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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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25 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Evolution of International Space Station Program Safety Review Processes and Tools

The International Space Station Program at NASA is constantly seeking to improve the processes and systems that support safe space operations. To that end, the ISS Program decided to upgrade their Safety and Hazard data systems with 3 goals: make safety and hazard data more accessible; better support the interconnection of different types of safety data; and increase the efficiency (and compliance) of safety-related processes. These goals are accomplished by moving data into a web-based structured data system that includes strong process support and supports integration with other information systems. Along with the data systems, ISS is evolving its submission requirements and safety process requirements to support the improved model. In contrast to existing operations (where paper processes and electronic file repositories are used for safety data management) the web-based solution provides the program with dramatically faster access to records, the ability to search for and reference specific data within records, reduced workload for hazard updates and approval, and process support including digital signatures and controlled record workflow. In addition, integration with other key data systems provides assistance with assessments of flight readiness, more efficient review and approval of operational controls and better tracking of international safety certifications. This approach will also provide new opportunities to streamline the sharing of data with ISS international partners while maintaining compliance with applicable laws and respecting restrictions on proprietary data. One goal of this paper is to outline the approach taken by the ISS Progrm to determine requirements for the new system and to devise a practical and efficient implementation strategy. From conception through implementation, ISS and NASA partners utilized a user-centered software development approach focused on user research and iterative design methods. The user-centered approach used on the new ISS hazard system utilized focused user research and iterative design methods employed by the Human Computer Interaction Group at NASA Ames Research Center. Particularly, the approach emphasized the reduction of workload associated with document and data management activities so more resources can be allocated to the operational use of data in problem solving, safety analysis, and recurrence control. The methods and techniques used to understand existing processes and systems, to recognize opportunities for improvement, and to design and review improvements are described with the intent that similar techniques can be employed elsewhere in safety operations. A second goal of this paper is to provide and overview of the web-based data system implemented by ISS. The software selected for the ISS hazard systemMission Assurance System (MAS)is a NASA-customized vairant of the open source software project Bugzilla. The origin and history of MAS as a NASA software project and the rationale for (and advantages of) using open-source software are documented elsewhere (Green, et al., 2009).

Ratterman, Christian D.↗
NASA NTRS2004

Preliminary Analysis of ISS Maintenance History and Implications for Supportability of Future Missions

The International Space Station (ISS) enables the study of supportability issues associated with long-duration human spaceflight. The ISS is a large, complex spacecraft that must be maintained by its crew. In contrast to the Space Shuttle Orbiter vehicle, but similar to spacecraft that will be component elements of future missions beyond low-Earth orbit, ISS does not return to the ground for servicing and provisioning of spares is severely constrained by transportation limits. Although significant technical support is provided by ground personnel, all hands-on maintenance tasks are performed by the crew. It is expected that future missions to distant destinations will be further limited by lack of resupply opportunities and will, eventually, become largely independent of ground support. ISS provides an opportunity to begin learning lessons that will enable future missions to be successful. Data accumulated over the first several years of ISS operations have been analyzed to gain a better understanding of maintenance-related workload. This analysis addresses both preventive and corrective maintenance and includes all U.S segment core systems. Systems and tasks that are major contributors to workload are identified. As further experience accrues, lessons will be learned that will influence future system designs so that they require less maintenance and, when maintenance is required, it can be performed more efficiently. By heeding the lessons of ISS it will be possible to identify system designs that should be more robust and point towards advances in both technology and design that will offer the greatest return on investment.

Watson, Kevin J.↗
DOE OSTI2025

Benchmark Tracking System for Performance Monitoring

Benchmarking is essential for high-performance software development, particularly for monitoring performance across code iterations. This project focused on enhancing the benchmarking process for Lamellar, an asynchronous runtime for High-Performance Computing (HPC) systems developed at Pacific Northwest National Laboratory. Prior to this work, benchmark results were difficult to track and compare across code versions, presenting significant challenges in identifying performance regressions and long-term trends. The primary objective was to establish a systematic, reproducible approach for measuring performance and detecting regressions following code commits. Our methodology involved three key components: standardizing benchmark outputs, implementing data versioning, and developing analysis tools. We standardized the benchmark output format to JSON Line records containing specific fields (execution time, hardware specifications, and environmental variables). To address data management challenges, we evaluated several options and eventually chose a git repository dedicated to benchmark data. We developed a suite of Python tools that processed benchmark results, enriched them with metadata, and facilitated search in the repository. The resulting system enables more efficient filtering and comparison of performance metrics across commit histories, hardware configurations, and benchmark variants through a unified query interface. Our implementation reduces computational overhead by first checking for existing results through configuration matching before initiating new benchmark runs, thereby conserving resources. The system has been validated by Lamellar developers. It organizes results by benchmark type and build configurations for efficient retrieval. Future developments include a planned Large Language Model interface for predicting benchmark performance, incorporating the criterion package for statistical analysis, which will enable automated detection of statistically significant performance changes, and integration with continuous integration pipelines. Despite these enhancements being reserved for future work, this project has successfully provided the Lamellar development team with a framework for maintaining consistent performance standards and identifying optimization opportunities across workloads and hardware environments.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1991

Systematic methods for knowledge acquisition and expert system development

Nine cooperating rule-based systems, collectively called AUTOCREW which were designed to automate functions and decisions associated with a combat aircraft's subsystems, are discussed. The organization of tasks within each system is described; performance metrics were developed to evaluate the workload of each rule base and to assess the cooperation between the rule bases. Simulation and comparative workload results for two mission scenarios are given. The scenarios are inbound surface-to-air-missile attack on the aircraft and pilot incapacitation. The methodology used to develop the AUTOCREW knowledge bases is summarized. Issues involved in designing the navigation sensor selection expert in AUTOCREW's NAVIGATOR knowledge base are discussed in detail. The performance of seven navigation systems aiding a medium-accuracy INS was investigated using Kalman filter covariance analyses. A navigation sensor management (NSM) expert system was formulated from covariance simulation data using the analysis of variance (ANOVA) method and the ID3 algorithm. ANOVA results show that statistically different position accuracies are obtained when different navaids are used, the number of navaids aiding the INS is varied, the aircraft's trajectory is varied, and the performance history is varied. The ID3 algorithm determines the NSM expert's classification rules in the form of decision trees. The performance of these decision trees was assessed on two arbitrary trajectories, and the results demonstrate that the NSM expert adapts to new situations and provides reasonable estimates of the expected hybrid performance.

Belkin, Brenda L.↗
NASA NTRS2012

Preliminary Exploration of Adaptive State Predictor Based Human Operator Modeling

Control-theoretic modeling of the human operator dynamic behavior in manual control tasks has a long and rich history. In the last two decades, there has been a renewed interest in modeling the human operator. There has also been significant work on techniques used to identify the pilot model of a given structure. The purpose of this research is to attempt to go beyond pilot identification based on collected experimental data and to develop a predictor of pilot behavior. An experiment was conducted to quantify the effects of changing aircraft dynamics on an operator s ability to track a signal in order to eventually model a pilot adapting to changing aircraft dynamics. A gradient descent estimator and a least squares estimator with exponential forgetting used these data to predict pilot stick input. The results indicate that individual pilot characteristics and vehicle dynamics did not affect the accuracy of either estimator method to estimate pilot stick input. These methods also were able to predict pilot stick input during changing aircraft dynamics and they may have the capability to detect a change in a subject due to workload, engagement, etc., or the effects of changes in vehicle dynamics on the pilot.

Trujillo, Anna C.↗
DOE OSTI2024

Early experiences evaluating the HPE/Cray ecosystem for AMD GPUs

Summary The Oak Ridge Leadership Computing Facility (OLCF) has a long history of supporting and promoting GPU‐accelerated computing starting with the deployment of the Titan supercomputer in 2021 and continuing with the Summit supercomputer which has a theoretical peak performance of approximately 200 petaflops. Because the majority of Summit's computational power comes from its 27,972 GPUs, users must port their applications to one of the supported programming models in order to make efficient use of the system. To prepare the transition to Frontier, the OLCF's exascale supercomputer, users will need to adapt to an entirely new ecosystem which will include new hardware and software technologies. First, users will need to familiarize themselves with the AMD Radeon GPU architecture. Furthermore, users who have been previously relying on CUDA will need to transition to the Heterogeneous‐Computing Interface for Portability (HIP) or one of the other supported programming models (e.g., OpenMP, OpenACC). In this work, we describe our initial experiences and lessons learned in porting three applications or proxy apps currently running on Summit to the HPE/Cray ecosystem to leverage the compute power from AMD GPUs: minisweep, GenASiS, and Sparkler. Each one is representative of current production workloads utilized at the OLCF, different programming languages, and different programming models.

Melesse Vergara, Verónica G.↗
NASA NTRS2003

Improved pulmonary function in working divers breathing nitrox at shallow depths

INTRODUCTION: There is limited data about the long-term pulmonary effects of nitrox use in divers at shallow depths. This study examined changes in pulmonary function in a cohort of working divers breathing a 46% oxygen enriched mixture while diving at depths less than 12 m. METHODS: A total of 43 working divers from the Neutral Buoyancy Laboratory (NBL), NASA-Johnson Space Center completed a questionnaire providing information on diving history prior to NBL employment, diving history outside the NBL since employment, and smoking history. Cumulative dive hours were obtained from the NBL dive-time database. Medical records were reviewed to obtain the diver's height, weight, and pulmonary function measurements from initial pre-dive, first year and third year annual medical examinations. RESULTS: The initial forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV1) were greater than predicted, 104% and 102%, respectively. After 3 yr of diving at the NBL, both the FVC and FEV1 showed a significant (p < 0.01) increase of 6.3% and 5.5%, respectively. There were no significant changes in peak expiratory flow (PEF), forced mid-expiratory flow rate (FEF(25-75%)), and forced expiratory flow rates at 25%, 50%, and 75% of FVC expired (FEF25%, FEF50%, FEF75%). Cumulative NBL dive hours was the only contributing variable found to be significantly associated with both FVC and FEV1 at 1 and 3 yr. CONCLUSIONS: NBL divers initially belong to a select group with larger than predicted lung volumes. Regular diving with nitrox at shallow depths over a 3-yr period did not impair pulmonary function. Improvements in FVC and FEV1 were primarily due to a training effect.

Non-programmatic↗