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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

AI to Predict Glass Compositions Satisfying Property and Cooling Rate Criteria

This project aimed to develop a predictive, artificial intelligence/machine learning-based model to identify glass compositions satisfying specified property requirements. Such a model would provide a systematic approach for narrowing down the nearly infinite range of possible compositions for glasses and minimize unnecessary experimental trial and error. A large empirical data set for training and testing the algorithm was obtained from the SciGlass database. It contains glass compositions and corresponding property data from a wide range of literature sources. However, the currently available form of this data, recently released under an open database license, is not conducive to easy querying and use. The data structure was deciphered and a customized parsing code developed to make this data more usable for the current and future work. Neural network models were developed and trained on viscosity data from the database and demonstrated potential for improving prediction accuracy over a traditional regression model.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Getting Started with Evaluations with Means and Uncertainties (EMU 3.0)

One of the most fundamental quantities in nuclear physics is the reaction cross section. A cross section represents the probability that a nuclear reaction will resolve through a given channel given a target nucleus and a projectile with a certain energy. A nuclear evaluation is a set of discrete data and interpolation rules to convert those discrete nuclear reaction data—such as the cross section—into a continuous function at arbitrary energies. Evaluated nuclear data files that can appear in Evaluated Nuclear Data File (ENDF) and Generalized Nuclear Data Structure (GNDS) formats, storing a “most-complete” discretized representation of nuclear data, based on both experimental measurements and theory models.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Compiler and Runtime Approaches to Enable Large-Scale Irregular Programs. Final report, July 2013 - July 2019

While regular algorithms, characterized by operations on dense matrices and arrays, have long been the mainstay of scientific, high-performance computing, irregular algorithms, which feature unpredictable accesses to pointer-based data structures, are becoming increasingly common in high performance computing, arising in graph analysis, data mining and visualization, among other domains. Unfortunately, the defining characteristics of irregular applications, their dynamic, unpredictable, data-dependent access patterns and data layouts, make achieving high performance on large scale systems difficult. Scaling applications to peta- and exa-scale requires carefully controlling communication and data movement and placement, an inherently difficult task when access patterns and data layouts are unpredictable! Most irregular applications that attain high performance must be painstakingly hand-written and hand-tuned, with few common principles or paradigms uniting various implementations and easing future development. Despite the increasing importance of irregular applications, there is little programmer knowledge, and even less compiler ability, devoted to optimizing them. This project aims to solve these problems. By allowing programmers to write irregular applications in high level forms, with at most a few annotations highlighting key structural properties, programmers can focus on developing their algorithms and methods. The compiler and run-time system can take on the tedious task of optimizing the application for execution at large scales, and can automatically provide efficient implementations. This will provide portability and ease maintenance for existing irregular applications, but, more importantly, open up whole new domains of computational science to large-scale, high-performance simulation codes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Bridging microscopy with molecular dynamics and quantum simulations: an atomAI based pipeline

Recent advances in (scanning) transmission electron microscopy have enabled a routine generation of large volumes of high-veracity structural data on 2D and 3D materials, naturally offering the challenge of using these as starting inputs for atomistic simulations. In this fashion, the theory will address experimentally emerging structures, as opposed to the full range of theoretically possible atomic configurations. However, this challenge is highly nontrivial due to the extreme disparity between intrinsic timescales accessible to modern simulations and microscopy, as well as latencies of microscopy and simulations per se. Addressing this issue requires as a first step bridging the instrumental data flow and physics-based simulation environment, to enable the selection of regions of interest and exploring them using physical simulations. Here we report the development of the machine learning workflow that directly bridges the instrument data stream into Python-based molecular dynamics and density functional theory environments using pre-trained neural networks to convert imaging data to physical descriptors. Additionally, the pathways to ensure structural stability and compensate for the observational biases universally present in the data are identified in the workflow. This approach is used for a graphene system to reconstruct optimized geometry and simulate temperature-dependent dynamics including adsorption of Cr as an ad-atom and graphene healing effects. However, it is universal and can be used for other material systems.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Challenges and Opportunities in Deep Reinforcement Learning With Graph Neural Networks: A Comprehensive Review of Algorithms and Applications

Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields, including pattern recognition, robotics, recommendation-systems, and gaming. Similarly, graph neural networks (GNN) have also demonstrated their superior performance in supervised learning for graph-structured data. In recent times, the fusion of GNN with DRL for graph-structured environments has attracted a lot of attention. Here, this paper provides a comprehensive review of these hybrid works. These works can be classified into two categories: (1) algorithmic enhancement, where DRL and GNN complement each other for better utility; (2) application-specific enhancement, where DRL and GNN support each other. This fusion effectively addresses various complex problems in engineering and life sciences. Based on the review, we further analyze the applicability and benefits of fusing these two domains, especially in terms of increasing generalizability and reducing computational complexity. Finally, the key challenges in integrating DRL and GNN, and potential future research directions are highlighted, which will be of interest to the broader machine learning community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Minimizing CGYRO HPC Communication Costs in Ensembles with XGYRO by Sharing the Collisional Constant Tensor Structure

First-principles fusion plasma simulations are both compute and memory intensive, and CGYRO is no exception. The use of many HPC nodes to fit the problem in the available memory thus results in significant communication overhead, which is hard to avoid for any single simulation. That said, most fusion studies are composed of ensembles of simulations, so we developed a new tool, named XGYRO, that executes a whole ensemble of CGYRO simulations as a single HPC job. By treating the ensemble as a unit, XGYRO can alter the global buffer distribution logic and apply optimizations that are not feasible on any single simulation, but only on the ensemble as a whole. The main saving comes from the sharing of the collisional constant tensor structure, since its values are typically identical between parameter-sweep simulations. This data structure dominates the memory consumption of CGYRO simulations, so distributing it among the whole ensemble results in drastic memory savings for each simulation, which in turn results in overall lower communication overhead.

CGYRO↗
DOE OSTI2022

Enhancing Risk Analysis of Accidental Release Using CFD Modeling and Machine Learning

This project report presents the results from the AU31 project that is entitled “Enhancing Risk Analysis of Accidental Release Using CFD Modeling and Machine Learning” performed between FY2021 and FY2022. The technical objective of this project is to enhance risk analysis of accidental release of radioactive materials and provide novel risk forecasting and capabilities for assessing unplanned hazardous chemical releases for the U.S. Department of Energy (DOE). The approach includes a cross-platform finite element analysis of airborne chemical release, machine learning (ML) for simulation and forecasting, and a big data approach to data management, transformation and analysis capable of handling petabyte-scale unstructured and structured data. The outcome of these findings were assembled and accessible as a final output via an intuitive web hosted geospatial-based user interface for demonstration and general dissemination. These new results also provide a mechanism of combining this knowledge in a risk analysis framework tool. The geospatial analysis and output can utilize 5 years of qualified meteorological data to illustrate projected plume footprints that are generated from computational aspects of this study. Specifically, we show the development of a geospatial risk analysis capabilities to respond to and plan for accidental release of radioactive materials and to provide a risk forecasting tool for assessing hazardous chemical releases for the DOE. The focus of this effort is germane to common practices in risk analysis; and it is highly relevant to the timely and transparent release of information related to materials at risk including hazardous materials and chemical vapors and rapid assessment of the potential impacts on the workforce at active sites across the DOE complex.

42 ENGINEERING↗
NASA NTRS1993

The use of linked lists in the simulation of controller-structure interaction

An algorithm for the computer simulation of large space structures under active control is considered. Linked lists are used in a matrix data structure to implement the trapezoidal rule on system differential equations. The use of the trapezoidal rule ensures that the numerical stability is equivalent to the system stability, which is essential for this type of simulation. The sparsity of the system matrices is exploited by the linked lists, and the algorithm efficiently steps through the lists in an orderly fashion. Results of simulations on a NASA large space structure experiment are reported.

Quan, R.↗
NASA NTRS1993

The use of linked lists in the simulation of controller-structure interaction

An algorithm for the computer simulation of large space structures under active control is considered. Linked lists are used in a matrix data structure to implement the trapezoidal rule on the system differential equations. The use of the trapezoidal rule ensures that the numerical stability is equivalent to the system stability, which is essential for this type of simulation. The sparsity of the system matrices is exploited by the linked lists, and the algorithm efficiently steps through the lists in an orderly fashion. Results of simulations on a NASA large space structure experiment are reported.

Quan, Ralph↗
NASA NTRS2003

The Digital Space Shuttle, 3D Graphics, and Knowledge Management

The Digital Shuttle is a knowledge management project that seeks to define symbiotic relationships between 3D graphics and formal knowledge representations (ontologies). 3D graphics provides geometric and visual content, in 2D and 3D CAD forms, and the capability to display systems knowledge. Because the data is so heterogeneous, and the interrelated data structures are complex, 3D graphics combined with ontologies provides mechanisms for navigating the data and visualizing relationships.

Gomez, Julian E.↗
NASA NTRS2002

Probabilistic #D data fusion for multiresolution surface generation

In this paper we present an algorithm for adaptive resolution integration of 3D data collected from multiple distributed sensors. The input to the algorithm is a set of 3D surface points and associated sensor models. Using a probabilistic rule, a surface probability function is generated that represents the probability that a particular volume of space contains the surface. The surface probability function is represented using an octree data structure; regions of space with samples of large conariance are stored at a coarser level than regions of space containing samples with smaller covariance. The algorithm outputs an adaptive resolution surface generated by connecting points that lie on the ridge of surface probability with triangles scaled to match the local discretization of space given by the algorithm, we present results from 3D data generated by scanning lidar and structure from motion.

3D data fusion multiresolution surface generation ↗
NASA NTRS1983

A flexible grid embedding technique with application to the Euler equations

An automated grid embedding procedure for solution of flows about complex geometries is described. The physical domain is subdivided into regions that can accommodate easily generated grids. The grids are organized in a hierarchical structure, and communication among grids is accomplished by interpolation of the flow variables at mesh boundaries. Algorithms for locating embedded boundaries, special treatment of the embedded grids, and the data structures required for manipulating the solution data are described. The method is demonstrated by solution of the Euler equations for transonic flow about a supercritical airfoil and a flapped airfoil.

Benek, J. A.↗
DOE OSTI2024

pyEGAF: Modernization of the EGAF database

One of the most comprehensive resources for thermal neutron-capture data is the Evaluated Gamma-ray Activation File (EGAF), containing data from prompt gamma activation analysis measurements carried out at the Budapest Research Reactor for 245 isotopes. Although these valuable datasets have been freely available for many years, the outdated and cryptic adopted format makes it difficult to utilize the data and it is not generally suitable for modern computational technologies. Furthermore, to help overcome these challenges, we have converted the datasets into an open standard JSON format. Additionally, we have developed a Python implementation of an open-source software package, pyEGAF, designed to interact with the JSON data structures for general purpose access, manipulation, and rapid assessment of the capture-gamma data in EGAF.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Structure of the peptidoglycan polymerase RodA resolved by evolutionary coupling analysis

The shape, elongation, division and sporulation (SEDS) proteins are a large family of ubiquitous and essential transmembrane enzymes with critical roles in bacterial cell wall biology. The exact function of SEDS proteins was for a long time poorly understood, but recent work has revealed that the prototypical SEDS family member RodA is a peptidoglycan polymerase—a role previously attributed exclusively to members of the penicillin-binding protein family4. This discovery has made RodA and other SEDS proteins promising targets for the development of next-generation antibiotics. However, little is known regarding the molecular basis of SEDS activity, and no structural data are available for RodA or any homologue thereof. Here we report the crystal structure of Thermus thermophilus RodA at a resolution of 2.9 Å, determined using evolutionary covariance-based fold prediction to enable molecular replacement. The structure reveals a ten-pass transmembrane fold with large extracellular loops, one of which is partially disordered. Here, the protein contains a highly conserved cavity in the transmembrane domain, reminiscent of ligand-binding sites in transmembrane receptors. Mutagenesis experiments in Bacillus subtilis and Escherichia coli show that perturbation of this cavity abolishes RodA function both in vitro and in vivo, indicating that this cavity is catalytically essential. These results provide a framework for understanding bacterial cell wall synthesis and SEDS protein function.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Let’s get our hands dirty: a comprehensive evaluation of DAQDB, key-value store for petascale hot storage

Data acquisition systems are a key component for successful data taking in any experiment. The DAQ is a complex distributed computing system and coordinates all operations, from the data selection stage of interesting events to storage elements. For the High Luminosity upgrade of the Large Hadron Collider, the experiments at CERN need to meet challenging requirements to record data with a much higher occupancy in the detectors. The DAQ system will receive and deliver data with a significantly increased trigger rate, one million events per second, and capacity, terabytes of data per second. An effective way to meet these requirements is to decouple real-time data acquisition from event selection. Data fragments can be temporarily stored in a large distributed key-value store. Fragments belonging to the same event can be then queried on demand, by the data selection processes. Implementing such a model relies on a proper combination of emerging technologies, such as persistent memory, NVMe SSDs, scalable networking, and data structures, as well as high performance, scalable software. In this paper, we present DAQDB (Data Acquisition Database) — an open source implementation of this design that was presented earlier, with an extensive evaluation of this approach, from the single node to the distributed performance. Furthermore, we complement our study with a description of the challenges faced and the lessons learned while integrating DAQDB with the existing software framework of the ATLAS experiment.

Abed Abud, Adam↗
DOE OSTI2018

ARCHIE: Data Analysis Acceleration with Array Caching in Hierarchical Storage

© 2018 IEEE. Scientific data analysis typically involves reading massive amounts of data that was generated by simulations, experiments, and observations. Performance of reading such large volumes of data from disk-based file systems is often poor because of the slow and mechanical components in the disks. Recent supercomputing systems are adding non-volatile storage layers in a hierarchy to handle the performance gap between fast main memory and slow disk-based storage. Software libraries for managing this hierarchy not only need efficient reading of data but also reduce user-involvement for cross-layer data movement. Furthermore, these libraries need to support array data access patterns into hierarchical storage management as scientific data is often organized in array-based data structures. Existing software typically manage individual storage layers requiring significant manual process in moving data among them. In this paper, we introduce a new array caching in hierarchical storage (ARCHIE) to accelerate array data analysis in a seamless fashion. ARCHIE evaluates array access patterns and prefetches data with array semantics between storage layers. Our evaluation shows that ARCHIE outperforms state-of-the-art file systems, i.e., Lustre and DataWarp, on a production supercomputing system by up to 5.8× in accessing data by scientific analysis applications.

Dong, Bin↗
DOE OSTI2019

BCM polytype structures from DFT - Data and Resources

This dataset contains structure files and results of density functional theory (DFT) calculations for the 12R (ground state) and 10H (metastable at ambient temperature) polytypes of BaCe0.25Mn0.75O3 (BCM). Starting from the experimentally determined average crystal structures, the structures were generated by sampling of the magnetic (12R and 10H) and atomic (10H) configurations based on DFT energies.

08 HYDROGEN↗
NASA NTRS1984

SPIRE Data-Base Management System

Spacelab Payload Integration and Rocket Experiment (SPIRE) data-base management system (DBMS) based on relational model of data bases. Data bases typically used for engineering and mission analysis tasks and, unlike most commercially available systems, allow data items and data structures stored in forms suitable for direct analytical computation. SPIRE DBMS designed to support data requests from interactive users as well as applications programs.

Fuechsel, C. F.↗