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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Developing a novel hierarchical approach for multiscale structural reliability predictions for ultra-high consequence applications

Microstructural variabilities are among the predominant sources of uncertainty in structural performance and reliability. We seek to develop efficient algorithms for multiscale calcu- lations for polycrystalline alloys such as aluminum alloy 6061-T6 in environments where ductile fracture is the dominant failure mode. Our approach employs concurrent multiscale methods, but does not focus on their development. They are a necessary but not sufficient ingredient to multiscale reliability predictions. We have focused on how to efficiently use concurrent models for forward propagation because practical applications cannot include fine-scale details throughout the problem domain due to exorbitant computational demand. Our approach begins with a low-fidelity prediction at the engineering scale that is sub- sequently refined with multiscale simulation. The results presented in this report focus on plasticity and damage at the meso-scale, efforts to expedite Monte Carlo simulation with mi- crostructural considerations, modeling aspects regarding geometric representation of grains and second-phase particles, and contrasting algorithms for scale coupling.

36 MATERIALS SCIENCE↗
DOE OSTI2016

An application of characteristic function in order to predict reliability and lifetime of aeronautical hardware

The forecasting of reliability and life of aeronautical hardware requires recognition of many and various destructive processes that deteriorate the health/maintenance status thereof. The aging of technical components of aircraft as an armament system proves of outstanding significance to reliability and safety of the whole system. The aging process is usually induced by many and various factors, just to mention mechanical, biological, climatic, or chemical ones. The aging is an irreversible process and considerably affects (i.e. reduces) reliability and lifetime of aeronautical equipment. Application of the characteristic function of the aging process is suggested to predict reliability and lifetime of aeronautical hardware. An increment in values of diagnostic parameters is introduced to formulate then, using the characteristic function and after some rearrangements, the partial differential equation. An analytical dependence for the characteristic function of the aging process is a solution to this equation. With the inverse transformation applied, the density function of the aging of aeronautical hardware is found. Having found the density function, one can determine the aeronautical equipment’s reliability and lifetime. The in-service collected or the life tests delivered data are used to attain this goal. Coefficients in this relationship are found using the likelihood function.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Reliability Prediction using FMEA, FTA, and Related Techniques [Slides]

Summary: We presented a reliability analysis framework. We made point estimates of reliability using reliability block diagrams, fault trees, success trees and estimates with uncertainty using expert elicitation, Monte Carlo simulation, Bayesian analysis. Expert elicitation of failure modes and probabilities is labor-intensive, but critical. Bayesian analysis updates information from expert elicitation with data from reliability and aging tests (aging/compatibility data are needed to estimate lower-bound reliabilities at end of life). Estimation by more than one method helps insure consistency and accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

An efficient and reliable predictive method for fluidized bed simulation

In past decades, the continuum approach was the only practical technique to simulate large-scale fluidized bed reactors because discrete approaches suffer from the cost of tracking huge numbers of particles and their collisions. This study significantly improved the computation speed of discrete particle methods in two steps: First, the time-driven hard-sphere (TDHS) algorithm with a larger time-step is proposed allowing a speedup of 20-60 times; second, the number of tracked particles is reduced by adopting the coarse-graining technique gaining an additional 2-3 orders of magnitude speedup of the simulations. A new velocity correction term was introduced and validated in TDHS to solve the over-packing issue in dense granular flow. The TDHS was then coupled with the coarse-graining technique to simulate a pilot-scale riser. The simulation results compared well with experiment data and proved that this new approach can be used for efficient and reliable simulations of large-scale fluidized bed systems.

42 ENGINEERING↗
DOE OSTI2015

Reliable prediction of three-body intermolecular interactions using dispersion-corrected second-order Møller-Plesset perturbation theory

Three-body and higher intermolecular interactions can play an important role in molecular condensed phases. Recent benchmark calculations found problematic behavior for many widely used density functional approximations in treating 3-body intermolecular interactions. Here, we demonstrate that the combination of second-order Møller-Plesset (MP2) perturbation theory plus short-range damped Axilrod-Teller-Muto (ATM) dispersion accurately describes 3-body interactions with reasonable computational cost. The empirical damping function used in the ATM dispersion term compensates both for the absence of higher-order dispersion contributions beyond the triple-dipole ATM term and non-additive short-range exchange terms which arise in third-order perturbation theory and beyond. Empirical damping enables this simple model to out-perform a non-expanded coupled Kohn-Sham dispersion correction for 3-body intermolecular dispersion. The MP2 plus ATM dispersion model approaches the accuracy of O(N{sup 6}) methods like MP2.5 or even spin-component-scaled coupled cluster models for 3-body intermolecular interactions with only O(N{sup 5}) computational cost.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Combining Three-Dimensional Modeling with Artificial Intelligence to Increase Specificity and Precision in Peptide–MHC Binding Predictions

The reliable prediction of the affinity of candidate peptides for the MHC is important for predicting their potential antigenicity and thus influences medical applications, such as decisions on their inclusion in T cell–based vaccines. In this study, we present a rapid, predictive computational approach that combines a popular, sequence-based artificial neural network method, NetMHCpan 4.0, with three-dimensional structural modeling. We find that the ensembles of bound peptide conformations generated by the programs MODELLER and Rosetta FlexPepDock are less variable in geometry for strong binders than for low-affinity peptides. In tests on 1271 peptide sequences for which the experimental dissociation constants of binding to the well-characterized murine MHC allele H-2D b are known, by applying thresholds for geometric fluctuations the structure-based approach in a standalone manner drastically improves the statistical specificity, reducing the number of false positives. Furthermore, filtering candidates generated with NetMHCpan 4.0 with the structure-based predictor led to an increase in the positive predictive value (PPV) of the peptides correctly predicted to bind very strongly (i.e., Kd < 100 nM) from 40 to 52% (p = 0.027). The combined method also significantly improved the PPV when tested on five human alleles, including some with limited data for training. Overall, an average increase of 10% in the PPV was found over the standalone sequence-based method. The combined method should be useful in the rapid design of effective T cell–based vaccines.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Charge transport model to predict intrinsic reliability for dielectric materials

Several lifetime models, mostly empirical in nature, are used to predict reliability for low-k dielectrics used in integrated circuits. There is a dispute over which model provides the most accurate prediction for device lifetime at operating conditions. As a result, there is a need to transition from the use of these largely empirical models to one built entirely on theory. Therefore, a charge transport model was developed to predict the device lifetime of low-k interconnect systems. The model is based on electron transport and donor-type defect formation. Breakdown occurs when a critical defect concentration accumulates, resulting in electron tunneling and the emptying of positively charged traps. The enhanced local electric field lowers the barrier for electron injection into the dielectric, causing a positive feedforward failure. The charge transport model is able to replicate experimental I-V and I-t curves, capturing the current decay at early stress times and the rapid current increase at failure. The model is based on field-driven and current-driven failure mechanisms and uses a minimal number of parameters. All the parameters have some theoretical basis or have been measured experimentally and are not directly used to fit the slope of the time-to-failure versus applied field curve. Despite this simplicity, the model is able to accurately predict device lifetime for three different sources of experimental data. The simulation's predictions at low fields and very long lifetimes show that the use of a single empirical model can lead to inaccuracies in device reliability.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Predicting laser weld reliability with stochastic reduced-order models. Predicting laser weld reliability

Laser welds are prevalent in complex engineering systems and they frequently govern failure. The weld process often results in partial penetration of the base metals, leaving sharp crack-like features with a high degree of variability in the geometry and material properties of the welded structure. Furthermore, accurate finite element predictions of the structural reliability of components containing laser welds requires the analysis of a large number of finite element meshes with very fine spatial resolution, where each mesh has different geometry and/or material properties in the welded region to address variability. We found that traditional modeling approaches could not be efficiently employed. Consequently, a method is presented for constructing a surrogate model, based on stochastic reduced-order models, and is proposed to represent the laser welds within the component. Here, the uncertainty in weld microstructure and geometry is captured by calibrating plasticity parameters to experimental observations of necking as, because of the ductility of the welds, necking – and thus peak load – plays the pivotal role in structural failure. The proposed method is exercised for a simplified verification problem and compared with the traditional Monte Carlo simulation with rather remarkable results.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Combined effects of horizontal and vertical resolution on reliable turbulence prediction at tidal energy sites: A systematic study in the Salish Sea, WA

Predicting turbulence characteristics with coastal ocean models is essential for tidal energy converter deployment. While large eddy simulation provides a detailed turbulence representation, computational limitations restrict its use to smaller domains. We systematically evaluate whether well-configured coastal models can provide reliable turbulence prediction through progressive refinement of 3D model representation. We implemented four model configurations (Levels 1–4) using terrain-following coordinates, isolating the impacts of horizontal resolution, vertical resolution, and layer distribution. We validated all configurations against field measurements from the Salish Sea, WA. Results show that tidal current velocity predictions remain unchanged regarding model configuration, but turbulence properties are sensitive to resolution refinement. Increasing vertical resolution alone proved insufficient; even with vertical sigma-levels rising from 11 to 41, significant underprediction persisted until finer horizontal resolution better captured bathymetric variations. The Level 4 configuration, incorporating geometric sigma-levels distribution, achieved turbulence prediction skill scores exceeding 0.90. Turbulence closure comparison revealed Mellor- Yamada 2.5 outperformed k-epsilon in TKE prediction (skill scores 0.84–0.94 versus 0.72–0.81) due to better boundary layer parameterization. This study shows that well-configured coastal models effectively bridge the gap between simplified tools and costly high-fidelity modeling, offering the tidal energy industry practical and cost-effective turbulence data at commercially relevant scales.

Marine Energy↗
DOE OSTI2017

Mechanical Reliability and Life Prediction of Coated Metallic Interconnects within Solid Oxide Fuel Cells

Metallic cell interconnects (IC) made of ferritic stainless steels, i.e., iron-based alloys, have been increasingly favored in the recent development of planar solid oxide fuel cells (SOFCs) because of their advantages in excellent imperviousness, low electrical resistance, ease in fabrication, and cost effectiveness [1]. Typical SOFC operating conditions inevitably lead to the formation of oxide scales on the surface of ferritic stainless steel, which could cause delamination, buckling, and spallation resulting from the mismatch of the coefficient of thermal expansion and eventually reduce the lifetime of the interconnect components. Various protective coating techniques have been applied to alleviate these drawbacks [2]. In the present work, a fracture-mechanics-based quantitative modeling framework has been established to predict the mechanical reliability and lifetime of the spinel-coated, surface-modified specimens under an isothermal cooling cycle. Analytical solutions have been formulated to evaluate the scale/substrate interfacial strength and determine the critical oxide thickness in terms of a variety of design factors, such as coating thickness, material properties, and uncertainties. The findings then are correlated with the experimentally measured oxide scale growth kinetics to quantify the predicted lifetime of the metallic interconnects.

Solid oxide fuel cell (SOFC), Ferritic stainless s↗
DOE OSTI2023

Investigating Various Failure Models on Commercial Silicon Carbide

Structures and components made from ceramic materials are often brittle and can fail by the unstable growth of existing flaws such as voids and cracks. There have been several failure criteria developed for ceramics, in the past and they are broadly categorized based on their dependency on crack geometry. The present work implements eight failure criteria using an open-source software package – srlife, which predicts the lifetime or failure probability of concentrated solar power (CSP) structural components. The present work also checks the viability of building a ceramic CSP receiver, by analyzing the reliability predictions from srlife for a SiC ceramic. The reliability predictions for a biaxial loading problem indicated the Shetty Mixed-Mode criterion gives the most conservative predictions. Whereas, in case of the CSP receiver, the predictions show that the co-planar strain energy criterion gives the most conservative predictions as it is agnostic towards the type of stress, and therefore, is not recommended to be used designing ceramic receivers.

Chaugule, Pawan (ORCID:0000000275706572)↗