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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

A research program in empirical computer science

During the grant reporting period our primary activities have been to begin preparation for the establishment of a research program in experimental computer science. The focus of research in this program will be safety-critical systems. Many questions that arise in the effort to improve software dependability can only be addressed empirically. For example, there is no way to predict the performance of the various proposed approaches to building fault-tolerant software. Performance models, though valuable, are parameterized and cannot be used to make quantitative predictions without experimental determination of underlying distributions. In the past, experimentation has been able to shed some light on the practical benefits and limitations of software fault tolerance. It is common, also, for experimentation to reveal new questions or new aspects of problems that were previously unknown. A good example is the Consistent Comparison Problem that was revealed by experimentation and subsequently studied in depth. The result was a clear understanding of a previously unknown problem with software fault tolerance. The purpose of a research program in empirical computer science is to perform controlled experiments in the area of real-time, embedded control systems. The goal of the various experiments will be to determine better approaches to the construction of the software for computing systems that have to be relied upon. As such it will validate research concepts from other sources, provide new research results, and facilitate the transition of research results from concepts to practical procedures that can be applied with low risk to NASA flight projects. The target of experimentation will be the production software development activities undertaken by any organization prepared to contribute to the research program. Experimental goals, procedures, data analysis and result reporting will be performed for the most part by the University of Virginia.

Knight, J. C.↗
NASA NTRS1993

WTEC monograph on instrumentation, control and safety systems of Canadian nuclear facilities

This report updates a 1989-90 survey of advanced instrumentation and controls (I&C) technologies and associated human factors issues in the U.S. and Canadian nuclear industries carried out by a team from Oak Ridge National Laboratory (Carter and Uhrig 1990). The authors found that the most advanced I&C systems are in the Canadian CANDU plants, where the newest plant (Darlington) has digital systems in almost 100 percent of its control systems and in over 70 percent of its plant protection system. Increased emphasis on human factors and cognitive science in modern control rooms has resulted in a reduced workload for the operators and the elimination of many human errors. Automation implemented through digital instrumentation and control is effectively changing the role of the operator to that of a systems manager. The hypothesis that properly introducing digital systems increases safety is supported by the Canadian experience. The performance of these digital systems has been achieved using appropriate quality assurance programs for both hardware and software development. Recent regulatory authority review of the development of safety-critical software has resulted in the creation of isolated software modules with well defined interfaces and more formal structure in the software generation. The ability of digital systems to detect impending failures and initiate a fail-safe action is a significant safety issue that should be of special interest to nuclear utilities and regulatory authorities around the world.

Uhrig, Robert E.↗
NASA NTRS1994

A study of software standards used in the avionics industry

Within the past decade, software has become an increasingly common element in computing systems. In particular, the role of software used in the aerospace industry, especially in life- or safety-critical applications, is rapidly expanding. This intensifies the need to use effective techniques for achieving and verifying the reliability of avionics software. Although certain software development processes and techniques are mandated by government regulating agencies, no one methodology has been shown to consistently produce reliable software. The knowledge base for designing reliable software simply has not reached the maturity of its hardware counterpart. In an effort to increase our understanding of software, the Langley Research Center conducted a series of experiments over 15 years with the goal of understanding why and how software fails. As part of this program, the effectiveness of current industry standards for the development of avionics is being investigated. This study involves the generation of a controlled environment to conduct scientific experiments on software processes.

Hayhurst, Kelly J.↗
NASA NTRS1995

Formal verification of an avionics microprocessor

Formal specification combined with mechanical verification is a promising approach for achieving the extremely high levels of assurance required of safety-critical digital systems. However, many questions remain regarding their use in practice: Can these techniques scale up to industrial systems, where are they likely to be useful, and how should industry go about incorporating them into practice? This report discusses a project undertaken to answer some of these questions, the formal verification of the AAMPS microprocessor. This project consisted of formally specifying in the PVS language a rockwell proprietary microprocessor at both the instruction-set and register-transfer levels and using the PVS theorem prover to show that the microcode correctly implemented the instruction-level specification for a representative subset of instructions. Notable aspects of this project include the use of a formal specification language by practicing hardware and software engineers, the integration of traditional inspections with formal specifications, and the use of a mechanical theorem prover to verify a portion of a commercial, pipelined microprocessor that was not explicitly designed for formal verification.

Srivas, Mandayam, K.↗
NASA NTRS1995

Formal methods in the design of Ada 1995

Formal, mathematical methods are most useful when applied early in the design and implementation of a software system--that, at least, is the familiar refrain. I will report on a modest effort to apply formal methods at the earliest possible stage, namely, in the design of the Ada 95 programming language itself. This talk is an 'experience report' that provides brief case studies illustrating the kinds of problems we worked on, how we approached them, and the extent (if any) to which the results proved useful. It also derives some lessons and suggestions for those undertaking future projects of this kind. Ada 95 is the first revision of the standard for the Ada programming language. The revision began in 1988, when the Ada Joint Programming Office first asked the Ada Board to recommend a plan for revising the Ada standard. The first step in the revision was to solicit criticisms of Ada 83. A set of requirements for the new language standard, based on those criticisms, was published in 1990. A small design team, the Mapping Revision Team (MRT), became exclusively responsible for revising the language standard to satisfy those requirements. The MRT, from Intermetrics, is led by S. Tucker Taft. The work of the MRT was regularly subject to independent review and criticism by a committee of distinguished Reviewers and by several advisory teams--for example, the two User/Implementor teams, each consisting of an industrial user (attempting to make significant use of the new language on a realistic application) and a compiler vendor (undertaking, experimentally, to modify its current implementation in order to provide the necessary new features). One novel decision established the Language Precision Team (LPT), which investigated language proposals from a mathematical point of view. The LPT applied formal mathematical analysis to help improve the design of Ada 95 (e.g., by clarifying the language proposals) and to help promote its acceptance (e.g., by identifying a verifiable subset that would meet the needs of safety-critical applications). The first LPT project, which ran from the fall of 1990 unti the end of 1992, produced studies of several language issues: optimization, sharing and storage, tasking and protected records, overload resolution, the floating point model, distribution, program erros, and object-oriented programming. The second LPT project, in 1994, formally modeled the dynamic semantics of a large part of the (almost) final language definition, looking especially for interactions between language features.

Guaspari, David↗
NASA NTRS1995

Safety analysis

We are engaged in a research program in safety-critical computing that is based on two case studies. We use these case studies to provide application-specific details of the various research issues, and as targets for evaluation of research ideas. The first case study is the Magnetic Stereotaxis System (MSS), an investigational device for performing human neurosurgery being developed in a joint effort between the Department of Physics at the University of Virginia and the Department of Neurosurgery at the University of Iowa. The system operates by manipulating a small permanent magnet (known as a 'seed') within the brain using an externally applied magnetic field. By varying the magnitude and gradient of the external magnetic field, the seed can be moved along a non-linear path and positioned at a site requiring therapy, e.g., a tumor. The magnetic field required for movement through brain tissue is extremely high, and is generated by a set of six superconducting magnets located in a housing surrounding the patient's head. The system uses two X-ray cameras positioned at right angles to detect in real time the locations of the seed and of X-ray opaque markers affixed to the patient's skull. the X-ray images are used to locate the objects of interest in a canonical frame of reference. the second case study is the University of Virginia Research Nuclear Reactor (UVAR). It is a 2 MW thermal, concrete-walled pool reactor. The system operates using 20 to 25 plate-type fuel assemblies placed on a rectangular grid plate. There are three scramable safety rods, and one non-scramable regulating rod that can be put in automatic mode. It was originally constructed in 1959 as a 1 MW system, and it was upgraded to 2 MW in 1973. Though only a research reactor rather than a power reactor, the issues raised are significant and can be related to the problems faced by full-scale reactor systems.

Knight, John C.↗
NASA NTRS1995

Software Safety Progress in NASA

NASA has developed guidelines for development and analysis of safety-critical software. These guidelines have been documented in a Guidebook for Safety Critical Software Development and Analysis. The guidelines represent a practical 'how to' approach, to assist software developers and safety analysts in cost effective methods for software safety. They provide guidance in the implementation of the recent NASA Software Safety Standard NSS-1740.13 which was released as 'Interim' version in June 1994, scheduled for formal adoption late 1995. This paper is a survey of the methods in general use, resulting in the NASA guidelines for safety critical software development and analysis.

Radley, Charles F.↗
NASA NTRS1996

A Framework for Performing V&V within Reuse-Based Software Engineering

Verification and validation (V&V) is performed during application development for many systems, especially safety-critical and mission-critical systems. The V&V process is intended to discover errors, especially errors related to critical processing, as early as possible during the development process. Early discovery is important in order to minimize the cost and other impacts of correcting these errors. In order to provide early detection of errors, V&V is conducted in parallel with system development, often beginning with the concept phase. In reuse-based software engineering, however, decisions on the requirements, design and even implementation of domain assets can be made prior to beginning development of a specific system. In this case, V&V must be performed during domain engineering in order to have an impact on system development. This paper describes a framework for performing V&V within architecture-centric, reuse-based software engineering. This framework includes the activities of traditional application-level V&V, and extends these activities into domain engineering and into the transition between domain engineering and application engineering. The framework includes descriptions of the types of activities to be performed during each of the life-cycle phases, and provides motivation for the activities.

Addy, Edward A.↗
NASA NTRS1993

Applying Formal Methods and Object-Oriented Design to Existing Flight Software

This paper describes a project appling formal methods to a portion of the shuttle on-orbit digital autopilot (DAP). Three objectives of the project were to: demonstrate the use of formal methods on a shuttle application, facilitate the incorporation and validation of new requirements for the system, and verify the safety-critical properties to be exhibited by the software.

Software Failures↗
NASA NTRS2026

Manufacturing and Scale-Up of Natural Fiber Composite eVTOL Propeller Skin and C-Channel

This report presents the manufacturing and scale-up of natural fiber composites for aerospace applications, conducted within NASA’s Convergent Aeronautics Solutions portfolio to advance aircraft capabilities and efficiency. The effort focused on the development and demonstration of two proof-of-concept articles, an Electric Vertical Take-off and Landing (eVTOL) propeller skin and a C-channel, produced through an iterative design and process optimization approach. Best practices and guidance on addressing key challenges with bio-based composite materials and manufacturing is provided. Ideally suited for non-load-bearing structural components, these emerging materials offer potential for weight reduction, vibration damping, noise mitigation, interference-free communication, and cost savings, without compromising the performance of safety-critical primary structures.

Natural fibers↗
NASA NTRSDate not supplied

Physics-Guided Deep Learning for Complex System Health Management and Decision Making

The landscape of complex engineered systems is rapidly evolving, from smart manufacturing facilities to next-generation transportation vehicles. As these systems become increasingly sophisticated and interconnected, the need for advanced health management systems grows ever more critical. These systems must go beyond simple monitoring, actively predicting potential failures before they occur. This paradigm shift from fixed maintenance schedules to condition-based predictions is key to optimizing system performance, enhancing safety, and paving the way for autonomous decision-making across various industries. Whether in industrial processes, energy systems, or advanced transportation, the ability to anticipate and prevent failures is becoming a cornerstone of operational excellence. To accurately predict the future health of any complex system, knowledge of its current health state and future operational conditions is essential. Recent advancements in data-driven algorithms have generated growing interest in artificial intelligence for industrial applications. However, the limitations of pure data-driven methods, particularly in industries where data acquisition is costly and limited, have become apparent. This has led to a focus on blending physics with data-driven algorithms, mitigating the drawbacks of both approaches while emphasizing their respective advantages. This research proposes a novel framework for integrating physics-based performance models with deep learning algorithms for the prognostics of complex safety-critical systems. In this approach, physics-based models serve as a blueprint, capturing fundamental system behaviors, while deep learning algorithms, leveraging real-world sensor data, fill in gaps and identify subtle patterns indicative of potential problems. This hybrid methodology, utilizing techniques such as Physics-Informed Neural Networks (PINNs), offers a powerful solution for predicting system health. By fusing domain knowledge with data-driven insights, this approach promises more accurate, adaptable, and reliable models for health prediction. The resulting framework is versatile, applicable across various sectors including aerospace, manufacturing, and energy systems, ultimately contributing to safer, more efficient operations in our increasingly complex technological landscape.

Diagnostics↗
DOE OSTI2025

Bridging the Gap Between Modern UX Design and Particle Accelerator Control Room Interfaces

Accelerator control systems often represent relatively complex and safety-sensitive human-machine interfaces within process control industries. These systems are technically robust and reflect the cumulative integration of solutions built and adapted across decades. One of the regular, unfortunate casualties of provisional accelerator control system updates is their human-system interfaces (HSIs) which often lag behind modern usability and design standards. An additional challenge is that although there is a multitude of established human factors (HF), and user experience (UX) principles for everyday digital applications, there are very few (if any) established principles for complex and safety-critical applications for an accelerator. This paper argues for the importance of established HF and UX principles (herein referred to as human-centered design principles) into the development of accelerator HSIs, emphasizing the need for clarity, consistency, responsiveness, and cognitive accessibility. Drawing from HF/UX best practices and human-centered design, this paper discusses how these approaches can enhance operator performance, reduce human error, and improve accelerator personnel collaboration. Case studies from Accelerator Control Operations Research Network (ACORN) at Fermilab are explored to demonstrate how interfaces built with human-centered design principles can scale with system complexity while remaining intuitive and efficient for diverse user roles including operators, machine experts, and engineers. By bridging the gap between traditional control system design and modern human-centered design methods, this paper provides a roadmap for evolving accelerator HSIs into more usable, maintainable, and effective tools.

Hill, Rachael [Idaho Natl. Lab.]↗
DOE OSTI2025

Safe Physics-Informed Machine Learning for Dynamics and Control

This tutorial paper focuses on safe physics-informed machine learning in the context of dynamics and control, providing a comprehensive overview of how to integrate physical models and safety guarantees. As machine learning techniques enhance the modeling and control of complex dynamical systems, ensuring safety and stability remains a critical challenge, especially in safety-critical applications like autonomous vehicles, robotics, medical decision-making, and energy systems. We explore various approaches for embedding and ensuring safety constraints, including structural priors, Lyapunov and Control Barrier Functions, predictive control, projections, and robust optimization techniques. Additionally, we delve into methods for uncertainty quantification and safety verification, including reachability analysis and neural network verification tools, which help validate that control policies remain within safe operating bounds even in uncertain environments. The paper includes illustrative examples demonstrating the implementation aspects of safe learning frameworks that combine the strengths of data-driven approaches with the rigor of physical principles, offering a path toward the safe control of complex dynamical systems.

Drgona, Jan↗
DOE OSTI2025

Role of Uncertainty Quantification in the Explainability of Large Language Models for the Nuclear Industry

The meteoric rise of generative artificial intelligence (AI) large language models (LLMs) has created an opportunity to utilize them to increase efficiencies in a multitude of industries. While LLMs carry great potential to revolutionize the manner in which work is performed, numerous known deficiencies limit their utility, including the black box nature of the models, the stochastic nature of the response (i.e., presenting the same prompt multiple times results in different responses), and the potential for hallucination. Widespread adoption of LLMs in safety-critical industries such as nuclear will require some form of explainability to assure end users that the LLM’s response to a given query is valid. Model uncertainty is inherently linked to the concepts of trust and explainability, and can be used to identify situations in which the model is insufficiently certain about its answer. Although uncertainty is not enough in and of itself to determine the suitability of an answer—a model can be very certain of an inaccurate answer—it still provides valuable supporting information. Practical methodologies for gauging or quantifying the uncertainty in LLM outputs are presented herein, along with examples based on nuclear-specific prompts.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Phenomena Identification and Ranking Table (PIRT) for Heat Pipes

This Phenomena Identification and Ranking Table (PIRT) report provides an evaluation of key phenomena affecting the performance and operational regimes of heat pipes, particularly in the context of heat pipe microreactors (HPMRs). Heat pipes are advanced passive thermal management devices that utilize phase change and capillary action to achieve efficient heat transfer. However, due to the complexity of the phenomena coupled in the heat pipe, including phase change, turbulent transition, and compressibility effects, among others, there is high uncertainty in identifying and ranking the important phenomena affecting the operation of heat pipes and the current knowledge for their modeling and simulation and experimental measurements and instrumentation. This PIRT exercise, conducted as a collaborative effort involving the Department of Energy (DOE) Microreactor Program (MRP), the Nuclear Regulatory Commission (NRC), and university partners systematically identifies, reviews, and prioritizes critical phenomena affecting the operation of heat pipes based on their importance and knowledge levels. The report analyzes phenomena with high importance and low knowledge, such as wick de-wetting, critical heat flux, contact angles, and pressure dynamics, discussing challenges and future research directions for improving their modeling and simulation and experimental measurements. Additionally, the report addresses phenomena with low knowledge that could impact heat pipe operation during non-normal or transient operation, including frozen startup, laminar to turbulent transition, geysering, wick priming, underfilling conditions, surface roughness of the wick, NCGs trapped in the wick, and the timescales of startup and shutdown. This comprehensive evaluation serves as a valuable resource for guiding future research and development efforts, supporting the successful integration of heat pipes into critical applications such as nuclear reactors, and contributing to the advancement of heat pipe technologies in safety-critical industries.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2026

Enhancing Operational Safety via Agentic Dialogue Hazard Identification Analysis

Operational safety in high-stakes domains such as industrial process control, autonomous, and safety-critical systems demand reliable hazard identification. While large language models (LLMs) have shown promise in automating safety analysis tasks, single-turn, monolithic inference is brittle: it lacks the self-correction, deliberation, and contextual refinement that safety engineers apply iteratively. In this paper, we introduce HAZDIAL, a framework that investigates whether structured agentic dialogue (multi-agent, multi-turn interactions) improves the quality of NLP-based hazard identification over single-pass baselines. We systematically compare two dialogue modalities: adversarial debate and constructive discussion, and propose an genetic algorithm-based agentic interaction optimization. We evaluate all configurations against a curated golden dataset using standard classification metrics (accuracy, precision, recall, F1) and a novel dialogue metrics. This work advances the intersection of dialogue systems, multi-agent reasoning, and AI safety, providing empirical evidence for dialogue-driven hazard analysis.

Das, Sanjay [ORNL] (ORCID:0009000542591915)↗