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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 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Deep level transient spectroscopy and photoluminescence studies of hole and electron traps in ZnSnP 2 bulk crystals

ZnSnP 2 , an emerging inorganic material for solar cells, was characterized by deep level transient spectroscopy (DLTS) and photoluminescence (PL). Acceptor- and donor-like traps with shallow energy levels were detected by DLTS analysis. The previous study based on first-principle calculation also suggested such traps were due to antisite defects of Zn and Sn. PL measurements also revealed sub-gap transitions related to these trap levels. Furthermore, DLTS found a trap with a deep level in ZnSnP 2 . A short lifetime of minority carrier in previous work might be due to such trap, coming from phosphorus vacancies and/or zinc interstitials suggested by the first-principle study.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

A Deep Reinforcement Learning-based Reserve Optimization in Active Distribution Systems for Tertiary Frequency Regulation

Federal Energy Regulatory Commission (FERC)Orders 841 and 2222 have recommended that distributed energy resources (DERs) should participate in energy and reserve markets; therefore, a mechanism needs to be developed to facilitate DERs’ participation at the distribution level. Although the available reserve from a single distribution system may not be sufficient for tertiary frequency regulation, stacked and coordinated contributions from several distribution systems can enable them participate in tertiary frequency regulation at scale. This paper proposes a deep reinforcement learning (DRL)-based approach for optimization of requested aggregated reserves by system operators among the clusters of DERs. The co-optimization of cost of reserve, distribution network loss, and voltage regulation of the feeders are considered while optimizing the reserves among participating DERs. The proposed framework adopts deep deterministic policy gradient (DDPG), which is an algorithm based on an actor-critic method. The effectiveness of the proposed method for allocating reserves among DERs is demonstrated through case studies on a modified IEEE 34-node distribution system.

deep reinforcement learning, distributed energy re↗
DOE OSTI2021

Deep Learning with Quantized Neural Networks for Gravitational-wave Forecasting of Eccentric Compact Binary Coalescence

We present the first application of deep learning forecasting for binary neutron stars, neutron star–black hole systems, and binary black hole mergers that span an eccentricity range e ≤ 0.9. In this work, we train neural networks that describe these astrophysical populations, and then test their performance by injecting simulated eccentric signals in advanced Laser Interferometer Gravitational-Wave Observatory (LIGO) noise available at the Gravitational Wave Open Science Center to (1) quantify how fast neural networks identify these signals before the binary components merge; (2) quantify how accurately neural networks estimate the time to merger once gravitational waves are identified; and (3) estimate the time-dependent sky localization of these events from early detection to merger. Our findings show that deep learning can identify eccentric signals from a few seconds (for binary black holes) up to tens of seconds (for binary neutron stars) prior to merger. A quantized version of our neural networks achieves 4× reduction in model size, and up to 2.5× inference speedup. These novel algorithms may be used to facilitate time-sensitive multimessenger astrophysics observations of compact binaries in dense stellar environments.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2002

A view of the future of NASA's Deep Space Network and associated systems

The current architecture of the Deep Space Network reflects its heritage of supporting past, and ongoing NASA missions. In the future, the size and character of the Agency's deep space mission fleet will significantly change. Consequently, the DSN must evolve to accomodate anticipated needs.

Deep Space Network operations↗
NASA NTRS2002

Statistical porcess control in Deep Space Network operation

This report describes how the Deep Space Mission System (DSMS) Operations Program Office at the Jet Propulsion Laboratory's (EL) uses Statistical Process Control (SPC) to monitor performance and evaluate initiatives for improving processes on the National Aeronautics and Space Administration's (NASA) Deep Space Network (DSN).

Deep Space Network operations statistical process ↗
NASA NTRS2001

Considerations on communications network protocols in deep space

Communications supporting deep space missions impose numerous unique constraints that impact the architectural choices made for cost-effectiveness. We are entering the era where networks that exist in deep space are needed to support planetary exploration. Cost-effective performance will require a balanced integration of applicable widely used standard protocols with new and innovative designs.

network protocols deep space wireless networks↗
NASA NTRS1998

Multiple Deep Jovian Atmospheric Entry Probes: Building on the Galileo Probe

Following on the very successful Galileo Entry Probe mission, studies underway at the Jet Propulsion Laboratory (JPL) address the feasibility and cost of the Jupiter Deep Multi-probes (JDMP) mission to deliver and support multiple deep (100 bar level or deeper) atmospheric entry probes to Jupiter.

JPL Galileo Entry Probe Mission Jupiter Deep Multi↗
NASA NTRS1993

Ground Antennas in NASA's Deep Space Telecommunications

Ground antennas are the major visible components of NASA's Deep Space Network (DSN). The role, key characteristics, and performance of these antennas in deep space telecommunications are described.

ground antenna antenna deep space network DSN tele↗
NASA NTRS1993

Results from the Galileo Laser Uplink: A JPL Demonstration of Deep-Space Optical Communications

The successful completion of the Galileo Optical Experiment (GOPEX), represented the accomplishment of a significant milestone in JPL's optical communication plan. The experiment demonstrated the first transmission of a narrow laser beam to a deep-space vehicle. Laser pulses were beamed to the Galileo spacecraft by Earth-based transmitters at the Table Mountain Facility (TMF), California, and Starfire Optical Range (SOR), New Mexico. The experiment took place over an eight-day period (December 9 through December 16, 1992) as Galileo receded from Earth on its way to Jupiter, and covered ranges from 1 to 6 million kilometers (15 times the Earth-Moon distance), the laser uplink from TMF covered the longest known range for laser beam transmission and detection. This demonstration is the latest in a series of accomplishments by JPL in the development of deep-space optical communications technology.

Lasers experiment optical communications Galileo d↗
NASA NTRS2006

Deep Impact Sequence Planning Using Multi-Mission Adaptable Planning Tools With Integrated Spacecraft Models

The Deep Impact mission was ambitious and challenging. JPL's well proven, easily adaptable multi-mission sequence planning tools combined with integrated spacecraft subsystem models enabled a small operations team to develop, validate, and execute extremely complex sequence-based activities within very short development times. This paper focuses on the core planning tool used in the mission, APGEN. It shows how the multi-mission design and adaptability of APGEN made it possible to model spacecraft subsystems as well as ground assets throughout the lifecycle of the Deep Impact project, starting with models of initial, high-level mission objectives, and culminating in detailed predictions of spacecraft behavior during mission-critical activities.

Deep Impact Mission↗
NASA NTRS2006

Implementation of an Antenna Array Signal Processing Breadboard for the Deep Space Network

The Deep Space Network Large Array will replace/augment 34 and 70 meter antenna assets. The array will mainly be used to support NASA's deep space telemetry, radio science, and navigation requirements. The array project will deploy three complexes in the western U.S., Australia, and European longitude each with 400 12m downlink antennas and a DSN central facility at JPL. THis facility will remotely conduct all real-time monitor and control for the network. Signal processing objectives include: provide a means to evaluate the performance of the Breadboard Array's antenna subsystem; design and build prototype hardware; demonstrate and evaluate proposed signal processing techniques; and gain experience with various technologies that may be used in the Large Array. Results are summarized..

array↗
NASA NTRS2006

Future Mission Trends and their Implications for the Deep Space Network

This viewgraph presentation discusses the direction of future missions and it's significance to the Deep Space Network. The topics include: 1) The Deep Space Network (DSN); 2) Past Missions Driving DSN Evolution; 3) The Changing Mission Paradigm; 4) Assessing Future Mission Needs; 5) Link Support Trends; 6) Downlink Rate Trends; 7) Uplink Rate Trends; 8) End-to-End Link Difficulty Trends; 9) Summary: Future Mission Trend Drivers; and 10) Conclusion: Implications for the DSN.

mission trends↗
NASA NTRS2007

Range Measurement as Practiced in the Deep Space Network

Range measurements are used to improve the trajectory models of spacecraft tracked by the Deep Space Network. The unique challenge of deep-space ranging is that the two-way delay is long, typically many minutes, and the signal-to-noise ratio is small. Accurate measurements are made under these circumstances by means of long correlations that incorporate Doppler rate-aiding. This processing is done with commercial digital signal processors, providing a flexibility in signal design that can accommodate both the traditional sequential ranging signal and pseudonoise range codes. Accurate range determination requires the calibration of the delay within the tracking station. Measurements with a standard deviation of 1 m have been made.

range measurement↗
NASA NTRS2006

Future Mission Trends and their Implications for the Deep Space Network

Planning for the upgrade and/or replacement of Deep Space Network (DSN) assets that typically operate for forty or more years necessitates understanding potential customer needs as far into the future as possible. This paper describes the methodology Deep Space Network (DSN) planners use to develop this understanding, some key future mission trends that have emerged from application of this methodology, and the implications of the trends for the DSN's future evolution. For NASA's current plans out to 2030, these trends suggest the need to accommodate: three times as many communication links, downlink rates two orders of magnitude greater than today's, uplink rates some four orders of magnitude greater, and end-to-end link difficulties two-to-three orders of magnitude greater. To meet these challenges, both DSN capacity and capability will need to increase.

future mission needs↗
NASA NTRS2012

Deep Drilling and Sampling via the Wireline Auto-Gopher Driven by Piezoelectric Percussive Actuator and EM Rotary Motor

The ability to penetrate subsurfaces and perform sample acquisition at depths of meters is critical for future NASA in-situ exploration missions to bodies in the solar system, including Mars and Europa. A corer/sampler was developed with the goal of acquiring pristine samples by reaching depths on Mars beyond the oxidized and sterilized zone. To developed rotary-hammering coring drill, called Auto-Gopher, employs a piezoelectric actuated percussive mechanism for breaking formations and an electric motor rotates the bit to remove the powdered cuttings. This sampler is a wireline mechanism that is incorporated with an inchworm mechanism allowing thru cyclic coring and core removal to reach great depths. The penetration rate is being optimized by simultaneously activating the percussive and rotary motions of the Auto-Gopher. The percussive mechanism is based on the Ultrasonic/Sonic Drill/Corer (USDC) mechanism that is driven by piezoelectric stack and that was demonstrated to require low axial preload. The Auto-Gopher has been produced taking into account the a lessons learned from the development of the Ultrasonic/Sonic Gopher that was designed as a percussive ice drill and was demonstrated in Antarctica in 2005 to reach about 2 meters deep. A field demonstration of the Auto-Gopher is currently being planned with objective of reaching as deep as 3 to 5 meters in tufa subsurface.

Auto-Gopher↗
NASA NTRS2012

Automating Mid- and Long-Range Scheduling for the NASA Deep Space Network

NASA has recently deployed a new mid-range scheduling system for the antennas of the Deep Space Network (DSN), called Service Scheduling Software, or S(sup 3). This system was designed and deployed as a modern web application containing a central scheduling database integrated with a collaborative environment, exploiting the same technologies as social web applications but applied to a space operations context. This is highly relevant to the DSN domain since the network schedule of operations is developed in a peer-to-peer negotiation process among all users of the DSN. These users represent not only NASA's deep space missions, but also international partners and ground-based science and calibration users. The initial implementation of S(sup 3) is complete and the system has been operational since July 2011. This paper describes some key aspects of the S(sup 3) system and on the challenges of modeling complex scheduling requirements and the ongoing extension of S(sup 3) to encompass long-range planning, downtime analysis, and forecasting, as the next step in developing a single integrated DSN scheduling tool suite to cover all time ranges.

Deep Space Network↗