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This paper validates that radiometric accuracy is maintained throughout the dynamic range of the instrument. As part of this study, a new look has been taken on the band-relative scale, and a decrease in the radiance reported for the Red and NIR Bands has resulted.
The mission of the Advanced Fuels Campaign (AFC) is to perform or support research, development, and demonstration (RD&D) activities to identify and mature innovative fuels and cladding materials and associated technologies with the potential to improve the performance and safety of current and future reactors; increase the efficient utilization of nuclear energy resources; contribute to enhancing proliferation resistance of the nuclear fuel cycle; and address challenges related to waste management and ultimate disposal. AFC pursues its mission objectives using a goal-oriented, science-based approach that seeks to establish a fundamental understanding of fuel and cladding behaviors under conditions that arise during fabrication, normal steady-state irradiation, off-normal transient scenarios, and storage/disposal. This approach includes advancing the theoretical understanding of fuel behavior, conducting fundamental and integral experiments, and supporting the mechanistic, multi-scale modeling of nuclear fuels to inform and guide fuel development projects, advance the technological readiness of promising fuel candidates, and ultimately support fuel qualification and licensing initiatives. In the area of advanced tools for the modeling and simulation of nuclear fuels, the AFC works in close partnership with the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, participating with NEAMS in developing mechanistic fuel behavior models and providing experimental data to inform and validate its most advanced tools. Specifically, AFC objectives in thecoming five year horizon include: 1. support the industry-led development of Accident Tolerant Fuel (ATF) technologies with improved reliability and performance under normal operations and enhanced tolerance during hypothetical accident scenarios, with implementation of lead test rods/assemblies of one or more ATF concepts in commercial reactor(s) by or before 2022 and batch reloads of near-term concept(s) in the 2023-2026 timeframe; 2. lead research and development on innovative fuel and cladding technologies for applications to future advanced reactors, especially fast-spectrum reactors, including reactors that utilize both once-through and recycle scenarios; 3. continue the development and demonstration of a multi-scale,science-based approach to fuel development and testing, andcontribute to the establishment of a state-of-the-art R&D infrastructure necessary to accelerate the development of new fuel concepts; and collaborate with NEAMS on the development and validation of multi-scale, multi-physics, and increasingly predictive fuel performance models and codes. 4. collaborate with NEAMS on the development and validation of multi-scale, multi-physics, and increasingly predictive fuel performance models and codes.
Foundation Models (FM) are AI models that are designed to replace a task or an application specific model. These FM can be applied to many different downstream applications. These FM are trained using self supervised techniques and can be built on any type of sequence data. The use of self supervised learning removes the hurdle for developing a large labeled dataset for training. Most FM use transformer architecture utilizes the notion of self attention which allows the network to model the influence of distant data points to each other both in space and time. The FM models exhibit emergent properties that are induced from the data. FM can be an important tool for science. The scale of these models results in better performance for different downstream applications and these applications show better accuracy over models built from scratch. FM drastically reduces the cost of entry to build different downstream applications both in time and effort. FM for selected science datasets such as optical satellite data, can accelerate applications ranging from data quality monitoring, feature detection and prediction. FM can make it easier to infuse AI into scientific research by removing the training data bottleneck and increasing the use of science data.
Here, there is a considerable body of work that describes the scaling of diblock copolymer micelle dimensions in dilute and semi-dilute solution based upon block degrees of polymerization and copolymer concentration. However, there is a lack of analogous information for semi-dilute ABA triblock copolymer gels, which consist of ABA triblock copolymer dissolved in midblock-selective (B-selective) solvent. The present study uses small angle X-ray scattering to extract micelle dimensions for numerous triblock copolymer gels that vary in copolymer identity (and hence block lengths) and copolymer concentration, as well as gels that contain various ratios of two unique triblock copolymers. Analysis of micelle structural data subsequently translates to universal scaling expressions for the micelle core radius – r A ≈ N A 0.53 N B -0.14 $\phi$ ABA 0.16 where N A and N B are the endblock and midblock degrees of polymerization, respectively, and $\phi$ ABA is the volume fraction of triblock copolymer in the gel – and for the intermicelle spacing – l AA ≈ N A 0.09 N B 0.29 $\phi$ ABA -0.35 . Each scaling expression describes the full collection of experimental data very well. Additionally, these scaling expressions are partially in line with expectations from semi-dilute diblock copolymer solution theory.
The Geostationary Fourier Transform Spectrometer (GeoFTS) is an imaging spectrometer designed for an earth science mission to measure key atmospheric trace gases and process tracers related to climate change and human activity. The GeoFTS instrument is a half meter cube size instrument designed to operate in geostationary orbit as a secondary "hosted" payload on a commercial geostationary satellite mission. The advantage of GEO is the ability to continuously stare at a region of the earth, enabling frequent sampling to capture the diurnal variability of biogenic fluxes and anthropogenic emissions from city to continental scales. The science goal is to obtain a process-based understanding of the carbon cycle from simultaneous high spatial resolution measurements of carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), and chlorophyll fluorescence (CF) many times per day in the near infrared spectral region to capture their spatial and temporal variations on diurnal, synoptic, seasonal and interannual time scales. The GeoFTS instrument is based on a Michelson interferometer design with a number of advanced features incorporated. Two of the most important advanced features are the focal plane arrays and the optical path difference mechanism. A breadboard GeoFTS instrument has demonstrated functionality for simultaneous measurements in the visible and IR in the laboratory and subsequently in the field at the California Laboratory for Atmospheric Remote Sensing (CLARS) observatory on Mt. Wilson overlooking the Los Angeles basin. A GeoFTS engineering model instrument is being developed which will make simultaneous visible and IR measurements under space flight like environmental conditions (thermal-vacuum at 180 K). This will demonstrate critical instrument capabilities such as optical alignment stability, interferometer modulation efficiency, and high throughput FPA signal processing. This will reduce flight instrument development risk and show that the GeoFTS design is mature and flight ready.
Under the Advanced Research for Integrated Energy Systems (ARIES) initiative, NREL will build out hydrogen system capabilities at the Flatirons campus. The hydrogen infrastructure at the Flatirons campus will include a research-ready megawatt-scale electrolyzer with hydrogen compression and storage, and a fuel cell system. This hardware will support H2@Scale goals by enabling integrated systems R&D (e.g., to demonstrate grid services, energy storage, renewable hydrogen production, and innovative end-use applications). The system will be coupled with the controllable grid interface at the Flatirons campus and will be designed with flexibility to provide a testbed for integrated electrolyzer R&D, hydrogen utilization R&D, and to study the science of scaling for hydrogen energy systems.
From an ecological perspective, the search for life on distant planets begins from several key assumptions. The first of these is that, viewed from a remote location in space, the signature of life on a distant planet will be the result of net gas exchange of organisms with their environment. On the basis of extensive biogeochemical measurements and biogenic trace gas fluxes in modem Earth environments, it is probable that certain groups of organisms both produce and consume the same trace gas(es) within a single bioprofile of Solid (porous) substrate or surface water. The net gas exchange rate with the atmosphere measured at the living surface is frequently the result of competing metabolic reactions, which may carried out by different functional groups of organisms located at dissimilar 'climatic' or chemical microsites within the same bioprofile. Biogenic gases produced at one (deep) level of a bioprofile may be consumed by another functional group of organisms located closer to the level of surface exchange with the atmosphere. A second key assumption is that the net biogenic fluxes of atmospheric gases on Earth can be used to infer relative abundance and functional composition of the major organisms on a distant planet. Examples of this principle include the presence of methanogenic microorganisms abundant today in freshwater ecosystems worldwide, which are major source of atmospheric methane and its seasonal variability in Earth's atmosphere. A third assumption is that scaling up biogenic gas fluxes from a single biological community to the planetary level requires flux measurements at the whole ecosystem level. This implies that measurements of biogenic gas exchange with the global atmosphere cannot be easily inferred from measurements of gas production rates of single organisms, which may have been isolated in some manner from the setting of their native ecosystem. Hence, the unit of biological organization used in modern Earth Science for scaling up to biosphere effects on atmospheric composition is the ecosystem level. These assumptions are the foundation for developing modern emission budgets for biogenic gases such as carbon dioxide, methane, carbon monoxide, isoprene, nitrous and nitric oxide, and ammonia. Such emission budgets commonly include information on seasonal flux patterns, typical diurnal profiles, and spatial resolution of at least one degree latitude/longitude for the globe. On the basis of these budgets, it is possible to compute 'base emission rates' for the major biogenic trace gases from both terrestrial and ocean sources, which may be useful benchmarks for defining the gas production rates of organisms, especially those from early Earth history, which are required to generate a detectable signal on a global atmosphere. This type of analysis is also the starting point for evaluation of the 'biological processes to global gas budget' extrapolation procedure described above for early Earth ecosystems.
The availability and disposition of data has assumed increasing importance in large-scale computational science. Data repositories are evolving to meet new classes of requirements: compliance with government access guidelines, support for reproducibility of experimental results, and long-term availability of data products. The Constellation public data repository at the Oak Ridge Leadership Computing Facility faces these issues while being situated in one of the most productive data centers in the world. While monitoring and operational data analysis are ingrained in the operation of the OLCF’s large-scale high performance computing platforms, data repositories do not have this history of support. Problems faced by Constellation range from data size (over 7 petabytes in current holdings) to analytic complexity (detailed curation is both absolutely necessary for many data sets and absolutely impossible for humans to accomplish in any practical manner) to deployment environment (OLCF storage resources are oriented toward the needs of the compute platforms). In this paper we describe some of the challenges for collecting monitoring and analytic data from a leadership public data repository. We also discuss various strategies we are pursuing in order to address these challenges, from manual data collection to plans for introducing machine learning-based curatorial techniques.
Large-scale computing has enabled numerous scientific discoveries, including ground-breaking achievements facilitated by the US Department of Energy (DOE) supercomputers and advances in applied mathematics and computer science. While important advances were made in energy efficiency to enable exascale computing, continued efforts are needed to dramatically improve the energy efficiency of the next generation of high-performance computing (HPC) systems and, more broadly, AI data centers. Without substantial improvements in energy efficiency, the energy consumption associated with computing could become a limiting factor for future scientific discovery, national security, and technological advancement.
Rapid technology developments are changing the way data-driven research is performed within the science community. With the emergence of cloud computing, this has quickly become a viable approach for enabling “science at scale”. Researchers are no longer hindered by obstacles of data management and data wrangling, allowing them to quickly discover, access and perform analysis on extremely large datasets. Infrastructures that move data out of institutional silos and into a computational platform, will ensure that data and tools are accessible to all users. NASA’s Interagency Implementation and Advanced Concepts Team (IMPACT) seeks to address these rapid technology developments by establishing Space Act Agreements with selected partners from the public-private sector working in the area of cloud computing. These agreements aim to explore new opportunities with commercial cloud providers to accelerate open science and enable discovery, access and use of data sets on the cloud. In addition, they will also help establish training workshops for the science community to help researchers utilize the cloud for science. In this talk, we will present an overview of current and new partnerships we are developing to support open science and open data initiatives.
The Mars 2020 Mission’s primary objective is to collect 20 geographically unique samples during its prime mission of one and a quarter Martian years, or just over 2 Earth years. Mission planners determined the project needed to develop a system that would enable the operations team to analyze engineering and science data, make science decisions, select viable rover targets at a millimeter resolution and validate an uplink bundle for a car sized rover with more complex science instruments than any previous Mars surface mission. All this had to be done within a five hour time frame. Doing this with a small team would be a challenge, but this had to be accomplished by a large team of engineers and scientists located across North America and Europe. Achieving this level of operational efficiency was unheard of in the prime mission. In addition, the mission had another set of requirements that had nothing to do with surface operations; the Mars 2020 Ground Data System (GDS) was also expected to comply with a new set of security requirements to keep up with the ever changing cybersecurity landscape. The Mars 2020 Ground Data System (GDS) is a re-architected version of the Mars Science Laboratory GDS. The primary goal was to integrate the lessons learned from previous Mars surface missions, accommodate a set of new requirements and capabilities required to ensure mission success, and comply with a new set of cybersecurity controls. The new architecture includes several unique qualities including a data lake, language-agnostic system-wide event-based operations, containerization, automated deployment, network segmentation, infrastructure-as-code, API-driven interfaces, and the first Mars surface GDS to operate primarily in the cloud. The new architecture enabled greater access to the system’s data, tighter integration with the operations team, and a higher level of traceability. The availability of the data also enabled a new set of capabilities previously not possible on surface missions. These new capabilities include an autonomous data to information, pipeline for downlink analysis, horizontal scaling of science data processing capabilities, autonomous round trip data tracking of science and engineering data, integration of flight system state into the tactical planning cycle, high fidelity targeting utilizing kinematic data, and hierarchical image and 3d meshes data representations. This paper will introduce the requirements for the Mars 2020 Mission, the heritage architecture, and the rationale for the changes to achieve the new architecture. The paper will continue to describe the fundamental changes made to the GDS architecture, how these changes enabled a more tightly integrated GDS, and the new capabilities that were enabled by the new architecture. The paper will conclude with the lessons learned from the process of rearchitecting a heritage GDS system and from the first 200 days of operations supporting over 800 users from around the world.
Energy is one of the most complex fields of study and an issue that influences nearly every aspect of modern life. Over the past century, combustion of fossil fuels, particularly in the transportation sector, has been the dominant form of energy release. Refining of petroleum and natural gas into liquid transportation fuels is also the centerpiece of the modern chemical industry used to produce materials, solvents, and other consumer goods. In the face of global climate change, the world is searching for alternative, sustainable means of producing energy carriers and chemical building blocks. The use of biofuels in engines predates modern refinery optimization and today represents a small but significant fraction of liquid transportation fuels burnt each year. Similarly, white biotechnology has been used to produce many natural products through fermentation. The evolution of recombinant DNA technology into modern synthetic biology has expanded the scope of biofuels and bioproducts that can be made by biocatalysts. This opinion examines the current trends in this research space, highlighting the substantial growth in computational tools and the growing influence of renewable electricity in the design of metabolic engineering strategies. In short, advanced biofuel and bioproduct synthesis remains a vibrant and critically important field of study whose focus is shifting away from the conversion of lignocellulosic biomass towards a broader consideration of how to reduce carbon dioxide to fuels and chemical products.
Better predictive models of mechanical failure in low-weight heat shield composites would aid material certification for missions with aggressive atmospheric entry conditions. In this study, we develop such a model for the rapid engineering analysis of the failure limits of phenolic impregnated carbon ablator (PICA) - a leading heat shield material whose structural component is a carbon fiber network. We hypothesize inelastic deformation failure mechanisms and model their behavior using molecular dynamics simulations to calculate the binding energy. We then upscale this binding energy to the macroscale using a renormalization argument. The approach delivers insightful and reasonably accurate macroscale predictions that compare favorably to experiments. In application, the model is validated for a particular variety of PICA by comparison to experiment and would then be used to study design scenarios in different entry conditions.
The massive amount of data produced during simulation on high-performance computers has grown exponentially over the past decade, exacerbating the need for streaming compression and decompression methods for efficient storage and transfer of this data---key to realizing the full potential of large-scale computational science. Lossy compression approaches such as JPEG when applied to scientific simulation data realized as a stream of images can achieve good compression rates but at the cost of introducing compression artifacts and loss of information. This paper develops a unified framework for in situ compression artifact removal in which the fully convolutional neural network architectures are combined with scalable training, transfer learning, and experience replay to achieve superior accuracy and efficiency while significantly decreasing the storage footprint as compared with the traditional optimization-based approaches. We demonstrate the proposed approach and compare it with compressed sensing postprocessing and other baseline deep learning models using climate simulations and nuclear reactor simulations, both of which are driven by hyperbolic partial differential equations. Our approach when applied to remove the compression artifacts on the JPEG-compressed nuclear reactor simulation data (using a transfer-trained model that was pretrained on the climate simulation data and updated incrementally as the nuclear reactor simulation progressed), achieved a significant improvement---mean peak signal-to-noise ratio of 42.438 as compared with 27.725 obtained with the compressed sensing approach.
This report investigates a laser-interference surface treatment as a non-contact, i.e., without major solid/liquid medium application or abrasion, and non-chemical surface preparation method for aerospace coating systems. It was proposed to use the laser-interference technique to structure surfaces of Al and/or Ti, creating "rough" surfaces with pre-engineered series of ridges and valleys at submicron scale. The science goal of this study was to develop an understanding of the surface microstructure, topology, and physical mechanisms that would improve adhesion and corrosion protection of Al2024-T3. Microstructure analysis indicates that the laser-interference structuring (LIS) was found to reduce the formation of CuMn-rich precipitates in Al 2024-T3 over a 500-800 nm depth from top surface. The X-cut and cross-hatch coating adhesion ratings indicate that the LIS specimens meet the performance requirements in the coating adhesion specifications by having a higher or identical ranking to those specimens prepared with current state-of-the-art chemical conversion or sulfuric acid anodizing. After the ASTM B117 corrosion exposure, it was found that the laser processed specimens exhibited only few blisters. It was found that the corrosion damage was minimized at a laser rastering speed of 4 mm/s, for which only 33% of specimens developed very minor corrosion damage. The ASTM D1654 creepage ratings, used to evaluate corrosion damage along the scribe lines, were found to be at least nine for all coated panels. These results indicate that the laser-interference technique with the additional acetone wiping has the potential to be further developed as a minor chemical surface preparation technique for chromate-containing epoxy primers coatings.
The United States (U.S.) is no longer the unambiguous leader in the vitally important field of high performance computing (HPC). Japan, the European Union (EU), and China have fielded systems that are on par with our fastest supercomputers. The supply chain for everything from semiconductors to scientific software is globally distributed. Yet our economic future and security depend critically on our ability to innovate faster than our competitors, and the speed of innovation depends increasingly on large-scale computational science and engineering and thus HPC. How should the United States respond to this challenge? This report seeks to initiate a new and potentially transformative national discussion on this vital question. The Department of Energy’s (DOE) Advanced Scientific Computing Research (ASCR) program is well-positioned to make informed, targeted decisions about where the United States should cooperate and where it should compete in the global market for scientific exploration and discovery. By setting its sights on problems critical to our nation and the world, by establishing productive new collaborations, and by making strategic investments, ASCR can restore and maintain U.S. scientific leadership in the critical areas described in this report while strengthening our research infrastructure and training a large, diverse cohort of scientists. In doing so, ASCR and its scientists will pave the way for a secure and prosperous future for America. For more than 30 years, the ASCR program has provided the HPC and networking capabilities and expertise needed to support DOE’s mission to advance the national, economic, and energy security of the United States. The program now faces the challenge of developing and deploying the next generation of HPC systems and technologies, as well as supporting the application of HPC and artificial intelligence (AI) technologies to a wide range of scientific and engineering research problems. Through its research and development efforts, the ASCR program must also advance the state of the art in HPC and accelerate the pace of scientific discovery and technological innovation. Fulfilling this promise will require significantly increased investments, as well as innovative policies and programs. This subcommittee is aware that we are making recommendations and calls for action at a time when federal resources are limited. We understand that a wide range of competing priorities must be balanced by the nation’s leaders and that there is a need to leverage resources in new ways and seek efficiencies in facilities and operations. However, we must not let these realities limit our imagination or silence our advocacy. The ASCR program is a key part of the U.S. research infrastructure and an important component of economic growth and U.S. competitiveness. ASCR has a responsibility to pursue its mission, including advanced scientific computing, applications of AI technologies, and the required advanced research facilities, with determination and enthusiasm. To fulfill the scientific enterprise’s responsibility to the nation, the ASCR program must not only develop and publish a clear vision with an associated list of goals, priorities, and recommendations but also demonstrate scientific leadership by consistently securing long-term funding. This will allow the program to build on its achievements to date, to realize its ambitious vision, and to make lasting contributions to the field.
Systems biology research spans a range of biological scales and science domains, and often requires a collaborative effort to collect and share data so that integration is possible. However, sharing data effectively is a challenging task that requires effort and alignment between collaborative partners, as well as coordination between organizations, repositories, and journals. As a community of systems biology researchers, we must get better at efficiently sharing data, and ensuring that shared data comes with the recognition and citations it deserves.
The Dark Energy Spectroscopic Instrument (DESI) completed its 5 month Survey Validation in 2021 May. Spectra of stellar and extragalactic targets from Survey Validation constitute the first major data sample from the DESI survey. This paper describes the public release of those spectra, the catalogs of derived properties, and the intermediate data products. In total, the public release includes good-quality spectral information from 466,447 objects targeted as part of the Milky Way Survey, 428,758 as part of the Bright Galaxy Survey, 227,318 as part of the Luminous Red Galaxy sample, 437,664 as part of the Emission Line Galaxy sample, and 76,079 as part of the Quasar sample. In addition, the release includes spectral information from 137,148 objects that expand the scope beyond the primary samples as part of a series of secondary programs. Here, we describe the spectral data, data quality, data products, Large-Scale Structure science catalogs, access to the data, and references that provide relevant background to using these spectra.