<interdisciplinary space-oriented research program in the physical, life, and engineering sciences< progress report
Physics research - plasmas, gas dynamics, magnetic effects, neutron absorption, shell theory, elasticity, and radial heat
PLASMA↗
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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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Learning in continuous action space for developing high dimensional potential energy models
Reinforcement learning (RL) approaches that combine a tree search with deep learning have found remarkable success in searching exorbitantly large, albeit discrete action spaces, as in chess, Shogi and Go. Many real-world materials discovery and design applications, however, involve multi-dimensional search problems and learning domains that have continuous action spaces. Exploring high-dimensional potential energy models of materials is an example. Traditionally, these searches are time consuming (often several years for a single bulk system) and driven by human intuition and/or expertise and more recently by global/local optimization searches that have issues with convergence and/or do not scale well with the search dimensionality. Here, in a departure from discrete action and other gradient-based approaches, we introduce a RL strategy based on decision trees that incorporates modified rewards for improved exploration, efficient sampling during playouts and a “window scaling scheme" for enhanced exploitation, to enable efficient and scalable search for continuous action space problems. Using high-dimensional artificial landscapes and control RL problems, we successfully benchmark our approach against popular global optimization schemes and state of the art policy gradient methods, respectively. We demonstrate its efficacy to parameterize potential models (physics based and high-dimensional neural networks) for 54 different elemental systems across the periodic table as well as alloys. We analyze error trends across different elements in the latent space and trace their origin to elemental structural diversity and the smoothness of the element energy surface. Broadly, our RL strategy will be applicable to many other physical science problems involving search over continuous action spaces.
36 MATERIALS SCIENCE↗
SMD Technology Highlights
Six technology highlights from the Science Mission Directorate (SMD) Heliophysics, Astrophysics, Earth, and Biological and Physical Sciences divisions. Technologies featured include 1. the 1653 m2 Solar Cruiser Solar Sail Propulsion system to enable missions to reach novel and otherwise difficult or impossible destinations for observing the Sun. 2. Technology sponsored by NASA and developed by MIT Space Nanotechnology Lab is developing high-performance space instrumentation for more powerful future X-ray telescopes that will study the dynamics of the high-energy universe. 3. Active thermal control technology that will help enable large ultra-stable telescopes needed to detect and characterize Earth-like planets around other stars. 4. SMD sponsored high-performance infrared sensors with reduced requirements for cooling onboard satellites and these sensors could potentially be flown on small satellite platforms like CubeSats. 5. A new technology that will utilize electron beams/ultraviolet light to remove dust that could help protect future space assets and astronauts from dangerous effects that arise from Moon/Mars dust exposure. 6. Studies using model organisms on ISS to understand the impact of the spaceflight environment on organisms in preparation for long-duration missions.
solar sail↗
Open Science for Plants in Space: Data Sharing, Standards, and Informatics for Reuse and Knowledge Discovery
Upcoming deep space missions rely on plants and crops for crew and ecosystem health. Access to space plant data enables scientists to gain a deeper understanding of biological responses to ionizing radiation, altered gravity, low atmospheric pressure, elevated CO2, and altered photoperiods. Open Science is the practice of making research available to all, while respecting diverse cultures, fostering collaborations with equity. 2023 is the ‘Year of Open Science’, and NASA has a 5-year Transform to Open Science (TOPS) mission designed to rapidly transform the agency toward an inclusive culture of open science. NASA’s Open Science Data Repository (OSDR) developed by NASA’s Biological and Physical Sciences Division provides access to data from space-relevant biological experiments. OSDR combines two databases, GeneLab and Ames Life Sciences Data Archive (ALSDA) to maximize access to standardized ‘omics (e.g., transcriptomics, proteomics) and phenotypic data (e.g., microscopy, biomass), respectively. OSDR started in 2014 with the creation of the first space-relevant FAIR (Findable, Accessible, Interoperable, Reusable) biological ‘omics repository (GeneLab), providing detailed metadata on investigation, sample, and assay levels. Today, GeneLab hosts 62 plant datasets which have led to 5 published peer-reviewed meta-analysis publications. Most of these publications were collaboration efforts under the OSDR Analysis Working Groups (AWGs). AWGs provide great opportunities for investigators to collaborate and set new standards for space-relevant data and metadata. The AWGs are welcoming any ASPB members interested in providing plant expertise for space biology. The addition of ALSDA to OSDR is also expanding analysis capability beyond ‘omics. Now is the time to get involved as a Subject Matter Expert as we establish the framework for modern plant data archiving through the AWGs. Investigators are invited to submit their space-relevant plant datasets to OSDR and visit the site to learn about the tools OSDR has to offer (osdr.nasa.gov/bio).
FAIR↗
Tracking Community Building in Open Science
Open Science is enabled by a vibrant community of researchers who regularly engage with the data, from its production to its organization, curation, archiving, dissemination, analysis, and publication. This presentation will examine community building in open science. The NASA Open Science Data Repository (OSDR) makes data available to the public following the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles. OSDR takes open science further with the OS Analysis Working Groups (AWGs) that facilitate community development and promotion. The primary activity of each AWG is to establish and validate analytical processes to generate higher-order data from data housed in OSDR. There are a number of these groups on various topics, including the Animal AWG, Plant AWG, Microbial AWG, Multi-Omics AWG, AI/ML AWG, and the Ames Life Sciences Data Archive (ALSDA) AWG. The international volunteers participating in these AWGs come from academia, citizen science initiatives, industry, and government. They include researchers, principal investigators, professors, trained hobbyists, and students from various domains and disciplines. Anyone may request to join the AWGs, and membership requests are vetted monthly by the group organizers before granting admission. Core to membership is demonstrated expertise through records of training, integrity, work in the professed domain(s), and good community standing. Regular virtual meetings are held for each AWG, with a varying cadence depending on the group's needs and goals. AWG communities share their expertise in research including cutting edge tools, software, frameworks, data formats, and libraries accelerating research collectively. This collaborative approach helps community members cross technology gaps and identify emerging challenges. These diverse communities encompass a wide range of individuals hailing from various sectors within the Science Mission Directorate and beyond. They serve as a means to promote and enhance transparency, accessibility, and inclusion. An annual AWG Symposium brings contributors together in person. Participation in AWGs can be synchronous or asynchronous, with some groups performing most of their work in off hours. Participants gain valuable skills and connections that allow them to add value to their communities and new organizations that they join, resulting in an expanded return on investment for the space life science community. Open science is increasingly a federal mandate and initiatives like NASA's Transform to Open Science and instruments like the Decadal Survey of Biological and Physical Sciences in Space demonstrate the need to carefully consider best practices in this domain. Here, we present greater detail about the makeup and participation metrics of the various AWGs affiliated with OSDR and details of successful peer-reviewed publication campaigns.
Christina M Johnson↗
Tracking Community Building in Open Science
Open Science is enabled by a vibrant community of researchers who regularly engage with the data, from its production to its organization, curation, archiving, dissemination, analysis, and publication. This presentation will examine community building in open science. The NASA Open Science Data Repository (OSDR) makes data available to the public following the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles. OSDR takes open science further with the OS Analysis Working Groups (AWGs) that facilitate community development and promotion. The primary activity of each AWG is to establish and validate analytical processes to generate higher-order data from data housed in OSDR. There are a number of these groups on various topics, including the Animal AWG, Plant AWG, Microbial AWG, Multi-Omics AWG, AI/ML AWG, and the Ames Life Sciences Data Archive (ALSDA) AWG. The international volunteers participating in these AWGs come from academia, citizen science initiatives, industry, and government. They include researchers, principal investigators, professors, trained hobbyists, and students from various domains and disciplines. Anyone may request to join the AWGs, and membership requests are vetted monthly by the group organizers before granting admission. Core to membership is demonstrated expertise through records of training, integrity, work in the professed domain(s), and good community standing. Regular virtual meetings are held for each AWG, with a varying cadence depending on the group's needs and goals. AWG communities share their expertise in research including cutting edge tools, software, frameworks, data formats, and libraries accelerating research collectively. This collaborative approach helps community members cross technology gaps and identify emerging challenges. These diverse communities encompass a wide range of individuals hailing from various sectors within the Science Mission Directorate and beyond. They serve as a means to promote and enhance transparency, accessibility, and inclusion. An annual AWG Symposium brings contributors together in person. Participation in AWGs can be synchronous or asynchronous, with some groups performing most of their work in off hours. Participants gain valuable skills and connections that allow them to add value to their communities and new organizations that they join, resulting in an expanded return on investment for the space life science community. Open science is increasingly a federal mandate and initiatives like NASA's Transform to Open Science and instruments like the Decadal Survey of Biological and Physical Sciences in Space demonstrate the need to carefully consider best practices in this domain. Here, we present greater detail about the makeup and participation metrics of the various AWGs affiliated with OSDR and details of successful peer-reviewed publication campaigns.
Christina M Johnson↗
How data science methods can improve the quality and efficiency of ICF and HEDP research
Data Science methods (many that are Bayesian based) are widely used in the physical sciences to estimate model parameters from experimental data, synthesize heterogeneous data, calibrate models, design experiments, and determine statistical significance of data. These methods provide a wealth of advantages over traditional analysis techniques because: 1) uncertainties are rigorously defined and propagated naturally through complex systems including covariance, 2) prior information is captured within the analysis framework (including rad-MHD and rad-hydro simulations), 3) competing models can be selected and/or ruled out using quantitative criteria, and 4) complex, heterogeneous data can be incorporated simultaneously. While these methods have been widely adopted as the gold standard in fields such as particle physics, astronomy, and biology, they have been slow to catch on in Inertial Confinement Fusion (ICF) and High Energy Density Physics (HEDP) research. Recently, several teams at LLNL, SNL, LANL, and the LLE have been exploring the use of these tools in their research and have found success. Here we propose that a concerted effort to consolidate these independent research efforts by developing and deploying common tools for use across the complex can revolutionize the way we approach data analysis, assimilation of theory and experiment, and decision making. The Bayesian formalism provides a means to accomplish this, but we are lacking certain infrastructure to make it happen on a large scale. Furthermore, once adopted, these techniques can be used to develop standards by which discoveries can be judged, similar to the so-called 5σ rule in high energy particle physics. Such standards may be used in the future to address the issue of unknown reproducibility in ICF and HED experiments caused by low shot rate and high cost per experiment. Our goals as a group are to advance the state of the art in HED measurement science by enabling: 1) better inferences from data with well-defined uncertainties, 2) better use of the data we have and continue to collect, 3) intelligent synthesis of data, 4) evaluation of the statistical significance of our data, and 5) informed decision making regarding the design of new experiments and instruments.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
5G Enabled Energy Innovation: Advanced Wireless Networks for Science
Digital wireless communication has become a foundational technology for the nation. The U.S. Department of Energy’s Office of Science (DOE-SC) is the Nation’s largest supporter of basic research in the physical sciences discovering new materials, designing advanced microelectronics, and understanding the physics of radio frequency signaling. The expanding national rollout of a new fifth-generation (5G) mobile network, coupled with the torrent of scientific data generated by next-generation devices such as battery-powered Internet of Things (IoT) sensors, has created an urgent need to enhance cutting-edge wireless technology. Breakthroughs in the deployment, integration, security, and operational range of wireless networking can provide new scientific capabilities for the next decade - from autonomous mobile instruments for scientific user facilities to intelligent sensors networks distributed over thousands of kilometers to study environmental processes. To realize this promise, however, we must continue to drive innovations in computing, artificial intelligence (AI), advanced materials, high-speed networking, and microelectronics. In March 2020, the DOE-SC convened a workshop to identify the potential opportunities and explore the scientific challenges of advanced wireless technologies.
42 ENGINEERING↗
Long Range Community Planning Process for Fusion Sciences and Plasma Physics (Final Technical Report)
This grant facilitated the APS-DPP Community Planning Process (DPP-CPP) which was a strategic planning process with the goals of identifying scientific and technological opportunities in the fields of Plasma Physics and Fusion Energy Science, and making consensus recommendations for a strategy to address these opportunities to the DOE Office of Fusion Energy Science Advisory Committee (FESAC). The DPP-CPP was initiated by the APS-DPP executive committee in order to help FESAC fulfill a charge from DOE to develop a strategic plan for the DOE Office of Fusion Energy Science (FES). In this charge, the DOE Deputy Director for Science, Dr. Stephen Binkley, requests “that the Fusion Energy Sciences Advisory Committee (FESAC) undertake a new long-range strategic planning activity for the Fusion Energy Sciences (FES) program. The strategic planning activity - to encompass the entire FES research portfolio (namely, burning plasma science and discovery plasma science) - should identify and prioritize the research required to advance both the scientific foundation needed to develop a fusion energy source, as well as the broader FES mission to steward plasma science.” The process as set out in the proposal (and below) was successful in meeting all of its stated goals culminating in the final report to the FESAC subcommittee given the charge.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
In Situ Resource Utilization Technology Research and Facilities Supporting the NASA's Human Systems Research and Technology Life Support Program
The NASA Microgravity Science program has transitioned research required in support of NASA s Vision for Space Exploration. Research disciplines including the Materials Science, Fluid Physics and Combustion Science are now being applied toward projects with application in the planetary utilization and transformation of space resources. The scientific and engineering competencies and infrastructure in these traditional fields developed at multiple NASA Centers and by external research partners provide essential capabilities to support the agency s new exploration thrusts including In-Situ Resource Utilization (ISRU). Among the technologies essential to human space exploration, the production of life support consumables, especially oxygen and; radiation shielding; and the harvesting of potentially available water are realistically achieved for long-duration crewed missions only through the use of ISRU. Ongoing research in the physical sciences have produced a body of knowledge relevant to the extraction of oxygen from lunar and planetary regolith and associated reduction of metals and silicon for use meeting manufacturing and repair requirements. Activities being conducted and facilities used in support of various ISRU projects at the Glenn Research Center and Marshall Space Flight Center will be described. The presentation will inform the community of these new research capabilities, opportunities, and challenges to utilize their materials, fluids and combustion science expertise and capabilities to support the vision for space exploration.
Schlagheck, Ronald A.↗
Unsupervised machine learning discovery of structural units and transformation pathways from imaging data
We show that unsupervised machine learning can be used to learn chemical transformation pathways from observational Scanning Transmission Electron Microscopy (STEM) data. To enable this analysis, we assumed the existence of atoms, a discreteness of atomic classes, and the presence of an explicit relationship between the observed STEM contrast and the presence of atomic units. With only these postulates, we developed a machine learning method leveraging a rotationally invariant variational autoencoder (VAE) that can identify the existing molecular fragments observed within a material. The approach encodes the information contained in STEM image sequences using a small number of latent variables, allowing the exploration of chemical transformation pathways by tracing the evolution of atoms in the latent space of the system. The results suggest that atomically resolved STEM data can be used to derive fundamental physical and chemical mechanisms involved, by providing encodings of the observed structures that act as bottom-up equivalents of structural order parameters. The approach also demonstrates the potential of variational (i.e., Bayesian) methods in the physical sciences and will stimulate the development of more sophisticated ways to encode physical constraints in the encoder–decoder architectures and generative physical laws and causal relationships in the latent space of VAEs.
97 MATHEMATICS AND COMPUTING↗
NASA-HBCU Space Science and Engineering Research Forum Proceedings
The proceedings of the Historically Black Colleges and Universities (HBCU) forum are presented. A wide range of research topics from plant science to space science and related academic areas was covered. The sessions were divided into the following subject areas: Life science; Mathematical modeling, image processing, pattern recognition, and algorithms; Microgravity processing, space utilization and application; Physical science and chemistry; Research and training programs; Space science (astronomy, planetary science, asteroids, moon); Space technology (engineering, structures and systems for application in space); Space technology (physics of materials and systems for space applications); and Technology (materials, techniques, measurements).
Sanders, Yvonne D.↗
Space life sciences perspectives for Space Station Freedom
It is now generally acknowledged that the life science discipline will be the primary beneficiary of Space Station Freedom. The unique facility will permit advances in understanding the consequences of long duration exposure to weightlessness and evaluation of the effectiveness of countermeasures. It will also provide an unprecedented opportunity for basic gravitational biology, on plants and animals as well as human subjects. The major advantages of SSF are the long duration exposure and the availability of sufficient crew to serve as subjects and operators. In order to fully benefit from the SSF, life sciences will need both sufficient crew time and communication abilities. Unlike many physical science experiments, the life science investigations are largely exploratory, and frequently bring unexpected results and opportunities for study of newly discovered phenomena. They are typically crew-time intensive, and require a high degree of specialized training to be able to react in real time to various unexpected problems or potentially exciting findings. Because of the long duration tours and the large number of experiments, it will be more difficult than with Spacelab to maintain astronaut proficiency on all experiments. This places more of a burden on adequate communication and data links to the ground, and suggests the use of AI expert system technology to assist in astronaut management of the experiment. Typical life science experiments, including those flown on Spacelab Life Sciences 1, will be described from the point of view of the demands on the astronaut. A new expert system, 'PI in a Box,' will be introduced for SLS-2, and its applicability to other SSF experiments discussed. (This paper consists on an abstract and ten viewgraphs.)
Young, Laurence R.↗
The Kon-Tiki Mission – Demonstrating Large Solar Sails for Deep Space Missions
Two key NASA strategic documents, Our Dynamic Space Environment: Heliophysics Science and Technology Roadmap for 2014-2033 and 2013 Solar and Space Physics: Science for a Technological Society, contain over a dozen references describing the value of solar sails to enable revolutionary new observational capabilities. Based on these needs, the NASA Marshall Space Flight Center (MSFC) developed the Kon-Tiki mission concept to mature solar sail technology for use in future Heliophysics missions, as well as missions of interest across a broad user community (e.g., space weather and Earth polar observatories). Kon-Tiki would serve as a pathfinder for missions that observe the solar environment from unique vantage points such as the Solar Polar Imager (SPI), opening a fundamentally new range of observational capabilities for the Heliophysics Program and for space weather monitoring. Observations away from the Sun-Earth line (SEL) present unique opportunities for answering the outstanding science questions of Heliophysics, for improving space-weather monitoring and prediction, and for revealing new discoveries about our Sun and solar system. High solar inclinations are particularly compelling. Investment in, and demonstration of, the technology needed to enable polar missions is essential to making this unique vantage point a reality in the next decade. Propellantless solar sails can be used to create artificial equilibria and indefinite station-keeping at locations sunward of L1 along the SEL, or at any desired offset from the SEL leading or trailing the Earth in its orbit. They can change the heliocentric inclination of a spacecraft from the ecliptic to as high as solar polar, stopping and remaining at any intermediate inclination orbit in between. Sails can be used to hover over the Earth’s poles, using solar photon pressure to offset the Earth’s gravitational attraction, creating functional equivalents of geostationary earth orbits. The Kon-Tiki mission would fly a small spacecraft with a large (>1200 square meter) solar sail containing embedded reflectivity control devices (RCDs) and photovoltaic cells. The mission concept includes successful deployment of the solar sail, validation of all sail subsystems, controlled station-keeping inside of the Sun-Earth L1 point, attitude control of the sail with the RCDs (including spinning and despinning), demonstration of pointing performance for science imaging, and finally an increase in heliocentric inclination (out of the ecliptic).
Johnson, Les↗
Space Biology Beyond LEO Instrumentation & Science Series - Science Working Group 2021 Annual Report
Human space exploration was never intended to stop within low Earth orbit (LEO). Although nearly all of biological research in space has taken place in LEO, on the Space Shuttle, International Space Station (ISS), and free-flyer CubeSat missions, NASA's recent shift in emphasis toward human exploration of the Moon and ultimately Mars necessitates a shift in the focus of its research in the biological sciences [1]. Specifically, in 2022 and beyond, the Division of Biological and Physical Sciences seeks to pivot toward a focus on Thriving In DEep Space (TIDES), furthering the fundamental research necessary for understanding risks and mitigation strategies for deep-space stressors on human crew, plants, and their microbiomes. This effort entails both research on model organisms to elucidate the molecular processes underlying the biological consequences of deep-space exposure, and research on the organisms that will be necessary companions to sustain life and facilitate resource utilization in long-duration missions.
R Craig Everroad↗
NASA Astronaut Medical Selection
Invited panel presentation to The National Academies of Sciences, Engineering, and Medicine - A Science Strategy for the Human Exploration of Mars: Panel on Biological and Physical Sciences and Human Factors.
astronaut medical selection↗
Embedding hard physical constraints in neural network coarse-graining of three-dimensional turbulence
In recent years, deep learning approaches have shown much promise in modeling complex systems in the physical sciences. A major challenge in deep learning of partial differential equations is enforcing physical constraints and boundary conditions. In this work, we propose a general framework to directly embed the notion of an incompressible fluid into convolutional neural networks, and apply this to coarse-graining of turbulent flow. These physics-embedded neural networks leverage interpretable strategies from numerical methods and computational fluid dynamics to enforce physical laws and boundary conditions by taking advantage the mathematical properties of the underlying equations. Here, we demonstrate results on three-dimensional fully developed turbulence, showing that this technique drastically improves local conservation of mass, without sacrificing performance according to several other metrics characterizing the fluid flow.
97 MATHEMATICS AND COMPUTING↗
A data-driven approach to sampling matrix selection for compressive sensing
Sampling is a fundamental aspect of any implementation of compressive sensing. Typically, the choice of sampling method is guided by the reconstruction basis. However, this approach can be problematic with respect to certain hardware constraints and is not responsive to domain-specific context. We propose a method for defining an order for a sampling basis that is optimal with respect to capturing variance in data, thus allowing for meaningful sensing at any desired level of compression. We focus on the Walsh-Hadamard sampling basis for its relevance to hardware constraints, but our approach applies to any sampling basis of interest. We illustrate the effectiveness of our method on the Physical Sciences Inc. Fabry-Perot interferometer sensor multispectral dataset, the Johns Hopkins Applied Physics Lab FTIR-based longwave infrared sensor hyperspectral dataset, and a Colorado State University Swiss Ranger depth image dataset. The spectral datasets consist of simulant experiments, including releases of chemicals such as GAA and SF6. We combine our sampling and reconstruction with the adaptive coherence estimator (ACE) and bulk coherence for chemical detection and we incorporate an algorithmic threshold for ACE values to determine the presence or absence of a chemical. We compare results across sampling methods in this context. We have successful chemical detection at a compression rate of 90%. For all three datasets, we compare our sampling approach to standard orderings of sampling basis such as random, sequency, and an analog of sequency that we term `frequency.' In one instance, the peak signal to noise ratio was improved by over 30% across a test set of depth images.
compressive sensing, hyperspectral imaging, Walsh-↗