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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Data reduction through optimized scalar quantization for more compact neural networks

Raw data generation for several existing and planned large physics experiments now exceeds TB/s rates, generating untenable data sets in very little time. Those data often demonstrate high dimensionality while containing limited information. Meanwhile, Machine Learning algorithms are now becoming an essential part of data processing and data analysis. Those algorithms can be used offline for post processing and post data analysis, or they can be used online for real time processing providing ultra low latency experiment monitoring. Both use cases would benefit from data throughput reduction while preserving relevant information: one by reducing the offline storage requirements by several orders of magnitude and the other by allowing ultra fast online inferencing with low complexity Machine Learning models. Moreover, reducing the data source throughput also reduces material cost, power and data management requirements. In this work we demonstrate optimized nonuniform scalar quantization for data source reduction. This data reduction allows lower dimensional representations while preserving the relevant information of the data, thus enabling high accuracy Tiny Machine Learning classifier models for online fast inferences. We demonstrate this approach with an initial proof of concept targeting the CookieBox, an array of electron spectrometers used for angular streaking, that was developed for LCLS-II as an online beam diagnostic tool. We used the Lloyd-Max algorithm with the CookieBox dataset to design an optimized nonuniform scalar quantizer. Optimized quantization lets us reduce input data volume by 69% with no significant impact on inference accuracy. When we tolerate a 2% loss on inference accuracy, we achieved 81% of input data reduction. Finally, the change from a 7-bit to a 3-bit input data quantization reduces our neural network size by 38%.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A Stochastic Approach to Reconstruct Gamma-Ray-burst Light Curves

Gamma-ray bursts (GRBs), as they are observed at high redshift ( z = 9.4), are vital to cosmological studies and investigating Population III stars. To tackle these studies, we need correlations among relevant GRB variables with the requirement of small uncertainties on their variables. Thus, we must have good coverage of GRB light curves (LCs). However, gaps in the LC hinder the precise determination of GRB properties and are often unavoidable. Therefore, extensive categorization of GRB LCs remains a hurdle. We address LC gaps using a stochastic reconstruction, wherein we fit two preexisting models (the Willingale model; W07; and a broken power law; BPL) to the observed LC, then use the distribution of flux residuals from the original data to generate data to fill in the temporal gaps. We also demonstrate a model-independent LC reconstruction via Gaussian processes. At 10% noise, the uncertainty of the end time of the plateau, its correspondent flux, and the temporal decay index after the plateau decreases by 33.3%, 35.03%, and 43.32% on average for the W07, and by 33.3%, 30.78%, 43.9% for the BPL, respectively. The uncertainty of the slope of the plateau decreases by 14.76% in the BPL. After using the Gaussian process technique, we see similar trends of a decrease in uncertainty for all model parameters for both the W07 and BPL models. These improvements are essential for the application of GRBs as standard candles in cosmology, for the investigation of theoretical models, and for inferring the redshift of GRBs with future machine-learning analyses.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1995

Computer Model Of Fragmentation Of Atomic Nuclei

High Charge and Energy Semiempirical Nuclear Fragmentation Model (HZEFRG1) computer program developed to be computationally efficient, user-friendly, physics-based program for generating data bases on fragmentation of atomic nuclei. Data bases generated used in calculations pertaining to such radiation-transport applications as shielding against radiation in outer space, radiation dosimetry in outer space, cancer therapy in laboratories with beams of heavy ions, and simulation studies for designing detectors for experiments in nuclear physics. Provides cross sections for production of individual elements and isotopes in breakups of high-energy heavy ions by combined nuclear and Coulomb fields of interacting nuclei. Written in ANSI FORTRAN 77.

Wilson, John W.↗
NASA NTRS2009

When Failure Means Success: Accepting Risk in Aerospace Development

Over the last three decades, NASA has been diligent in qualifying systems for human space flight. As the Agency transitions from operating the Space Shuttle, its employees must learn to accept higher risk levels to generate the data needed to certify its next human space flight system. The Marshall Center s Engineering workforce is developing the Ares I crew launch vehicle and designing the Ares V cargo launch vehicle for safety, reliability, and cost-effective operations. This presentation will provide a risk retrospective, using first-hand examples from the Delta Clipper-Experimental Advanced (DC-XA) and the X-33 single-stage-to-orbit flight demonstrators, while looking ahead to the upcoming Ares I-X uncrewed test flight. The DC-XA was successfully flown twice in 26 hours, setting a new turnaround-time record. Later, one of its 3 landing gears did not deploy, it tipped over, and was destroyed. During structural testing, the X-33 s advanced composite tanks were unable to withstand the forces to which it was subjected and the project was later cancelled. These are examples of successful failures, as the data generated are captured in databases used by vehicle designers today. More recently, the Ares I-X flight readiness review process was streamlined in keeping with the mission's objectives, since human lives are not at stake, which reflects the beginning of a cultural change. Failures are acceptable during testing, as they provide the lessons that actually lead to mission success. These and other examples will stimulate the discussion of when to accept risk in aerospace projects.

Dumbacher, Daniel L.↗
NASA NTRS2015

Performance of a Discrete Wavelet Transform for Compressing Plasma Count Data and its Application to the Fast Plasma Investigation on NASA's Magnetospheric Multiscale Mission

Plasma measurements in space are becoming increasingly faster, higher resolution, and distributed over multiple instruments. As raw data generation rates can exceed available data transfer bandwidth, data compression is becoming a critical design component. Data compression has been a staple of imaging instruments for years, but only recently have plasma measurement designers become interested in high performance data compression. Missions will often use a simple lossless compression technique yielding compression ratios of approximately 2:1, however future missions may require compression ratios upwards of 10:1. This study aims to explore how a Discrete Wavelet Transform combined with a Bit Plane Encoder (DWT/BPE), implemented via a CCSDS standard, can be used effectively to compress count information common to plasma measurements to high compression ratios while maintaining little or no compression error. The compression ASIC used for the Fast Plasma Investigation (FPI) on board the Magnetospheric Multiscale mission (MMS) is used for this study. Plasma count data from multiple sources is examined: resampled data from previous missions, randomly generated data from distribution functions, and simulations of expected regimes. These are run through the compression routines with various parameters to yield the greatest possible compression ratio while maintaining little or no error, the latter indicates that fully lossless compression is obtained. Finally, recommendations are made for future missions as to what can be achieved when compressing plasma count data and how best to do so.

Particles↗
NASA NTRSDate not supplied

Planning Bias: Planning as a Source of Sampling Bias

Many data-driven planning methods are trained on data generated by planners. It is well known that many statistical learning methods are sensitive to sampling bias, and yet there has been little or no attention to planning as a sampling method and its role in introducing sampling bias into planner-generated training data. Recently, it has been demonstrated that A**,* in the presence of problems with variable heuristic error, prefers some solutions over other equally cost-optimal solutions. But, as we discuss in this paper, mitigation may not be as simple as resolving arbitrary tie-breaking by sampling from ties uniformly at random. In this paper, we formalize an intuition of planning bias. We focus on problems which output a single solution. Diverse planning only complicates the problem by generalizing it to bias in the set of sets; we show how it is subject to bias in the single solution. We make some useful observations about deterministic algorithms in contrast to non-deterministic algorithms. We explain how information entropy may be a good way to measure planning bias, and discuss some issues in evaluating practical approaches to measurement. We address the intuition that uniform random tiebreaking should mitigate bias; and sketch a novel approach to constructing an appropriate random distribution for duplicate detection during forward search for unbiased A*. Finally, we suggest directions for future work.

Planning Scheduling Algorithms↗
DOE OSTI2019

Visualization of Simulation and TAP Interaction

The Temporal Analysis of Products (TAP) Reactor applies transient kinetics to analyze sophisticated chemical reaction mechanisms, rapidly changing temperature, flow rate, pressure or concentration and then monitoring the system’s time dependent response. A simulation tool exists for the TAP reactor, but it requires a visualization platform to interface effectively with researchers. This graphical user interface remedies this problem, fixing many of the difficulties caused by users interacting directly with the simulation code by providing an intuitive web application that satisfies the simulation input parameters for fast generation of data. This tool will ensure robust and repeatable simulation results, expedite the data generating process and enhance the overall ability of TAP to produce significant findings.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Data-Efficient Generation of Protein Conformational Ensembles with Backbone-to-Side-Chain Transformers

Excitement at the prospect of using data-driven generative models to sample configurational ensembles of biomolecular systems stems from the extraordinary success of these models on a diverse set of high-dimensional sampling tasks. Unlike image generation or even the closely related problem of protein structure prediction, there are currently no data sources with sufficient breadth to parametrize generative models for conformational ensembles. To enable discovery, a fundamentally different approach to building generative models is required: models should be able to propose rare, albeit physical, conformations that may not arise in even the largest data sets. Here, in this work, we introduce a modular strategy to generate conformations based on “backmapping” from a fixed protein backbone that (1) maintains conformational diversity of the side chains and (2) couples the side-chain fluctuations using global information about the protein conformation. Our model combines simple statistical models of side-chain conformations based on rotamer libraries with the now ubiquitous transformer architecture to sample with atomistic accuracy. Together, these ingredients provide a strategy for rapid data acquisition and hence a crucial ingredient for scalable physical simulation with generative neural networks.

36 MATERIALS SCIENCE↗
DOE OSTI2016

WE-AB-207B-07: Dose Cloud: Generating “Big Data” for Radiation Therapy Treatment Plan Optimization Research

Purpose: To provide a tool to generate large sets of realistic virtual patient geometries and beamlet doses for treatment optimization research. This tool enables countless studies exploring the fundamental interplay between patient geometry, objective functions, weight selections, and achievable dose distributions for various algorithms and modalities. Methods: Generating realistic virtual patient geometries requires a small set of real patient data. We developed a normalized patient shape model (PSM) which captures organ and target contours in a correspondence-preserving manner. Using PSM-processed data, we perform principal component analysis (PCA) to extract major modes of variation from the population. These PCA modes can be shared without exposing patient information. The modes are re-combined with different weights to produce sets of realistic virtual patient contours. Because virtual patients lack imaging information, we developed a shape-based dose calculation (SBD) relying on the assumption that the region inside the body contour is water. SBD utilizes a 2D fluence-convolved scatter kernel, derived from Monte Carlo simulations, and can compute both full dose for a given set of fluence maps, or produce a dose matrix (dose per fluence pixel) for many modalities. Combining the shape model with SBD provides the data needed for treatment plan optimization research. Results: We used PSM to capture organ and target contours for 96 prostate cases, extracted the first 20 PCA modes, and generated 2048 virtual patient shapes by randomly sampling mode scores. Nearly half of the shapes were thrown out for failing anatomical checks, the remaining 1124 were used in computing dose matrices via SBD and a standard 7-beam protocol. As a proof of concept, and to generate data for later study, we performed fluence map optimization emphasizing PTV coverage. Conclusions: We successfully developed and tested a tool for creating customizable sets of virtual patients suitable for large-scale radiation therapy optimization research.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

AMPX: A Modern cross section processing system for generating nuclear data libraries - 14619

Nuclear data libraries are essential for nuclear analysis code systems like SCALE. AMPX has been continuously developed since the early 1970's and is used to generate these data libraries for SCALE. Recently, Oak Ridge National Laboratory (ORNL) initiated a major modernization effort to position AMPX for continued development and deployment on modern computing platforms. An important step toward modernization has been to merge AMPX under the SCALE continuous integration (CI) development framework. As a result, AMPX is now developed under the SCALE software quality assurance plan, providing increased confidence in the quality of data libraries developed and deployed by AMPX. This merge allowed us to develop novel methods to generate self-shielding factors that combine SCALE's resonance self-shielding capabilities with the AMPX library-generation capabilities. AMPX processes Evaluated Nuclear Data Format (ENDF/B) files. The ENDF community is working to develop a more modern format that will replace the existing ENDF/B format. Due to strict space and memory requirements when AMPX was first developed, the original AMPX processing and reading of ENDF data were tightly integrated. On newer computer systems, this is no longer necessary. Now there is a separation between reading and processing which allows for simultaneous support of current and new ENDF formats. As a result, AMPX is positioned to adopt any new ENDF format when available. For end users, the modernization effort has resulted in improved processing capabilities for generating nuclear data libraries. The objective of the paper is to document the latest AMPX cross-section processing capabilities and provide the current status of the AMPX modernization effort. (authors)

42 ENGINEERING↗
DOE OSTI2024

Multitask methods for predicting molecular properties from heterogeneous data

Data generation remains a bottleneck in training surrogate models to predict molecular properties. We demonstrate that multitask Gaussian process regression overcomes this limitation by leveraging both expensive and cheap data sources. In particular, we consider training sets constructed from coupled-cluster (CC) and density functional theory (DFT) data. We report that multitask surrogates can predict at CC-level accuracy with a reduction in data generation cost by over an order of magnitude. Of note, our approach allows the training set to include DFT data generated by a heterogeneous mix of exchange–correlation functionals without imposing any artificial hierarchy on functional accuracy. More generally, the multitask framework can accommodate a wider range of training set structures—including the full disparity between the different levels of fidelity—than existing kernel approaches based on Δ-learning although we show that the accuracy of the two approaches can be similar. Consequently, multitask regression can be a tool for reducing data generation costs even further by opportunistically exploiting existing data sources.

Chemistry↗
DOE OSTI2014

Nanoscale Cluster Detection in Massive Atom Probe Tomography Data

Recent technological advances in atom probe tomography (APT) have led to unprecedented data acquisition capabilities that routinely generate data sets containing hundreds of millions of atoms. Detecting nanoscale clusters of different atom types present in these enormous amounts of data and analyzing their spatial correlations with one another are fundamental to understanding the structural properties of the material from which the data is derived. Extant algorithms for nanoscale cluster detection do not scale to large data sets. Here, a scalable, CUDA-based implementation of an autocorrelation algorithm is presented. It isolates spatial correlations amongst atomic clusters present in massive APT data sets in linear time using a linear amount of storage. Correctness of the algorithm is demonstrated using large synthetically generated data with known spatial distributions. Benefits and limitations of using GPU-acceleration for autocorrelation-based APT data analyses are presented with supporting performance results on data sets with up to billions of atoms. To our knowledge, this is the first nanoscale cluster detection algorithm that scales to massive APT data sets and executes on commodity hardware.

Seal, Sudip K↗
DOE OSTI2018

Spring Boot based REST API to Improve Data Quality Report Generation for Big Scientific Data: ARM Data Center Example

Web application technologies are growing rapidly with continuous innovation and improvements. This paper focuses on the popular Spring Boot [1] java-based framework for building web and enterprise applications and how it provides the flexibility for service-oriented architecture (SOA). One challenge with any Spring-based applications is its level of complexity with configurations. Spring Boot makes it easy to create and deploy stand-alone, production-grade Spring applications with very little Spring configuration. Example, if we consider Spring Model-View-Controller (MVC) framework [2], we need to configure dispatcher servlet, web jars, a view resolver, and component scan among other things. To solve this, Spring Boot provides several Auto Configuration options to setup the application with any needed dependencies. Another challenge is to identify the framework dependencies and associated library versions required to develop a web application. Spring Boot offers simpler dependency management by using a comprehensive, but flexible, framework and the associated libraries in one single dependency, which provides all the Spring related technology that you need for starter projects as compared to CRUD web applications. This framework provides a range of additional features that are common across many projects such as embedded server, security, metrics, health checks, and externalized configuration. Web applications are generally packaged as war and deployed to a web server, but Spring Boot application can be packaged either as war or jar file, which allows to run the application without the need to install and/or configure on the application server. In this paper, we discuss how Atmospheric Radiation Measurement (ARM) Data Center (ADC) at Oak Ridge National Laboratory, is using Spring Boot to create a SOA based REST [4] service API, that bridges the gap between frontend user interfaces and backend database. Using this REST service API, ARM scientists are now able to submit reports via a user form or a command line interface, which captures the same data quality or other important information about ARM data.

Guntupally, Kavya↗
DOE OSTI2015

Big Data: Next-Generation Machines for Big Science

Addressing the scientific grand challenges identified by the US Department of Energy’s (DOE’s) Office of Science’s programs alone demands a total leadership-class computing capability of 150 to 400 Pflops by the end of this decade. The successors to three of the DOE’s most powerful leadership-class machines are set to arrive in 2017 and 2018—the products of the Collaboration Oak Ridge Argonne Livermore (CORAL) initiative, a national laboratory–industry design/build approach to engineering nextgeneration petascale computers for grand challenge science. These mission-critical machines will enable discoveries in key scientific fields such as energy, biotechnology, nanotechnology, materials science, and high-performance computing, and serve as a milestone on the path to deploying exascale computing capabilities.

High-performance computing↗
DOE OSTI2021

Online data analysis and reduction: An important co-design motif for extreme-scale computers

A growing disparity between supercomputer computation speeds and I/O rates means that it is rapidly becoming infeasible to analyze supercomputer application output only after that output has been written to a file system. Instead, data-generating applications must run concurrently with data reduction and/or analysis operations, with which they exchange information via high-speed methods such as interprocess communications. The resulting parallel computing motif, online data analysis and reduction (ODAR), has important implications for both application and HPC systems design. Here we introduce the ODAR motif and its co-design concerns, describe a co-design process for identifying and addressing those concerns, present tools that assist in the co-design process, and present case studies to illustrate the use of the process and tools in practical settings.

Data Analysis↗