Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Language”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

The state of the Java universe

Speaker Bio: James Gosling received a B.Sc. in computer science from the University of Calgary, Canada in 1977. He received a Ph.D. in computer science from Carnegie-Mellon University in 1983. The title of his thesis was The Algebraic Manipulation of Constraints. He has built satellite data acquisition systems, a multiprocessor version of UNIX®, several compilers, mail systems, and window managers. He has also built a WYSIWYG text editor, a constraint-based drawing editor, and a text editor called Emacs, for UNIX systems. At Sun his early activity was as lead engineer of the NeWS window system. He did the original design of the Java programming language and implemented its original compiler and virtual machine. He has recently been a contributor to the Real-Time Specification for Java.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2011

Matlab Based LOCO

The LOCO algorithm has been used by many accelerators around the world. Although the uses for LOCO vary, the most common use has been to find calibration errors and correct the optics functions. The light source community in particular has made extensive use of the LOCO algorithms to tightly control the beta function and coupling. Maintaining high quality beam parameters requires constant attention so a relatively large effort was put into software development for the LOCO application. The LOCO code was originally written in FORTRAN. This code worked fine but it was somewhat awkward to use. For instance, the FORTRAN code itself did not calculate the model response matrix. It required a separate modeling code such as MAD to calculate the model matrix then one manually loads the data into the LOCO code. As the number of people interested in LOCO grew, it required making it easier to use. The decision to port LOCO to Matlab was relatively easy. It's best to use a matrix programming language with good graphics capability; Matlab was also being used for high level machine control; and the accelerator modeling code AT, [5], was already developed for Matlab. Since LOCO requires collecting and processing a relative large amount of data, it is very helpful to have the LOCO code compatible with the high level machine control, [3]. A number of new features were added while porting the code from FORTRAN and new methods continue to evolve, [7][9]. Although Matlab LOCO was written with AT as the underlying tracking code, a mechanism to connect to other modeling codes has been provided.

43 PARTICLE ACCELERATORS↗
DOE OSTI2011

A general higher-order remap algorithm for ALE calculations

A numerical technique for solving the equations of fluid dynamics with arbitrary mesh motion is presented. The three phases of the Arbitrary Lagrangian Eulerian (ALE) methodology are outlined: the Lagrangian phase, grid relaxation phase and remap phase. The Lagrangian phase follows a well known approach from the HEMP code; in addition the strain rate andflow divergence are calculated in a consistent manner according to Margolin. A donor cell method from the SALE code forms the basis of the remap step, but unlike SALE a higher order correction based on monotone gradients is also added to the remap. Four test problems were explored to evaluate the fidelity of these numerical techniques, as implemented in a simple test code, written in the C programming language, called Cercion. Novel cell-centered data structures are used in Cercion to reduce the complexity of the programming and maximize the efficiency of memory usage. The locations of the shock and contact discontinuity in the Riemann shock tube problem are well captured. Cercion demonstrates a high degree of symmetry when calculating the Sedov blast wave solution, with a peak density at the shock front that is similar to the value determined by the RAGE code. For a flyer plate test problem both Cercion and FLAG give virtually the same velocity temporal profile at the target-vacuum interface. When calculating a cylindrical implosion of a steel shell, Cercion and FLAG agree well and the Cercion results are insensitive to the use of ALE.

36 MATERIALS SCIENCE↗
DOE OSTI2011

Acceleration of Radiance for Lighting Simulation by Using Parallel Computing with OpenCL

We report on the acceleration of annual daylighting simulations for fenestration systems in the Radiance ray-tracing program. The algorithm was optimized to reduce both the redundant data input/output operations and the floating-point operations. To further accelerate the simulation speed, the calculation for matrix multiplications was implemented using parallel computing on a graphics processing unit. We used OpenCL, which is a cross-platform parallel programming language. Numerical experiments show that the combination of the above measures can speed up the annual daylighting simulations 101.7 times or 28.6 times when the sky vector has 146 or 2306 elements, respectively.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

Fishing for Clickbaits in Social Images and Texts with Linguistically-Infused Neural Network Models

This paper presents the results and conclusions of our participation in the Clickbait Challenge 2017 on automatic clickbait detection in social media. We first describe linguistically-infused neural network models and identify informative representations to predict the level of clickbaiting present in Twitter posts. Our models allow to answer the question not only whether a post is a clickbait or not, but to what extent it is a clickbait post e.g., not at all, slightly, considerably, or heavily clickbaity using a score ranging from 0 to 1. We evaluate the predictive power of models trained on varied text and image representations extracted from tweets. Our best performing model that relies on the tweet text and linguistic markers of biased language extracted from the tweet and the corresponding page yields mean squared error (MSE) of 0.04, mean absolute error (MAE) of 0.16 and R2 of 0.43 on the held-out test data. For the binary classification setup (clickbait vs. non-clickbait), our model achieved F1 score of 0.69. We have not found that image representations combined with text yield significant performance improvement yet. Nevertheless, this work is the first to present preliminary analysis of objects extracted using Google Tensorflow object detection API from images in clickbait vs. non-clickbait Twitter posts. Finally, we outline several steps to improve model performance as a part of the future work.

dlickbait challenge, Twitter, neural networks, mac↗
DOE OSTI2018

RuSentiment: An Enriched Sentiment Analysis Dataset for Social Media in Russian

This paper presents RuSentiment, a new dataset for sentiment analysis of social media posts in Russian, and a new set of comprehensive annotation guidelines that are extensible to other languages. RuSentiment is currently the largest in its class for Russian, with 30,521 posts annotated with Cohen’s kappa of 0.58 (3 annotations per post). To diversify the dataset, 6,749 posts were pre-selected with an active learning-style strategy. We report baseline classification results, and release the bestperforming embeddings trained on 3.2B tokens in Russian VKontakte posts.

data collection, sentiment analysis, machine learn↗
DOE OSTI2018

Home Energy Upgrade Data Standards: Home Upgrade Program Accelerator

This fact sheet explains how data standards like the Home Performance Extensible Markup Language (HPXML) can be used to establish a common way of defining, formatting, exchanging, and using data to describe residential buildings, energy conservation measures, and energy performance.

Home Performance Extensible Markup Language, HPXML↗
DOE OSTI2021

Machine Learning and Economic Models to Enable Risk-Informed Condition Based Maintenance of a Nuclear Plant Asset

The primary objective of this research is to address challenges in the implementation of risk-informed, condition-based predictive maintenance (PdM), which reduces operating costs while still maintaining the safety and reliability of commercial nuclear power plants (NPPs). To achieve the objective, risk models are being developed by taking advantage of advancements in data analytics, deep learning, machine learning (ML), and artificial intelligence (AI). The notable outcomes presented in the report include ? Development of a ML models using heterogeneous plant process and vibration data collected at different spatial and temporal resolutions from the Salem?s CWS to diagnose a circulating water pump (CWP) failure based on salient fault signatures. The developed diagnostic models are extendable to other faults associated with CWPs and CWP motors given associated fault signatures. ? Development of a natural language processing (NLP) technique to automatically classify the WO data into different categories. The developed NLP technique was validated on independent WO data. This automates the tedious and time-consuming activity of mining and classifying WOs by subject matter experts. ? Estimation of mean time between downtime (i.e., time duration between time instances when 1 or more CWPs are not available) and developed an approach to establish reliability of CWS components using unstructured WO data along with CWS plant process data. ? Formulation of economic model based on Markov chain models. The parameters of associated with the transition rate between different states of Markov chain models were estimated using WO data. The economic model formulation and discussion captures both time-independent and time-dependent parameter variation, leading to risk-informed decision-making.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Towards Semantic Search in Building Sensor Data

This paper presents a search engine system for sensor time series data and metadata in the context of building management. It takes natural language queries as input, retrieves sensor time series data, ranks them with respect to their relevance to a given query, and visualizes the time series as search results. In addition, the system allows users to interact with the search results: they can define events of interest in the visualized results and search across sensor data for similar events, i.e., the search by example scheme. Quantitative evaluations and user studies demonstrate the value of this system for managing building sensor data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2012

A preliminary user-friendly, digital console for the control room parameters supervision in old-generation Nuclear Plants

Improvements in the awareness of a system status is an essential requirement to achieve safety in every kind of plant. In particular, in the case of Nuclear Power Plants (NPPs), a progress is crucial to enhance the Human Machine Interface (HMI) in order to optimize monitoring and analyzing processes of NPP operational states. Firstly, as old-fashioned plants are concerned, an upgrading of the whole console instrumentation is desirable in order to replace an analog visualization with a full-digital system. In this work, we present a novel instrument able to interface the control console of a nuclear reactor, developed by using CompactRio, a National Instruments embedded architecture and its dedicated programming language. This real-time industrial controller composed by a real-time processor and FPGA modules has been programmed to visualize the parameters coming from the reactor, and to storage and reproduce significant conditions anytime. This choice has been made on the basis of the FPGA properties: high reliability, determinism, true parallelism and re-configurability, achieved by a simple programming method, based on LabVIEW real-time environment. The system architecture exploits the FPGA capabilities of implementing custom timing and triggering, hardware-based analysis and co-processing, and highest performance control algorithms. Data stored during the supervisory phase can be reproduced by loading data from a measurement file, re-enacting worthwhile operations or conditions. The system has been thought to be used in three different modes, namely Log File Mode, Supervisory Mode and Simulation Mode. The proposed system can be considered as a first step to develop a more complete Decision Support System (DSS): indeed this work is part of a wider project that includes the elaboration of intelligent agents and meta-theory approaches. A synoptic has been created to monitor every kind of action on the plant through an intuitive sight. Furthermore, another important aim of this work is the possibility to have a front panel available on a web interface: CompactRio acts as a remote server and it is accessible on a dedicated LAN. This supervisory system has been tested and validated on the basis of the real control console for the 1-MW TRIGA reactor RC-1 at the ENEA, Casaccia Research Center. In this paper we show some results obtained by recording each variable as the reactor reaches its maximum level of power. The choice of a research reactor for testing the developed system relies on its training and didactic importance for the education of plant operators: in this context a digital instrument can offer a better user-friendly tool for learning and training. It is worthwhile to remark that such a system does not interfere with the console instrumentation, the latter continuing to preserve the total control. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

Development of Data Acquisition System for nuclear thermal hydraulic out-of-pile facility using the graphical programming methods

This paper describes the design of data acquisition system (DAQ) that is connected to a PC and development of a feedback control system that maintains the coolant temperature of the process at a desired set point using a digital controller system based on the graphical programming language. The paper will provide details about the data acquisition unit, shows the implementation of the controller, and present test results. (authors)

42 ENGINEERING↗
DOE OSTI2020

Artificial Intelligence a D and D Enabler - 20552

The paper addresses the development of a specific search engine dedicated to Decommissioning and Dismantling (D and D) projects. The solution combines advanced model based system engineering methodologies and a data centric approach. It is based on an appropriate use of the last Natural Language processing techniques, the most innovative artificial intelligence algorithms and open source framework to handle large volume and diversity of data met in the case of a nuclear infrastructure we have to dismantle. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

A novel tool for improving the data collection process during control room modernization human-system interface testing and evaluation activities - 271

The U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program is developing a scientific basis to extend the existing U.S. nuclear power plant (NPP) operating life beyond the current 60-year licensing period and to ensure NPP long-term reliability, productivity, safety, and security. Under the Advanced Instrumentation, Information, and Control (II and C) Systems Technologies pathway, the LWRS Program conducts targeted research and development to address long-term aging and obsolescence of existing instrumentation and control technologies, as well as to implement and test new digital II and C technology that enables broad innovation and business improvement in the NPP operating model. To this end, the role of human factors engineering (HFE) is important as part of ensuring that the control room modernization (CRM) activities account for the operator's cognitive and physiological characteristics. Part of the HFE process for CRM entails conducting tests and evaluations (T and Es) that will inform the design of new human-system interface (HSI) technologies. Qualitative data that captures operator feedback and important human-system interactions are an important part of T and E. However, the analyzing this qualitative data in order to provide actionable design recommendations can be time-consuming and error prone. This paper presents an early version of a tool, which uses a combination of natural language processing and supervised machine learning to support the analysis of qualitative data. The objectives of this tool is to improve the process of (1) sifting through freeform text for improved design issue identification, (2) maintaining consistency in theme coding conventions, and (3) readily providing actionable design guidance that can be traceable to state-of-the-art HFE guidelines such as NUREG-0700. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Lessons Learned from AskGDR: Usage and Impact Analysis of the Geothermal Data Repository's AI Research Assistant: Preprint

In October of 2024, the Department of Energy's (DOE) Geothermal Data Repository (GDR) team officially launched AskGDR, an AI research assistant resulting from the integration of a Large Language Model (LLM) with the metadata and supporting documents associated with GDR datasets. AskGDR allows GDR users to ask deeper questions about the origin of datasets, the methods used to collect them, and the findings they help support. Using Retrieval Augmented Generation (RAG), AskGDR can be used to summarize findings spread across dozens of papers and technical reports or to extract relevant information describing a single data field. However, generative AI is experimental. The National Renewable Energy Laboratory (NREL) has been collecting metrics on AskGDR and documenting lessons learned during its deployment. This paper will outline the efficacy and impact of AskGDR through analysis of its use, operating costs, number and types of questions asked, and the quality of answers provided.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Condition-Based Maintenance of a Circulating Water System of a Canadian Nuclear Power Plant using Machine Learning and Statistical Tools

Canada Deuterium Uranium pressurized-heavy-water reactors (PHWR) are a type of nuclear power plant that generate clean and reliable energy. The scope of this work is to automate data analysis methodologies to inform a condition-based maintenance strategy of a circulating water system (CWS) of a PHWR. The multiunit CWS provides a continuous supply of water to cool steam condensers, even during transient scenarios, thereby improving the thermal efficiency. This work aims to develop a machine learning (ML) based approach to detect anomalies in heterogeneous data of a CWS in a PHWR to help inform a predictive maintenance strategy. The heterogeneous data include textual and numeric time series data for a PHWR. Natural-language-processing (NLP)-based models are used to analyze textual data contained in work orders and operator logs and an event-timeseries correlation detection method is applied to assist anomalies diagnoses for CWS. An ML model Robust Linear Model (RLM) is also used to remove the seasonal variations in the system variable distributions based on distributions of environmental variables. A machine learning model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), trained on both original data and data without any seasonal variations will then be used to detect if an anomaly exists. Thus, by moving to an automated methodology to detect, classify, and forecast anomalies, the maintenance strategy would be based on component condition instead of a time-based schedule.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Trustworthiness and Trust: Identifying Factors that Drive Successful Human-AI Interaction in Nuclear Power Plant Applications

Emerging technologies such as artificial intelligence (AI) and machine learning (ML) are rapidly evolving and considered a promising tool for efficient and continued safe operations of the U.S. nuclear power plants (NPPs). Emerging AI techniques like large language models (LLMs) are one such technology that may support personnel at existing NPPs perform work more efficiently. For example, operators may query the current operational status of a power plant via a chat interface leveraging LLMs to access plant-related information in an interactive manner rather than manually collecting various sensor data for tasks such as surveillances or completing work orders. This is a fundamental shift in the way operators currently perform their tasks today. The literature of human-automation interaction indicates that trust is a crucial factor that drives successful interaction between a human operator and an automated system, like an AI-infused NPP application. This work presents the results of a literature review on key factors that relate to trust in AI/LLM technologies for NPP applications. The relevant literature of human factors and cognitive engineering has identified various factors related to trust including trustworthiness, performance characteristics, operator skill and perceived risk. This preliminary literature review will guide development and evaluation of models involving the identified factors influencing trust in AI and develop a framework for human-centered design for interface between humans and AI. By addressing trust, this work supports developing a technical basis for designing key characteristics of AI/LLM to support calibrated trust, which will ultimately support wide-scale adoption of AI/LLM technologies, as well as ensure safe, effective, and reliable use.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Intern Poster

Large Language Models (LLMs) have skyrocketed in popularity after the release of ChatGPT in late 2022. Although LLMs are powerful tools, they can be subject to hallucinations, which is when an LLM (or any AI model) produces misleading/ nonsensical information. The objective is to determine if statistical methods can be used to detect hallucinations as an LLM generates its answer token by token (essentially word by word).

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2025

SEAFORML (Smart Exploration and Analysis For Optimal and Robust Machine Learning)

The poster discusses data analysis of the WAVgraph database and applied machine learning methods for it. The database is a long-term project that seeks to be a comprehensive repository of information on cyber threats and is updated regularly. It was previously unanalyzed and unexplored. The goal was to learn more about it and its contents in order to have a better understanding and enable better use. The data analysis and discovery enabled further exploration through natural language processing, similarity, and clustering methods. The poster shows some of the insights from the analysis and explains the methods used for the machine learning applications.

24 - POWER TRANSMISSION AND DISTRIBUTION↗