Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Reliability analysis”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Improving Limit Surface Search Algorithms in RAVEN Using Acceleration Schemes: Level II Milestone

The RAVEN code is becoming a comprehensive tool to perform Probabilistic Risk Assessment (PRA); Uncertainty Quantification (UQ) and Propagation; and Verification and Validation (V&V). The RAVEN code is being developed to support the Risk-Informed Safety Margin Characterization (RISMC) pathway by developing an advanced set of methodologies and algorithms for use in advanced risk analysis. The RISMC approach uses system simulator codes applied to stochastic analysis tools. The fundamental idea behind this coupling approach to perturb (by employing sampling strategies) timing and sequencing of events, internal parameters of the system codes (i.e., uncertain parameters of the physics model) and initial conditions to estimate values ranges and associated probabilities of figures of merit of interest for engineering and safety (e.g. core damage probability, etc.). This approach applied to complex systems such as nuclear power plants requires performing a series of computationally expensive simulation runs. The large computational burden is caused by the large set of (uncertain) parameters characterizing those systems. Consequently, exploring the uncertain/parametric domain, with a good level of confidence, is generally not affordable, considering the limited computational resources that are currently available. In addition, the recent tendency to develop newer tools, characterized by higher accuracy and larger computational resources (if compared with the presently used legacy codes, that have been developed decades ago), has made this issue even more compelling. In order to overcome to these limitations, the strategy for the exploration of the uncertain/parametric space needs to use at best the computational resources focusing the computational effort in those regions of the uncertain/parametric space that are “interesting” (e.g., risk-significant regions of the input space) with respect the targeted Figures Of Merit (FOM): for example, the failure of the system, subject of the analysis. These methodologies are named, in the RAVEN environment, adaptive sampling strategies. These methodologies infer system responses from surrogate models constructed from already existing samples (produced using high fidelity simulations) and suggest the most relevant location (coordinate in the input space) of the next sampling point to be explored in the uncertain/parametric domain. When using those methodologies, it is possible to understand features of the system response with a small number of carefully selected samples. This report focuses on the development and improvement of the limit surface search. The limit surface is an important concept in system reliability analysis. Without going into the details, which will be covered later in the report, the limit surface could be briefly described as an hyper-surface in the system uncertainty/parametric space separating the regions leading to a prescribed system outcome. For example, if the uncertainty/parametric space is the one generated by the reactor power level and the duration of the batteries, the system is a nuclear power plant and the system outcome discriminating variable is the clad failure in a station blackout scenario, then the limit surface separates the combinations of reactor power level and battery duration that lead to clad failure from the ones that do not.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

FORCE Integration with DRAFT and IDAES

Integrated energy systems (IES) combine, in mutually beneficial ways, power from variable renewable energy sources and nuclear power plants (NPP) to produce multiple commodities and improve economic viability under uncertain market conditions. Technical and economic analysis of IES requires modeling of complex processes with software models having enough fidelity to capture real-world dynamics while still being capable of running using reasonable computing resources. The open-source Framework for Optimization of Resources and Economics (FORCE) tool suite, developed at Idaho National Laboratory (INL), has enabled comprehensive modeling and simulation of IES. The capabilities within FORCE include grid portfolio optimization through the Holistic Energy Resource Optimization Network (HERON) and the transient process model analysis library HYBRID, among others. Recent developments for the FORCE toolset have centered on strengthening the flexibility and modularity to link with external models and other available software to enhance IES simulations. Adding versatility to the FORCE toolset improves capability resulting in better techno-economic simulation of nuclear and IES components (both in potential higher fidelity and accuracy to expected performance after deployment). It also helps leverage existing work in the IES field and improve efficiency in national code development. This report focuses on two such endeavors: integration of the Dynamic Reliability Analysis Framework Tool (DRAFT) and the Design Integration and Synthesis Platform to Advance Tightly Coupled Hybrid Energy Systems (DISPATCHES) software packages into FORCE.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2003

Effect of Individual Component Life Distribution on Engine Life Prediction

The effect of individual engine component life distributions on engine life prediction was determined. A Weibull-based life and reliability analysis of the NASA Energy Efficient Engine was conducted. The engine s life at a 95 and 99.9 percent probability of survival was determined based upon the engine manufacturer s original life calculations and assumed values of each of the component s cumulative life distributions as represented by a Weibull slope. The lives of the high-pressure turbine (HPT) disks and blades were also evaluated individually and as a system in a similar manner. Knowing the statistical cumulative distribution of each engine component with reasonable engineering certainty is a condition precedent to predicting the life and reliability of an entire engine. The life of a system at a given reliability will be less than the lowest-lived component in the system at the same reliability (probability of survival). Where Weibull slopes of all the engine components are equal, the Weibull slope had a minimal effect on engine L(sub 0.1) life prediction. However, at a probability of survival of 95 percent (L(sub 5) life), life decreased with increasing Weibull slope.

Zaretsky, Erwin V.↗
DOE OSTI2025

Evaluating switch lifetime in soft-switched single-stage differential-mode SST

The reliability of semiconductor switches in single-stage differential-mode solid-state transformers (DM-SSTs) has not been systematically evaluated under soft-switching operation and realistic grid conditions. This paper presents a switch-level reliability analysis for soft-switched and hard-switched DM-SST configurations by integrating converter-specific power loss modeling with empirical lifetime prediction. Analytical derivation of device current profiles specific to the DM-SST is used to characterize electrothermal stress, which is then mapped to lifetime using degradation models obtained from power cycling tests (PCTs). Applied to realistic SST load profiles and grid voltage variations, this approach provides a probabilistic prediction of switch lifetime for the DM-SST. Lifetime estimates for both SiC MOSFETs and Si IGBTs are presented, offering insight into device degradation under converter operating conditions. The results quantify the reliability benefits of soft switching in single-stage SSTs, highlighting how switching dynamics influence long-term switch degradation.

14 SOLAR ENERGY↗
DOE OSTI2020

Computational techniques for the analysis of small signals in high-statistics neutrino oscillation experiments

The current and upcoming generation of Very Large Volume Neutrino Telescopes – collecting unprecedented quantities of neutrino events – can be used to explore subtle effects in oscillation physics, such as (but not restricted to) the neutrino mass ordering. The sensitivity of an experiment to these effects can be estimated from Monte Carlo simulations. With the high number of events that will be collected, there is a trade-off between the computational expense of running such simulations and the inherent statistical uncertainty in the determined values. In such a scenario, it becomes impractical to produce and use adequately-sized sets of simulated events with traditional methods, such as Monte Carlo weighting. In this work we present a staged approach to the generation of expected distributions of observables in order to overcome these challenges. By combining multiple integration and smoothing techniques which address limited statistics from simulation it arrives at reliable analysis results using modest computational resources.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Emerging investigator series: quantification of multiwall carbon nanotubes in plant tissues with spectroscopic analysis

If agricultural plants are exposed to carbon nanotubes (CNTs), they can potentially take up the CNTs from growth media and translocate them to their different tissues. In addition, agricultural application of CNTs recently attracted increasing attention, as they could promote germination, enhance crop yield, and exhibit other benefits. For evaluating the environmental effects of CNTs and optimizing their agricultural application, it is essential to quantify CNTs in plant tissues. In this study, pristine (p-) and carboxyl-functionalized (c-) multiwall CNTs (MWCNTs) were extracted from plant tissues by a sequential digestion with nitric acid (HNO 3 ) and sulfuric acid (H 2 SO 4 ). The extracted MWCNTs were stabilized with nonionic surfactant Triton X-100 and analyzed with ultraviolet-visible (UV-vis) spectroscopic analysis to measure the concentration of the MWCNTs in plant (lettuce) tissues. The MWCNT concentration was linearly correlated with the absorbance at 800 nm. The detection limit for p- and c-MWCNTs was achieved at 0.10–0.12, 0.070–0.081, 0.019–0.18 μg mg -1 for leaf, stem, and root tissues, respectively. The developed method was applied for lettuce (Lactuca sativa, cv. black seeded Simpson) hydroponically grown with 5, 10, 20 mg L -1 of p-MWCNTs and c-MWCNTs in the culture solution. We detected 0.21 ± 0.05–4.57 ± 0.39 μg mg -1 p-MWCNTs and 0.20 ± 0.17–0.75 ± 0.25 μg mg -1 c-MWCNTs in the lettuce roots, positively correlated with the dose of CNTs in solution. We have come up with a method for rapid quantification of CNTs in plant tissues using a widely-accessible technique, which can enable reliable analysis of CNTs in plant tissues and provide critical information for evaluating the environmental implications and managing agricultural application of CNTs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Overview of Common Mode Outages in Power Systems

This paper is a result of ongoing activity carried out by Probability Applications for Common Mode Events (PACME) Task Force under the Reliability Risk and Probability Applications (RRPA) Subcommittee. The paper is intended to constitute a valid source of information and references about dealing with common-mode outages in power systems reliability analysis. This effort involves reviewing published literature and presenting state-of-the-art research and practical applications in the area of common-mode outages. Evaluation of available outage statistics show that there is a definite need for collective effort from academia and industry to not only recommended procedures for data collection and monitoring but also to provide appropriate mathematical models to assess such events.

Common mode outages↗
DOE OSTI2017

Uncertainty propagation of p-boxes using sparse polynomial chaos expansions

In modern engineering, physical processes are modelled and analysed using advanced computer simulations, such as finite element models. Furthermore, concepts of reliability analysis and robust design are becoming popular, hence, making efficient quantification and propagation of uncertainties an important aspect. In this context, a typical workflow includes the characterization of the uncertainty in the input variables. In this paper, input variables are modelled by probability-boxes (p-boxes), accounting for both aleatory and epistemic uncertainty. The propagation of p-boxes leads to p-boxes of the output of the computational model. A two-level meta-modelling approach is proposed using non-intrusive sparse polynomial chaos expansions to surrogate the exact computational model and, hence, to facilitate the uncertainty quantification analysis. The capabilities of the proposed approach are illustrated through applications using a benchmark analytical function and two realistic engineering problem settings. They show that the proposed two-level approach allows for an accurate estimation of the statistics of the response quantity of interest using a small number of evaluations of the exact computational model. This is crucial in cases where the computational costs are dominated by the runs of high-fidelity computational models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
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↗
NASA NTRS1999

Measuring the Chemical Potential of the Martian Regolith to Generate and Sustain Life

A critical component for identifying chemical biosignatures is the ability to assess in-situ the potential of an aqueous geochemical environment to generate and sustain life. On Mars or other solar bodies, in-situ chemical characterization could provide evidence as to whether the chemical composition of the regolith or evaporites in suspected ancient water bodies have been biologically influenced or possess the chemical parameters within which life may have existed, or may still exist. A variety of analytical techniques have been proposed for use in detecting and identify signatures of past or present life. These techniques fall into two groups; visual observation with instruments such as cameras or optical/atomic-force microscopes; or elemental chemical analysis with such instruments as X-ray fluorescence (XRF) and diffraction (XRD), a-proton backscatter (APX), y-ray, Mossbauer, Raman, IR, UV/VIS spectroscopies, gas chromatography (GC), or mass spectrometry (MS). Direct observation of an identifiable lifeform by the first set of instruments in a single sample is highly unlikely, especially for extinct organisms or on the surface. The later instruments can provide vital data as to the elemental mineralogy and geological history of the planet, but are highly inadequate for understanding the chemistry of the planet in terms of indigenous life or interactions with human explorers. Techniques such as XRD, XRF, and APX, provide elemental composition at high limits of detection. Some of this data can be extrapolated or interpolated to provide chemical parameters such as oxidation state or composition. Gas chromatography (GC) without standards and non-specific detectors, has little chance of identifying a mixture of unknown components. Combined with GC or by itself, mass spectrometry (MS) can provide identification of compounds, but in both cases the sample must be appropriately prepared for accurate and reliable analysis. Life as we know it, and probably identify it as such, requires an aqueous environment. Deciphering die chemical speciation of this aqueous environment is the key to recognizing therein the biosignatures of any extinct or present life forms. Identifying the soluble (ionic and nonionic) components by reacting a currently dormant environment can provide a "picture" of the thermodynamics and chemical components of a possibly bioactive environment. The only devices which can provide such information are electrochemical sensors based on the potentiometric ion selective electrodes (ISEs) and on dynamic techniques such as cyclic voltammetry (CV) and stripping voltammetry (SV). Such an array of devices can provide not only the chemical composition of a water-soluble Martian soil sample, but also several other vital chemical parameters such as pH, conductivity, redox potential, and dissolved gases. To address these issues we have been investigating the possible use of an electrochemically-based ion sensor array as a new integrated approach to quantitative analytical and chemometric electrochemical measurements. The sensor array will consist of specific and semispecific ion selective and amperometric transducers, which can simultaneously and continuously identify and semiquantitatively determine over 50 organic and inorganic analytes in water-based environments. Several individual sensors, based on the same principle, have been flight-tested and have been installed as part of the MECA instrumentation on the Mars 2001 Lander for in-situ analyses. However, the microfabrication, integration and multiplexing of such a large number of these sensors on a single substrate have not been previously attempted.

Kounaves, S. P.↗
NASA NTRS2023

Risk Trade-Space Analysis for Safe Human Expeditions to Mars

We assessed the integrated safety, health, and performance risk to crews on long-duration missions, specifically to Mars. Using a systems approach rather than one focused on individual countermeasures, we examined the trade space around several such risks to identify high-potential risk mitigation strategies and characterize aspects of Mars mission architectures that could lower aggregated risk. Current Mars Design Reference missions would require durations well over two years and would increase crew exposure to radiation and microgravity well beyond ISS levels, likely resulting in significantly reduced performance beyond our current capability to mitigate that could jeopardize mission success. A “fast Mars transit” round-trip mission concept was studied using an innovative flight dynamics approach to quantify the minimum total mission energy required for a Mars transit with total mission duration less than 400 days. This approach holds promise for sending humans to Mars and returning them safely with acceptable, potentially mitigatable, exposure to microgravity and radiation using current or near-term technologies. The fast transit concept would also result in fewer time-driven vehicle failures and enable sustainable deployment of humans and infrastructure to Mars on a regular cadence, allowing steady exploration and colonization of Mars. Finally, we conclude that reliance on the Low Earth Orbit (LEO) mission operations paradigm – i.e., one of near-complete real-time dependence on experts at Mission Control to manage the combined state of the mission, vehicle, and crew – is high risk given the communication delays and limited resupply of any Mars mission, and this risk is not eliminated by the shorter missions durations of fast transit scenarios. Based on historical trends, it is highly likely that the crew will face a high-consequence problem of uncertain origin during Mars transit when ground support will be greatly reduced. While it may be possible to reduce anomaly rates through improved reliability analysis and testing, and to reduce anomaly impacts through added robustness, such mitigations address only known failure modes and known uncertainties. Therefore, a radical shift in the Human-Systems Integration Architecture (HSIA) that defines the operational paradigm, systems design, and human-systems interactions is required to improve the risk posture to an acceptable level regardless of mission duration.

Mars↗
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↗
NASA NTRSDate not supplied

Strategies for Quantifying Human Space Flight Performance in the Crew Health and Performance System

The Crew Health and Performance-Probabilistic Risk Assessment (CHP-PRA) team at NASA Glenn Research Center is planning a customized approach to quantify human spaceflight performance changes with respect to changes to the CHP system functions and capabilities. Using the Directed Acyclic Graphs (DAG) initiated by NASA’s Human Systems Risk Board (HSRB) [1], the team is surveying potential candidate models and novel strategies that generate metrics suitable for supporting decision making related to how the CHP system may influence human system performance risk. One such investigation includes classic Human Reliability Analysis (HRA) models. Traditionally, HRA methods estimate the occurrence of human errors and their impact on the success of an activity when designing and operating a system. While humans perceive, interpret, decide on, and carry out a course of action, the factors affecting performance and error likelihood are commonly referred to as performance shaping factors (PSFs). Originally developed to alleviate safety concerns related to nuclear power plant operations, HRA methods such as THERP [2] and CREAM [3] dismantle an activity into tasks, requiring elemental steps to be executed, and assess their failure due to predefined PSFs. In this study, we compare generic HRA methods and those that incorporate some human spaceflight aspects, such as sleep conditions (SCREAM [4]), with respect to how they may be adopted to capture performance with an intention to mitigate detrimental outcomes elucidated by the HSRB DAGs. We suggest strategies to quantify astronaut performance specific to spaceflight activities and illustrate how such concepts may help in optimizing the CHP system capabilities with respect to Artemis missions.

dag↗
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↗