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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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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Estimating Lossy Compressibility of Scientific Data Using Deep Neural Networks

Simulation based scientific applications generate increasingly large amounts of data on high-performance computing (HPC) systems. To allow data to be stored and analyzed efficiently, data compression is often utilized to reduce the volume and velocity of data. However, a question often raised by domain scientists is the level of compression that can be expected so that they can make more informed decisions, balancing between accuracy and performance. In this letter, we propose a deep neural network based approach for estimating the compressibility of scientific data. To train the neural network, we build both general features as well as compressor-specific features so that the characteristics of both data and lossy compressors are captured in training. Our approach is demonstrated to outperform a prior analytical model as well as a sampling based approach in the case of a biased estimation, i.e., for SZ. However, for the unbiased estimation (i.e., ZFP), the sampling based approach yields the best accuracy, despite the high overhead involved in sampling the target dataset.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Two-level overlapping additive Schwarz preconditioner for training scientific machine learning applications

In this work we introduce a novel two-level overlapping additive Schwarz preconditioner for accelerating the training of scientific machine learning applications. The design of the proposed preconditioner is motivated by the nonlinear two-level overlapping additive Schwarz preconditioner. The neural network parameters are decomposed into groups (subdomains) with overlapping regions. In addition, the network’s feed-forward structure is indirectly imposed through a novel subdomain-wise synchronization strategy and a coarse-level training step. Through a series of numerical experiments, which consider physicsinformed neural networks and operator learning approaches, we demonstrate that the proposed two-level preconditioner significantly speeds up the convergence of the standard (LBFGS) optimizer while also yielding more accurate machine learning models. Moreover, the devised preconditioner is designed to take advantage of model-parallel computations, which can further reduce the training time.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Advancing river corridor science beyond disciplinary boundaries with an inductive approach to catalyse hypothesis generation

Abstract A unified conceptual framework for river corridors requires synthesis of diverse site‐, method‐ and discipline‐specific findings. The river research community has developed a substantial body of observations and process‐specific interpretations, but we are still lacking a comprehensive model to distill this knowledge into fundamental transferable concepts. We confront the challenge of how a discipline classically organized around the deductive model of systematically collecting of site‐, scale‐, and mechanism‐specific observations begins the process of synthesis. Machine learning is particularly well‐suited to inductive generation of hypotheses. In this study, we prototype an inductive approach to holistic synthesis of river corridor observations, using support vector machine regression to identify potential couplings or feedbacks that would not necessarily arise from classical approaches. This approach generated 672 relationships linking a suite of 157 variables each measured at 62 locations in a fifth order river network. Eighty four percent of these relationships have not been previously investigated, and representing potential (hypothetical) process connections. We document relationships consistent with current understanding including hydrologic exchange processes, microbial ecology, and the River Continuum Concept, supporting that the approach can identify meaningful relationships in the data. Moreover, we highlight examples of two novel research questions that stem from interpretation of inductively‐generated relationships. This study demonstrates the implementation of machine learning to sieve complex data sets and identify a small set of candidate relationships that warrant further study, including data types not commonly measured together. This structured approach complements traditional modes of inquiry, which are often limited by disciplinary perspectives and favour the careful pursuit of parsimony. Finally, we emphasize that this approach should be viewed as a complement to, rather than in place of, more traditional, deductive approaches to scientific discovery.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Room-Temperature Optical Detection of 14 CO 2 below the Natural Abundance with Two-Color Cavity Ring-Down Spectroscopy

Radiocarbon’s natural production, radiative decay, and isotopic rarity make it a unique tool to probe carbonaceous systems in the life and earth sciences. However, the difficulty of current radiocarbon ( 14 C) detection methods limits scientific adoption. Here, two-color cavity ring-down spectroscopy detects 14 CO 2 in room-temperature samples with an accuracy of one-tenth the natural abundance in 3 min. The intracavity pump–probe measurement uses two cavity-enhanced lasers to cancel out cavity ring-down rate fluctuations and strong one-photon absorption interference (>10 000 1/s) from hot-band transitions of CO 2 isotopologues. Selective, room-temperature detection of small 14 CO 2 absorption signals (<1 1/s) reduces the technical and operational burdens for cavity-enhanced measurements of radiocarbon, which can benefit a wide range of applications like biomedical research and field-detection of combusted fossil fuels.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

A reusable neural network pipeline for unidirectional fiber segmentation

Abstract Fiber-reinforced ceramic-matrix composites are advanced, temperature resistant materials with applications in aerospace engineering. Their analysis involves the detection and separation of fibers, embedded in a fiber bed, from an imaged sample. Currently, this is mostly done using semi-supervised techniques. Here, we present an open, automated computational pipeline to detect fibers from a tomographically reconstructed X-ray volume. We apply our pipeline to a non-trivial dataset by Larson et al . To separate the fibers in these samples, we tested four different architectures of convolutional neural networks. When comparing our neural network approach to a semi-supervised one, we obtained Dice and Matthews coefficients reaching up to 98%, showing that these automated approaches can match human-supervised methods, in some cases separating fibers that human-curated algorithms could not find. The software written for this project is open source, released under a permissive license, and can be freely adapted and re-used in other domains.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Efficient learning of accurate surrogates for simulations of complex systems

Machine learning methods are increasingly deployed to construct surrogate models for complex physical systems at a reduced computational cost. However, the predictive capability of these surrogates degrades in the presence of noisy, sparse or dynamic data. Here, we introduce an online learning method empowered by optimizer-driven sampling that has two advantages over current approaches: it ensures that all local extrema (including endpoints) of the model response surface are included in the training data, and it employs a continuous validation and update process in which surrogates undergo retraining when their performance falls below a validity threshold. We find, using benchmark functions, that optimizer-directed sampling generally outperforms traditional sampling methods in terms of accuracy around local extrema even when the scoring metric is biased towards assessing overall accuracy. Finally, the application to dense nuclear matter demonstrates that highly accurate surrogates for a nuclear equation-of-state model can be reliably autogenerated from expensive calculations using few model evaluations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Characterization and identification of HPC applications at leadership computing facility

High Performance Computing (HPC) is an important method for scientific discovery via large-scale simulation, data analysis, or artificial intelligence. Leadership-class supercomputers are expensive, but essential to run large HPC applications. The Petascale era of supercomputers began in 2008, with the first machines achieving performance in excess of one petaflops, and with the advent of new supercomputers in 2021 (e.g., Aurora, Frontier), the Exascale era will soon begin. However, the high theoretical computing capability (i.e., peak FLOPS) of a machine is not the only meaningful target when designing a supercomputer, as the resources demand of applications varies. A deep understanding of the characterization of applications that run on a leadership supercomputer is one of the most important ways for planning its design, development and operation. In order to improve our understanding of HPC applications, user demands and resource usage characteristics, we perform correlative analysis of various logs for different subsystems of a leadership supercomputer. This analysis reveals surprising, sometimes counter-intuitive patterns, which, in some cases, conflicts with existing assumptions, and have important implications for future system designs as well as supercomputer operations. For example, our analysis shows that while the applications spend significant time on MPI, most applications spend very little time on file I/O. Combined analysis of hardware event logs and task failure logs show that the probability of a hardware FATAL event causing task failure is low. Combined analysis of control system logs and file I/O logs reveals that pure POSIX I/O is used more widely than higher level parallel I/O. Based on holistic insights of the application gained through combined and co-analysis of multiple logs from different perspectives and general intuition, we engineer features to "fingerprint" HPC applications. We use t-SNE (a machine learning technique for dimensionality reduction) to validate the explainability of our features and finally train machine learning models to identify HPC applications or group those with similar characteristic. To the best of our knowledge, this is the first work that combines logs on file I/O, computing, and inter-node communication for insightful analysis of HPC applications in production.

Liu, Zhengchun↗
DOE OSTI2020

scisample

A package that implements a number of parameter sampling methods for scientific computing.

Krenn, ChristopherR.↗
DOE OSTI2023

Robust Machine Learning

UQ4ML is a code repository for a set of tools for the development of robust machine learning methods, uncertainty quantification and explainability of machine learning methods. The goal of these tools is to develop more robust and statistically rigorous machine learning methods for scientific applications. These tools are developed in Python, a high-level programming language that takes advantage of the Python ecosystem of high-quality open-source packages for machine learning.

Oyen, Diane↗
NASA NTRS1972

Comparative evolution of the inverse problems (Introduction to an interdisciplinary study of the inverse problems)

The progressive realization of the consequences of nonuniqueness imply an evolution of both the methods and the centers of interest in inverse problems. This evolution is schematically described together with the various mathematical methods used. A comparative description is given of inverse methods in scientific research, with examples taken from mathematics, quantum and classical physics, seismology, transport theory, radiative transfer, electromagnetic scattering, electrocardiology, etc. It is hoped that this paper will pave the way for an interdisciplinary study of inverse problems.

Sabatier, P. C.↗
NASA NTRS1976

Development of thermal control methods for specialized components and scientific instruments at very low temperatures (follow-on)

Many payloads currently proposed to be flown by the space shuttle system require long-duration cooling in the 3 to 200 K temperature range. Common requirements also exist for certain DOD payloads. Parametric design and optimization studies are reported for multistage and diode heat pipe radiator systems designed to operate in this temperature range. Also optimized are ground test systems for two long-life passive thermal control concepts operating under specified space environmental conditions. The ground test systems evaluated are ultimately intended to evolve into flight test qualification prototypes for early shuttle flights.

Wright, J. P.↗
NASA NTRS1983

International Halley watch amateur observers' manual for scientific comet studies. Part 1: Methods

The International Halley Watch is described as well as comets and observing techniques. Information on periodic Comet Halley's apparition for its 1986 perihelion passage is provided. Instructions are given for observation projects valuable to the International Halley Watch in six areas of study: (1) visual observations; (2) photography; (3) astrometry; (4) spectroscopic observations; (5) photoelectric photometry; and (6) meteor observations.

Edberg, S. J.↗
NASA NTRS1985

Observing the earth radiation budget from satellites - Past, present, and a look to the future

Satellite measurements of the radiative exchange between the planet earth and space have been the objective of many experiments since the beginning of the space age in the late 1950's. The on-going mission of the Earth Radiation Budget (ERB) experiments has been and will be to consider flight hardware, data handling and scientific analysis methods in a single design strategy. Research and development on observational data has produced an analysis model of errors associated with ERB measurement systems on polar satellites. Results show that the variability of reflected solar radiation from changing meteorology dominates measurement uncertainties. As an application, model calculations demonstrate that measurement requirements for the verification of climate models may be satisfied with observations from one polar satellite, provided there is information on diurnal variations of the radiation budget from the ERBE mission.

House, F. B.↗
NASA NTRS2021

NASA Ames Fatigue Countermeasures Laboratory and AA FRMS - A Working Relationship

American Airlines and NASA Ames Research Center have a long-standing partnership to conduct human factors fatigue research in airline operations. Since 2016, using a mechanism known as a Space Act Agreement, NASA Ames’ Fatigue Countermeasures Laboratory has worked with the American’s Fatigue Risk Management team to gather sleep and alertness data from volunteer flight crew members. Operations studied include long-range transpacific flights, timing of inflight rest breaks during augmented flights, and rates of acclimation related to flights making theater changes. This mutually beneficial collaborative working arrangement allows American to conduct operations of specific interest under the FAA’s Alternative Means of Compliance process and provides NASA with a means to gather and analyze data that can be used to inform operational safety-related decisions. Earlier this year, results from the study of inflight rest breaks was published in the journal Aerospace Medicine and Human Performance. A total of 500 American pilots responded to a survey with landing crew reporting more and better-quality sleep during break 2 than break 3. Subsequent ratings of sleepiness and alertness at TOD were significantly better for crew who used break 2. Findings from this study were reported to the FAA leading to an inflight fatigue mitigation capability for many pilots in the industry. An upcoming study is to evaluate workload and alertness levels during Caribbean Turn (DFW-SJO-DFW) operations. For this study, the FRMS team and NASA will ask volunteers to collect data on sleep, workload, performance, and alertness using scientifically valid methods The information gained in this study will help inform how workload and fatigue interact with duty duration during short-haul, daytime operations.

in-flight rest↗
NASA NTRS2021

Power Operations of the Mars Exploration Rovers

The rovers of the National Aeronautics and Space Administration’s (NASA) Mars Exploration Rovers (MER) project, Spirit (MER-A) and Opportunity (MER-B), safely landed on the surface of Mars three weeks apart during January 2004. Spirit and Opportunity were built and operated by the Jet Propulsion Laboratory (JPL), which is managed by the California Institute of Technology (Caltech) for NASA. Spirit landed at Gusev Crater, 14.8 degrees south of the equator, and operated continuously on the surface of Mars from January 4, 2004 until last contact from Spirit on March 22, 2010. Opportunity landed at Meridiani Planum, a location 2.5 degrees south of the Martian equator. Opportunity operated continuously on the Martian surface from January 25, 2004 until the last received transmission from Opportunity on June 10, 2018. The goal of the MER project was to determine if Mars ever had a habitable environment, in particular, if it ever had water. During their missions, both Spirit and Opportunity found evidence that liquid water once flowed on the surface of Mars. Both Spirit and Opportunity used a 1.33 m2 triple-junction solar array as their power sources. Based on observations of the original Mars rover, the Sojourner rover of the Mars Pathfinder mission that landed on Mars on July 4, 1997, the expectation was that the Martian dust would rapidly accumulate on the solar arrays of Spirit and Opportunity, and that the rovers would not have enough energy to continue operations after 90 Martian days (sols). Instead, due in part to lower dust accumulation rates than expected and numerous dust cleaning events, the Spirit and Opportunity rovers continued to operate on the Martian surface for over 2000 sols (MER-A) and 5000 sols (MER-B), respectively. During this time, the rovers experienced multiple Martian winters and several dust storms. Because the sources of solar array energy loss were known, the solar array energy output offered a method to scientifically estimate the loading and aeolian removal of dust from the solar arrays each sol. The MER Power operations team called this value that they calculated the solar array Dust Factor (DF). Dust Factor was defined as the fraction of sunlight that penetrates the accumulated dust on the surface of the solar array. A Dust Factor of 1.0 would indicate that the solar array was perfectly clean. A Dust Factor of 0.6 would indicate that only 60% of the available sunlight was able to penetrate the accumulated dust on the solar arrays. The MER Power subsystem operations team used the Multi-Mission Power Analysis Tool (MMPAT) to perform these Dust Factor calculations. The MMPAT software tool modeled the behavior of the solar arrays and the batteries as they interacted with the spacecraft power loads over the mission timeline. MMPAT also had knowledge (through telemetry and user inputs) of telemetered Power subsystem voltages and currents, atmospheric opacity (Tau), rover surface location, rover attitude, the planetary tilt and distance of Mars from the sun based on day of year, the instantaneous elevation of the sun based on time of day, temperatures (internal and external), terrain masking, and shadowing (due to the camera mast and antennas). Once all of the known sources of array energy loss are accounted, the remaining difference between the expected array energy and the actual array energy determines the solar array Dust Factor. The assumptions made while determining the Dust Factor are 1) that the single measured atmospheric opacity value (Tau) is constant over the course of the entire sol, 2) there is no measurable solar cell degradation, 3) there are no shorted strings and therefore, 4) all unexpected solar array energy losses are due to accumulated dust on the solar array. Although it cannot provide an absolute measure of dust loading, the determination of solar array Dust Factor provides a useful way of tracking dust accumulation and aeolian dust removal trends on Martian spacecraft. Any spacecraft on the Martian surface is vulnerable to dust, especially solar-powered spacecraft. The Spirit and Opportunity rovers were operational on the Martian surface for much longer than expected due in part to aeolian removal of dust from their solar arrays. The first few dust removal events were a pleasant surprise to the MER operations teams; however, over time a pattern began to arise. In over three Mars Years on the surface, the Power operations team tracked the solar array Dust Factor at Gusev Crater (location of MER-A) and observed that there were several significant dust removal and deposition events in Mars Year (MY) 27, even in the absence of a large dust storm. In MY 28 at Gusev Crater, the large atmospheric opacity (Tau) increase lagged significant dust removal events. Overall at Gusev Crater, there was a pattern of steady dust accumulation on the solar arrays, with a small number of significant dust cleaning events. At Meridiani Planum, where Opportunity rover operated for over seven Mars Years (late MY 26 to mid MY 34), a clear and consistent pattern of dust movement emerged. Meridiani Planum had a predictable, seasonally dependent pattern of gradual and continuous dust accumulation and removal. In summary, this paper explains the reasons for the development of the solar array Dust Factor and how it was used in mission operations. In particular, this paper describes the MMPAT software package, how it models array energy, including the important assumptions, model inputs and sources of error. And finally, this paper will show how the calculated solar array Dust Factor was used at Gusev Crater and Meridiani Planum to predict dust accumulation rates and weather patterns, and the importance of this generated data set for current and future solar- powered missions to Mars, such as the InSight lander and the planned Mars Sample Return rover.

Chin, Keith B.↗