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NASA NTRS1975

Main principles used for building up a data bank for the long-term storage and retrieval of scientific space data

Methodological, technical, and practical organizational problems are considered of constructing a long term data bank. Data processing flowcharts are presented which are based on an analysis of the tasks which the data bank must fulfill, an estimate of the existing and predicted data flows in the processing center, and the selection of the data storage media and the forms in which the data are presented. Questions related to the use of standard and special hardware and software in the data bank are discussed. Reasons are presented for the adopted structure of the document-fact type information retrieval system which permits the mechanization and automation of various stages in the retrieval and selection of the data.

Zlotin, G. N.↗

DOE OSTI2022

The SpacePy space science package at 12 years

For over a decade, the SpacePy project has contributed open-source solutions for the production and analysis of heliophysics data and simulation results. Here we introduce SpacePy’s functionality for the scientific user and present relevant design principles. We examine recent advances and the future of SpacePy in the broader scientific Python ecosystem, concluding with some of the work that has used SpacePy.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS2014

Anticipated Changes in Conducting Scientific Data-Analysis Research in the Big-Data Era

A Big-Data environment is one that is capable of orchestrating quick-turnaround analyses involving large volumes of data for numerous simultaneous users. Based on our experiences with a prototype Big-Data analysis environment, we anticipate some important changes in research behaviors and processes while conducting scientific data-analysis research in the near future as such Big-Data environments become the mainstream. The first anticipated change will be the reduced effort and difficulty in most parts of the data management process. A Big-Data analysis environment is likely to house most of the data required for a particular research discipline along with appropriate analysis capabilities. This will reduce the need for researchers to download local copies of data. In turn, this also reduces the need for compute and storage procurement by individual researchers or groups, as well as associated maintenance and management afterwards. It is almost certain that Big-Data environments will require a different "programming language" to fully exploit the latent potential. In addition, the process of extending the environment to provide new analysis capabilities will likely be more involved than, say, compiling a piece of new or revised code.We thus anticipate that researchers will require support from dedicated organizations associated with the environment that are composed of professional software engineers and data scientists. A major benefit will likely be that such extensions are of higherquality and broader applicability than ad hoc changes by physical scientists. Another anticipated significant change is improved collaboration among the researchers using the same environment. Since the environment is homogeneous within itself, many barriers to collaboration are minimized or eliminated. For example, data and analysis algorithms can be seamlessly shared, reused and re-purposed. In conclusion, we will be able to achieve a new level of scientific productivity in the Big-Data analysis environments.

Kuo, Kwo-Sen↗

DOE OSTI2020

Software-Defined Network for End-to-end Networked Science at the Exascale

Domain science applications and workflow processes are currently forced to view the network as an opaque infrastructure into which they inject data and hope that it emerges at the destination with an acceptable Quality of Experience. There is little ability for applications to interact with the network to exchange information, negotiate performance parameters, discover expected performance metrics, or receive status/troubleshooting information in real time. The work we presen here is motivated by a vision for a new smart network and smart application ecosystem that will provide a more deterministic and interactive environment for domain science workflows. The Software-Defined Network for End-to-end Networked Science at Exascale (SENSE) system includes a model-based architecture, implementation, and deployment which enables automated end-to-end network service instantiation across administrative domains. An intent based interface allows applications to express their high-level service requirements, an intelligent orchestrator and resource control systems allow for custom tailoring of scalability and real-time responsiveness based on individual application and infrastructure operator requirements. This allows the science applications to manage the network as a first-class schedulable resource as is the current practice for instruments, compute, and storage systems. Deployment and experiments on production networks and testbeds have validated SENSE functions and performance. Emulation based testing verified the scalability needed to support research and education infrastructures. Key contributions of this work include an architecture definition, reference implementation, and deployment. This provides the basis for further innovation of smart network services to accelerate scientific discovery in the era of big data, cloud computing, machine learning and artificial intelligence.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Insufficient reporting of x-ray photoelectron spectroscopy instrumental and peak fitting parameters (metadata) in the scientific literature

This study was motivated by earlier observations. It is a systematic examination of the adequacy of reporting of information (metadata) necessary to understand x-ray photoelectron spectroscopy (XPS) data collection and data analysis in the scientific literature. The information for this study was obtained from papers published in three high-quality journals over a six-month period in 2019 and throughout 2021. Each paper was evaluated to determine whether the authors had reported (percentages of the papers properly providing the information are given in parentheses) the spectrometer (66%), fitting software (15%), x-ray source (40%), pass energy (10%), spot size (5%), synthetic peak shapes in fits (10%), backgrounds in fits (10%), whether the XPS data are shown in the main body of the paper or in the supporting information (or both), and whether fitted or unfitted spectra were shown (80% of published spectra are fit). The Shirley background is the most widely used background in XPS peak fitting. The Al Kα source is the most widely used x-ray source for XPS data collection. CASAXPS is the most widely used fitting program for XPS data analysis. Further, there is good agreement between the results gathered during the two years of our survey. There are some hints the situation may be improving. This study also provides a list of the information/parameters that should be reported when XPS is performed.

47 OTHER INSTRUMENTATION↗

DOE OSTI2014

Advanced Simulation & Computing FY15 Implementation Plan Volume 2, Rev. 0.5

The Stockpile Stewardship Program (SSP) is a single, highly integrated technical program for maintaining the surety and reliability of the U.S. nuclear stockpile. The SSP uses nuclear test data, computational modeling and simulation, and experimental facilities to advance understanding of nuclear weapons. It includes stockpile surveillance, experimental research, development and engineering programs, and an appropriately scaled production capability to support stockpile requirements. This integrated national program requires the continued use of experimental facilities and programs, and the computational enhancements to support these programs. The Advanced Simulation and Computing Program (ASC) is a cornerstone of the SSP, providing simulation capabilities and computational resources that support annual stockpile assessment and certification, study advanced nuclear weapons design and manufacturing processes, analyze accident scenarios and weapons aging, and provide the tools to enable stockpile Life Extension Programs (LEPs) and the resolution of Significant Finding Investigations (SFIs). This requires a balance of resource, including technical staff, hardware, simulation software, and computer science solutions. As the program approaches the end of its second decade, ASC is intently focused on increasing predictive capabilities in a three-dimensional (3D) simulation environment while maintaining support to the SSP. The program continues to improve its unique tools for solving progressively more difficult stockpile problems (sufficient resolution, dimensionality, and scientific details), quantify critical margins and uncertainties, and resolve increasingly difficult analyses needed for the SSP. Where possible, the program also enables the use of high-performance simulation and computing tools to address broader national security needs, such as foreign nuclear weapon assessments and counternuclear terrorism.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2015

Advanced Simulation and Computing Implementation Plan, FY15, Rev. 1

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Computational Analysis of Coupled Geoscience Processes in Fractured and Deformable Media

Prediction of flow, transport, and deformation in fractured and porous media is critical to improving our scientific understanding of coupled thermal-hydrological-mechanical processes related to subsurface energy storage and recovery, nonproliferation, and nuclear waste storage. Especially, earth rock response to changes in pressure and stress has remained a critically challenging task. In this work, we advance computational capabilities for coupled processes in fractured and porous media using Sandia Sierra Multiphysics software through verification and validation problems such as poro-elasticity, elasto-plasticity and thermo-poroelasticity. We apply Sierra software for geologic carbon storage, fluid injection/extraction, and enhanced geothermal systems. We also significantly improve machine learning approaches through latent space and self-supervised learning. Additionally, we develop new experimental technique for evaluating dynamics of compacted soils at an intermediate scale. Overall, this project will enable us to systematically measure and control the earth system response to changes in stress and pressure due to subsurface energy activities.

58 GEOSCIENCES↗

DOE OSTI2023

Artificial Intelligence-Enhanced, Multi-Level, Modular System Design

As Moore’s Law and Dennard Scaling come to an end, it is becoming increasingly important to develop non-von Neumann computing architectures that can perform low-power computing in the domains of scientific computing, artificial intelligence, embedded systems, and edge computing. Next-generation computing technologies, such as neuromorphic computing and quantum computing, have the potential to revolutionize computing. However, in order to make progress in these fields, it is necessary to fundamentally change the current computing paradigm by codesigning systems across all system level, from materials to software. Because skilled labor is limited in the field of next-generation computing, we are developing artificial intelligence-enhanced tools to automate the codesign and co-discovery of next-generation computers. Here, we develop a method called Modular and Multi-level MAchine Learning (MAMMAL) which is able to perform analog codesign and co-discovery across multiple system levels, spanning devices to circuits. We prototype MAMMAL by using it to design simple passive analog low-pass filters. We also explore methods to incorporate uncertainty quantification into MAMMAL and to accelerate MAMMAL by using emerging technologies, such as crossbar arrays. Ultimately, we believe that MAMMAL will enable rapid progress in developing next-generation computers by automating the codesign and co-discovery of electronic systems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Integrate Latimer Controls' Solution into RTAC (CRADA Final Report, CRD-23-24672)

Latimer Controls, Inc. was awarded two vouchers under the Department of Energy's American-Made Solar Prize Round 6 to conduct collaborative research at a national laboratory. The National Renewable Energy Laboratory (NREL) was selected as a partner to assist Latimer Controls in the performance evaluation of its photovoltaic (PV) control software. This collaboration focuses on developing a hardware-in-the-loop (HIL) testbed at NREL, which will be used to test and validate the Latimer PV control technology in a realistic yet de-risked environment. Both Latimer and NREL teams will work together to analyze the collected test data, derive insights, and disseminate the scientific findings. Recent studies underscore the potential of solar energy as a zero-marginal-cost and zero-emission flexibility resource within the bulk power system, particularly when integrated with advanced control systems. To enhance the performance of such systems, Latimer Controls has developed leading-edge technologies, including machine learning (ML) algorithms and hierarchical inverter set-point allocation methods. These innovations are designed to estimate the operational headroom of large PV plants for grid integration and control. However, comprehensive validation under real-world conditions remains necessary. To address this gap, the concurrent CRADA project proposes the real-world application and validation of the Latimer Control solution within a HIL environment. Initially, the Latimer algorithm was developed and tested within MATLAB Simulink, a platform suitable for research-level simulations and iterative development. However, transitioning this technology to a real solar site as an industry-ready solution necessitates implementation in a format compatible with widely used solar power plant controllers. In this additional CRADA work, the MATLAB Simulink-based logic will be translated into Structured Text, a programming language compliant with IEC 61131 standards, which is commonly used for custom logic implementations in industry-leading programmable logic controllers (PLCs), such as the Schweitzer SEL real-time automation controller (RTAC). This transition will facilitate the deployment of the Latimer Control solution in real-world solar power plants, thereby advancing the technology towards commercialization.

14 SOLAR ENERGY↗

NASA NTRS1977

IPL Processing of the Viking Orbiter Images of Mars

The Viking orbiter cameras returned over 9000 images of Mars during the 6-month nominal mission. Digital image processing was required to produce products suitable for quantitative and qualitative scientific interpretation. Processing included the production of surface elevation data using computer stereophotogrammetric techniques, crater classification based on geomorphological characteristics, and the generation of color products using multiple black-and-white images recorded through spectral filters. The Image Processing Laboratory of the Jet Propulsion Laboratory was responsible for the design, development, and application of the software required to produce these 'second-order' products.

Ruiz, R. M.↗

NASA NTRSDate not supplied

Developing a Vision for Heliophysics Infrastructure: The LIKED Resource and the DIARieS Ecosystem

Heliophysics data and computational infrastracture are not equipped for 21st science, suffering from holes in the know-how to build better systems. Without a clear vision, efforts to improve the infrastructure have been incremental and incoherent. This poster presents both the vision and the technology required: an online LIbrary KnowledgE and Discovery (LIKED) resource for discovering and implementing knowledge, data, and infrastructure resources; and an online analysis ecosystem to simplify Discovery, Implementation, Analysis, Reproducibility, and Sharing (DIARieS) of scientific results and environments. The LIKED and DIARieS solutions adopt FAIR data principles and the best practices from the budding field of open science. The proposed new infrastructure components will close many of the current gaps in heliophysics’ infrastructure, such as the ability to search for data and knowledge by phenomenon across domains, and to find software and examples relevant to the desired data set (including model data). Further, these components will enable community members to more efficiently use the resources already present and improve upon the content via a community-curated and trusted library. Combining these solutions lowers the barriers to heliophysics resources for all, increasing the return on our investments. Finally, the structure behind these ideas are topic-agnostic, so they are fully extensible to other fields, leading to invaluable connections to other disciplines. Just as with the development and construction of a long-term satellite mission, we must work together as a community to build a vision of the infrastructure that will most benefit the community, and then collaborate to construct, assemble, and test all the necessary pieces individually and as a unit. Our purpose in presenting this work is to not only describe the proposed vision, but also to gather feedback from the community on this topic.

infrastructure↗

DOE OSTI2018

Forming IDEAS: Interactive Data Exploration & Analysis System

Modern cyber security operations collect an enormous amount of logging and alerting data. While analysts have the ability to query and compute simple statistics and plots from their data, current analytical tools are too simple to admit deep understanding. To detect advanced and novel attacks, analysts turn to manual investigations. While commonplace, current investigations are time-consuming, intuition-based, and proving insufficient. Our hypothesis is that arming the analyst with easy-to-use data science tools will increase their work efficiency, provide them with the ability to resolve hypotheses with scientific inquiry of their data, and support their decisions with evidence over intuition. To this end, we present our work to build IDEAS (Interactive Data Exploration and Analysis System). We present three real-world use-cases that drive the system design from the algorithmic capabilities to the user interface. Finally, a modular and scalable software architecture is discussed along with plans for our pilot deployment with a security operation command.

Bridges, Robert↗

DOE OSTI2015

HPCCloud: A Cloud/Web-Based Simulation Environment

Advanced modeling and simulation has enabled the design of a variety of innovative products and the analysis of numerous complex phenomenon. There are significant barriers to widespread adoption of these tools. In particular, advanced modeling and simulation: (1) is considered complex to use, (2) needs in-house expertise, and (3) requires high capital costs. In this paper, we describe the development of an end-to-end, advanced modeling and simulation cloud platform that encapsulates best practices for scientific computing in the cloud, and demonstrate using Hydra-TH as a prototypical application. As an alternative to traditional advanced modeling and simulation workflows, our Web-based approach simplifies the processes, decreases the need for in-house computational science and engineering experts, and lowers the capital investments. In addition to providing significantly improved, intuitive software, the environment offers reproducible workflows where the full lifecycle of data from input to final analyzed results can be saved, shared, and even published.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Optimizing I/O Performance of HPC Applications with Autotuning

Parallel Input output is an essential component of modern high-performance computing (HPC). Obtaining good I/O performance for a broad range of applications on diverse HPC platforms is a major challenge, in part, because of complex inter dependencies between I/O middleware and hardware. The parallel file system and I/O middleware layers all offer optimization parameters that can, in theory, result in better I/O performance. Unfortunately, the right combination of parameters is highly dependent on the application, HPC platform, problem size, and concurrency. Scientific application developers do not have the time or expertise to take on the substantial burden of identifying good parameters for each problem configuration. They resort to using system defaults, a choice that frequently results in poor I/O performance. We expect this problem to be compounded on exascale-class machines, which will likely have a deeper software stack with hierarchically arranged hardware resources. We present as a solution to this problem an autotuning system for optimizing I/O performance, I/O performance modeling, I/O tuning, and I/O patterns. We demonstrate the value of this framework across several HPC platforms and applications at scale.

Behzad, Babak↗

NASA NTRS1987

Maintaining an expert system for the Hubble Space Telescope ground support

The transformation portion of the Hubble Space Telescope (HST) Proposal Entry Processor System converts astronomer-oriented description of a scientific observing program into a detailed description of the parameters needed for planning and scheduling. The transformation system is one of a very few rulebased expert systems that has ever entered an operational phase. The day to day operations of the system and its rulebase are no longer the responsibility of the original developer. As a result, software engineering properties of the rulebased approach become more important. Maintenance issues associated with the coupling of rules within a rulebased system are discussed and a method is offered for partitioning a rulebase so that the amount of knowledge needed to modify the rulebase is minimized. This method is also used to develop a measure of the coupling strength of the rulebase.

Lindenmayer, Kelly↗

NASA NTRS1985

Preliminary plan for a Shuttle Coherent Atmospheric Lidar Experiment (SCALE)

A study has been completed to define a Shuttle experiment that solves the most crucial scientific and engineering problems involved in building a satellite Doppler wind profiler for making global wind measurements. The study includes: (1) a laser study to determine the feasibility of using the existing NOAA Windvan laser in the Space Shuttle spacecraft; (2) a preliminary optics and telescope design; (3) an accommodations study including power, weight, thermal, and control system requirements; and (4) a flight trajectory and operations plan designed to accomplish the required scientific and engineering goals. The experiment will provide much-needed data on the global distribution of atmospheric aerosols and demonstrate the technique of making wind measurements from space, including scanning the laser beam and interpreting the data. Engineering accomplishments will include space qualification of the laser, development of signal processing and lag angle compensation hardware and software, and telescope and optics design. All of the results of this limited Spacelab experiment will be directly applicable to a complete satellite wind profiler for the Earth Observation System/Space Station or other free-flying satellite.

Fitzjarrald, D.↗

NASA NTRS2002

Internet Voice Distribution System (IVoDS) Utilization in Remote Payload Operations

Due to limited crew availability to support science and the large number of experiments to be operated simultaneously, telescience is key to a successful International Space Station (ISS) science program. Crew, operations personnel at NASA centers, and researchers at universities and companies around the world must work closely together to perform scientific experiments on-board ISS. NASA has initiated use of Voice over Internet Protocol (VoIP) to supplement the existing HVoDS mission voice communications system used by researchers. The Internet Voice Distribution System (IVoDS) connects researchers to mission support "loops" or conferences via Internet Protocol networks such as the high-speed Internet 2. Researchers use IVoDS software on personal computers to talk with operations personnel at NASA centers. IVoDS also has the capability, if authorized, to allow researchers to communicate with the ISS crew during experiment operations. NODS was developed by Marshall Space Flight Center with contractors A2 Technology, Inc. FVC, Lockheed- Martin, and VoIP Group. IVoDS is currently undergoing field-testing with full deployment for up to 50 simultaneous users expected in 2002. Research is currently being performed to take full advantage of the digital world - the Personal Computer and Internet Protocol networks - to qualitatively enhance communications among ISS operations personnel. In addition to the current voice capability, video and data-sharing capabilities are being investigated. Major obstacles being addressed include network bandwidth capacity and strict security requirements. Techniques being investigated to reduce and overcome these obstacles include emerging audio-video protocols and network technology including multicast and quality-of-service.

Best, Susan↗