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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 NTRS2018

Earth Science Deep Learning: Applications and Lessons Learned

Deep learning has revolutionized computer vision and natural language processing with various algorithms scaled using high-performance computing. At the NASA Marshall Space Flight Center (MSFC), the Data Science and Informatics Group (DSIG) has been using deep learning for a variety of Earth science applications. This paper provides examples of the applications and also addresses some of the challenges that were encountered.

Training↗
NASA NTRSDate not supplied

Increasing accessibility to deep learning-based analytics for space biology: pretrained models, transfer learning, and analytics platform development

Biological systems react in complex ways to the stressors of spaceflight, and the data capturing these relationships is concomitantly high-dimensional and complex. Deep learning and machine learning approaches are increasingly popular as an analytical approach for space biosciences, due to their ability to model complex relationships in complex data. However, such approaches often require large datasets and extensive computational resources. New approaches that minimize data sizes and computational power needed to leverage machine learning, and resources that make these approaches accessible, are needed to increase accessibility and adoption of machine learning in the space biosciences. Transfer learning, in which a pretrained model of broad utility is trained on a large dataset, and subsequently reused on downstream applications for which data is more limited, is one approach to minimizing data and computational intensity of deep learning applications. This transfer learning approach results in more performant models in high-dimensional, low-sample-size settings such as space biology, as compared to training models on limited data from scratch. This presentation will outline efforts to generate pretrained models for the space biology community, and highlight transfer learning applications modeling microbial antibiotic resistance during spaceflight. Finally, in order to increase accessibility of these models and tools, as well as others, for the broader space biology community, we present a modeling and analysis platform facilitating machine learning applications in space biology. This platform streamlines machine learning training and analysis in a notebook format, facilitates download and use of space biology data from the NASA GeneLab database, and can be utilized on NASA-hosted servers or downloaded and hosted locally. This effort, as part of the AI4LS (Artificial Intelligence for Life in Space) working group, will increase accessibility, feasibility, and performance of machine learning approaches for the space biology community.

Adrienne Hoarfrost↗
NASA NTRS2019

A New Laser Beam Pointing Method Using Laser Arrays

A new method is described for optical data transmissions from satellites using laser arrays for laser beam pointing. It combines a lens system and a vertical-cavity surface-emitting laser (VCSEL)/Photodetector Array, both mature technologies, in a novel way. This system is applied to satellites in low-Earth orbit, (LEO). It can replace current architectures which use dynamical systems, (i.e., moving parts) to point the laser, and which may use vibration isolation platforms. Results of computer simulations show diffraction limited beam propagation. Possible additional applications are to planetary distances (deep space optical communications, (DSOC)), to optical multiple access, (OMA), to communication between a constellation of close satellites, and to satellites that use modulating retro-reflectors. Keywords: laser beam pointing, free-space optical communication, low-Earth orbit (LEO) data communications, integrated VCSEL/ photodetector arrays, deep space optical communications (DSOC), optical multiple access (OMA), constellation of close satellites, modulating retro-reflectors.

Goorjian, P. M.↗
NASA NTRS2018

A New Laser Beam Pointing Method Using Laser Arrays

A new method is described for optical data transmissions from satellites using laser arrays for laser beam pointing. It combines a lens system and a VCSEL/Photodetector Array, both mature technologies, in a novel way. This system is applied to satellites in LEO. It can replace current architectures which use dynamical systems, (i.e., moving parts) to point the laser, and which use vibration isolation platforms. Results of computer simulations show diffraction limited beam propagation. Possible additional applications are to planetary distances (deep space optical communications, DSOC) to optical multiple access, to communication between a constellation of close satellites, and to satellites that use modulating retro-reflectors.

Goorjian, Peter M.↗
NASA NTRSDate not supplied

In-Space Inspection Needs: Opportunities for advanced NDE tools such as x-ray CT for additively manufactured parts, in-situ resource utilization, geological applications, and more

It is now 50 years since the first human presence on the surface of the Moon and as we strive to return with women and men in the next few years, we embrace new technical challenges, goals, and innovative solutions to address 21st century objectives. These new ambitions carry fresh challenges and risks, with the field of NDE playing an increasingly more relevant role towards meeting these essential goals. In recent years, more advanced NDE tools have triggered a rapid expansion of applications for the space industry. In particular, x-ray Computed Tomography (CT) has proven to be a trusted and powerful asset for spaceflight hardware inspection, as well as applied geotechnical analysis for natural materials (e.g., rocks, soils) for NASA and across industry. However, such methods have yet to be extended to “deep space” applications such as those that are now part of the US National Space Policy Directive (SPD-1) and the accelerated push to return humans to the Moon (i.e., Artemis). For this reason, advancing these powerful Earth-based laboratory methods via new technologies, integrated computational solutions, and creative engineering approaches is directly aligned with national space policies, as well as with multiple NASA Strategic Plan priorities. The use of x-ray CT at scales as fine as a few microns or smaller can identify spacecraft part failure modes relevant to quality assurance for flight hardware and AM parts such as those recently developed for ISS. This technology could also identify valuable metallic phases within geological materials (i.e., rocks or drill cores), enabling resource-relevant triage of samples for In-Situ Resource Utilization (ISRU) and high science value sample return to Earth laboratories. There is also significant application for 3D imaging tools for medical use such as inspecting protective gear as well as bone density degradation studies which are critical in establishing a sustained presence in space. Timing for development of these tools for space use is advantageous as we prepare for new opportunities in the next few years and recognize recent commercial technology advancements which make it feasible. Moreover, as NASA strives to take full advantage of developments in AM technologies, including In-Space Manufacturing (ISM), it is widely recognized that NDE tools such as CT will play an essential role in acceptance of these parts for widespread use. New in-space 3D inspection tools with complimentary technology such as AI-based automated feature recognition (accelerated by machine learning), rapid compositional analysis, and advanced sample manipulation, would be a game-changing step toward a new class of crew-based laboratory sensors once human outposts on the Moon are established.

In-Space Non-Destructive Evaluation↗
DOE OSTI2026

Inverse design of hypoeutectoid pearlite steel microstructures using a deep learning and genetic algorithm optimization framework

Goal-oriented microstructure design in metallic materials is a challenging task due to complex structure-property relationships. Traditional experimental and computational approaches are time-intensive and economically inefficient, limiting their applicability for large-scale design space exploration. Here, in this work, we propose an end-to-end framework that integrates deep learning models with genetic optimization to design microstructures with targeted mechanical properties. Deep learning models enable accurate forward design, while their integration with genetic optimization enables efficient inverse design within a few hours, compared to days or weeks using conventional finite element simulations. The framework combines experimental characterization and finite element modeling to analyze the influence of microstructural features on the mechanical behavior of hypoeutectoid steels. Data from both experiments and simulations are used to train the deep learning models. To demonstrate its effectiveness, we apply the framework to 0.63% carbon steel with proeutectoid ferrite and pearlite phases, commonly used in industrial applications. In this study, 2D microstructures were used for modeling, selected primarily for computational efficiency and to establish proof of concept. The framework successfully optimizes microstructures for targeted yield strength, ultimate strength, and stress concentration factors while significantly reducing computational time. Beyond hypoeutectoid steels, this scalable framework can be extended to other material systems and integrated with additive manufacturing, offering an efficient approach for accelerating microstructure design for specific engineering applications.

ConvLSTM↗
NASA NTRS2009

DSN Data Visualization Suite

The DSN Data Visualization Suite is a set of computer programs and reusable Application Programming Interfaces (APIs) that assist in the visualization and analysis of Deep Space Network (DSN) spacecraft-tracking data, which can include predicted and actual values of downlink frequencies, uplink frequencies, and antenna-pointing angles in various formats that can include tables of values and polynomial coefficients. The data can also include lists of antenna-pointing events, lists of antenna- limit events, and schedules of tracking activities. To date, analysis and correlation of these intricately related data before and after tracking have been difficult and time-consuming. The DSN Data Visualization Suite enables operators to quickly diagnose tracking-data problems before, during, and after tracking. The Suite provides interpolation on demand and plotting of DSN tracking data, correlation of all data on a given temporal point, and display of data with color coding configurable by users. The suite thereby enables rapid analysis of the data prior to transmission of the data to DSN control centers. At the control centers, the same suite enables operators to validate the data before committing the data to DSN subsystems. This software is also Web-enabled to afford its capabilities to international space agencies.

Bui, Bach X.↗
NASA NTRS2013

A Situation Awareness Assistant for Human Deep Space Exploration

This paper presents the development and testing of a Virtual Camera (VC) system to improve astronaut and mission operations situation awareness while exploring other planetary bodies. In this embodiment, the VC is implemented using a tablet-based computer system to navigate through inter active database application. It is claimed that the advanced interaction media capability of the VC can improve situation awareness as the distribution of hu man space exploration roles change in deep space exploration. The VC is being developed and tested for usability and capability to improve situation awareness. Work completed thus far as well as what is needed to complete the project will be described. Planned testing will also be described.

Boy, Guy A.↗
NASA NTRS2016

High Data Rate Architecture (HiDRA)

One of the greatest challenges in developing new space technology is in navigating the transition from ground based laboratory demonstration at Technology Readiness Level 6 (TRL-6) to conducting a prototype demonstration in space (TRL-7). This challenge is com- pounded by the relatively low availability of new spacecraft missions when compared with aeronautical craft to bridge this gap, leading to the general adoption of a low-risk stance by mission management to accept new, unproven technologies into the system. Also in consideration of risk, the limited selection and availability of proven space-grade components imparts a severe limitation on achieving high performance systems by current terrestrial technology standards. Finally from a space communications point of view the long duration characteristic of most missions imparts a major constraint on the entire space and ground network architecture, since any new technologies introduced into the system would have to be compliant with the duration of the currently deployed operational technologies, and in some cases may be limited by surrounding legacy capabilities. Beyond ensuring that the new technology is verified to function correctly and validated to meet the needs of the end users the formidable challenge then grows to additionally include: carefully timing the maturity path of the new technology to coincide with a feasible and accepting future mission so it flies before its relevancy has passed, utilizing a limited catalog of available components to their maximum potential to create meaningful and unprecedented new capabilities, designing and ensuring interoperability with aging space and ground infrastructures while simultaneously providing a growth path to the future. The International Space Station (ISS) is approaching 20 years of age. To keep the ISS relevant, technology upgrades are continuously taking place. Regarding communications, the state-of-the-art communication system upgrades underway include high-rate laser terminals. These must interface with the existing, aging data infrastructure. The High Data Rate Architecture (HiDRA) project is designed to provide networked store, carry, and forward capability to optimize data flow through both the existing radio frequency (RF) and new laser communications terminal. The networking capability is realized through the Delay Tolerant Networking (DTN) protocol, and is used for scheduling data movement as well as optimizing the performance of existing RF channels. HiDRA is realized as a distributed FPGA memory and interface controller that is itself controlled by a local computer running DTN software. Thus HiDRA is applicable to other arenas seeking to employ next-generation communications technologies, e.g. deep space. In this paper, we describe HiDRA and its far-reaching research implications.

DTN↗
NASA NTRS2000

Deep Space Systems Technology Program Future Deliveries

NASA is in a period of frequent launches of low cost deep space missions with challenging performance needs. The modest budgets of these missions make it impossible for each to develop its own technology, therefore, efficient and effective development and insertion of technology for these missions must be approached at a higher level than has been done in the past. The Deep Space Systems Technology Program (DSST), often referred to as X2000, has been formed to address this need. The program is divided into a series of "Deliveries" that develop and demonstrate a set of spacecraft system capabilities with broad applicability for use by multiple missions. The First Delivery Project, to be completed in 2001, will provide a one MRAD-tolerant flight computer, power switching electronics, efficient radioisotope power source, and a transponder with services at 8.4 GHz and 32 GHz bands. Plans call for a Second Delivery in late 2003 to enable complete deep space systems in the 10 to 50 kg class, and a Third Delivery built around Systems on a Chip (extreme levels of electronic and microsystems integration) around 2006. Formulation of Future Deliveries (past the First Delivery) is ongoing and includes plans for such developments as highly miniaturized digital/analog/power electronics, optical communications, multifunctional structures, miniature lightweight propulsion, advanced thermal control techniques, highly efficient radioisotope power sources, and a unified flight ground software architecture to support the needs of future highly intelligent space systems. All developments are targeted at broad applicability and reuse, and will be commercialized within the US.

Salvo, Christopher G.↗
NASA NTRS1993

Chemical Vapor Deposition Of Silicon Carbide

Large single-crystal SiC boules from which wafers of large area cut now being produced commerically. Availability of wafers opens door for development of SiC semiconductor devices. Recently developed chemical vapor deposition (CVD) process produces thin single-crystal SiC films on SiC wafers. Essential step in sequence of steps used to fabricate semiconductor devices. Further development required for specific devices. Some potential high-temperature applications include sensors and control electronics for advanced turbine engines and automobile engines, power electronics for electromechanical actuators for advanced aircraft and for space power systems, and equipment used in drilling of deep wells. High-frequency applications include communication systems, high-speed computers, and microwave power transistors. High-radiation applications include sensors and controls for nuclear reactors.

Powell, J. Anthony↗
NASA NTRS1991

Effects of measurement errors on microwave antenna holography

The effects of measurement errors appearing during the implementation of the microwave holographic technique are investigated in detail, and many representative results are presented based on computer simulations. The numerical results are tailored for cases applicable to the utilization of the holographic technique for the NASA's Deep Space Network antennas, although the methodology of analysis is applicable to any antenna. Many system measurement topics are presented and summarized.

Rochblatt, David J.↗
DOE OSTI2024

ML-based Dimension Reduction Strategies

Deep learning (DL)--based surrogate models have achieved success in various applications in carbon capture and storage (CCS). However, the model training on high-dimensional spaces is computationally expensive and impractical for large-scale and complex geological models, because the models usually contain hundreds of thousands to millions of grid cells, each with a set of parameters. Furthermore, the high cost of generating training data with sufficient variation is another limitation of model training on high-dimensional spaces, which may result in overfitting and reduce the model efficiency and prediction performance. We proposed the workflow incorporating dimension reduction methods and deep learning models, which aim to extract the latent variables of input parameters and output state variables, and then build the mapping function at the latent spaces. The proposed workflow can significantly reduce the computational complexity in solving both forward and inverse problems compared to models trained on high-dimensional spaces. Dimensionality reduction models showed great potential in workflows for fast reservoir simulation, history matching, prior model generation, visualization, and more, ultimately enhancing DL model performance in related SMART Work Packages.

Hosseini, Seyyed↗
NASA NTRS1988

DSN 70-meter antenna X-band gain, phase, and pointing performance, with particular application for Voyager 2 Neptune encounter

The gain, phase, and pointing performance of the Deep Space Network (DSN) 70 m antennas are investigated using theoretical antenna analysis computer programs that consider the gravity induced deformation of the antenna surface and quadripod structure. The microwave effects are calculated for normal subreflector focusing motion and for special fixed-subreflector conditions that may be used during the Voyager 2 Neptune encounter. The frequency stability effects of stepwise lateral and axial subreflector motions are also described. Comparisons with recently measured antenna efficiency and subreflector motion tests are presented. A modification to the existing 70 m antenna pointing squint correction constant is proposed.

Slobin, S. D.↗
NASA NTRSDate not supplied

NASA's Small Spacecraft and Distributed Systems: Development and Demonstration of Technologies Enabling Swarms and New Spacecraft Platforms with AI and Edge Computing

NASA’s Small Spacecraft & Distributed Systems (SSDS) within the Research and Technology Mission Directorate (RTMD) expands U.S. capability to execute unique missions through targeted investment, rapid development, and flight demonstration of small spacecraft technologies applicable to exploration, science and the commercial space sector. SSDS strategically invests in technology development and on-orbit demonstrations executed across NASA, other government agencies, industry, and academia. The program’s University SmallSat Technology Partnerships initiative awards academic researchers with the opportunity to collaborate with NASA to mature innovative technology. Capabilities aligned with RTMD’s technology shortfalls and interests - power, processing, propulsion, sensors, communications, autonomous navigation, architectures, and advanced applications like artificial intelligence (AI), machine learning, and edge computing - are prioritized in SSDS investments. These investments enable distributed, autonomous, and cooperative small spacecraft systems that support swarm missions extending beyond low Earth orbit into cislunar and deep space. This paper highlights representative SSDS flight demonstrations that mature these capabilities to enable a future operational infrastructure needed to support sustained exploration of the Moon and beyond. SSDS’s investment strategy emphasizes rapid development and on-orbit demonstration to validate spacecraft technologies required for swarms and distributed mission architectures. The Starling swarm technology demonstration mission exemplifies this approach by advancing distributed spacecraft autonomy, cooperative operations, and space situational awareness. Extended flight testing and ongoing studies of next generation swarm configurations and on-orbit space traffic monitoring and management continue to inform future swarm designs. DiskSat’s four-spacecraft demonstration mission represents SSDS’s strategic vision to expand the design space for future small spacecraft through its commitment to advance novel platform concepts that can impact how science is performed on orbit. Continuing to invest in future platforms, the notional PY12 concept is a 12-spacecraft swarm hosting neuromorphic processors and is envisioned as an on-orbit testbed for AI, edge computing, and positioning, navigation and timing technologies. SSDS also invests in single-spacecraft technology demonstrations that underpin the success of future swarm missions and accelerate the availability of validated technologies across the small spacecraft ecosystem. Examples of such demonstrations include Pathfinder Technology Demonstrator-3 (PTD-3), which performed high-rate optical communications; PTD-R, which demonstrated a camera capable of simultaneous ultraviolet and short-wave infrared optical sensing; and CAPSTONE, the Cislunar Autonomous Positioning System Technology and Operations Navigation Experiment, which validated autonomous navigation in cislunar space. Collectively, SSDS-funded demonstrations advance capabilities across swarms and illustrate a coordinated investment strategy to mature high-impact technologies required for autonomous, distributed, and cooperative small spacecraft systems for low Earth orbit, cislunar, and deep space applications. Technology demonstrations strengthen SSDS partnerships with industry, academia, and other government agencies, and promote small spacecraft community adoption of capabilities required to close technical gaps for swarm missions.

Jan Stupl↗