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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Communications Reliability for Vehicle Grid Integration

Electric Vehicles (EVs) adoption rate has been steadily increasing in the US leading to a growing number of charging stations including faster DC (Direct Current) chargers and slower Level 1 and Level 2 AC (Alternating Current) chargers. This increase in demand for electricity is further exacerbated by recent developments in Artificial Intelligence (AI) technology, advanced manufacturing, and digitization. These factors will require electric utilities to upgrade their infrastructure to keep up with the increasing electrical demand (especially during peak hours). An easy way to counteract the need for these upgrades is to shift a major chunk of active charge sessions (durations where there is energy transfer from charger to EV's propulsion battery) to off-peak hours thereby flattening the load curve and making the infrastructure more resilient. This concept is known as Smart Charge Management (SCM). EV owners also benefit from SCM since it lowers their charging costs and consequently their transportation costs by prioritizing charging during off-peak hours. SCM takes advantage of EV's capability to act as a controllable load or DER (Distributed Energy Resource). This report summarizes the reliability analysis performed on the communication required for two of these SCM use-cases. This analysis only focuses on SCM strategies for unidirectional charging (energy transfer from EVSE to EV or V1G) and not bidirectional charging.

24 POWER TRANSMISSION AND DISTRIBUTION↗
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↗
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 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↗
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 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↗