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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Basic Research Needs for Innovation and Discovery of Transformative Experimental Tools

The scientific discoveries that expand the frontiers of human understanding, and that lead to the innovations of our technological world, require scientific tools and instrumentation to enable observation and manipulation of the physical world. As science advances, so too must its tools; the quest for deeper scientific insights and the drive to control chemistry and materials at the atomic and molecular levels require increasingly powerful and sophisticated instruments. A secure energy future requires technologies that use existing resources more efficiently, harness renewable resources, and efficiently store energy. The urgent demand for new energy technologies has ushered in a new era in scientific pursuit to decipher the complexity found at the core of chemical and materials processes, as articulated by the basic energy science research community in its series of Basic Research Needs (BRN) workshops. Historically, novel experimental tools and methods have been foundational in both scientific and technological advances — ranging from high-resolution microscopes that “see” atomic structures, to lithography that has enabled advances in semiconductors and computing. Today, expanding the frontiers of basic research requires new generations of instrumentation to reveal the intricacies of complex materials and chemical systems; energy systems in realistic working environments; and systems that are dynamical, far from equilibrium, and extremely heterogeneous. A concerted effort to invent, design, and build scientific instrumentation will enable new scientific breakthroughs and transformative technologies to address the most pressing energy challenges of the 21st century. To identify the highest priorities for the instrumentation innovation and development needed to address grand challenges in energy sciences, the U.S. Department of Energy’s Office of Basic Energy Sciences sponsored a workshop entitled, “Basic Research Needs for Innovation and Discovery of Transformative Experimental Tools” on June 1–3, 2016 near Washington, D.C. The workshop was attended by approximately 100 leading national and international scientific experts representing areas of basic energy sciences in chemistry, materials, physics, and biology, and included a mix of experimentalists and theorists.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Analysis and Optimization of Seismo-Acoustic Monitoring Networks with Bayesian Optimal Experimental Design

The Bayesian optimal experimental design (OED) problem seeks to identify data, sensor configurations, or experiments which can optimally reduce uncertainty. The goal of OED is to find an experiment that maximizes the expected information gain (EIG) about quantities of interest given prior knowledge about expected data. Therefore, within the context of seismic monitoring, we can use Bayesian OED to configure sensor networks by choosing sensor locations, types, and fidelity in order to improve our ability to identify and locate seismic sources. In this work, we develop the framework necessary to use Bayesian OED to optimize the ability to locate seismic events from arrival time data of detected seismic phases. In order to do utilize Bayesian OED we must develop four elements:1. A likelihood function that describes the uncertainty of detection and travel times; 2. A Bayesian solver that takes a prior and likelihood to identify the posterior; 3. An algorithm to compute EIG; and, 4. An optimizer that finds a sensor network which maximizes EIG. Once we have developed this framework, we can explore many relevant questions to monitoring such as: how and what multiphenomenology data can be used to optimally reduce uncertainty, how to trade off sensor fidelity and earth model uncertainty, and how sensor types, number, and locations influence uncertainty

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

mystic : software for autonomous discovery and design under uncertainty

Throughout the diverse range of science and engineering applications, there is a growing desire to develop computational methods that can reliably predict the behavior of complex systems. Specifically, there is a strategic need for tools that can robustly forecast the behavior of complex physical systems, where data may be high-dimensional, noisy, or sparse, and models of the system may be time-dependent or include uncertainty. We use mystic to build tools that leverage statistical learning, physics-informed learning, and active learning in the efficient generation of reliably predictive surrogates for complex physical systems. mystic is a robust, proven, open-source optimization and uncertainty quantification toolkit with over a decade of use in the design and optimization of neutron instrumentation, solar-powered drones, and gasguns, and in iterative tuning of models for Raman spectroscopy and elastoplastic materials strength. Recent developments have focused on automated learning of statistically robust surrogates under uncertainty, with applications in materials in extreme environments, nanostructures, materials simulations and strength models, and the failure of shielding under particle radiation. In 2020, McKerns demonstrated active learning of optimally robust surrogates with respect to new simulated data for molecular dynamics simulations of materials mixing in warm dense matter, and is currently applying active learning to the automated steering of particle accelerator beams and the optimal design and control of quantum optical sensor instrumentation.

42 ENGINEERING↗
DOE OSTI2013

Power consumption monitoring using additional monitoring device

Today, emphasis is placed on reducing power consumption. Computers are large consumers; therefore it is important to know the total consumption of computing systems. Since their optimal functioning requires quite strict environmental conditions, without much variation in temperature and humidity, reducing energy consumption cannot be made without monitoring environmental parameters. Thus, the present work uses a multifunctional electric meter UPT 210 for power consumption monitoring. Two applications were developed: software which carries meter readings provided by electronic and programming facilitates remote device and a device for temperature monitoring and control. Following temperature variations that occur both in the cooling system, as well as the ambient, can reduce energy consumption. For this purpose, some air conditioning units or some computers are stopped in different time slots. These intervals were set so that the economy is high, but the work's Datacenter is not disturbed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Nevada National Security Site Site-Directed Research and Development FY 2014 Annual Report

The reports contained herein are for project activities that occurred from October 2013 through September 2014. Project life cycle is indicated under the title as well as the original proposal number (in the following format: site abbreviation--ID #--originating fiscal year; e.g., STL-03-14). Each of the reports describes in detail the discoveries, achievements, and challenges encountered by our principal investigators. As SDRD, by definition, invests in “high-risk” and hopefully “high-payoff” research, the element of uncertainty is inherent. While many of our efforts are “successful” and result in positive outcomes or technology utilization, some fall short of expectations, but cannot be construed as “failure” in the negative sense. The latter is a natural and valid part of the process of advanced research and often leads to unforeseen new pathways to future discovery. Regardless, either result advances our knowledge base and increases our ability to identify solutions and/or avoid costly and unwarranted paths for future challenges. In summary, the SDRD program continues to provide an unfettered mechanism for innovation that returns multifold to our customers, to national security, and to the general public. The program is a vibrant R&D innovation engine, benefited by its discretionary pedigree, enhanced mission spectrum, committed resources, and sound competitiveness to yield maximum taxpayer benefit. The 25 projects described exemplify the creativity and ability of a diverse scientific and engineering talent base. The efforts also showcase an impressive capability and resource that can be brought to find solutions to a broad array of technology needs and applications relevant to the NNSS mission and national security. Further SDRD performance metrics can be found in the appendix at the end of this report.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Parallel sorting algorithm classification: is manual instrumentation necessary?

Understanding parallel algorithms is crucial for accelerating scientific simulations on complex, distributed memory, high-performance computers. Modern algorithm classification approaches learn semantics directly from source code to differentiate between algorithms, however, accessing source code is not always possible. We can learn about parallel algorithms from observing their performance, as programs running the same algorithms and using the same hardware should exhibit similar performance characteristics. We present an approach to learn algorithm classes from parallel performance data directly in order to classify algorithms without access to the source code. We extend previous work to enable classifying parallel sorting algorithms using automatic instrumentation instead of requiring manual region annotations in the source code. In this work, we design and demonstrate a study for classification of parallel sorting algorithms using parallel performance data collected from automatic instrumentation, and evaluate the performance of our new methodology on classification. We leverage Caliper to collect the performance data, Thicket for our exploratory data analysis (EDA), and PyTorch and Scikit-learn to evaluate the effectiveness of random forests, support vector machines (SVMs), decision trees, neural networks, and logistic regressions on parallel performance data. Additionally, we study noise in parallel performance data, whether the removal of noise and pre-processing of the data is necessary to accurately classify parallel sorting algorithms, and determine the effectiveness of features created from performance data. In conclusion, we demonstrate classification accuracy for these five different models of up to 97.7% across four different parallel algorithm classes.

Algorithm Classification↗
DOE OSTI2022

Enabling Modular Autonomous Feedback‐Loops in Materials Science through Hierarchical Experimental Laboratory Automation and Orchestration

Abstract Materials acceleration platforms (MAPs) operate on the paradigm of integrating combinatorial synthesis, high‐throughput characterization, automatic analysis, and machine learning. Within a MAP, one or multiple autonomous feedback loops may aim to optimize materials for certain functional properties or to generate new insights. The scope of a given experiment campaign is defined by the range of experiment and analysis actions that are integrated into the experiment framework. Herein, the authors present a method for integrating many actions within a hierarchical experimental laboratory automation and orchestration (HELAO) framework. They demonstrate the capability of orchestrating distributed research instruments that can incorporate data from experiments, simulations, and databases. HELAO interfaces laboratory hardware and software distributed across several computers and operating systems for executing experiments, data analysis, provenance tracking, and autonomous planning. Parallelization is an effective approach for accelerating knowledge generation provided that multiple instruments can be effectively coordinated, which the authors demonstrate with parallel electrochemistry experiments orchestrated by HELAO. Efficient implementation of autonomous research strategies requires device sharing, asynchronous multithreading, and full integration of data management in experimental orchestration, which to the best of the authors’ knowledge, is demonstrated for the first time herein.

36 MATERIALS SCIENCE↗
DOE OSTI2021

cuZ-Checker: A GPU-Based Ultra-Fast Assessment System for Lossy Compressions

Lossy compression is becoming an indispensable technique for the success of today's extreme-scale high-performance computing projects that produce vast volumes of data during scientific simulations or instrument data acquisitions. Comprehensively understanding the compression quality and performance of different lossy compressors is critical to selecting the best-fit compressors and using them properly and efficiently in practice. A few lossy compression assessment tools (e.g., Z-checker) have been developed, but none of them support the execution in a GPU environment. This is a significant gap because many recent extreme-scale applications and lossy compressors (e.g., cuSZ) can run entirely within GPUs. In this work, we develop an efficient lossy compression measuring system (called cuZ-Checker) on the GPU platform, which aims to perform the lossy compression quality and performance assessment completely within the GPU environment. Our contribution is threefold. (1) We develop a novel GPU-based lossy compression measuring framework using a computation pattern-based design approach. This approach classifies the computing-intensive metrics into three categories based on their patterns which creates large opportunities for kernel fusion and data reuse. (2) For each pattern in cuZ-Checker, we develop a CUDA kernel and provide fine-grained optimizations to boost its performance. (3) We thoroughly evaluate our cuZ-checker on a V100 GPU using four real-world scientific application datasets. Experiments show that cuZ-Checker can significantly accelerate the overall lossy compression assessment performance by 23X similar to 31X compared with the OpenMP-based multithreading CPU performance. To the best of our knowledge, this is the first lossy compression measuring system designed for GPU devices.

GPU↗
DOE OSTI2015

KINETIC EVOLUTION OF CORONAL HOLE PROTONS BY IMBALANCED ION-CYCLOTRON WAVES: IMPLICATIONS FOR MEASUREMENTS BY SOLAR PROBE PLUS

We extend the kinetic guiding-center model of collisionless coronal hole protons presented in Isenberg and Vasquez to consider driving by imbalanced spectra of obliquely propagating ion-cyclotron waves. These waves are assumed to be a small by-product of the imbalanced turbulent cascade to high perpendicular wavenumber, and their total intensity is taken to be 1% of the total fluctuation energy. We also extend the kinetic solutions for the proton distribution function in the resulting fast solar wind to heliocentric distances of 20 solar radii, which will be attainable by the Solar Probe Plus spacecraft. We consider three ratios of outward-propagating to inward-propagating resonant intensities: 1, 4, and 9. The self-consistent bulk flow speed reaches fast solar wind values in all cases, and these speeds are basically independent of the intensity ratio. The steady-state proton distribution is highly organized into nested constant-density shells by the resonant wave-particle interaction. The radial evolution of this kinetic distribution as the coronal hole plasma flows outward is understood as a competition between the inward- and outward-directed large-scale forces, causing an effective circulation of particles through the (v{sub ∥}, v{sub ⊥}) phase space and a characteristic asymmetric shape to the distribution. These asymmetries are substantial and persist to the outer limit of the model computation, where they should be observable by the Solar Probe Plus instruments.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

The possible disappearance of a massive star in the low-metallicity galaxy PHL 293B

ABSTRACT We investigate a suspected very massive star in one of the most metal-poor dwarf galaxies, PHL 293B. Excitingly, we find the sudden disappearance of the stellar signatures from our 2019 spectra, in particular the broad H lines with P Cygni profiles that have been associated with a massive luminous blue variable (LBV) star. Such features are absent from our spectra obtained in 2019 with the Echelle Spectrograph for Rocky Exoplanet- and Stable Spectroscopic Observation and X-shooter instruments of the European Southern Observatory’s Very Large Telescope. We compute radiative transfer models using cmfgen, which fit the observed spectrum of the LBV and are consistent with ground-based and archival Hubble Space Telescope photometry. Our models show that during 2001–2011, the LBV had a luminosity L* = 2.5–3.5 × 106 L⊙, a mass-loss rate $\dot{M} = 0.005{-}0.020 ~{\rm M}_{\odot }$ yr−1, a wind velocity of 1000 km s−1, and effective and stellar temperatures of Teff = 6000–6800 and T* = 9500–15 000 K. These stellar properties indicate an eruptive state. We consider two main hypotheses for the absence of the broad emission components from the spectra obtained since 2011. One possibility is that we are seeing the end of an LBV eruption of a surviving star, with a mild drop in luminosity, a shift to hotter effective temperatures, and some dust obscuration. Alternatively, the LBV could have collapsed to a massive black hole without the production of a bright supernova.

Allan, Andrew P.↗
DOE OSTI2014

Extracting grain-orientation-dependent data from in situ time-of-flight neutron diffraction. I. Inverse pole figures

The problem of calculating the inverse pole figure (IPF) is analyzed from the perspective of the application of time-of flight neutron diffraction to in situ monitoring of the thermomechanical behavior of engineering materials. On the basis of a quasi-Monte Carlo (QMC) method, a consistent set of grain orientations is generated and used to compute the weighting factors for IPF normalization. The weighting factors are instrument dependent and were calculated for the engineering materials diffractometer VULCAN (Spallation Neutron Source, Oak Ridge National Laboratory). The QMC method is applied to face-centered cubic structures and can be easily extended to other crystallographic symmetries. Examples include 316LN stainless steel in situ loaded in tension at room temperature and an Al–2%Mg alloy, substantially deformed by cold rolling and in situ annealed up to 653 K.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Energy Conservation Through Cloned Execution Of Simulations

High-performance computing facilities used for scientific computing draw enormous energy, some of them consuming many megawatt-hours. Saving the energy consumption of computations on such facilities can dramatically reduce the total cost of their operation and help reduce environmental effects. Here, we focus on a way to reduce energy consumption in many ensembles of simulations. Using the method of simulation cloning to exploit parallelism while also significantly conserving the computational and memory requirements, we perform a detailed empirical study of energy consumed on a large supercomputer consisting of hardware accelerator cards (graphical processing units, GPUs). We build on previous insights from mathematical analysis and implementation of cloned simulations that result in computational and memory savings by several orders-of-magnitude. Using instrumentation to track the power drawn by thousands of accelerator cards, we report significant aggregate energy savings from cloned simulations.

Yoginath, Srikanth↗
DOE OSTI2020

Cross-Cutting Software Solutions in Support of Experimental Analysis Challenges at National Scattering Facilities

Rapid improvements in instrumentation at neutron and light source combined with new capabilities in computing, data analytics, and machine learning offer unprecedented scientific capabilities to address fundamental and applied science questions in a wide area of subjects from quantum materials to biological systems. In order to make these capabilities accessible to the broader scientific community, significant developments are needed related to workflow spanning from experimental control to advanced materials simulations as well as the underpinning scientific software. In this chapter, we explore these needs and recent developments at the Spallation Neutron Source (SNS) at Oak Ridge National Laboratory. A central part of the workflow at the SNS is the data reduction framework Mantid which is developed as a partnership between European and US neutron-scattering facilities.

Proffen, Thomas↗
DOE OSTI2022

Auto Procedure Parsing: A Natural Language Processing Approach

Nuclear Power Plant (NPP) operating procedure is “a set of rules that describes how actions on the plant should be made if a certain system goal should be accomplished. U.S. NPPs use paper-based procedures (PBPs). PBPs are difficult to use. Common errors with PBPs are: following the wrong procedure, omit a step etc. Computer based procedures (CPBs) offer great improvement in ensuring plant safety. Some studies rely on experienced operators to understand the procedure content and then reorganize the procedure with digitally executable capabilities. Other studies utilize the procedure format to design rules to extract information from the operating procedures. Existing studies in procedure parsing are manual, laborious and extract limited information. This study aims to automatically extract critical information from operating procedures for generating computer interpretable representation of procedures. Such representation can further be used for automatic dynamic human reliability analysis and CPB design etc.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

A new international standard on cybersecurity for nuclear power plants: IEC 62645 - cybersecurity requirements - 161

This paper provides an overview of the IEC 62645, an actual standard by the International Electrotechnical Commission (IEC) focused on the issue of requirements for computer security programmes and system development processes to prevent and/or minimize the impact of cyber attacks against computer-based I and C systems. Developed since 2009 and published end of 2014, the standard is intended to be used for changing or establishing new security programmes for I and C systems of operating and new Nuclear Power Plants (NPP). This paper also presents the key issues being considered in the new revision to IEC 62645, currently in process. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Application of deep neural networks to improve diagnostic accuracy of rheumatoid arthritis using diffuse optical tomography

A set of deep neural network models for rheumatoid arthritis (RA) classification using a highway network, a convolutional neural network and a residual network is proposed based on the data of diffuse optical tomography (DOT) utilising near-infrared light, which ensures early diagnosis of pathophysiological changes resulting from inflammation. A numerical model of the finger is used to generate images to overcome the inherent problem of insufficient clinical DOT images available. The proposed deep neural network models are applied to automatically classify simulated DOT images of inflamed and non-inflamed joints and transfer learning is also used to improve the performance of the classification. The results demonstrate that all three deep neural network methods improve the diagnostic accuracy as compared to the widely applied support vector machine (SVM), especially for high inter-subject variability databases. In cases of distinct modelled severity of disease, residual network achieved the highest accuracy (> 99 %), and both of highway and convolutional neural networks reached 99 %, respectively. However, as the severity of the modelled disease is reduced, this accuracy is reduced to 75.2 % for residual networks. The results indicate that transfer learning can improve the performance of deep neural network methods on RA classification from DOT data and highlight their potential as a computer aided tool in DOT diagnostic systems. (paper)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Light Output Fitting Software

Light output response of scintillators is crucial to the utilization of organic scintillators as effective tools in radiation detection and measurement. While the response is a continuous distribution, the light output corresponding to the maximum energy deposition is crucial in effectively understanding and simulating a detector. There are a variety of fits derived in literature that will vary for every detector material. The Light Output Response Fitter, or LORF Program is a python script designed to easily and quickly compute and plot fits for a variety of scintillator light output models. It includes a stopping power library constructed from SRIM including Organic Glass, EJ309, EJ301, Stilbene, EJ276, and their deuterated counterparts by default, with the ability for the user to add custom stopping power libraries. The user is also capable of importing the python package and utilizing its in-built functions as appropriate. Uses for this capability include plotting and computing a model with known parameters.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

AutodiDAQt v1.1.0

AutodiDAQt automates and simplifies writing data acquisition software for spectroscopy and microscopy. After defining only how to communicate with instruments and hardware, autodiDAQt generates user interfaces for long running acquisition applications, handlings data collation and retention, and provides remote communication to analysis computers. This reduces the time to get experiments running from months to hours and increases reliability for scientific experiments. AutodiDAQt metaprograms from instrument drivers directly, where possible.

Stansbury, Conrad↗