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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Estimating epistemic uncertainty in soil parameters for nonlinear site response analyses: Introducing the Latin Hypercube Sampling technique

This study quantifies the effects of epistemic uncertainty in soil parameters on nonlinear (NL) site response analysis (SRA) results, validated against the data recorded at a well-instrumented geotechnical downhole array located in Japan. To this end, a one-dimensional soil column model of the Service Hall Array (SHA) near the Kashiwazaki-Kariwa Nuclear Power Plant (KKNPP) is developed using the finite element (FE) program LS-DYNA. The dynamic stress–strain relationship is characterized by a modified two-stage hyperbolic (MTH) NL backbone curve formulation capable of capturing soil behavior at both small- and large-shear strains. The model is then validated against the ground motion recordings to capture the model bias. The uncertainties associated with the shear-wave velocity profile (a small-strain soil property) and soil shear strength (a large-strain soil property) are incorporated in NL SRA to quantify their separate and joint randomization effects on the results. This study proposes using the Latin Hypercube Sampling (LHS) method as an efficient alternative to commonly used methods, such as Standard Monte Carlo (SMC), to account for uncertainty propagation in such reliability analysis. Both low-intensity and design-level records from the recordings at the SHA are applied to study the contribution of the small- and large-strain NL dynamic soil properties. Results from 46,200 NL FE analyses (23,100 per input motion) are presented. Measured and predicted site response, using recorded ground motions at this downhole array, is compared to assess the significance of soil parameter uncertainty on the observed ground motion dispersions. It is demonstrated that increasing the number of soft realizations and implementing higher level earthquake intensity lead to higher ground motion dispersion. Unlike past studies in randomization of Vs profiles with the SMC method, the LHS method is shown to have no significant effect on the predicted median surface response spectra and amplification factors (AFs) for this case study.

Engineering↗
DOE OSTI2017

Study on the assessment method of human error probabilities in the digitalized main control room - 170

Recently, nuclear power plants (NPPs) in Republic of Korea have been constructed with the new type of main control room (MCR) which is called as digitalized MCR. In digitalized MCR, as digital technologies are being adopted, the environment of MCR has been considerably changed. The operators may obtain the plant data via digitalized human system interfaces (HSIs), large display panel (LDP), computerized procedure system (CPS), soft controls and other system features. Accordingly, the necessity to consider the new environment of MCR has been raised when performing human reliability analysis (HRA) methods. However, none of HRA methods consider the newly installed MCRs. The goal of this paper is to propose new frameworks to assess the human error probabilities (HEPs) in digitalized MCR. Generally, the HEPs are obtained by summing the probabilities of diagnosis errors and the probabilities of execution errors and those probabilities are modified by performance shaping factors (PSFs). In this paper, two parts are included; (1) Assessing the probabilities of diagnosis errors, and (2) Assessing the probabilities of execution errors. In this paper, the process to develop the frameworks are introduced and we expect that the HEPs affecting the features of digitalized MCR can be calculated by using the proposed frameworks. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

General Multifidelity Surrogate Models: Framework and Active-Learning Strategies for Efficient Rare Event Simulation

Estimating the probability of failure for complex real-world systems using high-fidelity computational models is often prohibitively expensive, especially when the probability is small. Exploiting low-fidelity models can make this process more feasible, but merging information from multiple low-fidelity and high-fidelity models poses several challenges. Here, this paper presents a robust multi-fidelity surrogate modeling strategy in which the multi-fidelity surrogate is assembled using an active learning strategy using an on-the-fly model adequacy assessment set within a subset simulation framework for efficient reliability analysis. The multi-fidelity surrogate is assembled by first applying a Gaussian process correction to each low-fidelity model and assigning a model probability based on the model's local predictive accuracy and cost. Three strategies are proposed to fuse these individual surrogates into an overall surrogate model based on model averaging and deterministic/stochastic model selection. The strategies also dictate which model evaluations are necessary. No assumptions are made about the relationships between low-fidelity models, while the high-fidelity model is assumed to be the most accurate and most computationally expensive model. Through two analytical and two numerical case studies, including a case study evaluating the failure probability of Tristructural isotropic-coated (TRISO) nuclear fuels, the algorithm is shown to be highly accurate while drastically reducing the number of high-fidelity model calls (and hence computational cost).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

Polynomial meta-models with canonical low-rank approximations: Numerical insights and comparison to sparse polynomial chaos expansions

The growing need for uncertainty analysis of complex computational models has led to an expanding use of meta-models across engineering and sciences. The efficiency of meta-modeling techniques relies on their ability to provide statistically-equivalent analytical representations based on relatively few evaluations of the original model. Polynomial chaos expansions (PCE) have proven a powerful tool for developing meta-models in a wide range of applications; the key idea thereof is to expand the model response onto a basis made of multivariate polynomials obtained as tensor products of appropriate univariate polynomials. The classical PCE approach nevertheless faces the “curse of dimensionality”, namely the exponential increase of the basis size with increasing input dimension. To address this limitation, the sparse PCE technique has been proposed, in which the expansion is carried out on only a few relevant basis terms that are automatically selected by a suitable algorithm. An alternative for developing meta-models with polynomial functions in high-dimensional problems is offered by the newly emerged low-rank approximations (LRA) approach. By exploiting the tensor–product structure of the multivariate basis, LRA can provide polynomial representations in highly compressed formats. Through extensive numerical investigations, we herein first shed light on issues relating to the construction of canonical LRA with a particular greedy algorithm involving a sequential updating of the polynomial coefficients along separate dimensions. Specifically, we examine the selection of optimal rank, stopping criteria in the updating of the polynomial coefficients and error estimation. In the sequel, we confront canonical LRA to sparse PCE in structural-mechanics and heat-conduction applications based on finite-element solutions. Canonical LRA exhibit smaller errors than sparse PCE in cases when the number of available model evaluations is small with respect to the input dimension, a situation that is often encountered in real-life problems. By introducing the conditional generalization error, we further demonstrate that canonical LRA tend to outperform sparse PCE in the prediction of extreme model responses, which is critical in reliability analysis.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Design for reliability of BEoL and 3-D TSV structures – A joint effort of FEA and innovative experimental techniques

Copper-TSVs for 3D-IC-integration generate novel challenges for reliability analysis and prediction, e.g. the need to master multiple failure criteria for combined loading including residual stress, interface delamination, cracking and fatigue issues. So, the thermal expansion mismatch between copper and silicon leads to a stress situation in silicon surrounding the TSVs which is influencing the electron mobility and as a result the transient behavior of transistors. Furthermore, pumping and protrusion of copper is a challenge for Back-end of Line (BEoL) layers of advanced CMOS technologies already during manufacturing. These effects depend highly on the temperature dependent elastic-plastic behavior of the TSV-copper and the residual stresses determined by the electro deposition chemistry and annealing conditions. That’s why the authors pushed combined simulative/experimental approaches to extract the Young’s-modulus, initial yield stress and hardening coefficients in copper-TSVs from nanoindentation experiments, as well as the temperature dependent initial yield stress and hardening coefficients from bow measurements due to electroplated thin copper films on silicon under thermal cycling conditions. A FIB trench technique combined with digital image correlation is furthermore used to capture the residual stress state near the surface of TSVs. The extracted properties are discussed and used accordingly to investigate the pumping and protrusion of copper-TSVs during thermal cycling. Moreover, the cracking and delamination risks caused by the elevated temperature variation during BEoL ILD deposition are investigated with the help of fracture mechanics approaches.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Different long-term trends of extra-tropical cyclones and windstorms in ERA-20C and NOAA-20CR reanalyses: Extra-tropical cyclones and windstorms in 20th century reanalyses

NOAA 20th century and ERA-20C reanalysis datasets are evaluated regarding the representation of extra-tropical cyclones and windstorms over the Northern and Southern Hemisphere during the respective 6-month winter seasons. The results indicate substantial differences in low-frequency variability between the two datasets – especially in the first half of the 20th century – expressed in different signs and/or magnitudes of long-term trends. This is hampering a reliable analysis of real long-term trends of cyclone and windstorm activity. However, higher-frequency variability is in good agreement between both datasets especially for the Northern Hemisphere.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Using Explainable Artificial Intelligence to Predict Perovskite Solar Cell Electrical Metastability from Operando Photoluminescence Images in Accelerated Stress Testing

Metal halide perovskite (MHP) solar cells exhibit a metastable response to bias governed by coupled ionic–electronic processes, complicating the conventional reciprocity relation between luminescence intensity and device open-circuit voltage (V oc ). This limits the use of luminescence as a diagnostic for device screening or accelerated stress testing, motivating new approaches that can interpret photoluminescence (PL) signals under nonequilibrium conditions. From the artificial intelligence perspective, we develop an explainable deep learning framework that integrates convolutional neural networks (CNN), long short-term memory (LSTM) layers, and an attention mechanism to learn spatiotemporal features from operando photoluminescence PL image sequences. The model achieves a mean absolute error of ±0.027 V in predicting open-circuit voltage transients and reduces extreme-tail errors by up to 78% compared to physics-based reciprocity calculations. Gradient-weighted Class Activation Mapping (Grad-CAM) provides interpretability by highlighting physically meaningful regions such as electrode edges and emergent defect features. From the engineering application perspective, this framework enables accurate, contactless prediction of device V oc and identification of degradation-relevant features during accelerated aging of perovskite solar cells. This approach demonstrates how explainable AI can enhance operando diagnostics and reliability analysis in photovoltaic devices under nonequilibrium conditions.

14 SOLAR ENERGY↗
DOE OSTI2024

Assessing decision boundaries under uncertainty

In order to make design decisions, engineers may seek to identify regions of the design domain that are acceptable in a computationally efficient manner. A design is typically considered acceptable if its reliability with respect to parametric uncertainty exceeds the designer’s desired level of confidence. Despite major advancements in reliability estimation and in design classification via decision boundary estimation, the current literature still lacks a design classification strategy that incorporates parametric uncertainty and desired design confidence. To address this gap, this paper offers a novel interpretation of the acceptance region by defining the decision boundary as the hypersurface which isolates the designs that exceed a user-defined level of confidence given parametric uncertainty. This work addresses the construction of this novel decision boundary using computationally efficient algorithms that were developed for reliability analysis and decision boundary estimation. The approach proposed in this paper is verified on two physical examples from structural and thermal analysis using Support Vector Machines and Efficient Global Optimization-based contour estimation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Assessment of HRA method predictions against operating crew performance: Part I: Study background, design and methodology

This is the first in a series of four papers document ing two large -scale human reliability analysis (HRA) empirical studies – the International HRA Empirical Study and the US HRA Empirical Study. The two studies are the first major efforts in recent years to benchmark HRA methods by comparing HRA method predictions against actual operator performance in accident scenarios simulated on nuclear power plant (NPP) simulators. When a met hod was applied by different analyst teams, the predictions of the different teams were also compared against each other. The purpose of the comparisons was to obtain insights concerning the strengths and weaknesses of the HRA methods addressed in the studies as well as the factors contributing to inter-analyst (or intra-method) variability. The background and motivation for the studies is first presented in the article. Then, the overall study design is described with a discussion on high-level tasks and participants. Next, the methodology to achieve the high-level tasks is discussed. Finally, concluding remarks are made concerning lessons learned on benchmarking HRA methods with crew performance of scenarios on NPP simulators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Assessment of HRA method predictions against operating crew performance: Part III: Conclusions and achievements

This is the last in a series of three papers documenting two large-scale human reliability analysis (HRA) empirical studies – the International HRA Empirical Study and the US HRA Empirical Study. The goal of the two studies was to develop an empirically-based understanding of the performance, strengths, and weaknesses of HRA methods by comparing HRA method predictions against actual operator performance in simulated accident scenarios on nuclear power plant (NPP) simulators. This paper first addresses areas where there is convergence between the two studies and where differences lie. Then it summarizes the combined insights and conclusions, including key findings on HRA in general through lessons learned about the HRA methods assessed in the studies and specific recommendations for improving guidance, practice and methods. Then it discusses the relevance and usefulness of simulator data for HRA in general. Finally, it presents the key achievements and overall conclusions of the two studies taken together.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Identifying human failure events (HFEs) for external hazard probabilistic risk assessment

In recent years, several advancements in nuclear power plant (NPP) probabilistic risk assessment (PRA) have been driven by increased understanding of external hazards, plant response, and uncertainties. However, major sources of uncertainty associated with external hazard PRA remain. One important source is how risk-significant human actions that are carried out to enable plant response and recovery from natural hazards cause the close coupling of physical impacts on plants and overall plant risk during these hazard events. This makes human reliability and human-plant interactions important elements to consider in resolving PRA gaps in external hazards. One of the challenges in considering human response in external hazard probabilistic risk assessment (XHPRA) is that most existing human reliability analysis (HRA) models were not developed for assessing actions outside the control room (termed ex-control room actions) and hazard response. To support this new scope, HRA models will need to be developed or modified to support identification of human activities, causal factors, and uncertainties inherent in external hazard response, thereby providing insights regarding event timing and physical event conditions as they relate to human performance. In this study, there are two main objectives: (1) evaluate the applicability of an existing cognitive-based HRA method, Phoenix, to ex-control room actions, and (2) identify sources of uncertainty to be characterized or reduced in order to make this method suitable for XHPRA. The first step of such work is performed by assessing the suitability of existing HRA methods to support identifying human failure events (HFEs) for human response to flooding hazards. These HFEs are human actions or inactions that are involved in human responses to flooding hazards and could contribute to the loss of a critical function for the plant in the scenario being examined. Here, in this work, decomposition analyses using the cognitive-based Phoenix HRA model are used to identify HFEs. The Phoenix method was found to be suitable for analyzing ex-control room actions as well as identifying specific HFEs and underlying crew failure modes (CFMs). However, the method's suitability for use in ex-control room actions would benefit from expanding the available CFMs to accommodate a larger variety of physical and communication tasks.

42 ENGINEERING↗
DOE OSTI2024

PIP-II cryoplant non thermal cycling updates

The Proton Improvement Plan-II (PIP-II) is a crucial upgrade to the Fermilab accelerator complex, featuring a new 800-MeV Superconducting Radio-Frequency (SRF) linear accelerator (LINAC) with 23 cryomodules operating at 2 K. The LINAC thermohydraulic conditions are satisfied by the cryogenic subsystems: Cryogenic Distribution System (CDS), a helium refrigerator cold box (CB), a warm compression station (WCS) and a helium recovery system (RSYS). The accelerator has a strict requirement of non-thermal cycling of the LINAC cryomodules during planned and unplanned subsystem outages. This paper presents an integrated reliability analysis of the PIPII cryoplant. The study evaluates both normal and abnormal operating modes, with a focus on identifying integrated scenarios that put subsystems components under stress. The conclusions of this study will help build redundancy to mitigate LINAC thermal cycling risks during planned and unplanned subsystem outages.

43 PARTICLE ACCELERATORS↗
DOE OSTI2019

Damping Selection Strategy for Maximum Energy on Wave Energy Power Converters

An optimal damping selection strategy on the power electronics in wave energy application is introduced to increase the total energy produced from the power converter in a wave energy converter (WEC) system. A damping map for maximum energy is built for the selection. The map is based on an irregular wave-towire simulation and reliability analysis on the IGBT module. From the yearly wave mission profile, the damping selection strategy is proved to provide 40% more energy than the conventional optimal power with passive damping selection in the WEC power converter.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

Understanding Impacts of Frequency Calculations on Underfrequency Load Shedding

Power system fundamental frequency-dependent protection decisions and control decisions are increasingly common in the distribution system space. Understanding the algorithms used in frequency measurements is critical to understanding the decisions made by the many intelligent electronic devices in the power system. These decisions are critically important for reliability analysis for interconnected power systems as they bear directly on resource planning to mitigate under-frequency conditions that would result in under-frequency load shedding (UFLS). This work aims to improve the understanding of frequency measurement algorithms, evaluate the intelligent electronic devices that use frequency measurements for decision making, and understand the algorithms' direct impact on frequency related decision making. First, this work presents background information on the use of frequency measurements in protection logic, specifically UFLS. Second, the paper presents a relay hardware evaluation test bed used to detect and protect systems from under-frequency events. Finally, the paper presents the dynamic events used to evaluate commercially available, off-the-shelf relay equipment and the results from the relay evaluation.

COMTRADE↗
DOE OSTI2022

Mechanistic modeling of lifetime distribution of SiC/SiC composite claddings

Silicon carbide (SiC) fiber-reinforced SiC matrix (SiC/SiC) composites have emerged as a new material candidate for fuel claddings in light water reactors. Recent studies showed that the load capacity of SiC/SiC materials exhibits a considerable statistical variation. Therefore, reliability analysis plays a critical role in design of SiC/SiC composite claddings. Here, this paper presents a probabilistic model for the lifetime distribution of SiC/SiC composites. The model is anchored by a multiaxial stress-based failure criterion and subcritical damage accumulation mechanism. Based on the kinetics of subcritical damage growth, the lifetime distribution of a laboratory test specimen for any given loading history can be calculated. A finite weakest-link model is used to extrapolate the lifetime distribution of test specimens to full-length claddings. It is shown that the damage accumulation mechanism has a strong influence on the lifetime distribution of the cladding. This finding highlights the importance of understanding the static fatigue behavior of SiC/SiC composites. The present analysis also demonstrates an intricate length effect on the failure probability of the cladding, which is expected to play a crucial role in design extrapolation.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Preliminary safety evaluation of an aircraft impact on a near-surface radioactive waste repository

The aircraft impact accident has become very significant in the design of a nuclear facilities, particularly, after the tragic September 2001 event, that raised the public concern about the potential damaging effects that the impact of a large civilian airplane could bring in safety relevant structures. The aim of this study is therefore to preliminarily evaluate the global response and the structural effects induced by the impact of a military or commercial airplane (actually considered as a 'beyond design basis' event) into a near surface radioactive waste (RWs) disposal facility. The safety evaluation was carried out according to the International safety and design guidelines and in agreement with the stress tests requirements for the security track. To achieve the purpose, a lay out and a scheme of a possible near surface repository, like for example those of the El Cabril one, were taken into account. In order to preliminarily perform a reliable analysis of such a large-scale structure and to determine the structural effects induced by such a types of impulsive loads, a realistic, but still operable, numerical model with suitable materials characteristics was implemented by means of FEM codes. In the carried out structural analyses, the RWs repository was considered a 'robust' target, due to its thicker walls and main constitutive materials (steel and reinforced concrete). In addition to adequately represent the dynamic response of repository under crashing, relevant physical phenomena (i.e. penetration, spalling, etc.) were simulated and analysed. The preliminary assessment of the effects induced by the dynamic/impulsive loads allowed generally to verify the residual strength capability of the repository considered. The obtained preliminary results highlighted a remarkable potential to withstand the impact of military/large commercial aircraft, even in presence of ongoing concrete progressive failure (some penetration and spalling of the concrete wall) of the impacted area. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

SU-F-T-233: Evaluation of Treatment Delivery Parameters Using High Resolution ELEKTA Log Files

Purpose: As modern linacs have become more technologically advanced with the implementation of IGRT and IMRT with HDMLCs, a requirement for more elaborate tracking techniques to monitor components’ integrity is paramount. ElektaLog files are generated every 40 milliseconds, which can be analyzed to track subtle changes and provide another aspect of quality assurance. This allows for constant monitoring of fraction consistency in addition to machine reliability. With this in mind, it was the aim of the study to evaluate if ElektaLog files can be utilized for linac consistency QA. Methods: ElektaLogs were reviewed for 16 IMRT patient plans with >16 fractions. Logs were analyzed by creating fluence maps from recorded values of MLC locations, jaw locations, and dose per unit time. Fluence maps were then utilized to calculate a 2D gamma index with a 2%–2mm criteria for each fraction. ElektaLogs were also used to analyze positional errors for MLC leaves and jaws, which were used to compute an overall error for the MLC banks, Y-jaws, and X-jaws by taking the root-meansquare value of the individual recorded errors during treatment. Additionally, beam on time was calculated using the number of ElektaLog file entries within the file. Results: The average 2D gamma for all 16 patient plans was found to be 98.0±2.0%. Recorded gamma index values showed an acceptable correlation between fractions. Average RMS values for MLC leaves and the jaws resulted in a leaf variation of roughly 0.3±0.08 mm and jaw variation of about 0.15±0.04 mm, both of which fall within clinical tolerances. Conclusion: The use of ElektaLog files for day-to-day evaluation of linac integrity and patient QA can be utilized to allow for reliable analysis of system accuracy and performance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Sequence Diagrams & PFMEA Table - VGI [SWR-25-107]

As part of the VGI work under the National Charging Experience (ChargeX) Consortium, reliability analysis of communication interfaces for multiple SCM/VGI use-cases was performed using a Process Failure Modes and Analysis (PFMEA) style framework. This repository hosts all the relevant files for each of these use-cases which include: *A visual representation of their communication architecture: Image file (.png) *UML sequence diagram: Plant-UML source file (.puml). Visio file (.vsdx) and image file (.png) derived from the UML sourceX` *The PFMEA table: Excel file (.xlsx) These files are meant to serve as a starting point and can be adapted to company / organization specific SCM implementation.

Gadamsetty, Pranav [National Renewable Energy Labo↗