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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Extending the Remote Control Capabilities in the CMS Detector Control System with Remote Procedure Call Services

The CMS Detector Control System (DCS) is implemented as a large distributed and redundant system, with applications interacting and sharing data in multiple ways. The CMS XML-RPC is a software toolkit implementing the standard Remote Procedure Call (RPC) protocol, using the Extensible Mark-up Language (XML) and a custom lightweight variant using the JavaScript Object Notation (JSON) to model, encode and expose resources through the Hypertext Transfer Protocol (HTTP). The CMS XML-RPC toolkit complies with the standard specification of the XML-RPC protocol that allows system developers to build collaborative software architectures with self-contained and reusable logic, and with encapsulation of well-defined processes. The implementation of this protocol introduces not only a powerful communication method to operate and exchange data with web-based applications, but also a new programming paradigm to design service-oriented software architectures within the CMS DCS domain. This paper presents details of the CMS XML-RPC implementation in WinCC Open Architecture (OA) Control Language using an object-oriented approach.

43 PARTICLE ACCELERATORS↗
DOE OSTI2016

HySafe research priorities workshop report Summary of the workshop organized in cooperation with US DOE and supported by EC JRC in Washington DC November 10-11 2014.

The HySafe research priorities workshop is held on the even years between the International Conference on Hydrogen Safety (ICHS) which is held on the odd years. The research priorities workshop is intended to identify the state-of-the-art in understanding of the physical behavior of hydrogen and hydrogen systems with a focus on safety. Typical issues addressed include behavior of unintended hydrogen releases, transient combustion phenomena, effectiveness of mitigation measures, and hydrogen effects in materials. In the workshop critical knowledge gaps are identified. Areas of research and coordinated actions for the near and medium term are derived and prioritized from these knowledge gaps. The stimulated research helps pave the way for the rapid and safe deployment of hydrogen technologies on a global scale. To support the idea of delivering globally accepted research priorities for hydrogen safety the workshop is organized as an internationally open meeting. In attendance are stakeholders from the academic community (universities, national laboratories), funding agencies, and industry. The industry participation is critically important to ensure that the research priorities align with the current needs of the industry responsible for the deployment of hydrogen technologies. This report presents the results of the HySafe Research Priorities Workshop held in Washing- ton, D.C. on November 10-11, 2014. At the workshop the participants presented updates (since the previous workshop organized two years before in Berlin, Germany) of their research and development work on hydrogen safety. Following the workshop, participants were asked to provide feedback on high-priority topics for each of the research areas discussed and to rank research area categories and individual research topics within these categories. The research areas were ranked as follows (with the percentage of the vote in parenthesis): 1. Quantitative Risk Assessment (QRA) Tools (23%) 2. Reduced Model Tools (15%) 3. Indoor (13%) 4. Unintended Release-Liquid (11%) 5. Unintended Release-Gas (8%) 6. Storage (8%) 7. Integration Platforms (7%) 8. Hydrogen Safety Training (7%) 9. Materials Compatibility/Sensors (7%) 10. Applications (2%) The workshop participants ranked the need for Quantitative Risk Analysis (QRA) tools as the top priority by a large margin. QRA tools enable an informed expert to quantify the risk asso- ciated with a particular hydrogen system in a particular scenario. With appropriate verification and validation such tools will enable: * system designers to achieve a desired level of risk with suitable risk mitigation strategies, * permitting officials to determine if a particular system installation meets the desired risk level (performance based Regulations, Codes, and Standards (RCS) rather than prescrip- tive RCS), and * allow code developers to develop code language based on rigorous and validated physical models, statistics and standardized QRA methodologies. Another important research topic identified is the development of validated reduced physical models for use in the QRA tools. Improvement of the understanding and modeling of specific release phenomena, in particular liquid releases, are also highly ranked research topics. Acknowledgement The International Association HySafe, represented here by the authors, would like to thank all participants of the workshop for their valuable contributions. Particularly appreciated is the active participation of the industry representatives and the steady support by the European Com- mission's Joint Research Centre (JRC). Deep gratitude is owed for the great support by the United States Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy's Fuel Cell Technologies Office (EERE/FCTO) for the organization of the 2014 version of the hydrogen safety research priorities workshop. This page intentionally left blank.

08 HYDROGEN↗
DOE OSTI2022

Recent advances and applications of deep learning methods in materials science

Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Computationally Efficient Learning of Quality Controlled Word Embeddings for Natural Language Processing

Deep learning (DL) has been used for many natural language processing (NLP) tasks due to its superior performance as compared to traditional machine learning approaches. In DL models for NLP, words are represented using word embeddings, which capture both semantic and syntactic information in text. However, 90-95% of the DL trainable parameters are associated with the word embeddings, resulting in a large storage or memory footprint. Therefore, reducing the number of word embedding parameters is critical, especially with the increase of vocabulary size. In this work, we propose a novel approximate word embeddings approach for convolutional neural networks (CNNs) used for text classification tasks. The proposed approach significantly reduces the number of model trainable parameters without noticeably sacrificing in computing performance accuracy. Compared to other techniques, our proposed word embeddings technique does not require modifications to the DL model architecture. We evaluate the performance of the the proposed word embeddings on three classification tasks using two datasets, composed of Yelp and Amazon reviews. The results show that the proposed method can reduce the number of word embeddings parameters by 98% and 99% for the Yelp and Amazon datasets respectively, with no drop in computing accuracy.

Alawad, Mohammed↗
DOE OSTI2022

Accelerating Hydraulic Fracture Imaging by Deep Transfer Learning

Deep transfer learning has a great success story in computer vision (CV), natural language processing, and many other fields. In this communication, we are going to push forward the deep transfer learning to the hydraulic fracture imaging problem by proposing a two-step approach: 1) train a convolutional neural network (CNN) to reconstruct target geometries by a relatively large amount of approximated field patterns generated from a simplified model and 2) fine-tune the top layers of transferred CNN by a small amount of true field patterns generated through a full model. The advantages include the rapid generation of large amount of data through the simplified model and the high reconstruction accuracy through a careful design of the deep transfer learning. Here, the CNN trained through the deep transfer learning can accurately reconstruct the lateral extent and direction of fractures with unseen conductivity and white Gaussian noise, showing a notable acceleration/accuracy enhancement over the previous CNN trained by mixing a small number of true data into the approximated data as data augmentation.

02 PETROLEUM↗
DOE OSTI2021

QLiG: Query Like a Graph For Subgraph Matching

A graph is a natural and flexible modeling approach to represent entities and relationships between them in real-world. A Knowledge Graphs (KG) is a specialized graph with formal and structured representation of facts, relationships, annotated with semantic descriptions. Subgraph matching is one of the fundamental graph problems to identify relationships, interactions and activities of interest within a large graph. A query specification is a collection of abstract components, operations, and constraints to express a pattern. The specification can be implemented in different ways based on underlying data model. Various graph query specifications have been developed over the years and have led to the development of different open-sourced and vendor-specific query languages. Such specification are modeled as an extension of relational algebra used to develop relational query languages such as SQL. Such relational concepts do not inherently support graph queries. There is a need to represent graph queries in terms on graph-based components to expedite query construction by non-database experts. We present a graph-based query approach QLiG (pronounced cleeg), to perform subgraph matching in Labeled Property Graph. We present the query specifications, salient features, and a use case to show functional examples.

Purohit, Sumit↗
DOE OSTI2018

A library of building occupant behaviour models represented in a standardised schema

During the past four decades, a significant body of literature has reported on the impacts of occupant behaviour (OB) on building technologies, operation, and energy consumption. A large number of data-driven behavioural models have been developed based on field data. These models lack standardisation and consistency, leading to difficulties in applications and comparison. To address this concern, an ontology was developed using the drivers-needs-actions-systems (DNAS) framework. Recent work has been carried out to implement the theoretical DNAS framework into an eXtensible Markup Language (XML) schema, titled ‘occupant behaviour XML’ (obXML) which is a practical implementation of OB models that can be integrated into building performance simulation (BPS) programs. This paper presents a newly developed library of OB models represented in the standardised obXML schema format. This library provides ready-to-use examples for BPS users to employ more accurate occupant representation in their energy models. The library, which contains an initial effort of 52 OB models, was made publicly available for the BPS community. As part of the library development process, limitations of the obXML schema were identified and addressed, and future improvements were proposed. Authors hope that by compiling this library building, energy modellers from all over the world can enhance their BPS models by integrating more accurate and robust OB patterns.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

EXSCLAIM!: Harnessing materials science literature for self-labeled microscopy datasets

This work introduces the EXSCLAIM! toolkit for the automatic extraction, separation, and caption-based natural language annotation of images from scientific literature. EXSCLAIM! is used to show how rule-based natural language processing and image recognition can be leveraged to construct an electron microscopy data set containing thousands of keyword-annotated nanostructure images. Moreover, it is demonstrated how a combination of statistical topic modeling and semantic word similarity comparisons can be used to increase the number and variety of keyword annotations on top of the standard annotations from EXSCLAIM! With large-scale imaging datasets constructed from scientific literature, users are well positioned to train neural networks for classification and recognition tasks specific to microscopy-tasks often otherwise inhibited by a lack of sufficient annotated training data.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Inferring Convolutional Neural Networks' Accuracies from Their Architectural Characterizations

The challenge of choosing an appropriate convolutional neural network (CNN) architecture for specific applications and different data sets is still poorly understood in the literature. This is problematic, since CNNs have shown strong promise for analyzing scientific data from many domains including particle imaging detectors. In this paper, we proposed a systematic language that is useful for comparison between different CNN's architectures before training time. This helps us predict whether a network can perform better than a certain threshold accuracy before training up to 70% accuracy using simple machine learning models. Additionally, we found a coefficient of determination of 0.966 for an Ordinary Least Squares model in a regression task to predict accuracy of a large population of networks.

Hoang, Duc↗
DOE OSTI2017

RuleBuilder

RuleBuilder is a tool for drawing graphs represented by the BioNetGen rule-based modeling language (BNGL), a domain-specific language used to formulate mathematical models of biochemical interaction networks. This framework provides an intuitive plain-text representation of such networks using a graph-based formalism. The interactions between the model's components are graph-rewriting rules that denote the minimum context needed for the interaction to occur. In some cases the context required for reactions to occur can grow quite large, and the lengthy textual description of the interaction can be difficult to interpret or debug. RuleBuilder is designed to make this process simpler through visualization of individual rule or pattern strings. The software assists in the reading of existing models by converting the BNGL strings of interest into a graph-based representation composed of nodes and edges. RuleBuilder also enables the user to build the visual representation directly from its interface, composing the BNGL string as components and operators are added to the graphical object.

Fricke, Matthew↗
DOE OSTI2023

Towards POI-based large-scale land use modeling: spatial scale, semantic granularity, and geographic context

The combination of spatial distribution, semantic characteristics, and sometimes temporal dynamics of POIs inside a geographic region can capture its unique land use characteristics. Most previous studies on POI-based land use modeling research focused on one geographic region and select one spatial scale and semantic granularity for land use characterization. There is a lack of understanding on the impact of spatial scale, semantic granularity, and geographic context on POI-based land use modeling, particularly large-scale land use modeling. In this study, we developed a scalable POI-based land use modeling framework and examined the impact of these three factors on POI-based land use characterization using data from three geographic regions. We developed a unified semantic representation framework for POI semantics that can help fuse heterogeneous POI data sources. Then, by combining POIs with a neural network language model, we developed a spatially explicit approach to learn the embedding representation of POIs and AOIs. We trained multiple supervised classifiers using AOI embeddings as input features to predict AOI land use at different semantic granularities. The classification performance of different land use classes was analyzed and compared across three geographic regions to identify the semantic representativeness of POI-based AOI embedding and the impact of geographic context.

58 GEOSCIENCES↗
DOE OSTI2013

AirfoilPrep.py Documentation: Release 0.1.0

AirfoilPrep.py provides functionality to preprocess aerodynamic airfoil data. Essentially, the module is an object oriented version of the AirfoilPrep spreadsheet with additional functionality and is written in the Python language. It allows the user to read in two-dimensional aerodynamic airfoil data, apply three-dimensional rotation corrections for wind turbine applications, and extend the datato very large angles of attack. This document discusses installation, usage, and documentation of the module.

17 WIND ENERGY↗
DOE OSTI2025

NEAR: Neural Embeddings for Amino acid Relationships

Protein language models (PLMs) have recently demonstrated potential to supplant classical protein database search methods based on sequence alignment, but are slower than common alignment-based tools and appear to be prone to a high rate of false labeling. Here, we present NEAR, a method based on neural representation learning that is designed to improve both speed and accuracy of search for likely homologs in a large protein sequence database. NEAR’s ResNet embedding model is trained using contrastive learning guided by trusted sequence alignments. It computes per-residue embeddings for target and query protein sequences, and identifies alignment candidates with a pipeline consisting of residue-level k-NN search and a simple neighbor aggregation scheme. Tests on a benchmark consisting of trusted remote homologs and randomly shuffled decoy sequences reveal that NEAR substantially improves accuracy relative to state-of-the-art PLMs, with lower memory requirements and faster embedding and search speed. While these results suggest that the NEAR model may be useful for standalone homology detection with increased sensitivity over standard alignment-based methods, in this manuscript we focus on a more straightforward analysis of the model’s value as a high-speed pre-filter for sensitive annotation. In that context, NEAR is at least 5x faster than the pre-filter currently used in the widely-used profile hidden Markov model (pHMM) search tool HMMER3, and also outperforms the pre-filter used in our fast pHMM tool, nail.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Application of Variance-Based Sensitivity Analysis to a Large System Dynamics Model

Variance-based sensitivity methods can provide insights into large computational models. We present a novel application of sensitivity analysis to the Biomass Scenario Model (BSM) a large and complex system dynamics model of the developing biofuels industry in the United States. We apply a two-stage sensitivity approach consisting of an initial sensitivity screening, followed by a variance decomposition approach. Identifying key system levers and quantifying their strength is not straightforward in complex system dynamics models that have numerous feedbacks and nonlinear results. Variance-based sensitivity analysis (VBSA) offers a systematic, global approach to assessing system dynamics models because it addresses nonlinear responses and interactive effects. Especially when a large model's size makes manual exploration of the input space difficult and time-consuming, the approach can help to provide a comprehensive understanding of interactions that drive model behaviors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Computational Workflow for Accelerated Molecular Design Using Quantum Chemical Simulations and Deep Learning Models

Efficient methods for searching the chemical space of molecular compounds are needed to automate and accelerate the design of new functional molecules such as pharmaceuticals. Given the high cost in both resources and time for experimental efforts, computational approaches play a key role in guiding the selection of promising molecules for further investigation. Here, we construct a workflow to accelerate design by combining approximate quantum chemical methods [i.e. density-functional tight-binding (DFTB)], a graph convolutional neural network (GCNN) surrogate model for chemical property prediction, and a masked language model (MLM) for molecule generation. Property data from the DFTB calculations are used to train the surrogate model; the surrogate model is used to score candidates generated by the MLM. The surrogate reduces computation time by orders of magnitude compared to the DFTB calculations, enabling an increased search of chemical space. Furthermore, the MLM generates a diverse set of chemical modifications based on pre-training from a large compound library. We utilize the workflow to search for near-infrared photoactive molecules by minimizing the predicted HOMO-LUMO gap as the target property. Our results show that the workflow can generate optimized molecules outside of the original training set, which suggests that iterations of the workflow could be useful for searching vast chemical spaces in a wide range of design problems.

Blanchard, Andrew↗
DOE OSTI2021

A pre-training and self-training approach for biomedical named entity recognition

Named entity recognition (NER) is a key component of many scientific literature mining tasks, such as information retrieval, information extraction, and question answering; however, many modern approaches require large amounts of labeled training data in order to be effective. This severely limits the effectiveness of NER models in applications where expert annotations are difficult and expensive to obtain. In this work, we explore the effectiveness of transfer learning and semi-supervised self-training to improve the performance of NER models in biomedical settings with very limited labeled data (250-2000 labeled samples). We first pre-train a BiLSTM-CRF and a BERT model on a very large general biomedical NER corpus such as MedMentions or Semantic Medline, and then we fine-tune the model on a more specific target NER task that has very limited training data; finally, we apply semi-supervised self-training using unlabeled data to further boost model performance. We show that in NER tasks that focus on common biomedical entity types such as those in the Unified Medical Language System (UMLS), combining transfer learning with self-training enables a NER model such as a BiLSTM-CRF or BERT to obtain similar performance with the same model trained on 3x-8x the amount of labeled data. We further show that our approach can also boost performance in a low-resource application where entities types are more rare and not specifically covered in UMLS.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Spatio-temporal multivariate cluster evolution analysis for detecting and tracking climate impacts

Recent years have seen a growing concern about climate change and its impacts. While Earth System Models (ESMs) can be invaluable tools for studying the impacts of climate change, the complex coupling processes encoded in ESMs and the large amounts of data produced by these models, together with the high internal variability of the Earth system, can obscure important source-to-impact relationships. Here, this paper presents a novel and efficient unsupervised data-driven approach for detecting statistically-significant impacts and tracing spatio-temporal source-impact pathways in the climate through a unique combination of ideas from anomaly detection, clustering and Natural Language Processing (NLP). Using as an exemplar the 1991 eruption of Mount Pinatubo in the Philippines, we demonstrate that the proposed approach is capable of detecting known post-eruption impacts/events. We additionally describe a methodology for extracting meaningful sequences of post-eruption impacts/events by using NLP to efficiently mine frequent multivariate cluster evolutions, which can be used to confirm or discover the chain of physical processes between a climate source and its impact(s).

Anomaly detection↗
DOE OSTI2022

Resilience Design Patterns: A Structured Approach to Resilience at Extreme Scale (V.2.0)

Reliability is a serious concern for future extreme-scale high-performance computing (HPC) systems. Projections based on the current generation of HPC systems and technology roadmaps suggest the prevalence of very high fault rates in future systems. The errors resulting from these faults will propagate and generate various kinds of failures, which may result in outcomes ranging from result corruptions to catastrophic application crashes. Therefore, the resilience challenge for extreme-scale HPC systems requires coordination between various hardware and software technologies that are capable of handling a broad set of fault models at accelerated fault rates. Also, due to practical limits on power consumption in future HPC systems, they are likely to embrace innovative architectures, increasing the levels of hardware and software complexities. Therefore, the techniques that seek to improve resilience must navigate the complex trade-off space between resilience and the overheads to power consumption and performance. While the HPC community has developed various resilience solutions, application-level techniques as well as system-based solutions, the solution space of HPC resilience techniques remains fragmented. There are no formal methods to integrate the various HPC resilience techniques into composite solutions, nor are there methods to holistically evaluate the adequacy and efficacy of such solutions in terms of their protection coverage, and their performance & power efficiency characteristics. Additionally, few implementations of current resilience solutions are portable to newer architectures and software environments that will be deployed on future systems. We developed a new structured approach to the management of HPC resilience using the concept of resilience-based design patterns. In general, a design pattern is a repeatable solution to a commonly occurring problem. We identified the well-known solutions that are commonly used to deal with faults, errors and failures in HPC systems. In the initial design patterns specification (version 1.0), we described the various solutions, which address specific problems in the design of resilient HPC environments, in the form of patterns. Each pattern describes a problem caused by a fault, error or failure event in an HPC environment, and then describes the core of the solution of the problem in such a way that this solution may be adapted to different systems and implemented at different layers of the system stack. The catalog of these resilience design patterns provides designers with a collection of design elements. To construct complete resilience solutions using combinations of various patterns, we defined a framework that enhances HPC designers' understanding of the important constraints and the opportunities for the design patterns to be implemented and deployed at various layers of the system stack. The design framework is also useful for establishing interfaces and mechanisms to coordinate flexible fault management across hardware and software components, as well as to consider the trade-off between performance, resilience, and power consumption when constructing a solution. The resilience design patterns specification version 1.1 included more detailed explanations of the pattern solutions, the context in which the patterns are applicable, and the implications for hardware or software design. It also provided several additional examples and detailed case studies to demonstrate the use of patterns to build realistic solutions. In version 1.2 of the specification document, we have improved the pattern descriptions, including graphical representations of the pattern components. These improvements are largely based on critical comments, feedback and suggestions received from pattern experts and readers of the previous versions of the specification. The pattern classification has been modified to further clarify the relationships between pattern categories. This version of the specification also introduces a pattern language for resilience design patterns. The pattern language presents the patterns in the catalog as a network, revealing the relations among the resilience patterns. The language provides designers with the means to explore alternative techniques for handling a specific fault model that may have different efficiency and complexity characteristics. Using the pattern language also enables the design and implementation of comprehensive resilience solutions as a set of interconnected resilience patterns that can be instantiated across layers of the system stack. The overall goal of this work is to provide hardware and software designers, as well as the users and operators of HPC systems, a systematic methodology for the design and evaluation of resilience technologies in HPC systems that keep scientific applications running to a correct solution in a timely and cost-efficient manner despite frequent faults, errors, and failures of various types. Version 2.0 expands the resilience design pattern classification and catalog to include self-stabilization patterns and reliability, availability and performance models for each structural pattern.

97 MATHEMATICS AND COMPUTING↗