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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

UQpy: A general purpose Python package and development environment for uncertainty quantification

In this paper, we present the UQpy software toolbox, an open-source Python package for general uncertainty quantification (UQ) in mathematical and physical systems. The software serves as both a user-ready toolbox that includes many of the latest methods for UQ in computational modeling and a convenient development environment for Python programmers advancing the field of UQ. The paper presents an introduction to the software's architecture and existing capabilities, divided in the code in a set of modules centered around different UQ tasks such as sampling methods, generation of random processes and random fields, probabilistic inverse modeling, reliability analysis, surrogate modeling, and active learning. The paper also highlights the importance of the RunModel module, which is used to drive simulations in the uncertainty analyses performed in UQpy. This module conveniently allows the user to define computational models directly in Python, or to run simulations from a third-party software in serial or in parallel. To illustrate the various capabilities, two examples are tracked throughout the paper and analyzed repeatedly for various UQ tasks. The first is a Python model solving a nonlinear structural dynamics problem, used to illustrate UQpy's capabilities in sampling and forward propagation of high dimensional random vectors (stochastic processes), and probabilistic inference. The second model is a third-party Abaqus finite element model solving the thermomechanical response of a beam structure. This example is used to illustrate UQpy's capabilities in variance reduction sampling techniques, reliability analysis, surrogate modeling and active learning techniques.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Assessment of HRA method predictions against operating crew performance: Part II: Overall simulator data, HRA method predictions, and intra-method comparisons

This is the third in a series of four papers documenting two large-scale human reliability analysis (HRA) empirical studies – the International HRA Empirical Study and the US HRA Empirical Study. Here, 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. However, since in most cases only a single HRA team applied a given method in the International study, it was often difficult to separate analyst effects from variability in results related to the methods themselves. Since at least two HRA teams used each of the HRA methods in the US Study, intra-method comparisons were performed to identify method strengths and weaknesses independent of analyst specific effects where possible. This paper first summarizes the intra-method comparison results from the U.S. Study. Then, it discusses the reasons for the observed HRA predictive differences and the underlying methodological and guidance limitations that permitted the differences to arise. In the fourth paper, this information is combined wit h the results of the comparisons of method predictions to the actual crew data, from both the International and U.S. Studies, to develop the final conclusions about overall strengths and weaknesses of HRA methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

PV Field Reliability Status--Analysis of 100,000 Solar Systems

In this paper, we analyzed annual production data from 100 000 photovoltaic systems as well as comments relating to their performance and maintenance. Our analysis revealed that 80% to 90% of all systems performed within 10% of the predicted production or better. Also, 56% of the systems were still performing above P50 or the median at 5 years. However, a small but significant tail of about 7000 systems perform below P90 expectations. In general, residential systems have a lower rate of failure than utility or commercial systems. Despite higher rates of component failures, utility systems lose less power than residential or commercial systems. This outcome is likely due to closer monitoring and better operations and maintenance practices. Inverters are still the components that reportedly fail most often (4%-6%), but other failures such as unspecified repair and meters cause more production loss. Reported module failures are relatively rare (0.2%) and are within the range of historical values. Installation quality affects performance and safety as indicated by data showing connector, wiring, breaker, and fuse failures due to undersizing, electrical design, and improper connection. Lastly, early detection of degradation and proactive response resulted in less impact on production than reactive, unplanned repairs.

14 SOLAR ENERGY↗
DOE OSTI2022

Physics-informed machine learning assisted uncertainty quantification for the corrosion of dissimilar material joints

Jointing techniques like the Self-Piercing Riveting (SPR), Resistance Spot Welding (RSW) and Rivet-Weld (RW) joints are used for mass production of dissimilar material joints due to their high performance, short cycle time, and adaptability. However, the service life and safety usage of these joints can be largely impacted by the galvanic corrosion due to the difference in equilibrium potentials between the metals with the presence of electrolyte. Here, in this paper, we focus on Al-Fe galvanic corrosion and develop physics-informed machine learning based surrogate model for statistical corrosion analysis, which enables the reliability analysis of dissimilar material joints under corrosion environment. In this study, a physics-based finite element (FE) corrosion model has been developed to simulate the galvanic corrosion between a Fe cathode and an Al anode. Geometric and environmental factors including crevice gap, roughness of anode, conductivity, and the temperature of the electrolyte are investigated. Further, a thorough Uncertainty Quantification (UQ) analysis is conducted for the overall corrosion behavior of the Fe-Al joints. It is found that the electrolyte conductivity has the largest effects on the material loss and needs to be managed closely for better corrosion control. This will help in designing and manufacturing joints with improved corrosion performance.

42 ENGINEERING↗
DOE OSTI2019

Chapter 4: Drivetrain Analysis for Reliable Design

This chapter described common design practices for the wind-turbine gearboxes and main bearings, along with modelling and simulation techniques employed to support and evaluate the development process. These design criteria and requirements are based on several major failure modes for gears and bearings. However, not all of the failure modes are understood well enough to be included in the design standards primarily because of the complexity of the modelling and loading conditions. Drivetrain modelling for design was discussed, and most of the modelling tools used in design are static in nature.

17 WIND ENERGY↗
DOE OSTI2015

Analysis of reliable sub-ns spin-torque switching under transverse bias magnetic fields

The switching process of a magnetic spin-valve nanosystem subject to spin-polarized current pulses is considered. The dependence of the switching probability on the current pulse duration is investigated. The further application of a transverse field along the intermediate anisotropy axis of the particle is used to control the quasi-random relaxation of magnetization to the reversed magnetization state. The critical current amplitudes to realize the switching are determined by studying the phase portrait of the Landau-Lifshtz-Slonczewski dynamics. Macrospin numerical simulations are in good agreement with the theoretical prediction and demonstrate reliable switching even for very short (below 100 ps) current pulses.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Analysis of Reliabilities under Different Path Loss Models in Urban/Sub-Urban Vehicular Networks

This paper studies the impact of propagation path losses in urban/sub-urban vehicular networks. The impact has been analyzed considering both the network-layer and application-layer reliabilities. We have built a realistic vehicular simulation environment and performed an extensive simulation experiment in a semi-urban traffic environment. The results show that there are significant performance differences between LOS (Line-of-Sight)/OLOS (Obstructed-LOS)/NLOS (Non-LOS) path loss model and any other studied loss model. With a moderate communication distance between a transmitter and a receiver (300 m), while with the other existing loss models the network-level reliability is around 60%, with the LOS/OLOS/NLOS model that falls below 30%. Moreover, the application-level reliability of LOS/OLOS/NLOS model is not more than 55% for the delay sensitive safety application.

Ali, G. Nawaz↗
DOE OSTI2026

Dataset for Top Model Decision Tree: Selecting Segmentation Models for Reliable Quantitative Analysis in Low- and Ultralow-Dose CryoEM

Motivation Multiple deep learning model architectures can be used to segment bacterial membranes in cryoEM images. However, an AI-based tool advancement is often presented with only a single segmentation model for broad use, and this single model may show inconsistent results across datasets from different users. Here, we present the Top Model Decision Tree, a model screening framework to screen for the best model to generate bacterial inner and outer membrane masks based on user priorities. We use pre-trained segmentation models from YOLOv11, YOLO26, U-Net, Detectron2 and SAM3 fine-tuned on bacterial inner and outer membranes imaged with cryoEM. Run the Framework This notebook must be opened in Google Colab. Mount Google Drive and run with a GPU-based runtime. Open the notebook and follow steps to git clone in folders and files within this repository. There will be a repeating top_model_decision_tree.ipynb (notebook clone) that will not be used. Save your .png binary mask files and .csv table outputs within your Google Drive or download before closing the notebook. The models and all analysis/training scripts are available at [GitHub: https://github.com/Lynnicia/CryoEM_membranes_top_model_decision_tree and https://github.com/Sireesiru/Semantic-Segmentation-of-bacterial-cell-envelope-using-U-Nets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Defining Human Failure Events for Petroleum Risk Analysis

In this paper, an identification and description of barriers and human failure events (HFEs) for human reliability analysis (HRA) is performed. The barriers, called target systems, are identified from risk significant accident scenarios represented as defined situations of hazard and accident (DSHAs). This report serves as the foundation for further work to develop petroleum HFEs compatible with the SPAR-H method and intended for reuse in future HRAs.

02 PETROLEUM↗
DOE OSTI2015

Challenges in leveraging existing human performance data for quantifying the IDHEAS HRA method

Our article documents an exploratory study for collecting and using human performance data to inform human error probability (HEP) estimates for a new human reliability analysis (HRA) method, the IntegrateD Human Event Analysis System (IDHEAS). The method was based on cognitive models and mechanisms underlying human behaviour and employs a framework of 14 crew failure modes (CFMs) to represent human failures typical for human performance in nuclear power plant (NPP) internal, at-power events [1]. A decision tree (DT) was constructed for each CFM to assess the probability of the CFM occurring in different contexts. Data needs for IDHEAS quantification are discussed. Then, the data collection framework and process is described and how the collected data were used to inform HEP estimation is illustrated with two examples. Next, five major technical challenges are identified for leveraging human performance data for IDHEAS quantification. Furthermore, these challenges reflect the data needs specific to IDHEAS. More importantly, they also represent the general issues with current human performance data and can provide insight for a path forward to support HRA data collection, use, and exchange for HRA method development, implementation, and validation.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
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 OSTI2014

One Size Does Not Fit All: Human Failure Event Decomposition and Task Analysis

In the probabilistic safety assessments (PSAs) used in the nuclear industry, human failure events (HFEs) are determined as a subset of hardware failures, namely those hardware failures that could be triggered or exacerbated by human action or inaction. This approach is top-down, starting with hardware faults and deducing human contributions to those faults. Elsewhere, more traditionally human factors driven approaches would tend to look at opportunities for human errors first in a task analysis and then identify which of those errors is risk significant. The intersection of top-down and bottom-up approaches to defining HFEs has not been carefully studied. Ideally, both approaches should arrive at the same set of HFEs. This question remains central as human reliability analysis (HRA) methods are generalized to new domains like oil and gas. The HFEs used in nuclear PSAs tend to be top-down—defined as a subset of the PSA—whereas the HFEs used in petroleum quantitative risk assessments (QRAs) are more likely to be bottom-up—derived from a task analysis conducted by human factors experts. The marriage of these approaches is necessary in order to ensure that HRA methods developed for top-down HFEs are also sufficient for bottom-up applications. In this paper, I first review top-down and bottom-up approaches for defining HFEs and then present a seven-step guideline to ensure a task analysis completed as part of human error identification decomposes to a level suitable for use as HFEs. This guideline illustrates an effective way to bridge the bottom-up approach with top-down requirements.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2013

Differential Power Processing for Increased Energy Production and Reliability of Photovoltaic Systems

Conventional energy conversion architectures in photovoltaic (PV) systems are often forced to tradeoff conversion efficiency and power production. This paper introduces an energy conversion approach that enables each PV element to operate at its maximum power point (MPP) while processing only a small fraction of the total power produced. This is accomplished by providing only the mismatch in the MPP current of a set of series-connected PV elements. Differential power processing increases overall conversion efficiency and overcomes the challenges associated with unmatched MPPs (due to partial shading, damage, manufacturing tolerances, etc.). Several differential power processing architectures are analyzed and compared with Monte Carlo simulations. Local control of the differential converters enables distributed protection and monitoring. Reliability analysis shows significantly increased overall system reliability. Simulation and experimental results are included to demonstrate the benefits of this approach at both the panel and subpanel level.

Shenoy, PS↗