SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
SEASAT-A satellite data processing and modelling capabilities address scientific and applications problems in the domains of oceanic, atmospheric, and solid earth geophysics studies by the use of active radar, and passive microwave and infrared instruments. A data flow map depicts the interrelationship between SEASAT-A instruments and other sources of information and the geophysical parameters to be determined.
The capabilities of the first Space Shuttle scientific payload for remote sensing observations of the earth are evaluated, and the instruments used are from the Office of Space and Terrestrial Applications (OSTA-1). The payload gathered 10 million square kilometers of data over the earth's oceans, 6 million square kilometers of data over land masses, and global atmospheric data on carbon monoxide concentration. Shuttle imaging radar collected data over a 10,000 km sweep from Spain to Australia, a Shuttle multispectral infrared radiometer obtained 400,000 ten-channel spectra in clear weather conditions over four continents, and the Ocean Color Experiment detected water color for interpretation of major water constituents. The mission is considered to be a success based on the amount of data collected, and scientific and technical insight gained for the development of Shuttle flight instruments.
Agency objective are: Strategic Planning Transformation. Advanced Planning Organizational Roles. Public Involvement in Strategic Planning. Strategic Roadmaps and Schedule. Capability Roadmaps and Schedule. Purpose of NRC Review. Capability Roadmap Development (Progress to Date).
This report details the observations from a two-day virtual workshop, held May 12-13, 2020, focused on whether, and how, artificial intelligence (AI) could assist humans in strategic planning, specifically in science and technology prioritization. The participants identified several “key challenges” that AI might tackle in this area. To further understand the value of these key challenges the workshop then developed related test cases that would demonstrate specifically how AI/machine learning (ML) could provide assistance to humans. Approximately 40 subject matter experts (SMEs), with backgrounds in AI, strategic planning for science, and scientific data, were gathered for the conference. This report collates the details of the output of the workshop. The “best” test cases include (in no particular order):Use of AI to assist in selecting Decadal Survey priorities. * Use of AI to identify new, or previously unidentified, science topics for prioritization. * Using AI to better label and increase discoverability of scientific literature and proposals. * Use of AI to enhance current observation capabilities for scientific missions. * Using AI to mitigate biases in selection of proposal reviewers and membership of advisory committees. Examination of these test cases indicates that Natural Language Processing (NLP) is a common capability found in most of the ”best” (top-rated) test cases and is a valuable, multi-purpose tool which enables ML in this area.
Social computing has the potential to contribute to scientific research. Ongoing developments in information and communications technology improve capabilities for enabling scientific research, including research fostered by social computing capabilities. The recent emergence of e-Science practices has demonstrated the benefits from improvements in the technological infrastructure, or cyber-infrastructure, that has been developed to support science. Cloud computing is one example of this e-Science trend. Our own work in the area of software reuse offers methods that can be used to improve new technological development, including cloud computing capabilities, to support scientific research practices. In this paper, we focus on software reuse and its potential to contribute to the development and evaluation of information systems and related services designed to support new capabilities for conducting scientific research.
The objective of this project is to demonstrate a new imaging technology with unprecedented capabilities for scientific exploration. This technology innovation will enable extremely high-resolution imaging capability, otherwise unattainable with conventional approaches. It is based on a unique combination of novel diffractive optical sensing (photon sieve), advanced image processing, and small satellite formation flying. The spatial resolution of current solar instruments are more than 100 times coarser than the size of energy dissipation features in coronal heating (<100 km) since conventional optics cannot be figured to near diffraction-limited accuracy at extreme ultraviolet (EUV) wavelengths. This project will demonstrate the feasibility of imaging of a few milli-arcseconds (MAS) to study the structure and development of these dissipation regions.
Scientific discoveries rely heavily on efficient access, search, and management of massive data sets. Data management technologies have, for decades, provided foundational capabilities for scientific computing. Just as storage, input/output (I/O), and data management have been fundamental to simulation-based science for many years, so too are capable data-management technologies key to the success of today’s scientific workflows utilizing data intensive and machine learning (ML) techniques. The Department of Energy, Office of Science, Advanced Scientific Computing Research (ASCR) program has invested broadly in data-management research focused on high-performance computing (HPC) systems, from parallel file systems that store data to application software that makes these systems more productive. Still, advances in technology combined with growing diversity of supported science strongly motivate continued investment in this area. In January 2022, ASCR convened a workshop to identify priority research directions in the area of data management for high-performance and scientific computing. Attendees were challenged to identify promising approaches that would support the breadth of the DOE mission, including the explosion of artificial intelligence (AI) uses and the growing needs of experimental and observational science. Technological and science drivers were identified and considered as they relate to key aspects of data management such as interfaces, architectural design, and FAIR principles (Findable, Accessible, Interoperable, and Reusable). The thoughts of the workshop participants were distilled into a set of four priority research directions with the potential for high impact on DOE science. These research directions are summarized in the following pages.
The development of long-duration ballooning techniques to support flights of 1-2 ton payloads for periods up to 2 weeks, possibly even longer, offers a near-space scientific mission capability with an order of magnitude improvement over traditional balloon flights. This revolution in scientific research ballooning began with the solution of the manufacturing difficulties that plagued the program in the first half of the 1980's, and it has culminated in the early 1990's with three successive circumnavigations of the Antarctic continent in 9 to 14 day flights. A complementary capablity in the Northern hemisphere, which would approximately double the number of flights that could be supported each year, is needed to accommodate the trend for conventional payloads to be modified, or developed, for long-duration flights. Plans are already underway to employ the order-of-magnitude increased flight time for support of multi-flight research programs that will produce results comparable to some space missions. An overview of the current status and near-term plans for ballooning will be presented, along with a discussion of some major science initiatives that have been enabled.
Convective storms vertically transport water vapor and condensate from Earth’s surface to the upper troposphere. Life on Earth is fundamentally linked to this transport which determines the hydrological cycle, and the intensity of severe weather responsible for the destruction of life and property. Despite advances in high-resolution modeling and better observational capabilities, the scientific community continues to be confronted with knowledge gaps about convective storms that limit our predictive capabilities. The ongoing developments in the high-resolution Energy Exascale Earth System Model (E3SM), large eddy simulations, and AI-based analytics to evaluate uncertainties are expected to provide a comprehensive framework for new scientific discovery. The model-experiment (MODEX) approach suggests that the aforementioned advancements in model development and AI-based inference techniques should be complemented by similar advancements in the experimental (observational) side so that the former does not outstrip the ability of the latter to provide meaningful constraints. What are the recent advancements in observations that will provide the necessary leap forward in improving our predictive capabilities? To address this question, we propose a new experimental paradigm called Multisensor Agile Adaptive Sampling (MAAS) that capitalizes on advancements in communications (5G), computational resources (edge/fog computing), sensor capabilities, and machine learning (ML) and AI techniques (Kollias et al., 2020). The MAAS framework allows for the collection of higher spatiotemporal resolution and quality observations of convective storms than is traditionally possible. The MAAS framework is scalable and applicable to atmospheric observatories such as those operated by the Department of Energy (DoE) Atmospheric Radiation Measurement (ARM) facility.
The overarching goal of this project is to develop a scalable black-box training capability for scientific machine learning (SciML) problems that are non-trainable with existing automatic differentiation (AD)-based algorithms. AD assumes that a loss function can be decomposed into a sequence of elementary operations whose derivatives are known. This assumption is violated when the loss function includes a black-box physical model (e.g., a legacy simulator). The current strategy, converting a black-box simulator to an AD-enabled code via differential programming, is inflexible and time-, labor-consuming. Thus, black-box optimization is a main workhorse for training SciML models, e.g., in scientific reinforcement learning, hyper-parameter fine tuning, designing SciML models with adversarial robustness, etc.
Laboratory Directed Research & Development (LDRD) projects at Ames National Laboratory are compelling, collaborative, and mission-oriented research that accelerate progress in our strategic directions. The aim of these projects is to strengthen Ames National Laboratory’s emerging scientific leaders and core capabilities, to further enhance our scientific and technological vitality by stimulating R&D innovation in support of the Laboratory’s foundational strengths and strategic initiatives, and to build capabilities to respond to rapidly emerging R&D opportunities of clear potential benefit to DOE’s mission. LDRD funding strengthens our core capabilities either by exploiting targets of opportunity in expanding current capabilities, or by exploring and implementing new capabilities that provide scientific advantages and position Ames National Laboratory to be a leader in existing and emerging scientific fields. For FY2022, the Ames National Laboratory LDRD focused on transformative science to accelerate progress in our strategic directions, especially the initiatives discussed below. Of particular interest are areas that span multiple disciplines, or that develop new technical capabilities that enhance our ability to address our strategic directions.
Secure environments face a steadily increasing threat from the introduction of scientific equipment that includes extensive software components. Devices such as powerful microscopes, 3d printers, programmable logic controllers, and many others, increase our capability and scientific output. Unfortunately, they now frequently include an entire computing stack with custom software bundled along with entire operating systems. Our ability to introspect and understand binary software is extremely limited, opening up the possibility of malicious third-parties including software that compromises our critical missions.
Laboratory Directed Research & Development (LDRD) projects at Ames National Laboratory perform compelling, collaborative, and mission-oriented research that accelerates progress in our strategic directions. The aim of these projects is to strengthen Ames National Laboratory’s emerging scientific leaders and core capabilities, to further enhance our scientific and technological vitality by stimulating R&D innovation in support of the Laboratory’s foundational strengths and strategic initiatives, and to build capabilities to respond to rapidly emerging R&D opportunities of clear potential benefit to DOE’s mission. LDRD funding strengthens our core capabilities either by exploiting targets of opportunity in expanding current capabilities, or by exploring and implementing new capabilities that provide scientific advantages and position Ames National Laboratory to be a leader in existing and emerging scientific fields.
The NASA/JPL aircrft program has been in existence for more than a decade. However, during the past two years, there has been a dramatic increase in the capability and scientific utility of this system as an L-band, 4-polarization synthetic aperture radar (SAR) system has become operational. The system is capable of simultaneously and coherently acquiring four independent complex polarization measurements for every pixel in the image. This capability has been exploited by a number of experimenters to generate several new data types that appear to contain significantly more information than was previously available from single or dual polarized SAR systems. The system will ultimately be capable of estimating the entire complex scattering matrix for every pixel on the ground. Within the next year the system will be extended to C-band so that it will be possible to simultaneously acquire 4-polarization imagery at L-band and C-band. This system will be the prototype for the SIR-C experiment that will have similar capabilities and be flown on the Space Shuttle in 1989.
Ionizing radiation poses a significant risk to humans living and working in space. The major sources of radiation are solar disturbances and galactic cosmic rays. The components of this radiation are energetic charged particles, protons, as well as fully ionized nuclei of all elements. The biological effects of these particles cannot be extrapolated in a straightforward manner from available data on x-rays and gamma-rays. A radiation protection program that meets the needs of spacefaring nations must have a solid scientific basis, capable not only of predicting biological effects, but also of making reliable estimates of the uncertainty in these predictions. A strategy leading to such predictions is proposed, and scientific requirements arising from this strategy are discussed.
This report highlights significant strides made by Oak Ridge National Laboratory’s Oak Ridge Leadership Computing Facility (OLCF) in advancing computational research and infrastructure. Through the Advanced Computing Ecosystem (ACE) strategic initiative, OLCF has been successfully integrated with Doe’s Integrated Research Infrastructure (IRI) program, establishing itself as a critical framework for enhancing scientific computing capabilities across various domains. This report outlines the activities, accomplishments, and future directions of ACE, emphasizing its role in developing cutting-edge technologies, supporting science pilots, and fostering collaborations that drive scientific innovation.
EXPRESS Racks have been supporting payload science operations onboard the International Space Station (ISS) since April of 2001. EXPRESS is an acronym that stands for "EXpedite the PRocessing of Experiments to Space Station." This name reflects NASA's focus to simplify the process of manifesting experiments and maximizing scientific research capabilities by providing a robust, remotely operated payload support platform. The EXPRESS Rack System was developed by NASA's Marshall Space Flight Center (MSFC) and built by The Boeing Company in Huntsville, Alabama. Eight EXPRESS racks were built and five are currently onboard the ISS supporting science operations. The design and development of the EXPRESS Rack System is a long story that has been documented in previous publications. This paper briefly describes the facilities used to develop and verify flight software, test operational capabilities. It then traces the advancements made in the operational capabilities of the EXPRESS Racks from the time they were launched on STS-100 through the present. The paper concludes with a description of potential enhancements that will make the EXPRESS racks one of the most advanced and capable remote science platforms ever developed.
Today, our calling to explore is greater than ever before, and here at Marshall Space Flight Center—we make human deep space exploration possible. A key goal for Artemis is demonstrating and perfecting capabilities on the Moon for technologies needed for humans to get to Mars. This year’s report features 10 of the Agency’s 16 Technology Areas, and I am proud of Marshall’s role in creating solutions for so many of these daunting technical challenges. Many of these projects will lead to sustainable in-space architecture for human space exploration that will allow us to travel to the Moon, on to Mars, and beyond. Others are developing new scientific instruments capable of providing an unprecedented glimpse into our universe. NASA has led the charge in space exploration for more than six decades, and through the Artemis program we will help build on our work in low Earth orbit and pave the way to the Moon and Mars. At Marshall, we leverage the skills and interest of the international community to conduct scientific research, develop and demonstrate technology, and train international crews to operate further from Earth for longer periods of time than ever before— first at the lunar surface, then on to our next giant leap, human exploration of Mars. While each project in this report seeks to advance new technology and challenge conventions, it is important to recognize the diversity of activities and people supporting our mission. This report not only showcases the Center’s capabilities and our partnerships, it also highlights the progress our people have achieved in the past year. These scientists, researchers and innovators are why Marshall and NASA will continue to be a leader in innovation, exploration, and discovery for years to come.