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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Utilizing the ISS Mission as a Testbed to Develop Cognitive Communications Systems

The ISS provides an excellent opportunity for pioneering artificial intelligence software to meet the challenges of real-time communications (comm) link management. This opportunity empowers the ISS Program to forge a testbed for developing cognitive communications systems for the benefit of the ISS mission, manned Low Earth Orbit (LEO) science programs and future planetary exploration programs. In November, 1998, the Flight Operations Directorate (FOD) started the ISS Antenna Manager (IAM) project to develop a single processor supporting multiple comm satellite tracking for two different antenna systems. Further, the processor was developed to be highly adaptable as it supported the ISS mission through all assembly stages. The ISS mission mandated communications specialists with complete knowledge of when the ISS was about to lose or gain comm link service. The current specialty mandated cognizance of large sun-tracking solar arrays and thermal management panels in addition to the highly-dynamic satellite service schedules and rise/set tables. This mission requirement makes the ISS the ideal communications management analogue for future LEO space station and long-duration planetary exploration missions. Future missions, with their precision-pointed, dynamic, laser-based comm links, require complete autonomy for managing high-data rate communications systems. Development of cognitive communications management systems that permit any crew member or payload science specialist, regardless of experience level, to control communications is one of the greater benefits the ISS can offer new space exploration programs. The IAM project met a new mission requirement never previously levied against US space-born communications systems management: process and display the orientation of large solar arrays and thermal control panels based on real-time joint angle telemetry. However, IAM leaves the actual communications availability assessment to human judgment, which introduces unwanted variability because each specialist has a different core of experience with comm link performance. Because the ISS utilizes two different frequency bands, dynamic structure can be occasionally translucent at one frequency while it can completely interdict service at the other frequency. The impact of articulating structure on the comm link can depend on its orientation at the time it impinges on the link. It can become easy for a human specialist to cross-associate experience at one frequency with experience at the other frequency. Additionally, the specialist's experience is incremental, occurring one nine-hour shift at a time. Only the IAM processor experiences the complete 24x7x365 communications link performance for both communications links but, it has no "learning capability." If the IAM processor could be endowed with a cognitive ability to remember past structure-induced comm link outages, based on its knowledge of the ISS position, attitude, communications gear, array joint angles and tracking accuracy, it could convey such experience to the human operator. It could also use its learned communications link behaviors to accurately convey the availability of future communications sessions. Further, the tool could remember how accurately or inaccurately it predicted availability and correct future predictions based on past performance. The IAM tool could learn frequency-specific impacts due to spacecraft structures and pass that information along as "experience." Such development would provide a single artificial intelligence processor that could provide two different experience bases. If it also "knew" the satellite service schedule, it could distinguish structure blockage from schedule or planet blockage and then quickly switch to another satellite. Alternatively, just as a human operator could judge, a cognizant comm system based on the IAM model could "know" that the blockage is not going to last very long and continue tracking a comm satellite, waiting for it to track away from structure. Ultimately, once this capability was fully developed and tested in the Mission Control Center, it could be transferred on-orbit to support development of operations concepts that include more advanced cognitive communications systems. Future applications of this capability are easily foreseen because even more dynamic satellite constellations with more nodes and greater capability are coming. Currently, the ISS fully employs a 300 million bit-per-second (Mbps) return link for harvesting payload science. In the coming eighteen months, it will step up to 600 Mbps. Already there is talk of a 1.2 billion bit-per-second (Gbps) upgrade for the ISS and laser comm links have already been tested from the ISS. Every data rate upgrade mandates more complicated and sensitive communications equipment which implies greater expertise invested in the human operator. Future on-orbit cognizant comm systems will be needed to meet greater performance demands aboard larger, far more complicated spacecraft. In the LEO environment, the old-style one-satellite-per-spacecraft operations concept will give way to a new concept of a single customer spacecraft simultaneously using multiple comm satellites. Much more highly-dynamic manned LEO missions with decades of crew members potentially increase the demand for communications link performance. A cognizant on-board communications system will meet advanced communications demands from future LEO missions and future planetary missions. The ISS has fledgling components of future exploration programs, both LEO and planetary. Further, the Flight Operations Directorate, through the IAM project, has already begun to develop a communications management system that attempts to solve advanced problems ideally represented by dynamic structure impacting scheduled satellite service. With an earnest project to integrate artificial intelligence into the IAM processor, the ISS Program could develop a cognizant communications system that could be adapted and transferred to future on-orbit avionics designs.

Jackson, Dan↗
NASA NTRS2006

Spread and SpreadRecorder An Architecture for Data Distribution

The Space Acceleration Measurement System (SAMS) project at the NASA Glenn Research Center (GRC) has been measuring the microgravity environment of the space shuttle, the International Space Station, MIR, sounding rockets, drop towers, and aircraft since 1991. The Principle Investigator Microgravity Services (PIMS) project at NASA GRC has been collecting, analyzing, reducing, and disseminating over 3 terabytes of collected SAMS and other microgravity sensor data to scientists so they can understand the disturbances that affect their microgravity science experiments. The years of experience with space flight data generation, telemetry, operations, analysis, and distribution give the SAMS/ PIMS team a unique perspective on space data systems. In 2005, the SAMS/PIMS team was asked to look into generalizing their data system and combining it with the nascent medical instrumentation data systems being proposed for ISS and beyond, specifically the Medical Computer Interface Adapter (MCIA) project. The SpreadRecorder software is a prototype system developed by SAMS/PIMS to explore ways of meeting the needs of both the medical and microgravity measurement communities. It is hoped that the system is general enough to be used for many other purposes.

Wright, Ted↗
NASA NTRS1998

High Performance Database Management for Earth Sciences

The High Performance Database Research Center at Florida International University is completing the development of a highly parallel database system based on the semantic/object-oriented approach. This system provides exceptional usability and flexibility. It allows shorter application design and programming cycles and gives the user control via an intuitive information structure. It empowers the end-user to pose complex ad hoc decision support queries. Superior efficiency is provided through a high level of optimization, which is transparent to the user. Manifold reduction in storage size is allowed for many applications. This system allows for operability via internet browsers. The system will be used for the NASA Applications Center program to store remote sensing data, as well as for Earth Science applications.

Rishe, Naphtali↗
NASA NTRS1991

Determination of the Critical Parameters for Remote Microscope Control

As part of a program to determine the capabilities of Telescience as applied to Microgravity Materials Science the need for a remotely controlled microscope was recognized. For this purpose we equipped a microscope with an X-Y-Z positioning device and motors on the zoom and focus controls. Computer control of these devices allowed remote operation. A standard TV camera was mounted to the computer controlled video board which could compress the image in resolution and grey scale. The operator control console was programmed to display three still video pictures as well as provide command access. A standard data transfer network was used to transmit the video data files and the command interaction was via a high speed phone modem. This system, with the microscope in the Microgravity Materials Science Laboratory (MMSL) at LeRC and the control at RPI, was used to determine the accuracy of setting, time required to achieve setting and the operator ease factor. It was found that the focus setting could be established well within the resolution limit of the TV system and that each motion took about 50 seconds and approximately 12 minutes was required to reach ?best? focus. These times could be reduced significantly with operator experience. The operators were provided with ancillary equipment which provided assistance in making the necessary decisions and they reported satisfaction with the control.

Hahn, R. C.↗
NASA NTRS1984

Artificial intelligence applications concepts for the remote sensing and earth science community

The following potential applications of AI to the study of earth science are described: (1) intelligent data management systems; (2) intelligent processing and understanding of spatial data; and (3) automated systems which perform tasks that currently require large amounts of time by scientists and engineers to complete. An example is provided of how an intelligent information system might operate to support an earth science project.

Campbell, W. J.↗
NASA NTRS1996

Radarsat Processing System at ASF

Radarsat is a Canadian polar orbiting remote sensing satellite scheduled for launch in September 1995. Its lone instrument on-board is a synthetic aperture radar (SAR) that is capable of operating in a number of imaging modes including the first operational ScanSAR mode. As one of the data reception, processing and archive facility for Radarsat data, Alaska SAR Facility (ASF) has responded to its Science users by establishing a Radarsat processing system to handle the data processing of all Radarsat modes. This task involves enhancements to the high throughput hardware based Alaska SAR Processor (ASP) to handle standard mode Radarsat data in addition to its existing ERS and JERS capabilities, the addition of the new ScanSAR Processor (SSP) to process the Radarsat ScanSAR mode data, and the introduction of a Precision Processor (PP) to accommodate the special Radarsat modes such as fine resolution and wide swath. For raw data ingestion and distribution to the appropriate SAR processor, a new Control Processor (CP) and Raw Data Scanner (RDS) subsystem is also incorporated.

Radarsat↗
NASA NTRSDate not supplied

VERNE: Revealing the Mysteries and Histories of Venus

Introduction: The three Venus missions that were recently selected for upcoming flight (VERITAS, DAVINCI+, and EnVision) will be incredibly valuable to our understanding of Venus’ history, geology, and atmosphere. However, even once completed, key gaps in our knowledge of Venus, and more generally the formation and active processes on rocky, Earth-like planets, will still persist. Remaining questions include 1) how global intrinsic magnetic fields might be maintained on rocky worlds, and how they could then go extinct, and 2) what role atmospheric sulfur chemistry plays in climates of Earth-like planets, which is an increasingly timely subject as Earth’s own atmospheric sulfur content is climbing due to human activity. These questions require in-situ observations from Venus’ cloud deck, at the altitudes at which the UV absorber exists. The Venus Environment Research and Novel Exploration (VERNE) mission will address these questions with an aerial platform that will drift around the equatorial region of the planet for 9 days. VERNE will collect data to determine the identity of the mysterious UV absorber, while also taking magnetic field measurements over the tesserae, the regions on Venus that are most likely to retain remanent crustal magnetization, in order to understand the potential role of a past intrinsically-generated global magnetic field on Venus. Mission Objectives: The two major science objectives that drive the VERNE mission are: 1) Determine the identity of the Venusian unknown ultraviolet absorber(s). First observed approximately a century ago [1], the composition of Venus’ ultraviolet (UV) absorber is one of the oldest mysteries in Venus atmospheric chemistry [2,3]. While several candidate UV absorbers (mostly sulfur species) have been proposed, no consensus has been reached on its composition and its specific interactions with the atmosphere. Determining the identity of the UV absorber will aid climate models by showing how and where incident solar energy is absorbed by the atmosphere and make progress towards understanding the chemical and energetic processes taking place above Venus’ upper cloud deck [4]. 2) Determine if Venus retains evidence of a past, internally-generated magnetic field. While Venus does not currently have an intrinsically-generated magnetic field, evidence for the existence of a past field on Venus and a timeline of its decay will fill in a more holistic picture of the evolution of Venus’ geological record and atmosphere. As the oldest geologic units on the surface, Venus’ tesserae may still have remanent crustal magnetization signatures within the rocky composition [5]. In any case, the signatures detected will provide insight into Venus’ past geological and core dynamo activity. Mission Summary: VERNE includes a 3-part flight system made up of 1) an entry system with a HEEET (Heatshield for Extreme Entry Environment Technology) aeroshell, 2) an orbiter for data relay, and 3) an in-situ balloon and gondola. After entry into Venus’ atmosphere, the balloon will be deployed within the upper cloud deck, at an altitude of 62 km, over a tesserae region. The in-situ data collection will last for the duration of 2 full circumnavigations of the planet, which will take ~9 days. Instrumentation Suite: The four instruments that comprise VERNE’s instrument payload will enable the identification of the unknown UV absorber and the detection of remanent crustal magnetization if it exists in the tesserae. The proposed instrument suite cycle during mission operations is shown in Fig. 1. The four instruments are described below: 1) Adams (ion neutral mass spectrometer) has a range of 18-257 AMU and will detect and distinguish the mixing ratios of O₂, H₂O, H₂SO₄, S, S₂, S₃, S₄, S₅, S₆, S₇, S₈, SO, SO₂, OSSO, SO₃, Cl₂, FeCl₃ and other trace sulfur and organic species. The spatial (longitudinal) and temporal (day/night) variations will be observed throughout 2 circumnavigations with a sample cadence of 12 minutes. 2) Shelley (nephelometer) will determine the size distribution of the aerosols (0.4 to 36 um) in the atmosphere. With a size resolution of <0.7 um, it can determine which mode of H2SO4 is present and characterize the large (>30 um) organic particles previously detected by the Venera and Galileo missions [6,7]. 3) Herbert (UV imager) will measure UV radiance at 283 nm (the wavelength of SO2 absorption) and 365 nm (the unknown part of the absorber). UV images will be taken concurrently with the INMS and nephelometer to correlate UV absorption with abundances of the UV absorber species. 4) Vonnegut (magnetometer) has a range of >600 nT and a precision and accuracy of 1 nT. If magnetized by a past field, the crust may have retained a magnetization of up to 3 A/m2 [5]. With a noise floor of 10 nT, the magnetometer will be able to detect RCM from an altitude of 62 km, even if the thickness of the magnetized crust is just 1 km (Fig. 2). Mission Concept Design: VERNE will be launched with a mass of 3300 kg in an intermediate-high performance class vehicle with a 4-m fairing. The 475-day mission includes 466 days for the cruise, coasting, and orbit initialization phases before entry, descent, and balloon deployment. During the 9-day science phase, the aerial platform will make 2 circumnavigations of the planet at an altitude of 62 km. The INMS and the nephelometer will acquire data for 2 hours during the daytime and nighttime during each circumnavigation, while the UV imager will be on for the duration of the daytime, and the magnetometer will be operational throughout the entirety of the science phase. Data will be stored and processed with the JPL-designed Sphinx command and data handling system. Data will be sent from the balloon to the orbiter using an S-band relay link, stored on the orbiter, and then forwarded to Earth where it will be received by the DSN. Conclusion: VERNE will fill key gaps in our understanding of the history and ongoing processes related to the geology and atmosphere of Venus and rocky worlds in general. Even with adequate flight system contingencies and expected costs below the $900M New Frontiers cost cap, VERNE is not without its risks and challenges. Further trade spaces to explore include 1) using solely battery power vs. including solar panels to increase the mission duration and 2) investigating the use of lightweight materials and 3D-printed structures to reduce the gondola mass, among others. Acknowledgments: We would like to thank the JPL Planetary Science Summer School, especially our mentors Troy Hudson, Karl Mitchell, and Leslie Lowes, as well as our Team-X study lead Al Nash and the members of Team-X. We’d additionally like to thank our review panel for asking insightful questions and providing valuable feedback. References: [1] Ross, F. E. (1928) Astrophysical J., 68, 57-92. [2] Rossow, W. B. et al. (1980) J. Geophysical Research, 85, 8107-8128. [3] Pinto, J. P. et al. (2021) Nature Communications, 12, 175. [4] Titov, D. V. et al. (2007) Cosmic Research, 21, 401. [5] O’Rourke, J. et al. (2019) Geophysical Research Lett., 46, 5768–5777. [6] Limaye, S. S. et al. (2018) Astrobiology, 18(9), 1181-1198. [7] Grinspoon, D. H. et al. (2013) Planetary and Space Sci., 41(7), 515-542. [8] Parker, R. L. (2003) J. Geophysical Research, 108, 5006. *The cost information contained in this document is of a budgetary and planning nature and is intended for informational purposes only. It does not constitute a commitment on the part of JPL and/or Caltech.

H Alpert↗
NASA NTRS2019

ISS Science Payload Command & Data Handling

For decades, the International Space Station (ISS) has provided a distinctive platform in low Earth orbit for experimental research. In support of this platform is a family of avionics systems that enables reliable data distribution of the many science payloads installed, and future internal and external payloads. This poster provides an update to the ISS avionics hardware system architecture, including design change successes, test bed architecture, and performance upgrades in the operation of all ISS avionics. We conclude with an outlook on future avionics system enhancements required to support additional modules and payload expansions taking place on the ISS, and how these avionics systems translate to a lunar gateway.

1553↗
NASA NTRS2018

Automated Impact Assessment: A New Approach to ISS Payload Operations Anomaly Response

The International Space Station (ISS) Payload Operations and Integration Center (POIC) is undergoing rapid growth as the space station program focuses on science and commercial activities. The ISS is expanding its onboard capabilities to support additional science activities. In parallel, the POIC is expanding the capabilities of our operations tools to support the higher pace of payload activities being executed each week. An effect of these changes is that anomaly resolution has become more challenging. In the event of a real-time system fault, operators are responsible for analyzing telemetry displays, anomaly monitoring tools, documentation, and system models in order to produce failure impacts and recovery strategies. This approach to operations relies on the operator to ingest, process, and analyze information from an array of deterministic sources to provide actionable data on impacted systems and activities. Changing the existing approach of anomaly response is necessary if the ISS community is to succeed in the age of science and commercialization of space. The creation of a tool that captures deterministic technical systems knowledge and integrates existing telemetry, documentation, and planning information will allow the burden of impact assessment to be automated, thereby allowing the operator to focus on non-deterministic tasks, such as recovering failed systems and restoring critical payload operations.

Hall, R. Mason↗
NASA NTRS1993

R and T report: Goddard Space Flight Center

The 1993 Research and Technology Report for Goddard Space Flight Center is presented. Research covered areas such as (1) flight projects; (2) space sciences including cosmology, high energy, stars and galaxies, and the solar system; (3) earth sciences including process modeling, hydrology/cryology, atmospheres, biosphere, and solid earth; (4) networks, planning, and information systems including support for mission operations, data distribution, advanced software and systems engineering, and planning/scheduling; and (5) engineering and materials including spacecraft systems, material and testing, optics and photonics and robotics.

Soffen, Gerald A.↗
NASA NTRS2017

Inflight Performance of the SDO Fine Pointing Science Mode

The Solar Dynamics Observatory (SDO) was successfully launched and deployed from its Atlas V launch vehicle on February 11, 2010. Three months later, on May 14, 2010, the fully commissioned heliophysics laboratory was handed over to Space Systems Mission Operations to begin its science mission. SDO is an Explorer-class mission now operating in a geosynchronous orbit, sending data 24 hours per day to a dedicated ground station in White Sands, New Mexico. It carries a suite of instruments designed to observe the Sun in multiple wavelengths at unprecedented resolution. The Atmospheric Imaging Assembly (AIA) includes four telescopes with 4096x4096 focal plane CCDs that can image the full solar disk in seven extreme ultraviolet and three ultraviolet-visible wavelengths. The Extreme Ultraviolet Variability Experiment (EVE) collects time-correlated data on the activity of the Sun's corona. The Helioseismic and Magnetic Imager (HMI) enables study of pressure waves moving through the body of the Sun.

Control↗
NASA NTRSDate not supplied

Mission Design & Operations Approach for the HelioSwarm Mission

HelioSwarm: The Nature of Turbulence in Space Plasmas is a transformational mission to explore the dynamic three-dimensional mechanisms controlling the physics of plasma turbulence, a ubiquitous process occurring in the heliosphere and plasmas throughout the universe. Turbulence is the process by which energy contained in fluctuating magnetic fields and plasma motion cascades from large to smaller spatial scales. HelioSwarm achieves its science goals by making simultaneous measurements across a wide range of measurement baselines, spanning magnetohydrodynamic scales (1000’s of km) to sub-ion heating scales (10’s of km), using a novel nine-spacecraft swarm. The swarm operates in a high-altitude lunar resonant Earth orbit (two-week period, ~63 RE apogee, ~13 RE perigee), giving it access to both the pristine solar wind and regions of strongly driven turbulence (specifically the magnetosphere and foreshock), and utilizes customized relative orbital motion of the swarm members to produce the range of measurement baselines and configurations. The swarm comprises eight “node” spacecraft, manufactured by Blue Canyon Technologies, and a “hub” spacecraft produced by Northrop Grumman Corp. The hub serves as a communications relay, with all communications between the ground and the nodes flowing through it. Mission operations are conducted within the Multi-Mission Operations Center at the NASA Ames Research Center and science operations at the University of New Hampshire, Durham. HelioSwarm was selected in 2022 as one of NASA’s newest Heliophysics Explorer missions to proceed from mission concept into mission implementation, with a target launch in 2029. This paper provides an overview of the mission’s science goals and objectives, the mission design, and the concept of operations, with an emphasis on how the swarm aspects of the mission both enable the science measurements and present unique operational challenges. The paper then describes the proposed development approach for the mission operations system and ground data system which relies on a selective combination of scaling strategies to meet the challenges.

HelioSwarm↗
NASA NTRS2016

Benchmark Comparison of Cloud Analytics Methods Applied to Earth Observations

Cloud computing has the potential to bring high performance computing capabilities to the average science researcher. However, in order to take full advantage of cloud capabilities, the science data used in the analysis must often be reorganized. This typically involves sharding the data across multiple nodes to enable relatively fine-grained parallelism. This can be either via cloud-based file systems or cloud-enabled databases such as Cassandra, Rasdaman or SciDB. Since storing an extra copy of data leads to increased cost and data management complexity, NASA is interested in determining the benefits and costs of various cloud analytics methods for real Earth Observation cases. Accordingly, NASA's Earth Science Technology Office and Earth Science Data and Information Systems project have teamed with cloud analytics practitioners to run a benchmark comparison on cloud analytics methods using the same input data and analysis algorithms. We have particularly looked at analysis algorithms that work over long time series, because these are particularly intractable for many Earth Observation datasets which typically store data with one or just a few time steps per file. This post will present side-by-side cost and performance results for several common Earth observation analysis operations.

science data management↗
NASA NTRS1994

The GRO remote terminal system

In March 1992, NASA HQ challenged GSFC/Code 531 to propose a fast, low-cost approach to close the Tracking Data Relay Satellite System (TDRSS) Zone-of-Exclusion (ZOE) over the Indian Ocean in order to provide global communications coverage for the Compton Gamma Ray Observatory (GRO) spacecraft. GRO had lost its tape recording capability which limited its valuable science data return to real-time contacts with the TDRS-E and TDRS-W synchronous data relay satellites, yielding only approximately 62 percent of the possible data obtainable. To achieve global coverage, a TDRS spacecraft would have to be moved over the Indian Ocean out of line-of-sight control of White Sands Ground Terminal (WSGT). To minimize operations life cycle costs, Headquarters also set a goal for remote control, from the WSGT, of the overseas ground station which was required for direct communications with TDRS-1. On August 27, 1992, Code 531 was given the go ahead to implement the proposed GRO Relay Terminal System (GRTS). This paper describes the Remote Ground Relay Terminal (RGRT) which went operational at the Canberra Deep Space Communications Complex (CDSCC) in Canberra, Australia in December 1993 and is currently augmenting the TDRSS constellation in returning between 80-100 percent of GRO science data under the control of a single operator at WSGT.

Zillig, David J.↗
NASA NTRS1998

SeaWiFS Science Algorithm Flow Chart

This flow chart describes the baseline science algorithms for the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Data Processing System (SDPS). As such, it includes only processing steps used in the generation of the operational products that are archived by NASA's Goddard Space Flight Center (GSFC) Distributed Active Archive Center (DAAC). It is meant to provide the reader with a basic understanding of the scientific algorithm steps applied to SeaWiFS data. It does not include non-science steps, such as format conversions, and places the greatest emphasis on the geophysical calculations of the level-2 processing. Finally, the flow chart reflects the logic sequences and the conditional tests of the software so that it may be used to evaluate the fidelity of the implementation of the scientific algorithm. In many cases however, the chart may deviate from the details of the software implementation so as to simplify the presentation.

Darzi, Michael↗
DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗
NASA NTRS1982

NASA-ESA Spacelab systems and programs; Proceedings of the Seminar, Washington, DC, April 23, 24, 1981

Topics discussed include the development status of the Space Shuttle and Spacelab, with attention to Spacelab subsystem performance capabilities and Shuttle-Spacelab flight operations; Spacelab data management and software for science applications, ESA and Space Shuttle pointing systems, and the Space Shuttle's Office of Space and Terrestrial Applications (OSTA)-1 payload. Also covered are the lessons learned from the first Spacelab mission, the pallet-only mode verification flight of the second Spacelab mission, the objectives of the Spacelab mission D1, its role in the German space program, and its implementation, the impact of Spacelab on space-based life sciences research, the high resolution, large area modular reflector array X-ray telescope to be used by Spacelab, and Space Shuttle contamination effects on UV coronagraphic observations.

Moore, J. W.↗
NASA NTRS2021

SNPP VIIRS Day Night Band: Ten Years of On-Orbit Calibration and Performance

Aboard the polar-orbiting SNPP satellite, the VIIRS instrument has been in operation since launch in October 2011. It is a visible and infrared radiometer with a unique panchromatic channel capability designated as a day-night band (DNB). This channel covers wavelengths from 0.5 to 0.9 µm and is designed with a near-constant spatial resolution for Earth observations 24 h a day. The DNB operates at 3 gain stages (low, middle, and high) to cover a large dynamic range. An onboard solar diffuser (SD) is used for calibration in the low gain stage, and to enable the derivation of gain ratios between the different stages. In this paper, we present the SNPP VIIRS DNB calibration performed by the NASA VIIRS characterization support team (VCST). The DNB calibration algorithms are described to generate the calibration coefficient look up tables (LUTs) for the latest NASA Level 1B Collection 2 products. We provide an evaluation of DNB on-orbit calibration performance. This activity supports the NASA Earth science community by delivering consistent VIIRS sensor data products via the Land Science Investigator-led Processing Systems, including the SD degradation applied for DNB calibrations in detector gain and gain ratio trending. The DNB stray light contamination and its correction are highlighted. Performance validations are presented using comparisons to the calibration methods employed by NOAA’s operational Interface Data Processing Segment. Further work on stray light corrections is also discussed.

VIIRS↗