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Results for “reliability prediction”

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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Firefly: The Case for a Holistic Understanding of the Global Structure and Dynamics of the Sun and the Heliosphere

This white paper presents the current knowledge gaps in understanding how magnetic fields control solar (and by extension, stellar) activity in timescales from minutes to years and discusses a transformative observational approach to fill those gaps. The solar activity drives space weather as the result of dynamic magnetic fields forming in the solar interior and evolving continuously until reaching levels of complexity in the atmosphere that trigger eruptions. However, we do not fully understand how solar and, more generally, stellar magnetic fields are generated and how they evolve through the eruptive states. The major obstacle is our reliance on observations from a single viewpoint, particularly in the ecliptic plane. This vantage point can only provide limited information for (i) understanding the generation of solar magnetic fields deep in the convection zone; (ii) determining the origin of the solar cycle and predicting its timing and strength; (iii) explaining the causes of solar activity and their triggers; (iv) reliably predicting when and how CMEs will impact Earth and other planets; (v) fathoming the structure and dynamics of the corona as it creates the heliosphere; (vi) understanding the energization and transport of energetic particles; etc.

Nour E. Raouafi↗
NASA NTRS1983

Burner liner thermal/structural load modelling

The objective of this program is to develop a thermal data transfer computer program module for the burner liner thermal structural load modeling program. This will be accomplished by (1) reviewing existing methodologies for thermal data transfer and selecting three heat transfer codes for application in this program; (2) evaluating the selected codes to establish criteria for developing a computer program module to transfer thermal data from the heat transfer codes to selected stress analysis codes; (3) developing the automated thermal load transfer module; and (4) verifying and documenting the module. In aircraft turbine engine hot section components, cyclic thermal stresses are the most important damage mechanism. Consequently, accurate and reliable prediction of thermal loads is essential to improving durability. To achieve this goal, a considerable effort over the past 20 years has been devoted to the acquisition of engine temperature test data, as well as the development of accurate, reliable, and efficient computer codes for the prediction of steady state and transient temperatures and for the calculation of elastic and inelastic cyclic stresses and strains in hot section components. There is a need for continued development of these codes, because the availability of more accurate analysis techniques for complex configurations has enabled engine designers to use more sophisticated designs to achieve higher cycle efficiency and reduce weight.

Maffeo, R. J.↗
DOE OSTI2024

Graph neural networks for CO 2 solubility predictions in Deep Eutectic Solvents

Deep Eutectic Solvents (DESs) are a promising class of solvents for CO 2 capture. DESs are complex mixtures that can be designed to optimize CO solubility and overall capture process efficiency. However, the vast design landscape of DES mixtures makes experimental investigation prohibitive; as such, there is a need for computational models that can quickly and efficiently navigate the design space and inform data collection efforts. In this work, we propose Graph Neural Network (GNN) models for predicting CO 2 solubility for DESs; the GNN leverages a mixture graph representation that captures the molecular structure of the DES components as well as their intermolecular interactions. Here, we compare the GNN framework against alternative architectures (neural networks, graph convolution networks, and random forests) and data representations (molecular fingerprints, sigma profiles, and graphs). We show that the proposed approach offers superior predictive performance; specifically, we show that solubility can be predicted reliably directly from molecular structure (without the need of using sigma profiles as proposed in previous studies). This result is important, as obtaining sigma profiles requires expensive density functional theory computations. We also explored the ability of GNNs to predict solubility for new DES mixtures and operating conditions. We found that the model extrapolates across temperature reliably. However, we also found deficiencies in the ability of the model to predict solubility for DES mixtures, pressures, and molar ratio not included in the training sets; we show that this is due to an inherent lack of chemical diversity in datasets available in the literature. The proposed computational capabilities can thus help navigate the design space of DES and inform data collection efforts. Our models, data, and benchmarks are shared as Python code implemented in Jupyter notebooks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1990

System Predicts Critical Runway Performance Parameters

Runway-navigation-monitor (RNM) and critical-distances-process electronic equipment designed to provide pilot with timely and reliable predictive navigation information relating to takeoff, landing and runway-turnoff operations. Enables pilot to make critical decisions about runway maneuvers with high confidence during emergencies. Utilizes ground-referenced position data only to drive purely navigational monitor system independent of statuses of systems in aircraft.

Millen, Ernest W.↗
NASA NTRS1992

Regional climates in the GISS global circulation model - Synoptic-scale circulation

A major weakness of current general circulation models (GCMs) is their perceived inability to predict reliably the regional consequences of a global-scale change, and it is these regional-scale predictions that are necessary for studies of human-environmental response. For large areas of the extratropics, the local climate is controlled by the synoptic-scale atmospheric circulation, and it is the purpose of this paper to evaluate the synoptic-scale circulation of the Goddard Institute for Space Studies (GISS) GCM. A methodology for validating the daily synoptic circulation using Principal Component Analysis is described, and the methodology is then applied to the GCM simulation of sea level pressure over the continental United States (excluding Alaska). The analysis demonstrates that the GISS 4 x 5 deg GCM Model II effectively simulates the synoptic-scale atmospheric circulation over the United States. The modes of variance describing the atmospheric circulation of the model are comparable to those found in the observed data, and these modes explain similar amounts of variance in their respective datasets. The temporal behavior of these circulation modes in the synoptic time frame are also comparable.

Hewitson, B.↗
NASA NTRS1990

Recalibrating software reliability models

In spite of much research effort, there is no universally applicable software reliability growth model which can be trusted to give accurate predictions of reliability in all circumstances. Further, it is not even possible to decide a priori which of the many models is most suitable in a particular context. In an attempt to resolve this problem, techniques were developed whereby, for each program, the accuracy of various models can be analyzed. A user is thus enabled to select that model which is giving the most accurate reliability predicitons for the particular program under examination. One of these ways of analyzing predictive accuracy, called the u-plot, in fact allows a user to estimate the relationship between the predicted reliability and the true reliability. It is shown how this can be used to improve reliability predictions in a completely general way by a process of recalibration. Simulation results show that the technique gives improved reliability predictions in a large proportion of cases. However, a user does not need to trust the efficacy of recalibration, since the new reliability estimates prodcued by the technique are truly predictive and so their accuracy in a particular application can be judged using the earlier methods. The generality of this approach would therefore suggest that it be applied as a matter of course whenever a software reliability model is used.

Brocklehurst, Sarah↗
NASA NTRS1992

Comparison of level-1 and level-2 buckling and postbuckling solutions

The accuracy of the solutions obtained with the Level-1 computational modules of DISDECO (Delft Interactive Shell Design Code) are investigated by comparing them with solutions obtained with ANILISA, a Level-2 computational module. Whereas the Level-1 solutions are based on the use of trigonometric functions to represent the out-of-plane displacement W and involve Galerkin type approximations to reduce the governing partial differential equations to simple algebraic expressions, the Level-2 solutions employ a truncated Fourier expansion in the circumferential direction and solve the resulting ordinary differential equations forming a nonlinear eigenvalue problem by a Stodola like technique. This involves the numerical integration of the set of ordinary differential equations, which in turn makes it possible to satisfy the specified boundary conditions exactly. It is shown that in many applications the Level-1 predictions concerning the critical buckling load of the perfect structure and its imperfection sensitivity are quite accurate and reliable. For those cases where this is not true, it is shown how the pre and postprocessing facilities of DISDECO can be used efficiently to arrive at a reliable prediction. This involves always the use of the Level-2 computational module ANILISA with its rigorous prebuckling solution and its feature which makes it possible to satisfy the specified boundary conditions exactly.

Arbocz, J.↗
NASA NTRS1973

Optimization of structures on the basis of fracture mechanics and reliability criteria

Systematic summary of factors which are involved in optimization of given structural configuration is part of report resulting from study of analysis of objective function. Predicted reliability of performance of finished structure is sharply dependent upon results of coupon tests. Optimization analysis developed by study also involves expected cost of proof testing.

Heer, E.↗
NASA NTRS2018

Estimating Software Reliability for Space Launch Vehicles in Probabilistic Risk Assessment (PRA)

It is acutely recognized in the Probabilistic Risk assessment (PRA) field that software plays a defining role in overall system reliability for all modern systems across a wide variety of industries. Regardless if the software is embedded firmware for working components or elements, part of a Human-Machine-Interface, or automated command and control logic, the success of the software to fulfill its function under nominal and off-nominal environments will be a dominant contributor to system reliability. It is also recognized that software reliability prediction and estimation is one of the more challenging and questionable aspects of any PRA or system analyses due to the nature of software and its integration with physics based systems. Irrespective of this dichotomy, any incorporation of software reliability methods requires that the contributions are accountable, quantitative, and tractable. This paper provides a brief overview of software reliability methods, establishes some minimum requirements that the methods should incorporate for completeness, and provides a logic structure for applying software reliability. Model resolution will be discussed that supports current testing plans and trade studies. We will provide initial recommendations for use in the NASA PRA and present a future dynamic option for software and PRA. Space Launch Vehicle Software is recognized to be reliable in static conditions, yet relatively vulnerable to a set of failure modes in changing environments/flight phases. Two quantitative methods were chosen to incorporate software reliability into a Space Launch Vehicle PRA accounting for phase adjustments. One method predicts latent software failure using statistical methods, and the second provides estimates of coding errors and software operating system failures based on test and historical data, respectively. Software uncertainty will also be discussed. We determined that recommendations for PRA software reliability should be modeled at the software module level where multiple software components compose a module and combinations of the software architecture can lead to a functional failure.

Novack, Steven↗
NASA NTRS2018

Estimating Software Reliability for Space Launch Vehicles in Probabilistic Risk Assessment (PRA)

It is acutely recognized in the Probabilistic Risk Assessment (PRA) field that software plays a defining role in overall system reliability for all modern systems across a wide variety of industries. Regardless of whether the software is embedded firmware for working components or elements, part of a Human-Machine-Interface, or automated command and control logic, the success of the software to fulfill its function under nominal and off-nominal environments will be a dominant contributor to system reliability. It is also recognized that software reliability prediction and estimation is one of the more challenging and questionable aspects of any PRA or system analyses due to the nature of software and its integration with physics based systems. Irrespective of this dichotomy, any incorporation of software reliability methods requires that the contributions are accountable, quantitative, and tractable. This paper provides a brief overview of software reliability methods, establishes some minimum requirements that the methods should incorporate for completeness, and provides a logic structure for applying software reliability. Model resolution will be discussed that supports current testing plans and trade studies. We will provide initial recommendations for use in the National Aeronautics and Space Administration (NASA) PRA and present a future dynamic option for software and PRA. Space Launch Vehicle software is recognized to be reliable in static conditions, yet relatively vulnerable to a set of failure modes in changing environments/flight phases. Two quantitative methods were chosen to incorporate software reliability into a Space Launch Vehicle PRA accounting for phase adjustments. One method predicts latent software failure using statistical methods, and the second provides estimates of coding errors and software operating system failures based on test and historical data. Software uncertainty will also be discussed. It is determined that recommendations for PRA software reliability should be modeled at the software module level where multiple software components compose a module and combinations of the software architecture can lead to a functional failure.

Steven D. Novack↗
DOE OSTI2025

Utilizing machine learning to predict tensile ductility and yield strength of CoNiV-based multi-principal elements alloys

This study explores the use of machine learning (ML) as a computational tool to accelerate the design of multi-principal element alloys (MPEAs) with improved tensile elongation. An ML model was trained using available experimental data from the literature along with theoretically derived features to predict yield strength (YS) and ductility. A subset of ML-predicted compositions—CoNiVFe, CoNiVTi, CoNiVTiFe, and CoCrNiVTi—was synthesized and evaluated through tensile testing. The ML model underpredicted YS by approximately 20–30 % and overpredicted ductility by 60–70 % for Ti-containing alloys. Microstructural analysis revealed that Ti segregation at interdendritic regions contributed to early fracture, leading to discrepancies in ductility predictions. Ti segregation at these regions likely drives the increased YS due to segregation strengthening. In contrast, the CoNiVFe alloy showed good agreement with both experimental YS and elongation, with prediction errors of ∼10.2 % and ∼20.7 %, respectively. Microstructural characterization revealed minimal segregation in this alloy, suggesting that the ML model can reliably predict the properties of alloys with little to no segregation. These findings highlight the capability of ML in predicting YS with good accuracy but underscore its limitations in capturing defect-driven failure mechanisms such as segregation-induced embrittlement.

36 MATERIALS SCIENCE↗
NASA NTRS1995

Reliability and Maintainability model (RAM) user and maintenance manual

This report documents the procedures for utilizing and maintaining the Reliability and Maintainability Model (RAM) developed by the University of Dayton for the NASA Langley Research Center (LaRC). The RAM model predicts reliability and maintainability (R&M) parameters for conceptual space vehicles using parametric relationships between vehicle design and performance characteristics and subsystem mean time between maintenance actions (MTBM) and manhours per maintenance action (MH/MA). These parametric relationships were developed using aircraft R&M data from over thirty different military aircraft of all types. This report describes the general methodology used within the model, the execution and computational sequence, the input screens and data, the output displays and reports, and study analyses and procedures. A source listing is provided.

Ebeling, Charles E.↗
NASA NTRS2013

Statistical Issues in Galaxy Cluster Cosmology

The number and growth of massive galaxy clusters are sensitive probes of cosmological structure formation. Surveys at various wavelengths can detect clusters to high redshift, but the fact that cluster mass is not directly observable complicates matters, requiring us to simultaneously constrain scaling relations of observable signals with mass. The problem can be cast as one of regression, in which the data set is truncated, the (cosmology-dependent) underlying population must be modeled, and strong, complex correlations between measurements often exist. Simulations of cosmological structure formation provide a robust prediction for the number of clusters in the Universe as a function of mass and redshift (the mass function), but they cannot reliably predict the observables used to detect clusters in sky surveys (e.g. X-ray luminosity). Consequently, observers must constrain observable-mass scaling relations using additional data, and use the scaling relation model in conjunction with the mass function to predict the number of clusters as a function of redshift and luminosity.

Galaxy↗
NASA NTRS1972

Investigation of a mixed compression axisymmetric inlet at Mach number 5.3

The hypersonic diffuser portion of an uncooled high performance mixed compression, axisymmetric inlet suitable for subsonic burning engines was designed and tested. Performance of a model with a 25.4-cm capture diameter was measured in a wind tunnel and the results were compared with theoretical predictions calculated by a comprehensive computer program. All tests were conducted at a Mach number of 5.3 at a total temperature of 667 K and a total pressure of 11.57 atm. The angle of attack ranged from 0 to + or - 3 deg. Performance at angle of attack remained high. Reasonably high performance in the throat (maximum throat pitot-pressure recovery of 77 percent and an average value of 58 percent) was obtained at 0 deg angle of attack with relatively large amounts of boundary-layer bleed (11 to 22 percent of the capture mass flow). The computer program used in the design of this inlet is considered marginally adequate for predicting hypersonic inlet flow fields. Although the program as it now exists is very useful, an improved computer program that more accurately predicts the boundary layer and the shock-wave-boundary-layer interaction and accounts for boundary-layer bleed should be developed for reliability predicting hypersonic inlet flow fields.

Latham, E. A.↗
DOE OSTI2024

A framework for strategic discovery of credible neural network surrogate models under uncertainty

The widespread integration of deep neural networks in developing data-driven surrogate models for high-fidelity simulations of complex physical systems highlights the critical necessity for robust uncertainty quantification techniques and credibility assessment methodologies, ensuring the reliable deployment of surrogate models in consequential decision-making. Here, this study presents the Occam Plausibility Algorithm for surrogate models (OPAL-surrogate), providing a systematic framework to uncover predictive neural network-based surrogate models within the large space of potential models, including various neural network classes and choices of architecture and hyperparameters. The framework is grounded in hierarchical Bayesian inferences and employs model validation tests to evaluate the credibility and prediction reliability of the surrogate models under uncertainty. Leveraging these principles, OPAL-surrogate introduces a systematic and efficient strategy for balancing the trade-off between model complexity, accuracy, and prediction uncertainty. The effectiveness of OPAL-surrogate is demonstrated through two modeling problems, including the deformation of porous materials for building insulation and turbulent combustion flow for ablation of solid fuels within hybrid rocket motors.

42 ENGINEERING↗
NASA NTRSDate not supplied

Role of PHM in Autonomous Decision-Making: Aerospace applications

There is an increased need for onboard decision-making capabilities in cyber-physical systems be it in energy, automotive, aviation, space, or other industries as they aim for increased efficiency, resiliency, and mission assurance capabilities. Emerging next-gen technologies such as multi-rover planetary missions, distributed satellites, unmanned ground and aerial vehicle operations and smart grid systems rely on in-time risk assessment and autonomous decision-making. One critical piece of the autonomy puzzle is reliable prediction of system behavior under time-varying and potentially uncertain environmental conditions. Further, if agent states change during operation such as initiation of faults or degradation, reliable diagnostic tools need to be investigated. In this tutorial, we will revise approaches that integrates existing physics-based and data-driven models of agents interacting with probability models of the environment and component operation state. Role of existing PHM methodologies as they feed into decision-making under uncertainty will be studied. Balancing critical trade-offs between high-fidelity prognostic models, prediction time-horizons and the computational requirements for in-time cost-effective decision-making will be discussed through the implementation of surrogate models. Finally, the audience will be introduced to a real-time application of in-time trajectory planning of an unmanned aerial system (UAS) based on its PHM assessments under uncertain and varying wind conditions.

decision-making↗