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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Storage management in Ada. Three reports. Volume 1: Storage management in Ada as a risk to the development of reliable software. Volume 2: Relevant aspects of language. Volume 3: Requirements of the language versus manifestations of current implementations

The risk to the development of program reliability is derived from the use of a new language and from the potential use of new storage management techniques. With Ada and associated support software, there is a lack of established guidelines and procedures, drawn from experience and common usage, which assume reliable behavior. The risk is identified and clarified. In order to provide a framework for future consideration of dynamic storage management on Ada, a description of the relevant aspects of the language is presented in two sections: Program data sources, and declaration and allocation in Ada. Storage-management characteristics of the Ada language and storage-management characteristics of Ada implementations are differentiated. Terms that are used are defined in a narrow and precise sense. The storage-management implications of the Ada language are described. The storage-management options available to the Ada implementor and the implications of the implementor's choice for the Ada programmer are also described.

Auty, David↗
NASA NTRS2011

Executable Architecture Research at Old Dominion University

Executable Architectures allow the evaluation of system architectures not only regarding their static, but also their dynamic behavior. However, the systems engineering community do not agree on a common formal specification of executable architectures. To close this gap and identify necessary elements of an executable architecture, a modeling language, and a modeling formalism is topic of ongoing PhD research. In addition, systems are generally defined and applied in an operational context to provide capabilities and enable missions. To maximize the benefits of executable architectures, a second PhD effort introduces the idea of creating an executable context in addition to the executable architecture. The results move the validation of architectures from the current information domain into the knowledge domain and improve the reliability of such validation efforts. The paper presents research and results of both doctoral research efforts and puts them into a common context of state-of-the-art of systems engineering methods supporting more agility.

Tolk, Andreas↗
NASA NTRS2018

PYSAT: Python Satellite Data Analysis Toolkit

A common problem in space science data analysis is combining complementary data sources that are provided and analyzed in different formats and programming languages. The Python Satellite Data Analysis Toolkit (pysat) addresses this issue by providing an open source toolkit that implements the general process of space science data analysis, from beginning to end, in an instrumentindependent manner. This toolkit uses an Instrument object that enables systematic analysis of science data from a variety of platforms within a single interface. Basic functions such as downloading, loading, and cleaning are included for all supported instruments. Common analysis routines are also included, which are instrument and data source independent. A nanokernel is used to provide instrument independence, it is attached to the Instrument object and mediates the systematic and arbitrary modification of loaded data. Pysat uses the nanokernel to improve the rigor of time series analysis, support onthefly orbit determination, and cleanly span file breaks. Pysat's functions and higherlevel scientific analysis features are validated through the use of unit testing. Further adoption by the community provides a set of scientific results produced by a common core, constituting a distributed heritage that supports the validity of the underlying processing and scientific output. These features are used to demonstrate consistency between derived electron density profiles and measured ion drifts, particularly downward ion drifts in the afternoon hours during extreme solar minimum. Pysat builds upon open source Python software that is freely available and encourages communitydriven development.

Stoneback, R.A.↗
NASA NTRS1995

Cross-Cultural Barriers to Effective Communication in Aviation

Recent research on communication and performance in airline flight crews has led to a concept of shared mental models that is associated with effective, efficient team coordination in problem solving and decision making situations. Elements that characterize efficient communication have been identified. This research, however, was based strictly on US crews. More recent studies supported by NASA have identified cultural factors that influence communication among team members who vary in their status and roles. Research is just beginning to identify commonalities and culturally distinct strategies for accomplishing joint tasks. ASRS incident reports have been analyzed to identify language barriers in flight that have safety consequences. Implications of these concepts and findings for multi-cultural command and control will be explored.

Orasanu, Judith↗
NASA NTRS2018

Modeling with Scala

The activities and the associated formalisms for modeling and programming have many commonalities. In this paper we emphasize this point of view by modeling two examples in the programming language Scala, which have previously been modeled in the VDM specification language, and the Promela modeling language of the SPIN model checker respectively. The latter Scala model uses an internal DSL for hierarchical state machines, and a simple randomized testing framework exposing the same errors as found with SPIN. We believe, as the examples illustrate, that this use of a modern programming language for modeling is promising, especially if utilizing internal DSLs.

Joshi, Rajeev↗
NASA NTRS1997

Steady-State Cycle Deck Launcher Developed for Numerical Propulsion System Simulation

One of the objectives of NASA's High Performance Computing and Communications Program's (HPCCP) Numerical Propulsion System Simulation (NPSS) is to reduce the time and cost of generating aerothermal numerical representations of engines, called customer decks. These customer decks, which are delivered to airframe companies by various U.S. engine companies, numerically characterize an engine's performance as defined by the particular U.S. airframe manufacturer. Until recently, all numerical models were provided with a Fortran-compatible interface in compliance with the Society of Automotive Engineers (SAE) document AS681F, and data communication was performed via a standard, labeled common structure in compliance with AS681F. Recently, the SAE committee began to develop a new standard: AS681G. AS681G addresses multiple language requirements for customer decks along with alternative data communication techniques. Along with the SAE committee, the NPSS Steady-State Cycle Deck project team developed a standard Application Program Interface (API) supported by a graphical user interface. This work will result in Aerospace Recommended Practice 4868 (ARP4868). The Steady-State Cycle Deck work was validated against the Energy Efficient Engine customer deck, which is publicly available. The Energy Efficient Engine wrapper was used not only to validate ARP4868 but also to demonstrate how to wrap an existing customer deck. The graphical user interface for the Steady-State Cycle Deck facilitates the use of the new standard and makes it easier to design and analyze a customer deck. This software was developed following I. Jacobson's Object-Oriented Design methodology and is implemented in C++. The AS681G standard will establish a common generic interface for U.S. engine companies and airframe manufacturers. This will lead to more accurate cycle models, quicker model generation, and faster validation leading to specifications. The standard will facilitate cooperative work between industry and NASA. The NPSS Steady-State Cycle Deck team released a batch version of the Steady-State Cycle Deck in March 1996. Version 1.1 was released in June 1996. During fiscal 1997, NPSS accepted enhancements and modifications to the Steady-State Cycle Deck launcher. Consistent with NPSS' commercialization plan, these modifications will be done by a third party that can provide long-term software support.

VanDrei, Donald E.↗
DOE OSTI2025

Deformable phrase level attention: A flexible approach for improving AI based medical coding

Objective: Improving the AI-driven automated medical encoding of clinical text plays a vital role in gathering information on the occurrence of diseases to improve population-level health. This work presents a novel attention mechanism designed to enhance text classification models and ensure appropriate classification of medical concepts in unstructured electronic health records. Materials and Methods: We developed a deformable, phrase-level attention mechanism to identify important lexical word-level and contextual phrase-level information from clinical text documents. We evaluated conventional and transformer-based deep learning models that we extended with our attention mechanism on the extraction of critical cancer information (e.g., site, subsite, laterality, histology, behavior) from 629,908 electronic pathology reports and on the automated medical encoding of 52,722 hospital discharge summaries. Results: Transformer-based models with the deformable, phrase-level attention mechanism achieved the best performance on the extraction of critical cancer information from pathology reports. Conventional- and transformer-based models show similar or better performance than their baseline counterparts on the automated medical encoding of clinical documents. Discussion: The addition of phrase-level information allowed models extended with our proposed method to outperform standard word-level attention. Our method showed favorable properties for the real-world application in terms of model robustness and phenotyping. These results indicate that our method is promising for automated data harmonization for common data models. Conclusion: This work proposes a novel deformable, phrase-level attention mechanism that enhances text classification models in the extraction of medical concepts from clinical text documents. We demonstrate strong performances on two clinical text datasets and showcase real-world deployability of our method.

Automated medical encoding↗
NASA NTRS2002

Information architecture for a planetary 'exploration web'

'Web services' is a common way of deploying distributed applications whose software components and data sources may be in different locations, formats, languages, etc. Although such collaboration is not utilized significantly in planetary exploration, we believe there is significant benefit in developing an architecture in which missions could leverage each others capabilities. We believe that an incremental deployment of such an architecture could significantly contribute to the evolution of increasingly capable, efficient, and even autonomous remote exploration.

middleware messaging IT infrastructure web service↗
DOE OSTI2026

SetGo: Metadata Readiness for Scientific AI Datasets

Scientific datasets intended for AI use require both computational readiness for model training and metadata readiness for discovery, sharing, and reuse. The Readiness Engine for Data Integration (REDI) addresses computational readiness, but no corresponding tool evaluates whether a dataset’s metadata are sufficiently complete, governed, and standards-compliant for publication and agent-based consumption. Existing FAIR assessors operate only on published repository records, and no single system covers FAIR compliance, licensing, provenance, governance, reproducibility, and catalog readiness together. We present SetGo, an open-source Python toolkit that assesses and repairs metadata readiness across these six dimensions before a dataset is published or archived. Applied to four scientific corpora, SetGo surfaces deficiencies that general-purpose tools do not detect: ERA5 climate metadata scores 4% on ACDD 1.3 compliance; materials datasets fail OPTIMADE species-definition requirements; and PDB-derived proteomics data carries licensing terms incompatible with standard SPDX identifiers. Guided enrichment raises overall FAIR scores from 52–57% to 81–91%, and a single setgo publish command pushes to Hugging Face Hub, CKAN, or OpenMetadata with ML Commons Croissant 1.0 metadata sidecars. To support interactive and automated workflows, SetGo integrates with coding agents powered by large language models (LLMs) through a /setgo skill that enables natural-language execution of the full assess–enrich–publish loop, with user involvement limited to supplying missing metadata values.

Wilkinson, Sean [ORNL] (ORCID:0000000214437479)↗
NASA NTRS1991

An aircraft model for the AIAA controls design challenge

A generic, state-of-the-art, high-performance aircraft model, including detailed, full-envelope, nonlinear aerodynamics, and full-envelope thrust and first-order engine response data is described. While this model was primarily developed Controls Design Challenge, the availability of such a model provides a common focus for research in aeronautical control theory and methodology. An implementation of this model using the FORTRAN computer language, associated routines furnished with the aircraft model, and techniques for interfacing these routines to external procedures is also described. Figures showing vehicle geometry, surfaces, and sign conventions are included.

Brumbaugh, Randal W.↗
NASA NTRS2005

The M-Integral for Computing Stress Intensity Factors in Generally Anisotropic Materials

The objective of this project is to develop and demonstrate a capability for computing stress intensity factors in generally anisotropic materials. These objectives have been met. The primary deliverable of this project is this report and the information it contains. In addition, we have delivered the source code for a subroutine that will compute stress intensity factors for anisotropic materials encoded in both the C and Python programming languages and made available a version of the FRANC3D program that incorporates this subroutine. Single crystal super alloys are commonly used for components in the hot sections of contemporary jet and rocket engines. Because these components have a uniform atomic lattice orientation throughout, they exhibit anisotropic material behavior. This means that stress intensity solutions developed for isotropic materials are not appropriate for the analysis of crack growth in these materials. Until now, a general numerical technique did not exist for computing stress intensity factors of cracks in anisotropic materials and cubic materials in particular. Such a capability was developed during the project and is described and demonstrated herein.

Warzynek, P. A.↗
NASA NTRS2005

Use of Field Programmable Gate Array Technology in Future Space Avionics

Fulfilling NASA's new vision for space exploration requires the development of sustainable, flexible and fault tolerant spacecraft control systems. The traditional development paradigm consists of the purchase or fabrication of hardware boards with fixed processor and/or Digital Signal Processing (DSP) components interconnected via a standardized bus system. This is followed by the purchase and/or development of software. This paradigm has several disadvantages for the development of systems to support NASA's new vision. Building a system to be fault tolerant increases the complexity and decreases the performance of included software. Standard bus design and conventional implementation produces natural bottlenecks. Configuring hardware components in systems containing common processors and DSPs is difficult initially and expensive or impossible to change later. The existence of Hardware Description Languages (HDLs), the recent increase in performance, density and radiation tolerance of Field Programmable Gate Arrays (FPGAs), and Intellectual Property (IP) Cores provides the technology for reprogrammable Systems on a Chip (SOC). This technology supports a paradigm better suited for NASA's vision. Hardware and software production are melded for more effective development; they can both evolve together over time. Designers incorporating this technology into future avionics can benefit from its flexibility. Systems can be designed with improved fault isolation and tolerance using hardware instead of software. Also, these designs can be protected from obsolescence problems where maintenance is compromised via component and vendor availability.To investigate the flexibility of this technology, the core of the Central Processing Unit and Input/Output Processor of the Space Shuttle AP101S Computer were prototyped in Verilog HDL and synthesized into an Altera Stratix FPGA.

Ferguson, Roscoe C.↗
NASA NTRS2012

Modeling and Analysis of Mixed Synchronous/Asynchronous Systems

Practical safety-critical distributed systems must integrate safety critical and non-critical data in a common platform. Safety critical systems almost always consist of isochronous components that have synchronous or asynchronous interface with other components. Many of these systems also support a mix of synchronous and asynchronous interfaces. This report presents a study on the modeling and analysis of asynchronous, synchronous, and mixed synchronous/asynchronous systems. We build on the SAE Architecture Analysis and Design Language (AADL) to capture architectures for analysis. We present preliminary work targeted to capture mixed low- and high-criticality data, as well as real-time properties in a common Model of Computation (MoC). An abstract, but representative, test specimen system was created as the system to be modeled.

Driscoll, Kevin R.↗
DOE OSTI2025

Automation of Vulnerability and Patch Management: Information Extraction, Association, and Optimization

Vulnerability and patch management is an integral part of a robust cybersecurity program, yet it grows increasingly complex due to the sheer amount of data that must be analyzed. Particularly in Operational Technology (OT) environments, analysis must be done manually because of the lack of automated solutions. Additionally, there are many steps in this process, from the initial discovery of the vulnerability to the implementation of its remediation, and each step in the process requires different data in order to be performed effectively. In this work, we provide approaches and strategies to assist operators in industrial or OT environments throughout the vulnerability management cycle. Security advisories provide key information about mitigation strategies, or actions that can be taken when a patch is unavailable or cannot be installed. Details of these strategies are not shared in public vulnerability databases and must be found manually. We approach this problem by designing a solution to automatically identify that information within vendor security advisories and retrieve it for operator use. We start with an approach that requires domain-specific knowledge of certain frequently-seen reference websites. Next, an approach that can work on an arbitrary website but relies on certain keywords. Finally, an approach that uses Natural Language Processing (NLP) methods and does not require specific knowledge or keywords. Each of these approaches is more general than its predecessor; we demonstrate high accuracy for all approaches Advisories also often contain details of affected products in non-standard or natural language formats. While this information can be easily understood when read by an operator, the non-standard format acts as a barrier to effective automation. We provide an approach for the first step in this process: identifying vendors in security advisories and mapping them to a standard framework for representing digital assets and software products. We evaluate five established string similarity algorithms, plus one of our own design that combines string similarity and information theory, on the task of mapping vendors to their corresponding entries in the Common Platform Enumeration (CPE) repository. Our results show that our proposed metric outperforms all others. Due to the constraints on time, finances, and personnel for organizations, Large Language Models (LLMs) may seem like attractive opportunities for security operators to speed up information gathering; however, it is still not clear whether LLMs can handle vulnerability management tasks well. To answer this question, we perform an empirical study of LLMs’ ability to provide consistent, accurate information about vulnerabilities in order to guide organizations in their adoption of LLMs. We observe poor performance for all models tested, suggesting that these models are not well-suited to the consistent retrieval of accurate vulnerability information. Finally, once vulnerabilities have been identified and any additional information has been obtained, operators must decide which remediation actions to implement based on their available resources. This already-complex problem becomes even more so when we consider that a vulnerability may have multiple avenues for remediation. We formulate this scenario as two knapsack problems and provide solutions, which we then compare against several existing strategies for vulnerability prioritization seen in real operational environments.

McClanahan, Kylie↗
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 OSTI2024

Machine-Learning-Based Mapping and Modeling of Solar Energy with Ultra-High Spatiotemporal Granularity

Despite the rapid growth of solar energy, we still lack a dynamic, high-fidelity database that tracks the spatiotemporal variations of solar PVs and their associated infrastructures across different places at a spatially resolved scale. The absence of such data presents a barrier to various applications such as solar PV growth projection, solar energy integration, solar incentive design, and climate risk assessment. In this project, we aim to bridge this gap by developing AI-based algorithms to extract granular information about solar PV installations and their associated infrastructures (i.e., distribution grids) from widely available unstructured data like remote sensing images and street views. As a result, we have built the Solar Energy Atlas, a fine-grained, large-scale geospatial overlay of distributed solar PVs and distribution grids. On top of it, we have advanced the understanding of solar adoption and distribution grid vulnerability to climate-induced extremes. Our major contributions can be summarized as follow: (1) By developing new AI algorithms, we have built the most comprehensive solar PV spatiotemporal database covering the entire US. This is the first time we obtained the exact GPS locations, size, subtype, and installation year information for rooftop solar PVs across the US. This database can be used for solar PV growth projection, solar energy integration, solar energy policy analysis and design, and spatially-resolved climate risk assessment. (2) Leveraging this database, we have uncovered the socioeconomic driving factors that are correlated with earlier onset of solar adoption and higher saturated adoption levels. We have identified the heterogeneity in the effects of different types of financial incentives on solar adoption and provided implications for tailoring incentive design based on local income levels to promote equitable solar adoption. (3) We have developed a distribution grid GIS mapping algorithm which can obtain granular geospatial and topology information about distribution grids using multi-modal open data, reducing the dependency on hard-to-obtain smart meter data of conventional approaches. It shows effectiveness in both the U.S. and Sub-Saharan Africa. Using this algorithm, we have uncovered the non-uniform vulnerability of distribution grids to wildfires in California in the aspects of undergrounding protection and Distributed Energy Resources (DER) preparedness. This has provided important implications for improving the affordability and equity of grid adaptation approaches. (3) We have made our produced database publicly available and provided user-friendly interface to enable various stakeholders and the general public to interact with the data. We have also integrated the produced data into the Data Commons platform to enable the public to access the data and correlate it with other location-specific characteristics simply using natural language as queries. The impact of our project is three-fold: (1) New algorithms for mapping solar PVs and distribution grids across space and time, which are open source to facilitate researchers and industry; (2) New databases of solar PVs and distribution grids that have been made publicly available for engineering, social, and policy applications; (3) New understandings and actionable insights on the potential approaches to promoting solar adoption and reducing energy infrastructure vulnerabilities. In this report, we start by discussing the project background and motivation (section 5), followed by the overview of project objectives (section 6). Results and discussion for each task are presented in section 7. Significant accomplishments are summarized in section 8. This report will be concluded by discussing the paths forwards (section 9), products (section 10), and team roles (section 11).

14 SOLAR ENERGY↗
NASA NTRS1988

Dynamic substructuring for shock spectrum analysis using component mode synthesis

Component mode synthesis was used to analyze different types of structures with MSC NASTRAN. The theory and technique of using Multipoint Constraint Equations (MPCs) to connect substructures to each other or to a common foundation is presented. Computation of the dynamic response of the system from shack spectrum inputs was automated using the DMAP programming language of the MSC NASTRAN finite element code.

Mcpheeters, Barton W.↗