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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Parallel Computing for Probabilistic Response Analysis of High Temperature Composites

The objective of this Phase I research was to establish the required software and hardware strategies to achieve large scale parallelism in solving PCM problems. To meet this objective, several investigations were conducted. First, we identified the multiple levels of parallelism in PCM and the computational strategies to exploit these parallelisms. Next, several software and hardware efficiency investigations were conducted. These involved the use of three different parallel programming paradigms and solution of two example problems on both a shared-memory multiprocessor and a distributed-memory network of workstations.

Sues, R. H.↗
NASA NTRS2004

Applications of Response Surface-Based Methods to Noise Analysis in the Conceptual Design of Revolutionary Aircraft

Due to the growing problem of noise in today's air transportation system, there have arisen needs to incorporate noise considerations in the conceptual design of revolutionary aircraft. Through the use of response surfaces, complex noise models may be converted into polynomial equations for rapid and simplified evaluation. This conversion allows many of the commonly used response surface-based trade space exploration methods to be applied to noise analysis. This methodology is demonstrated using a noise model of a notional 300 passenger Blended-Wing-Body (BWB) transport. Response surfaces are created relating source noise levels of the BWB vehicle to its corresponding FAR-36 certification noise levels and the resulting trade space is explored. Methods demonstrated include: single point analysis, parametric study, an optimization technique for inverse analysis, sensitivity studies, and probabilistic analysis. Extended applications of response surface-based methods in noise analysis are also discussed.

Hill, Geoffrey A.↗
NASA NTRS2006

NASA Taxonomies for Searching Problem Reports and FMEAs

Many types of hazard and risk analyses are used during the life cycle of complex systems, including Failure Modes and Effects Analysis (FMEA), Hazard Analysis, Fault Tree and Event Tree Analysis, Probabilistic Risk Assessment, Reliability Analysis and analysis of Problem Reporting and Corrective Action (PRACA) databases. The success of these methods depends on the availability of input data and the analysts knowledge. Standard nomenclature can increase the reusability of hazard, risk and problem data. When nomenclature in the source texts is not standard, taxonomies with mapping words (sets of rough synonyms) can be combined with semantic search to identify items and tag them with metadata based on a rich standard nomenclature. Semantic search uses word meanings in the context of parsed phrases to find matches. The NASA taxonomies provide the word meanings. Spacecraft taxonomies and ontologies (generalization hierarchies with attributes and relationships, based on terms meanings) are being developed for types of subsystems, functions, entities, hazards and failures. The ontologies are broad and general, covering hardware, software and human systems. Semantic search of Space Station texts was used to validate and extend the taxonomies. The taxonomies have also been used to extract system connectivity (interaction) models and functions from requirements text. Now the Reconciler semantic search tool and the taxonomies are being applied to improve search in the Space Shuttle PRACA database, to discover recurring patterns of failure. Usual methods of string search and keyword search fall short because the entries are terse and have numerous shortcuts (irregular abbreviations, nonstandard acronyms, cryptic codes) and modifier words cannot be used in sentence context to refine the search. The limited and fixed FMEA categories associated with the entries do not make the fine distinctions needed in the search. The approach assigns PRACA report titles to problem classes in the taxonomy. Each ontology class includes mapping words - near-synonyms naming different manifestations of that problem class. The mapping words for Problems, Entities and Functions are converted to a canonical form plus any of a small set of modifier words (e.g. non-uniformity NOT + UNIFORM.) The report titles are parsed as sentences if possible, or treated as a flat sequence of word tokens if parsing fails. When canonical forms in the title match mapping words, the PRACA entry is associated with the corresponding Problem, Entity or Function in the ontology. The user can search for types of failures associated with types of equipment, clustering by type of problem (e.g., all bearings found with problems of being uneven: rough, irregular, gritty ). The results could also be used for tagging PRACA report entries with rich metadata. This approach could also be applied to searching and tagging failure modes, failure effects and mitigations in FMEAs. In the pilot work, parsing 52K+ truncated titles (the test cases that were available), has resulted in identification of both a type of equipment and type of problem in about 75% of the cases. The results are displayed in a manner analogous to Google search results. The effort has also led to the enrichment of the taxonomy, adding some new categories and many new mapping words. Further work would make enhancements that have been identified for improving the clustering and further reducing the false alarm rate. (In searching for recurring problems, good clustering is more important than reducing false alarms). Searching complete PRACA reports should lead to immediate improvement.

Malin, Jane T.↗
NASA NTRS2005

Probabilistic Parameter Uncertainty Analysis of Single Input Single Output Control Systems

The current standards for handling uncertainty in control systems use interval bounds for definition of the uncertain parameters. This approach gives no information about the likelihood of system performance, but simply gives the response bounds. When used in design, current methods of m-analysis and can lead to overly conservative controller design. With these methods, worst case conditions are weighted equally with the most likely conditions. This research explores a unique approach for probabilistic analysis of control systems. Current reliability methods are examined showing the strong areas of each in handling probability. A hybrid method is developed using these reliability tools for efficiently propagating probabilistic uncertainty through classical control analysis problems. The method developed is applied to classical response analysis as well as analysis methods that explore the effects of the uncertain parameters on stability and performance metrics. The benefits of using this hybrid approach for calculating the mean and variance of responses cumulative distribution functions are shown. Results of the probabilistic analysis of a missile pitch control system, and a non-collocated mass spring system, show the added information provided by this hybrid analysis.

Smith, Brett A.↗
NASA NTRS1996

NESSUS/NASTRAN Interface

The NESSUS and NASTRAN computer codes were successfully integrated. The enhanced NESSUS code will use NASTRAN for the structural Analysis and NESSUS for the probabilistic analysis. Any quantities in the NASTRAN bulk data input can be random variables. Any NASTRAN result that is written to the output2 file can be returned to NESSUS as the finite element result. The interfacing between NESSUS and NASTRAN is handled automatically by NESSUS. NESSUS and NASTRAN can be run on different machines using the remote host option.

Millwater, Harry↗
NASA NTRS2018

Demonstration of Probabilistic Sensitivity Analyses Tools on the Structural Response of a Representative Inflatable Space Structure

This work provides an initial step toward demonstrating a probabilistic numerical simulation capability to support trade studies and the development of a certification plan for inflatable space habitats. This study concentrates on interpreting the results from probabilistic analysis and numerical simulation tools to identify parameter sensitivities for a novel inflatable airlock concept, specifically the Non‐Axisymmetric Inflatable Pressure Structure (NAIPS) that was designed and tested under NASA's Minimalistic Advanced Softgoods Hatch (MASH) Program. A brief overview of the finite element model is provided along with the probabilistic sensitivity analysis approach. The sensitivity studies required a model that was numerically stable and efficient enough that hundreds of simulations could be completed in the allotted time. Therefore, the existing full model was simplified by: extracting a quarter symmetry section of the dome; focusing on a single inflation pressure; and replacing the non‐linear material stress‐strain curves with linear, isotropic materials defined by elastic moduli. Responses of interest include the sensitivity of various structural component loads to material properties, cord lengths, inflation pressure and friction. Multiple sensitivity studies were completed and three are reported here. The first study focused on utilizing wide input parameter ranges to provide an opportunity to assess numerical robustness. The next two studies narrowed the parameter ranges to enable focus on understanding uncertainty at a fixed operating condition. The completion of the sensitivity studies improved understanding of the interdependence of multiple inputs on the responses. In addition, numerical stability of the simulations over wide parameter ranges, shows the feasibility of incorporating uncertainty‐based methods in the design and certification of inflatable space habitats. With the experience and trust gained, it is anticipated that these same methods will be applied to nonlinear, orthotropic models in the future.

Lyle, Karen H.↗
NASA NTRS1990

Integrated analysis and applications

An overview is presented of current research activities which, in a broad context, are focused on the development and verification of integrated structural analysis and optimal design capabilities for advanced aerospace propulsion and power systems. The overview encompasses a variety of subject areas including: (1) composite materials; (2) advanced structural analysis; (3) constitutive modeling; (4) computational simulation; (5) probabilistic analysis; and (6) multidisciplinary optimization. Typical results are presented which illustrate the benefit and utility of the emerging technologies as applied to propulsion and power system structures.

Hopkins, Dale A.↗
NASA NTRS1987

Efficient probabilistic fracture mechanics analysis

A systematic and efficient method for probabilistic fracture mechanics analysis is proposed. The method is based on a most-probable-point-locus concept. The locus is obtained iteratively where the initial locus is determined using the linear approximation of the service life N(X) function about the mean values of the random variables X. Linear and quadratic approximations of N(X) are established locally at the most probable points, and the reliability analysis methods are used to compute the cumulative probabilities. By using two examples, the proposed method is demonstrated to be efficient and accurate. One example involved a random loading and N(X) was computed using cycle-by-cycle integration. The method is general and can be applied to other performance functions. It is particularly suitable when the computation of the performance function is time consuming such that Monte Carlo simulation is prohibitively costly.

Wu, Y.-T.↗
NASA NTRS1992

Structural system reliability calculation using a probabilistic fault tree analysis method

The development of a new probabilistic fault tree analysis (PFTA) method for calculating structural system reliability is summarized. The proposed PFTA procedure includes: developing a fault tree to represent the complex structural system, constructing an approximation function for each bottom event, determining a dominant sampling sequence for all bottom events, and calculating the system reliability using an adaptive importance sampling method. PFTA is suitable for complicated structural problems that require computer-intensive computer calculations. A computer program has been developed to implement the PFTA.

Torng, T. Y.↗
DOE OSTI2022

PyApprox: Enabling efficient model analysis

PyApprox is a Python-based one-stop-shop for probabilistic analysis of scientific numerical models. Easy to use and extendable tools are provided for constructing surrogates, sensitivity analysis, Bayesian inference, experimental design, and forward uncertainty quantification. The algorithms implemented represent the most popular methods for model analysis developed over the past two decades, including recent advances in multi-fidelity approaches that use multiple model discretizations and/or simplified physics to significantly reduce the computational cost of various types of analyses. Simple interfaces are provided for the most commonly-used algorithms to limit a user’s need to tune the various hyper-parameters of each algorithm. However, more advanced work flows that require customization of hyper-parameters is also supported. An extensive set of Benchmarks from the literature is also provided to facilitate the easy comparison of different algorithms for a wide range of model analyses. This paper introduces PyApprox and its various features, and presents results demonstrating the utility of PyApprox on a benchmark problem modeling the advection of a tracer in ground water.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2013

Towards Real-time, On-board, Hardware-Supported Sensor and Software Health Management for Unmanned Aerial Systems

Unmanned aerial systems (UASs) can only be deployed if they can effectively complete their missions and respond to failures and uncertain environmental conditions while maintaining safety with respect to other aircraft as well as humans and property on the ground. In this paper, we design a real-time, on-board system health management (SHM) capability to continuously monitor sensors, software, and hardware components for detection and diagnosis of failures and violations of safety or performance rules during the flight of a UAS. Our approach to SHM is three-pronged, providing: (1) real-time monitoring of sensor and/or software signals; (2) signal analysis, preprocessing, and advanced on the- fly temporal and Bayesian probabilistic fault diagnosis; (3) an unobtrusive, lightweight, read-only, low-power realization using Field Programmable Gate Arrays (FPGAs) that avoids overburdening limited computing resources or costly re-certification of flight software due to instrumentation. Our implementation provides a novel approach of combining modular building blocks, integrating responsive runtime monitoring of temporal logic system safety requirements with model-based diagnosis and Bayesian network-based probabilistic analysis. We demonstrate this approach using actual data from the NASA Swift UAS, an experimental all-electric aircraft.

System & Software Health Management↗
NASA NTRS1990

Computational methods for probability of instability calculations

This paper summarizes the development of the methods and a computer program to compute the probability of instability of a dynamic system than can be represented by a system of second-order ordinary linear differential equations. Two instability criteria based upon the roots of the characteristics equation or Routh-Hurwitz test functions are investigated. Computational methods based on system reliability analysis methods and importance sampling concepts are proposed to perform efficient probabilistic analysis. Numerical examples are provided to demonstrate the methods.

Wu, Y.-T.↗
NASA NTRS2015

Intelligent Hardware-Enabled Sensor and Software Safety and Health Management for Autonomous UAS

Unmanned Aerial Systems (UAS) can only be deployed if they can effectively complete their mission and respond to failures and uncertain environmental conditions while maintaining safety with respect to other aircraft as well as humans and property on the ground. We propose to design a real-time, onboard system health management (SHM) capability to continuously monitor essential system components such as sensors, software, and hardware systems for detection and diagnosis of failures and violations of safety or performance rules during the ight of a UAS. Our approach to SHM is three-pronged, providing: (1) real-time monitoring of sensor and software signals; (2) signal analysis, preprocessing, and advanced on-the- y temporal and Bayesian probabilistic fault diagnosis; (3) an unobtrusive, lightweight, read-only, low-power hardware realization using Field Programmable Gate Arrays (FPGAs) in order to avoid overburdening limited computing resources or costly re-certi cation of ight software due to instrumentation. No currently available SHM capabilities (or combinations of currently existing SHM capabilities) come anywhere close to satisfying these three criteria yet NASA will require such intelligent, hardwareenabled sensor and software safety and health management for introducing autonomous UAS into the National Airspace System (NAS). We propose a novel approach of creating modular building blocks for combining responsive runtime monitoring of temporal logic system safety requirements with model-based diagnosis and Bayesian network-based probabilistic analysis. Our proposed research program includes both developing this novel approach and demonstrating its capabilities using the NASA Swift UAS as a demonstration platform.

Robotics↗
NASA NTRS2015

Towards Real-Time, On-Board, Hardware-Supported Sensor and Software Health Management for Unmanned Aerial Systems

For unmanned aerial systems (UAS) to be successfully deployed and integrated within the national airspace, it is imperative that they possess the capability to effectively complete their missions without compromising the safety of other aircraft, as well as persons and property on the ground. This necessity creates a natural requirement for UAS that can respond to uncertain environmental conditions and emergent failures in real-time, with robustness and resilience close enough to those of manned systems. We introduce a system that meets this requirement with the design of a real-time onboard system health management (SHM) capability to continuously monitor sensors, software, and hardware components. This system can detect and diagnose failures and violations of safety or performance rules during the flight of a UAS. Our approach to SHM is three-pronged, providing: (1) real-time monitoring of sensor and software signals; (2) signal analysis, preprocessing, and advanced on-the-fly temporal and Bayesian probabilistic fault diagnosis; and (3) an unobtrusive, lightweight, read-only, low-power realization using Field Programmable Gate Arrays (FPGAs) that avoids overburdening limited computing resources or costly re-certification of flight software. We call this approach rt-R2U2, a name derived from its requirements. Our implementation provides a novel approach of combining modular building blocks, integrating responsive runtime monitoring of temporal logic system safety requirements with model-based diagnosis and Bayesian network-based probabilistic analysis. We demonstrate this approach using actual flight data from the NASA Swift UAS.

Unmanned Aerial System↗
NASA NTRS2018

Life Qualification of Hall Thrusters By Analysis and Test

High power Hall thrusters under development for deep space robotic and human exploration missions have demanding lifetime and operational requirements that make thruster life qualification challenging. The required burn times make it infeasible to conduct multiple tests with durations exceeding the life requirement, and complex wearout phenomena and the need for power throttling during missions complicate analyses and testing. The traditional qualification approach, a single life test that typically demonstrates 50 to 100% margin on the required lifetime, does not provide enough information to demonstrate low failure risk. It is a weak source of statistical information about the location of the peak in the failure probability distribution and provides no information on the width of the distribution. Testing does provide information that can be used to validate physics-based models of failure processes, however, and the combination can be used to assess mission risk. Validated, conservative, deterministic analysis can be used to demonstrate that most failure processes have such large margins that more detailed analysis is unnecessary. For a subset of the failure modes a more detailed probabilistic analysis is required. Pole erosion in a 12.5 kW Hall thruster is used as a detailed example of this qualification process.

Brophy, John R.↗
DOE OSTI2024

Probabilistic Grid Reliability Analysis with Energy Storage Systems

SAND2025-12025O The Probabilistic Grid Reliability Analysis with Energy Storage Systems (ProGRESS) software tool is an open-source tool for assessing the resource adequacy of the evolving electric power grid integrated with energy storage systems (ESS). This tool uses a simulation engine to create diverse scenarios that test the limits of the modern power grid consisting of a high-volume ESS and variable energy resources (VER). Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Nguyen, Tu↗