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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Ground-motions site and event specificity: Insights from assessing a suite of simulated ground motions in the San Francisco Bay Area

This article presents the results of a research that is part of a larger collaborative effort between the Lawrence Berkeley National Laboratory and the Pacific Earthquake Engineering Research Center, funded by the US Department of Energy Office of Cybersecurity, Energy Security and Emergency Response. The main objective of this study is to assess a suite of near and far-field simulated ground motions obtained from 20 realizations of an M7 Hayward Fault earthquake in the San Francisco Bay Area, California USA, and inform the selection of rupture simulation parameters leading to strong motions. To this aim, comparisons are conducted with NGA-W2 and directivity ground-motion models and a selected population of records. An archetypal steel moment-resisting frame is utilized to assess infrastructure response distributions. The analyses carried out for each simulated event and subdomain with consistent properties in terms of shallow shear-wave velocity proved to be instrumental for better interpreting the differences between simulated motions and empirical models. The main reasons identified for variances between simulations and empirical relationships included (1) directivity effects fully captured by the simulations across the full breadth of rupture models; (2) site vicinity to ruptures that incorporate large-slip patches, particularly if these are in the forward-directivity direction; and (3) presence of geologic structures that can “trap” seismic waves and produce ground motions with large amplitude and long signal duration. The analyses carried out in this work provide a path for interpreting ground-motion site and event specificity obtained from a suite of physics-based simulations, differing only in the rupture model characterization, to inform the selection of simulation scenarios for site-specific engineering analyses under strong excitations. Evidence from this work points to the possibility that current hazard models may underestimate ground-motion intensities in areas where the combined effect of directivity and site conditions results in large ground-motion amplitudes.

58 GEOSCIENCES↗
DOE OSTI2021

Modeling and Understanding of Rear Junction Double-Side Passivated Contact Solar Cells with Selective Area TOPCon on Front

Device modeling is performed to propose > 25% efficient industry-compatible rear junction double-side passivated contacts solar cell structure with full area p-TOPCon on the rear and selective area n-TOPCon under the front grid pattern (selective TOPCon). Here, this design enables the use of thicker TOPCon (>100nm) on the front for traditional screen-printed contacts without incurring metal-induced damage, high parasitic absorption loss, and compromise in lateral transport or carrier collection on the front. Rear junction design with appropriate bulk lifetime and resistivity combination eliminate the need for heavy doping in the front field region because carriers can flow through the bulk Si without appreciable FF loss. High VOC is maintained because high-quality Si surface passivation in the field region by Al 2 O 3 /SiN gives J 0 comparable to the TOPCon. Our device modeling specifies the practically achievable properties and parameters for each region, including full area rear p-TOPCon, selective area front n-TOPCon, bulk and contacts, to achieve 25.4% efficiency screen-printed bifacial rear junction selective TOPCon cells.

14 SOLAR ENERGY↗
DOE OSTI2020

30 cm Drop Modeling

This report documents modeling and analysis of the 30 cm cask drop scenario for spent nuclear fuel (SNF) casks. The 30 cm drop is an important hypothetical dynamic loading scenario for SNF because 10 CFR 71.71 defines testing requirements for normal conditions of transport (NCT) of SNF packages, and free drop (of 30 cm for the heaviest packages) tests are required. Other NCT conditions specified in 10 CFR 71.71 include vibration normally incident to transport, and DOE’s multimodal transportation test (MMTT) and related analysis concluded that mechanical shock and vibration incident to transportation is negligible for SNF structural integrity because the loads are so small. The SNF cask 30 cm drop has a lot more potential to cause damage to SNF because the kinetic energy and momentum of the system is stopped abruptly during the cask impact event. Test data and model predictions both agree that the SNF assemblies are subjected to significantly higher loads in a cask drop event than normal transportation shock and vibration, but currently there is no reason to expect gross structural damage of the fuel assembly or widespread SNF cladding failure as a result of a 30 cm cask drop. The ultimate goal of this work is to close the knowledge gap of stress profiles on SNF rods and fuel assembly components during NCT, and this report describes the progress toward reaching that goal as well as a plan for finishing this work over the next year or two. Key remaining tasks include refining the fuel assembly model, calculating a selection of specific fuel assembly cases to populate the damage model, and finally running the damage model to predict the full general response range of 30 cm drop loads on SNF.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Xanthos-Lake Dataset

The Xanthos-Lake v1.0 dataset provides the input data, trained machine-learning models, and simulation outputs needed to characterize lake water balance, snow and ice conditions, and mixing-layer temperature within the Xanthos global hydrological modeling framework. The dataset supports lake representation across a wide range of lake sizes and hydroclimatic conditions by combining xLSIM, a basin-specific machine-learning emulator of lake snow, ice, ice-cover fraction, and mixing-layer temperature, with the Xanthos-Lake water-balance model. The archive contains NetCDF datasets used to train and evaluate xLSIM, trained model weights, processed meteorological and lake-property inputs, and basin- and lake-category-specific simulation outputs. These materials are organized into four primary data groups, described below. Snowice_model_inputs: Contains the NetCDF input data used to train xLSIM. The xLSIM machine-learning framework uses three lake-based datasets. The meteorological forcing dataset provides monthly relative humidity, specific humidity, surface wind speed, maximum and minimum air temperature, downward longwave and shortwave radiation, snowfall, surface air pressure, and total precipitation. Lake surface area is included as an additional static predictor. The target-state dataset provides lake ice thickness, snow depth, snow cover, and lake mixing-layer temperature, while a companion lake-surface dataset provides the lake ice-cover fraction. Before training, ice thickness and snow depth are converted from meters to centimeters, mixing-layer temperature is converted from kelvin to degrees Celsius and constrained to nonnegative values, and ice-cover fraction is converted from a fraction to a percentage. The predictor variables are normalized using statistics calculated across the selected lakes and time steps. Snowice_model_outputs: Contains the NetCDF outputs generated by xLSIM. For each basin, xLSIM produces a file containing observed and predicted lake-state variables for the training, validation, and testing periods. The modeled variables include lake ice thickness, snow depth, snow cover, mixing-layer temperature, and lake ice-cover fraction. For basins without a sufficiently persistent snow-and-ice signal, the emulator predicts only mixing-layer temperature. The outputs also include training and validation loss histories, the selected model configuration, identifiers of the lakes used in training, and SHAP-based feature-importance information at the global, lake, and seasonal-regime levels. The trained machine-learning model weights are provided separately within the dataset archive. Together, these files support model evaluation and subsequent coupling with the Xanthos-Lake water-balance framework. XanthosLAKES: Contains the NetCDF input data used by the Xanthos-Lake framework. Monthly meteorological inputs include relative and specific humidity, downward shortwave and longwave radiation, mean, maximum, and minimum air temperature, wind speed, precipitation, snowfall, and surface air pressure. Static lake-property datasets provide lake identifiers, geographic locations, surface area, volume, mean depth, elevation, drainage area, fetch, outlet-routing information, and associated Xanthos grid-cell attributes. Separate bathymetric datasets provide the coefficients of the area–depth and volume–depth relationships for each aggregated lake unit. GLEV-based records provide observed lake surface area and evaporation data used to initialize lake states, define reference conditions, and calibrate and evaluate the model. Xanthos-Lake Outputs: Contains the basin- and lake-category-specific NetCDF outputs generated by Xanthos-Lake. Monthly variables include lake surface area, storage volume, outlet discharge, evaporation rate, evaporation volume, lake–groundwater exchange, lake inflow, ice thickness, snow depth, snow-cover fraction, ice-cover fraction, and mixing-layer temperature. The files also contain lake-specific calibration and validation statistics, including normalized root-mean-square error, mean absolute error, Nash–Sutcliffe efficiency, Kling–Gupta efficiency, and percent bias. Stored calibrated and derived parameters include the weir discharge coefficient, fractional freeboard, groundwater exchange coefficient, reference water level, corresponding reference surface area and storage volume, weir-width adjustment factor, and the fraction of routed inflow entering the lake. Basin identifiers, lake category, simulation period, calibration and validation periods, and parameter-schema information are retained as NetCDF metadata.

Abeshu, Guta [Pacific Northwest National Laborator↗
DOE OSTI2025

CMIP6-based Dynamically Downscaled Hydroclimate Projection over the Conterminous US

This dataset presents a suite of downscaled hydro-climate projections over the conterminous United States (CONUS) based on multiple selected Global Climate Models (GCMs) from the Coupled Models Intercomparison Project phase 6 (CMIP6). The CMIP6 GCMs are downscaled dynamically using Regional Climate Model version 4 (RegCM4). Each ensemble member covers the 1980-2019 baseline and 2020-2059 near-future periods under the high-end (SSP585) emission scenario. This dataset is formulated to support the SECURE Water Act Section 9505 Assessment for the US Department of Energy (DOE) Water Power Technologies Office (WPTO). For further details on this dataset, please refer to Kao et al. (2022) and Rastogi et al. (2022).

13 HYDRO ENERGY↗
DOE OSTI2023

Extended Donnan-Manning theory for selective ion partition and transport in ion exchange membrane

The use of ion exchange membranes (IEMs) for electrochemical ion-ion separation leverages the selectivity of the IEMs toward like-charged species via valence difference and/or other ion membrane interactions. A mechanistic model that relates selectivity with membrane structural and chemical properties is lacking in the literature. Here, in this study, we extend the Manning’s counter-ion condensation model for describing ion partition and ion mobility inside IEMs to mixed salts scenarios. We evaluate the extended Donnan-Manning model against experimental data from literature and compare the performance the Donnan-Manning model to that of the ideal Donnan model and the Donnan-Affinity model. Our analysis shows that, despite its structural complexity, the Donnan-Manning model has less fitting parameters than the Donnan-Affinity model and generally outperforms the two other models in predicting counter-ion and co-ion partition. With the assumption of a higher mobility of condensed ions than that of uncondensed ions, the generalized Manning’s model can also predict counter-ion mobility selectivity for cation exchange membranes, but its performance for predicting mobility selectivity for anion exchange membranes is still unsatisfactory.

42 ENGINEERING↗
DOE OSTI2022

Direct measurement of storage and loss behavior in AFM force–distance experiments using the modified Fourier transformation

Force–distance curve experiments are commonly performed in atomic force microscopy (AFM) to obtain the viscoelastic characteristics of materials, such as the storage and loss moduli or compliances. The classic methods used to obtain these characteristics consist of fitting a viscoelastic material model to the experimentally obtained AFM data. Here, we demonstrate a new method that utilizes the modified discrete Fourier transform to approximate the storage and loss behavior of a material directly from the data, without the need for a fit. Additionally, one may still fit a model to the resulting storage and loss behavior if a parameterized description of the material is desired. In contrast to fitting the data to a model chosen a priori, departing from a model-free description of the material's frequency behavior guides the selection of the model, such that the user may choose the one that is most appropriate for the particular material under study. To this end, we also include modified Fourier domain descriptions of commonly used viscoelastic models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

$\nu_μ$ Disappearance in MicroBooNE using the Deep Learning 1$\mu$1$p$ Selection

We test a 3+1 model with the MicroBooNE data using a 1µ1p selection developed using Deep-Learning-based reconstruction. In order to test this model we apply a muon neutrino disappearance effect to the selection, and search across a grid of oscillation model parameters using a Feldman Cousins technique. We determine MicroBooNE’s sensitivity across this model parameter space, and perform several validation studies to test this study’s robustness. Finally, we examine the allowed and excluded regions per MicroBooNE’s data at 90% confidence, using a data set corresponding to 6.67 × 10 20 protons on target. The null model remains allowed, and several of the high-disappearance models are excluded.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

$\nu_μ$ Disappearance in MicroBooNE using the Deep Learning 1$\mu$1$p$ Selection

We test a 3+1 model with the MicroBooNE data using a 1µ1p selection developed using Deep-Learning-based reconstruction. In order to test this model we apply a muon neutrino disappearance effect to the selection, and search across a grid of oscillation model parameters using a Feldman Cousins technique. We determine MicroBooNE’s sensitivity across this model parameter space, and perform several validation studies to test this study’s robustness. Finally, we examine the allowed and excluded regions per MicroBooNE’s data at 90% confidence, using a data set corresponding to 6.67 × 10 20 protons on target. The null model remains allowed, and several of the high-disappearance models are excluded.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Solvent selection for biphasic extraction of 5-hydroxymethylfurfural via multiscale modeling and experiments

We introduce a comprehensive conceptual framework for selecting solvents for reactive extraction in biphasic organic-water systems and demonstrate it for the separation of HMF (5-hydroxylmethylfurfural), a platform chemical produced in the acid-catalyzed dehydration of hexoses. We first perform in silico screening of ~2500 solvents, from the ADFCRS-2018 database using the ADF COSMO-RS implementation, and classification, based on the solvent partition coefficient. We then determine experimentally the partition coefficients for HMF, fructose, and products of HMF rehydration (levulinic acid (LA), and formic acid (FA)), the mutual water-organic solvent solubilities, and the separation factors in >50 select solvents spanning multiple homologous series at room temperature and a typical reaction temperature with in situ sampling. We find that COSMO-RS is excellent for screening purposes (typical error in most cases within a factor of ~2). Increased temperatures lead to significant reduction in partitioning, and room temperature measurements are clearly inadequate for solvent selection. Upon down selecting classes of solvents based on separation performance, we perform experimental thermal stability and reaction compatibility studies of a small set of solvents at relevant reactive-extraction temperatures. We discover that many substituted phenols exhibit an order-of-magnitude increase in partitioning compared to conventional solvents due chiefly to hydrogen bond interactions and show the necessary stability but retain a significant fraction of water and LA, factors that need to be considered in technoeconomic analysis. In contrast, anilines, aldehydes, and acids are good to excellent regarding separation but incompatible with this specific reaction medium. Furthermore, this multifaceted framework can be extended to other biomass-derived products and processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

An open-source framework for balancing computational speed and fidelity in production cost models

Studies of bulk power system operations need to incorporate uncertainty and sensitivity analyses, especially around exposure to weather and climate variability and extremes, but this remains a computational modeling challenge. Commercial production cost models (PCMs) have shorter runtimes, but also important limitations (opacity, license restrictions) that do not fully support stochastic simulation. Open-source PCMs represent a potential solution. They allow for multiple, simultaneous runs in high-performance computing environments and offer flexibility in model parameterization. Yet, developers must balance computational speed (i.e. runtime) with model fidelity (i.e. accuracy). In this paper, we present Grid Operations (GO), a framework for instantiating open-source, scale-adaptive PCMs. GO allows users to search across parameter spaces to identify model versions that appropriately balance computational speed and fidelity based on experimental needs and resource limits. Results provide generalizable insights on how to navigate the fidelity and computational speed tradeoff through parameter selection. We show that models with coarser network topologies can accurately mimic market operations, sometimes better than higher-resolution models. It is thus possible to conduct large simulation experiments that characterize operational risks related to climate and weather extremes while maintaining sufficient model accuracy.

42 ENGINEERING↗
DOE OSTI2022

Development of Short-Term Forecasting Models Using Plant Asset Data and Feature Selection

Nuclear power plants collect and store large volumes of heterogeneous data from various components and systems. With recent advances in machine learning (ML) techniques, these data can be leveraged to develop diagnostic and short-term forecasting models to better predict future equipment condition. Maintenance operations can then be planned in advance whenever degraded performance is predicted, thus resulting in fewer unplanned outages and the optimization of maintenance activities. This enables lower maintenance costs and improves the overall economics of nuclear power. This paper focuses on developing a short-term forecasting process that leverages a feature selection process to distill large volumes of heterogeneous data and predict specific equipment parameters. A variety of feature selection methods, including Shapley Additive Explanations (SHAP) and variance inflation factor (VIF), were used to select the optimal features as inputs for three ML methods: long short-term memory (LSTM) networks, support vector regression (SVR), and random forest (RF). Each combination of model and input features was used to predict a pump bearing temperature both 1 and 24 hours in advance, based on actual plant system data. The optimal inputs for the LSTM and SVR were selected using the SHAP values, while the optimal input for the RF consisted solely of the response variable itself. Each model produced similar 1-hour-ahead predictions, with root mean square errors (RMSEs) of roughly 0.006. For the 24-hour-ahead predictions, differences could be seen between LSTM, SVR, and RF, as reflected by model performances of 0.036 +- 0.014, 0.0026 +- 0, and 0.063 +- 0.004 RMSE, respectively. As big data and continuous online monitoring become more widely available, the proposed feature selection process can be used for many applications beyond the prediction of process parameters within nuclear infrastructure.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Performance evaluation of automated data-driven feature extraction and selection methods for practical and scalable building energy consumption prediction models

Here, this study quantifies the impact of automated feature engineering methods (feature extraction and selection) on the quality and accuracy of machine learning models that predict building energy consumption. The case study compares model performance for three main scenarios: baseline (no feature extraction and selection), feature extraction only, and feature extraction combined with feature selection (filter and/or wrapper methods) for fully trained machine learning models for 200 metered/sub-metered energy measurements across 118 real buildings. For consistency, the same machine learning model architecture (a black box deep learning neural network with probabilistic forecast output) was used for all scenarios. Based on results, all feature engineering methods provided noticeable prediction accuracy improvements (e.g., 29%-68% median prediction improvement) compared to baseline scenarios. However, in this application, feature selection methods provide little practical value due to their limited performance gains and high computational cost. Smarter algorithm development supported by better computational environments will be needed before feature selection methods can reliably and efficiently improve predictive model performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Modeling of transport in carrier-selective contacts in silicon heterojunction solar cells

High-performance silicon heterojunction (SHJ) solar cells use carrier-selective contact structures based on hydrogentated amorphous Si (a-Si:H) to maximize collection of photogenerated carriers. The high open circuit voltages observed experimentally in SHJ cells require that the carrier-selective contacts provide selectivity and passivation. However, a microscopic understanding of the dynamics of carrier transport through the a-Si layer is currently lacking. In this paper, we explicitly simulate the transport of holes across the a-Si:H(i) layer using a novel kinetic Monte Carlo approach. The hole-selective contact structure investigated in this paper uses p-type doped a-Si:H(p) and intrinsic a-Si:H(i) on an n-type crystalline silicon wafer, where the selectivity is provided by the a-Si:H(p) and the passivation is provided by the a-Si:H(i). However, in addition to the passivation provided by the a-Si:H(i), this layer also creates a potential barrier to the collection of photogenerated holes. There have been experimental studies in the literature that have suggested that multi-phonon processes are the main transport mechanism that assists in the transport of holes across the intrinsic a-Si:H barrier. Simulations presented here show that multi-phonon injection of holes into the a-Si:H(i) layer is the rate limiting step for transport across the a-Si:H(i) layer. Our results indicate that multi-phonon transport is strongly dependent on the electric field at the a-Si:H(i)/c-Si heterointerface as well. Transport simulations presented in this paper are consistent with experimental findings that multi-phonon processes limit transport across the a-Si:H(i) layer and are responsible for photocurrent suppression at the a-Si:H(i)/c-Si heterointerface when these processes are slower than the associated incident hole flux due to photo-excitation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Recent advances in integrated hydrologic models: Integration of new domains

Over the past several decades, hydrologic models have advanced from independent models of the surface and subsurface to integrated models that can capture the terrestrial hydrologic cycle within one framework. In recent years, these coupled frameworks have seen the inclusion of biogeochemical processes, ecohydrology, sedimentation and erosion, cold region hydrology, anthropogenic activities, and atmospheric processes. This expansion is the result of increased computational, data, and modeling capabilities and capacities, as well as improved understanding of the processes that drive these integrated systems. Here, in this study, we review these recent advances to integrate new processes and systems into existing terrestrial hydrologic models and highlight the significant challenges and opportunities that remain. We identify that with so many models currently available and in development, selecting the most appropriate model is difficult, and we suggest a path for new or novice modelers to find the most appropriate code based on their needs. In addition, data required to parameterize and calibrate these models can often constrain their applicability and usefulness. However, advances in environmental sensors and measurement technology, in addition to data assimilation of non-traditional data (e.g. remote sensing, qualitative data) are providing new ways of addressing this issue. As we expand hydrologic models to integrate more processes and systems, our computational demands also increase. Recent and emerging advances in computational platforms, including cloud and quantum computing, in addition to the use of machine learning to capture some processes, will continue to support the use of increasingly larger and more complex, process-based models. Finally, we highlight that it is critical to develop state-of-the-science models that are accessible to all model users, not just those applied for research and development. We encourage continued development of diverse modeling platforms, considering the user needs, data availability, and computational resources.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Fast and fault-tolerant model predictive control of MMCs under selective harmonic elimination

Selective harmonic elimination (SHE) is a suitable pulse-width modulation (PWM) tech-nique for some applications of modular multilevel converters (MMC) with lower numberof semiconductor submodules (SM). However, SHE can make it difficult to control circu-lating currents in the MMC. Moreover, the MMC is vulnerable to SM switch failures thatreduce its output power magnitude and quality. This paper proposes a fault-tolerant modelpredictive control strategy for MMCs operating with SHE (FT SHE-MPC). The effects ofswitch failure on phase voltage waveforms are studied first. Then, a method is proposedto design the FT SHE waveform to preserve its harmonic performance. Next, the pro-posed FT SHE-MPC is designed to increase the capacitor voltages during an SM failure tomaintain the fundamental component of the faulty phase voltage and control the circulat-ing currents, maintaining balance among line voltages and the output power of the MMC.Additionally, the proposed method offers as much as 60% faster FT transition time and asmuch as 36% lower capacitor voltage ripple. The advantages of the proposed method areverified through offline and real-time simulation studies on a three-phase nine-level MMCin PSCAD/EMTDC software and RTDS.

42 ENGINEERING↗
DOE OSTI2020

Validation of Continuous-Energy ENDF/B-VIII.0 16 O, 56 Fe, and 63,65 Cu Cross Sections for Nuclear Criticality Safety Applications

Recently completed cross-section evaluations sponsored in part by the Nuclear Criticality Safety Program were incorporated into the 2018 release of the ENDF/B-VIII.0 cross-section library. Evaluated isotopes of interest to the nuclear data and criticality safety community include 16 O, 56 Fe, and 63,65 Cu. For performance validation, benchmark models defined in the International Criticality Safety Benchmark Evaluation Project Handbook were selected based on energy-integrated k eff sensitivities to total cross sections of interest and compared with experimental values. Of the 102 benchmark configurations that were utilized, 63 are sensitive to 16 O, 32 sensitive to 63,65 Cu, and 25 sensitive to 56 Fe. Selected benchmarks were modeled in SCALE 6.2.3 Criticality Safety Analysis Sequence (CSAS) continuous-energy Monte Carlo k eff calculations with ENDF/B-VII.1, with a hybrid ENDF/B-VII.1 with ENDF/B-VIII.0 data substituted for individual isotopes of interest, and with ENDF/B-VIII.0. ENDF/B-VIII.0 showed improved agreement with experimental k eff for 56 Fe, 63 Cu, elemental copper, and full library substitution while producing lessened agreement for 16 O and 65 Cu. Furthermore, with full library and isotope-specific ENDF/B-VIII.0 performance, a best-case ENDF library was formed by excluding underperforming isotopes’ ENDF/B-VIII.0 data, reverting 16 O and 65 Cu cross sections to ENDF/B-VII.1. This resulted in the average relative deviation between calculated and experimental data improving from 1.45σ for the ENDF/B-VIII.0 library to 1.32σ for the best-case library, relative to benchmark uncertainty.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗