Results for “Technical Languages” — Page 4

DOE OSTI2022

CALORIE: A Constraint Language and Optimizing Runtime for Exascale Power Management (Final Report)

This final technical report summarizes the key accomplishments on the CALORIE project, a DOE Early Career award received by PI Henry Hoffmann at the University of Chicago. CALORIE’s main goal was to create principled methodologies, tools, and practices to help scientists and high-performance computing (HPC) operators maximize the performance and insights obtained from scientific computing applications in the face of exascale power constraints. The project accomplished these goals by completing the following objectives: • Designing a language for describing application goals (including constraints and objectives) and system capabilities. The application goals will include things like which simulations or simulations plus in situ analysis will be run together, what the power constraints are, and what requirements there are for in situ analysis (for example, a desired frame rate for visualization). The system capabilities include all components that can be adjusted to tradeoff power and performance. • Designing a runtime system that takes specified goals and capabilities and dynamically determines what capabilities to use to meet the goals. This runtime adapts to changes in goals, application behavior, or available capabilities to automatically maintain the goals despite unexpected disturbances. • Developing a foundational understanding of how power constraints affect the problem of scheduling applications in large-scale systems. This report provides an overview of the accomplishments related to each of these key objectives.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1988

The proposal entry processor: Telescience applications for Hubble Space Telescope science operations

The Proposal Entry Processor (PEP) System supports the submission, entry, technical evaluation review, selection and implementation of Hubble Space Telescope (HST) observing proposals. The PEP system is described concentrating on features which illustrate principles of telescience as applied to the HST. These principles are applicable to other observatories, both space and ground based. The PEP proposal forms allow a scientist to specify scientific objectives without becoming needlessly involved in implementation details. The Remote Proposal Submission System (RPSS) allows proposers to submit proposals electronically via Telenet, SPAN, and other networks. The RPSS performs syntax and sematic checks on proposals. The PEP uses a fourth generation database system to store proposal information and to allow general queries and reports. The Transformation subsystem uses an expert system written in OPS5 to cast a scientific description of an observing program into parameters used by the planning and scheduling system. The TACOS system is a natural language database which supports the proposal selection process. Technical evaluations for resource usage and duplicate science are performed using rulebased systems.

Jackson, Robert↗

NASA NTRS1987

The crustal dynamics intelligent user interface anthology

The National Space Science Data Center (NSSDC) has initiated an Intelligent Data Management (IDM) research effort which has, as one of its components, the development of an Intelligent User Interface (IUI). The intent of the IUI is to develop a friendly and intelligent user interface service based on expert systems and natural language processing technologies. The purpose of such a service is to support the large number of potential scientific and engineering users that have need of space and land-related research and technical data, but have little or no experience in query languages or understanding of the information content or architecture of the databases of interest. This document presents the design concepts, development approach and evaluation of the performance of a prototype IUI system for the Crustal Dynamics Project Database, which was developed using a microcomputer-based expert system tool (M. 1), the natural language query processor THEMIS, and the graphics software system GSS. The IUI design is based on a multiple view representation of a database from both the user and database perspective, with intelligent processes to translate between the views.

Short, Nicholas M., Jr.↗

DOE OSTI2026

A Benchmarking Framework for Evaluating Large Language Model Capabilities in Nuclear Reactor Safety Applications

Large language models (LLMs) are increasingly capable of answering technical questions, synthesizing domain knowledge, and supporting engineering workflows. For nuclear science and engineering, these capabilities require careful, domain-specific evaluation before they can be credibly incorporated into safety-related activities, regulatory review, or technical decision support. This paper presents preliminary results from benchmarking framework for evaluating LLM capabilities in nuclear contexts. The framework is organized into three evaluation categories: nuclear fundamentals, general dual-use knowledge, and plant specific knowledge. These categories are intended to distinguish general nuclear engineering competence from broader technical reasoning and more context-dependent nuclear knowledge. Initial evaluations focus on nuclear fundamentals using questions representative of the knowledge expected of a nuclear professional engineer. Results indicate that contemporary frontier models perform at a high level and substantially exceed the performance of older model generations, with some models approaching saturation of the current benchmark. These findings suggest both the rapid improvement of LLM capabilities in specialized technical domains and the need for more discriminating evaluation methods. The paper presents the benchmark structure, preliminary model-comparison results, and ongoing work. This work supports development of verifiable, responsible, and safety-conscious methods for assessing AI systems in nuclear engineering applications.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗

NASA NTRS1972

FGRAAL: Technical documentation

Implementation of FGRAAL, a FORTRAN-extended graph algorithmic language for the Univac 1108 is described. The report contains the description of the implemented data structures for sets, lists and graphs. It summarizes the changes made for the RALPH compiler to accommodate the special statements of FGRAAL; gives the calling sequences generated by the changed compiler; and describes the object time subroutine package.

Mesztenyi, C. K.↗

NASA NTRS2026

Integrated Computational Materials Engineering (ICME) Capability Maturity Levels for Ecosystems Enabling Digital Transformation

Digital engineering (DE) and integrated computational materials engineering (ICME) are widely recognized as critical enablers of faster, more affordable, and more reliable aerospace systems. However, many organizations have struggled to realize the promised return on investment (ROI) from digital initiatives. A primary reason is the absence of a shared, decision-focused framework that distinguishes simple digitization of existing workflows from true digital transformation that fundamentally changes how engineering decisions are made. This paper introduces an ICME capability maturity framework that fills this gap. The framework defines six cumulative ICME capability maturity levels (CMLs), explicitly tied to decision authority, engineering integration, optimization, and uncertainty management across material, process, structure, and performance scales. It is designed to complement established readiness metrics such as technology readiness levels (TRLs), manufacturing readiness levels (MRLs), and integration readiness levels (IRLs), by addressing a missing dimension: the conditions required for model-informed decision authority across scales. A unifying figure and capability table illustrate the six-level ICME Capability Maturity Framework, showing how organizations progress from digitization—with limited or negative ROI—to true digital transformation, where ICME-enabled workflows deliver measurable improvements in decision quality, cycle time, risk reduction, and reuse. The framework is intended for both technical practitioners and executive leadership, providing a common language to assess current state, guide roadmaps, align software ecosystem investments, and set realistic expectations for digital transformation outcomes. A regulatory-relevant statement clarifying the relationship between ICME capability and existing certification frameworks is provided.

ICME↗

NASA NTRSDate not supplied

Document Classification Techniques for Aviation Letters of Agreement

Often when working with technical documents, it is helpful to classify them into specific categories. In this paper, we conduct a thorough review of natural language processing techniques to perform this classification task on Letters of Agreement (LOAs), technical aviation documents outlining rules for utilizing US airspace. We evaluate multiple techniques, including Transfer Learning, for representing the text in the documents as embeddings: unigram and bigram Term Frequency Inverse Document Frequency (TFIDF), Word2Vec, Doc2Vec, GloVe and RoBERTa. We investigate a wide range of classification models: K-Nearest Neighbors, Random Forest, Support Vector Machines (SVM), Logistic Regression, Naive Bayes, Feed-Forward Neural Network, Convolutional Neural Networks (CNNs) and Long-Short Term Memory (LSTM). By comparing the different methods, we found the best overall approach for our task was to use unigram TFIDF representations with SVM while also gaining insight into how the other methodologies performed on a small technical datasets.

Aayushi Batra↗

DOE OSTI2025

A case study in contrastive learning information combination: Application to technical forensics of additive manufacturing filament source identification

Combination of information from disparate data sources into a single decision is a core challenge in many fields, including the field of technical forensics. Technical forensics (TF) utilizes technical characterization of questioned samples to determine properties of that sample; these properties are then used to infer information of forensic interest, such as provenance, age, or attribution. TF is utilized in traditional forensic applications, such as the attribution of material fragments from an explosive, and in nuclear forensic applications, such as the attribution of actinides which have been interdicted out of regulatory control. The challenge of combining information from disparate sources, described alternately by many terms including “Data Fusion” and “Data Integration”, is exacerbated in the technical forensics domain due to at least two factors: the challenge of interpreting each information source singularly, and the relatively small data set sizes available. Extensive literature exists attempting to combine technical forensics information sources, both in manual and automated processes. These attempts are often bespoke to the specific information sources (such as the bi-, tri-, or quad-isotope chart (Moody, Grant, and Hutcheon 2005)), with some emerging examples of simple early- and late- fusion (, respectively). Simultaneous to the information combination efforts described in the previous paragraph, the field of natural language processing attempted (and largely succeeded) in combining information from multiple non-technical information sources. The ecosystem of “multi-modal” language models, which can take text and images as input, and generate text and images as output, became large and diverse by 2025 (Khan et al. 2025). In a generalized sense, many of these methods are trained by learning neural networks which can convert raw text or images into a vector of numbers describing the text or image, hereafter called “embeddings” and the neural networks performing the conversion are called “embedders”. By using a separate embedder for text and images, finding coincident text and images (such as images with their captions), and optimizing the parameters of the embedders such that the embeddings for the text and the image are similar, the field has found a bridge between text and images (Girdhar et al. 2023). It is the contention of the authors of this report that this insight is not limited to text and images but instead can be extended to any modality which can be found coincidently. The subject of the rest of this report is the application of this method to example multi-modal technical forensic data. Some details about the data used in this report are not appropriate for this report, and are included in a companion report (PNNL-38669).

36 MATERIALS SCIENCE↗

NASA NTRS2024

Expanding Impact with the James Webb Space Telescope: Combining Art, Science, and Technology for Public Outreach

This archived content may contain outdated information or references that may not reflect current policy or programs. NASA’s James Webb Space Telescope revealed its first images on July 12, 2022 and has been used for groundbreaking science since. Scientific exploration transcends international borders, making a mission like Webb feasible with collaborators including the European Space Agency (ESA), and Canadian Space Agency (CSA). Communication about NASA’s discoveries about the universe, so essential for involving the public, likewise needs to be inclusive to all. Art is a tool for cutting across the boundaries of language, culture, and ability, and serves as a bridge between the technical and non-technical communities. Art makes the beauty and nuance of science and engineering accessible in ways that may not be readily perceived to those outside of those communities of practice. Equitable and inclusive outreach endeavors are challenging to implement while considering language translations, culture context, sensory methods, and technology capabilities. Formats including “Unfolding the Universe'' virtual reality platform has allowed for a wider audience to interact with the Webb images including sounds, visual aids, and talks by the scientists and engineers who worked on Webb. This VR platform has been expanded to highlight the ESA contributors to the mission and will be showcased internationally. The Webb first images were also translated into sounds as a method of inclusion for anyone with sight disabilities. We will explore some successful outreach methods and suggestions for inclusivity in future.

Outreach↗

NASA NTRSDate not supplied

Equitable and Inclusive Public Outreach with the James Webb Space Telescope: Combining Art, Science, and Technology

This archived content may contain outdated information or references that may not reflect current policy or programs. NASA’s James Webb Space Telescope revealed its first images on July 12, 2022 and has been used for groundbreaking science since. Scientific exploration transcends international borders, making a mission like Webb feasible with collaborators including the European Space Agency (ESA), and Canadian Space Agency (CSA). Communication about NASA’s discoveries about the universe, so essential for involving the public, likewise needs to be inclusive to all. Art is a tool for cutting across the boundaries of language, culture, and ability, and serves as a bridge between the technical and non-technical communities. Art makes the beauty and nuance of science and engineering accessible in ways that may not be readily perceived to those outside of those communities of practice. Equitable and inclusive outreach endeavors are challenging to implement while considering language translations, culture context, sensory methods, and technology capabilities. Formats including “Unfolding the Universe'' virtual reality platform has allowed for a wider audience to interact with the Webb images including sounds, visual aids, and talks by the scientists and engineers who worked on Webb. This VR platform has been expanded to highlight the ESA contributors to the mission and will be showcased internationally. The Webb first images were also translated into sounds as a method of inclusion for anyone with sight disabilities. We will explore some successful outreach methods and suggestions for inclusivity in future.

Outreach↗

NASA NTRSDate not supplied

Equitable and Inclusive Public Outreach with the James Webb Space Telescope: Combining Art, Science, and Technology

This archived content may contain outdated information or references that may not reflect current policy or programs. NASA’s James Webb Space Telescope revealed its first images on July 12, 2022 and has been used for groundbreaking science since. Scientific exploration transcends international borders, making a mission like Webb feasible with collaborators including the European Space Agency (ESA), and Canadian Space Agency (CSA). Communication about NASA’s discoveries about the universe, so essential for involving the public, likewise needs to be inclusive to all. Art is a tool for cutting across the boundaries of language, culture, and ability, and serves as a bridge between the technical and non-technical communities. Art makes the beauty and nuance of science and engineering accessible in ways that may not be readily perceived to those outside of those communities of practice. Equitable and inclusive outreach endeavors are challenging to implement while considering language translations, culture context, sensory methods, and technology capabilities. Formats including “Unfolding the Universe'' virtual reality platform has allowed for a wider audience to interact with the Webb images including sounds, visual aids, and talks by the scientists and engineers who worked on Webb. This VR platform has been expanded to highlight the ESA contributors to the mission and will be showcased internationally. The Webb first images were also translated into sounds as a method of inclusion for anyone with sight disabilities. We will explore some successful outreach methods and suggestions for inclusivity in future.

Outreach↗

NASA NTRS1981

Breaking the Language Barrier

Preparation for the Apollo Soyuz mission entailed large-scale informational exchange that was accomplished by a computerized translation system. Based on this technology of commercial machine translation, a system known as SYSTRAN II was developed by LATSEC, Inc. and the World Translation Company of Canada. This system increases the output of a human translator by five to eight times, affording cost savings by allowing a large increase in document production without hiring additional people. Extra savings accrue from automatic production of camera-ready copy. Applications include translation of service manuals, proposals and tenders, planning studies, catalogs, list of parts and prices, textbooks, technical reports and education/training materials. System is operational for six language pairs. Systran users include Xerox Corporation, General Motors of Canada, Bell Northern Research of Canada, the U.S. Air Force and the European Commission. The company responsible for the production of SYSTRAN II has changed its name to SYSTRAN.

Source record↗

DOE OSTI2025

solposx: A Python package for determining solar position and atmospheric refraction

solposx is a Python package of reference algorithms for calculating the sun’s position and atmospheric refraction. The package includes 11 solar position algorithms and 6 refraction models from the past 50 years. All functions follow a standardized design pattern, making it easy to compare different algorithms. The provided algorithm implementations have been thoroughly vetted, making the package a valuable research tool and a reliable reference for implementing solar position algorithms in other programming languages or applications.

Jensen, Adam R. [Technical University of Denmark, ↗

NASA NTRS1982

An analysis of information design and packaging for an inelastic market

Producers of technical reports can increase the potential use of the technical report, as an information product, by making certain changes to the sequential, language, and presentation components. These improvements, which effect the overall design and packaging of the product, are discussed. These improvements are likely to have their greatest impact on the marginal technical report user. However, these improvements are not without economic consideration. For this reason, cost factors should be calculated to ensure that benefits to the user and/or increased utility will outweight the cost to the producer.

Pinelli, T. E.↗

DOE OSTI2023

Large Language Models (LLMs) for Energy Systems Research

The integration of Large Language Models (LLMs) in energy systems research promises transformative results, as demonstrated in this work, particularly in the realms of information retrieval and legal document analysis. We have developed a chat-based interface, specifically designed to query an extensive corpus of technical reports from the National Renewable Energy Laboratory (NREL). This interface capitalizes on the natural language processing capabilities of LLMs, providing future consumers of NREL research with a user-friendly platform to access and extract valuable information from technical documents, thus enhancing the dissemination of research to the public. In addition to information retrieval, we have employed LLMs to extract renewable energy siting ordinances from a variety of legal documents, a task traditionally driven by significant human labor. This automated extraction not only supports the ongoing development of the high-impact NREL siting ordinance database but also ensures the database's accuracy and comprehensiveness. Crucially, we have augmented the performance of LLMs through the integration of a decision tree framework, resulting in a substantial improvement in extraction accuracy. Comparative analysis with manual efforts has shown that this approach not only rivals but also significantly surpasses human accuracy, heralding increased reliability in legal document analysis for energy systems research. To democratize access to these advancements and foster collaborative research, we introduce the "Energy Language Model" (ELM), an open-source software package. ELM encapsulates the methodologies and tools developed in this work, providing researchers and practitioners with a robust toolkit to conduct similar analyses within their respective domains. Through these contributions, this work underscores the immense potential of LLMs in revolutionizing energy systems research, improving accuracy, efficiency, and accessibility in the field.

automation↗

NASA NTRS1981

Toward a theory of distributed word expert natural language parsing

An approach to natural language meaning-based parsing in which the unit of linguistic knowledge is the word rather than the rewrite rule is described. In the word expert parser, knowledge about language is distributed across a population of procedural experts, each representing a word of the language, and each an expert at diagnosing that word's intended usage in context. The parser is structured around a coroutine control environment in which the generator-like word experts ask questions and exchange information in coming to collective agreement on sentence meaning. The word expert theory is advanced as a better cognitive model of human language expertise than the traditional rule-based approach. The technical discussion is organized around examples taken from the prototype LISP system which implements parts of the theory.

Rieger, C.↗

NASA NTRS1979

Contemporary soaring nomenclature

Considerable technical progress took place during the past two decades in the field of soaring. In contrast, basic terminology in many languages is lagging seriously. English, one of the leading languages, is no exception. Because of this situation, misunderstandings occur which under some circumstances may result in undesirable consequences, hindering further technical developments as well as soaring activities. Definitions were established and compiled by mid-1973, followed by minor additions (1974 and 1977).

Jenko, S. O.↗

NASA NTRS2024

Logic Programming with Extensible Types

Logic programming allows structuring code in terms of predicates or relations, rather than functions. Although logic programming languages present advantages in terms of declarativeness and conciseness, the introduction of static types has not become part of most popular logic programming languages, increasing the difficulty of testing and debugging of logic programming code. This paper demonstrates how to implement logic programming in Haskell, thus empowering logic programs with types, and functional programs with relations or predicates. We do so by combining three ideas. First, we use extensible types to generalize a type by a parameter type function. Second, we use a sum type as an argument to introduce optional variables in extensible types. Third, we implement a unification algorithm capable of working with any data structure, provided that certain operations are implemented for the given type. We demonstrate our proposal via a series of increasingly complex examples inspired by educational texts in logic programming, and leverage the host language's features to make new notation convenient for users, showing that the proposed approach is not just technically possible but also practical.

logic programming↗