Microelectronics and space vehicle reliability.
NASA program for microelectronic circuit and space vehicle reliability
CIRCUIT RELIABILITY↗
SEARCH · Engineering Papers
Results for “reliablity”
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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Reliability prediction activities in the Apollo program.
Reliability prediction, modeling and analysis activities in Apollo program
PERFORMANCE PREDICTION↗
Reliable cabling for space vehicles.
Cabling material problems associated with military standard connectors for space vehicles, discussing reliability, fabrication, assembly and design
SPACECRAFT ELECTRONIC EQUIPMENT↗
CSPs Assembly Reliability
Availability of board solder joint reliability information is critical to the wider implementation of Chip Scale Packages (CSPs).
Reliability↗
Review and Document Reliability Issues With Hybrid Microcircuits
Reliability issues and lessons learned are described for hybrid microcircuits (including multi-chip modules) used for space applications.
reliability hybrid microcircuits electronic parts ↗
CASRE ?? Eay-to-Use Software Reliability Measurement Tool
This paper describes the implementation of a software reliability measurement tool, CASRE, that incorporates the methematical modeling capabilities of the public domain tool SMERFS, and is being implemented in a Microsoft Windows environment.
software reliability Computer-Aided Software Relia↗
Software Reliability Modeling Techniques and Tools
Presents software reliability modeling techniques and tools.
Reliability measurement reliability function failu↗
Assurance of Electronic Parts for Aerospace System Reliability: Past, Present, and Future
Over the last two to three decades, the balance of influence within the electronics industry has shifted decisively towards the consumer electronics and information technology products, away from the defense or aerospace markets. This shift towards the commercial markets has changed certain ground rules in the electronics industry. Reliance of the aerospace industry, in particular, on components from supply chains dedicated to other products, or cross-vertical product integration, creates a supply web and introduces significant risks that are not necessarily present for volume-driven electronic products. Many of the unique risks in the aerospace industry emanate from the key characteristics of this industry. Although selection and usage of commercial electronics in NASA flight hardware is possible at a comparable (and in many cases lower) level of risk to that of employing parts that are screened to military or NASA specifications, for a large majority of flight projects it is difficult to distinguish the difference in on-orbit performance or reliability for parts screened at any level, or commercial parts that have no additional screening performed by the government. This paper identifies several bases for establishing context and developing unique or alternative approaches for successful parts selection and application within NASA and across the commercial space community.
electronic parts↗
Formative vs. Summative Dependence in Human Reliability Analysis
Dependence in human reliability analysis (HRA) is the concept that once an initial human error has occurred, subsequent human errors are more likely. The approach almost universally adopted in HRA was first introduced in the Technique for Human Error Rate Prediction (THERP). In THERP, calculated human error probabilities (HEPs) are adjusted for dependence in the final step of quantification. This final adjustment for dependence involves anchoring the HEP to a set of values corresponding to low through complete dependence. The effect is to increase the HEP. In this paper, we propose formative dependence, which occurs at the onset of quantification. We demonstrate that dependence should be considered earlier in the calculation process, because it is not actually necessary to calculate the HEP when it is subsequently overridden by the dependence anchors. By applying dependence first in the calculation, processing steps can be eliminated, making for more efficient analysis.
99 GENERAL AND MISCELLANEOUS↗
A mathematical approach to using the forgetting curve to evaluate experience and training factors in human reliability analysis
Traditional human reliability analysis (HRA) methods have difficulty dealing with the dynamic nature of factors such as time and rely on static and expert-judgment-based assessments of performance-shaping factors (PSFs) across limited levels. In this study, we introduce a mathematical approach for dynamically evaluating the experience and training PSF. Our proposed method integrates the psychological concept of the “forgetting curve” to evaluate how PSFs are impacted by the number of trainings and the time elapsed since training. To confirm the validity of the model, we provide experimental data fitted by identifying the quantitative relationship between training and human performance. This research enables dynamic and objective assessments, thus reducing reliance on subjective expert judgment and improving the accuracy of HRA.
99 - GENERAL AND MISCELLANEOUS↗
Recent advances and challenges in optimization models for expansion planning of power systems and reliability optimization
Optimization models for expansion planning of power systems aim to determine capacities, investment timing, and location of power systems to satisfy the power demands while minimizing the total cost. The models have become complex in recent years to reflect both regulations on conventional energy sources and the increasing penetration of renewable energy sources (RES). This paper reviews the basic concepts and optimization models for expansion planning of power systems. We first explain the definition and features of generation expansion planning (GEP), transmission expansion planning (TEP), and generation and transmission expansion planning (GTEP). To address the computational challenges of large-scale expansion planning problems, we review several simplifications including temporal and spatial aggregation, and decomposition methods. This paper also addresses power system reliability defined as the probability of satisfying the load demand while withstanding failures of components. Finally, the goal of this paper is to provide a research overview, discuss trends in expansion planning of power systems, and suggest directions for future research.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Harnessing distributed GPU computing for generalizable graph convolutional networks in power grid reliability assessments
Although machine learning (ML) has emerged as a powerful tool for rapidly assessing grid contingencies, prior studies have largely considered a static grid topology in their analyses. This limits their application, since they need to be re-trained for every new topology. Here, this paper explores the development of generalizable graph convolutional network (GCN) models by pre-training them across a range of grid topologies and contingency types. We found that a GCN model with auto-regressive moving average (ARMA) layers with a line graph representation of the grid offered the best predictive performance in predicting voltage magnitudes (VM) and voltage angles (VA). We introduced the concept of phantom nodes to consider disparate grid topologies with a varying number of nodes and lines. For pre-training the GCN ARMA model across a variety of topologies, distributed graphics processing unit (GPU) computing afforded us significant training scalability. The predictive performance of this model on grid topologies that were part of the training data is substantially better than the direct current (DC) approximation. Although direct application of the pre-trained model to topologies that are not part of the grid is not particularly satisfactory, fine-tuning with small amounts of data from a specific topology of interest significantly improves predictive performance. In general, this paper highlights the feasibility of training large-scale GNN models to assess the reliability of power grids by considering a wide variety of grid topologies and contingency types. With the advent of foundational models in ML and the exponential increase in GPU computing clusters, generalizable ML models will significantly enhance how utilities manage power systems and make decisions in real-time or near-real-time.
24 - POWER TRANSMISSION AND DISTRIBUTION↗
CAMERA: A method for cost-aware, adaptive, multifidelity, efficient reliability analysis
Estimating probability of failure in aerospace systems is a critical requirement for flight certification and qualification. Failure probability estimation involves resolving tails of probability distributions, and Monte Carlo sampling methods are intractable when expensive high-fidelity simulations have to be queried. Here, we propose a method to use models of multiple fidelities that trade accuracy for computational efficiency. Specifically, we propose the use of multifidelity Gaussian process models to efficiently fuse models at multiple fidelity, thereby offering a cheap surrogate model that emulates the original model at all fidelities. Furthermore, we propose a novel sequential acquisition function based experiment design framework that can automatically select samples from appropriate fidelity models to make predictions about quantities of interest at the highest fidelity. We use our proposed approach in an importance sampling setting and demonstrate our method on the failure level set and probability estimation on synthetic test functions and two real-world applications, namely, the reliability analysis of a gas turbine engine blade using a finite element method and a transonic aerodynamic wing test case using Reynolds-averaged Navier-Stokes equations. We show that our method predicts the failure boundary and probability more accurately and at a fraction of the computational cost compared with using just a single expensive high-fidelity model. Finally, we show that our sequential approach is guaranteed to asymptotically converge to the true failure boundary with high probability.
97 MATHEMATICS AND COMPUTING↗
A framework to collect human reliability analysis data for nuclear power plants using a simplified simulator and student operators
Data scarcity in human reliability analysis (HRA) has been a major challenge in the quantification process. Many institutes have collected HRA data through experiments using full-scope simulators with actual operators. Nevertheless, there are still some limitations to relying solely on full-scope studies. This paper aims to propose how full-scope data collection studies can be supported through the Simplified Human Error Experimental Program (SHEEP). The SHEEP framework was developed by Idaho National Laboratory (INL) to collect HRA data through a simplified simulator and student operators. This paper introduces the major tasks in the SHEEP framework, with a particular focus on differences that arise due to participant type (i.e., student vs. actual operator), based on experiments using a simplified simulator (i.e., the Rancor Microworld). This paper also describes whether the data collected via this approach could support a representative full-scope data collection study (i.e., the HuREX study) based on the experimental data.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Reliability estimation of an advanced nuclear fuel using coupled active learning, multifidelity modeling, and subset simulation
Tristructural isotropic (TRISO)-coated particle fuel is a robust nuclear fuel and determining its reliability is critical for the success of advanced nuclear technologies. However, TRISO failure probabilities are small and the associated computational models are expensive. Here we used coupled active learning, multifidelity modeling, and subset simulation to estimate the failure probabilities of TRISO fuels using several 1D and 2D models. With multifidelity modeling, we replaced expensive high-fidelity (HF) model evaluations with information fusion from two low-fidelity (LF) models. For the 1D TRISO models, we considered three multifidelity modeling strategies: only Kriging, Kriging LF prediction plus Kriging correction, and deep neural network (DNN) LF prediction plus Kriging correction. While the results across these multifidelity modeling strategies compared satisfactorily, strategies employing information fusion from two LF models called the HF model least often. Next, for the 2D TRISO model, we considered two multifidelity modeling strategies: DNN LF prediction plus Kriging correction (data-driven) and 1D TRISO LF prediction plus Kriging correction (physics-based). The physics-based strategy, as expected, consistently required the fewest calls to the HF model. However, the data-driven strategy had a lower overall simulation time since the DNN predictions are instantaneous, and the 1D TRISO model requires a non-negligible simulation time.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Lead-Free Flexible Perovskite Solar Cells with Interfacial Native Oxide Have >10% Efficiency and Simultaneously Enhanced Stability and Reliability
Here we demonstrate an innovative compositional and interfacial engineering approach to achieve Pb-free flexible perovskite solar cells (f-PSCs) with an unprecedented combination of the highest efficiency reported to date (10.43%), together with enhanced operational stability and mechanical reliability. The key to this approach is alloying of an FASnI3 metal halide perovskite (MHP) thin film with Ge2+ to stabilize the Sn2+ oxidation state in the MHP, together with the use of a NiOx hole-transport layer. We show that this results in the in situ formation of a thin amorphous GeO2 native oxide layer at the NiOx/FASn0.9Ge0.1I3 interface. This layer not only passivates that critical interface but also enhances the interfacial mechanical bonding.
efficiency↗
Package Development for Reliability Testing of Perovskites
Metal halide perovskite solar cells have reached a critical point in their development. At a current certified record efficiency of 25.7% for a single-junction, research-scale cell, they now garner serious attention from the solar cell industry as a promising route to widespread, low-cost photovoltaics in single- or tandem-junction configurations. However, more work to demonstrate their durability under real-world outdoor test conditions is necessary to ensure the long-term success and deployment of the technology. Differences in chemistry, processes, and their combination result in unique performance limiters for both efficiency and stability. Further, as many active formulations and layer/cell stack combinations are sensitive to temperature, air, and moisture, it is important to separate intrinsic limitations to stability relative to these extrinsic sources. This presents a particular need for the development of an appropriate and reliable package for environmental (i.e., accelerated and outdoor) testing that will permit these different factors to be evaluated and understood.
14 SOLAR ENERGY↗
Data Reliability in BIM and Performance Analytics: A Survey of Contemporary AECO Practice
As awareness around building energy consumption increases, practitioners are encouraged to consider performance aspects regarding the built environment more closely and find ways to improve its efficiency. Improvements in building information modeling (BIM) and building performance simulation (BPS) tools present opportunities to facilitate information communication with a wider range of stakeholders. The building sector can benefit from the integration of performance informatics; however, there has been limited success in utilizing available technologies that promote data integration and management in favor of enriching our knowledge and understanding of buildings as artifacts of information. This phenomenon was investigated by conducting a survey, together with a review of relevant literature, to depict the relevant challenges and opportunities for the architecture, engineering, construction, and owner-operated (AECO) industry, as it undergoes digital transformation, as well as the working practices that have formed around them. It is argued that the current tools available to practitioners do not support effective data serialization between design and analytics processes, affecting the collaboration between team members. Lastly, a series of functional goals are proposed to support a higher level of reliability in the ways information is mobilized, by rethinking the technologies and methods for organizing information systems.
42 ENGINEERING↗