting research and technology, advanced development, engineering and implementation, and DSN operations is presented. The functions and facilities of the DSN are described.
The very long baseline interferometry capability as well as the planned capability of the deep space network (DSN) are described. Major emphasis is placed on the following: VLBI development; operational VLBI for DSN calibration; and VLBI for spacecraft navigation.