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At least 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

An Approach to Dynamic Human Reliability Analysis Using the EMRALD Dynamic Risk Assessment Tool

There is a need for research into dynamic human reliability analysis (HRA) (i.e., simulation- or computation-based HRA), as many researchers have emphasized the importance of applying dynamic approaches to probabilistic safety assessments (PSAs). This study proposes the Procedure-based Investigation Method of EMRALD Risk Assessment – Human Reliability Analysis (PRIMERA-HRA), a dynamic HRA approach based on the Event Modeling Risk Assessment Using Linked Diagram (EMRALD) software developed by Idaho National Laboratory (INL). This study suggests how this software can be used to model human actions and evaluate error probabilities. The applicability of this approach is also be investigated in light of an extended loss of AC power (ELAP) scenario. This paper then discusses the major insights derived by comparing this dynamic HRA approach against the static one.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Dynamics of Fungal and Bacterial Biomass Carbon in Natural Ecosystems: Site-level Applications of the CLM-Microbe Model

Explicitly representing microbial processes has been recognized as a key improvement to Earth system models for the realistic projections of soil carbon (C) and climate dynamics. The CLM-Microbe model builds upon the CLM4.5 and explicitly represents two major soil microbial groups, fungi and bacteria. Based on the compiled time-series data of fungal (FBC) and bacterial (BBC) biomass C from nine biomes, we parameterized and validated the CLM-Microbe model, and further conducted sensitivity analysis and uncertainty analysis for simulating C cycling. The model performance was evaluated with mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) for relative change in FBC and BBC. The CLM-Microbe model is able to reasonably capture the seasonal dynamics of FBC and BBC across biomes, particularly for tropical/subtropical forest, temperate broadleaf forest, and grassland, with MAE < 0.49 for FBC and <0.36 for BBC and RMSE <0.52 FBC and <0.39 for BBC, while R2 values are relatively smaller in some biomes (e.g., shrub) due to small sample sizes. We found good consistencies between simulated and observed FBC (R 2 =0.70, P<0.001) and BBC (R 2 =0.26, P<0.05) on average across biomes, but the model is not able to fully capture the large variation in observed FBC and BBC. Sensitivity analysis shows the most critical parameters are turnover rate, carbon-to-nitrogen ratio of fungi and bacteria, and microbial assimilation efficiency. This study confirms that the explicit representation of soil microbial mechanisms enhances model performance in simulating C variables such as heterotrophic respiration and soil organic C density. The further application of the CLM-Microbe model would deepen our understanding of microbial contributions to the global C cycle.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Reduced Order Model of Transactive Bidding Loads

Transactive energy (TE) has been identified to provide better grid efficiency and reliability by market-based transactive exchanges between energy producers and energy consumers. Simulations of TE systems are crucial to evaluate the benefits and impacts of different transactive mechanisms. However, such simulations can be time consuming due to the information exchange between various participants and complex co-simulation environments. In this paper, we develop a reduced order model to speed up the simulation of transactive systems in TE simulation platform (TESP) while achieving very low error between the reduced order and full model results. Specifically, the developed reduced order model consists of an aggregate responsive load agent which utilizes two Recurrent Neural Networks (RNNs) with Long Short-Term Memory units (LSTMs) to enable transactive elements to collectively participate in the TE system. The proposed aggregate responsive load (ARL) agent is able to produce similar transactive behaviors to the full simulation model while achieving significant simulation time reduction. Finally, we also show that the developed model enables generalization of simulation results across different dates and across different number of loads included in the simulations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Improving Computational Efficiency of Mechanical Finite Element Method Simulations for PV Modules: Preprint

In this work we have elucidated the trade off between structural mechanics FEM model accuracy and computation time by employing lower fidelity viscoelastic models. Results indicate that computation time can easily be cut in half while only expecting a potential maximum error of 10 % by considering lower fidelity models. A novel approach to produce the Prony Series fit for viscoelastic characterization has also been presented. Employing this approach, we were able to achieve a further 10 % reduction in computation time without further sacrificing simulation accuracy.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2020

Modified Isotherm Modeling to Predict Cs Exchange with Crystalline Silicotitanate in Tank Waste Simulants

The U.S. Department of Energy is working to expedite processing of Hanford tank waste supernate at the Hanford Waste Treatment and Immobilization Plant. To support this goal, Washington River Protection Solutions is designing a Tank Side Cesium Removal (TSCR) system for suspended solids and cesium (Cs/ 137 Cs) removal from Hanford tank waste supernate. The ion exchange media selected for Cs removal at TSCR is crystalline silicotitanate (CST) that is manufactured in a nearly spherical form by Honeywell UOP LLC (UOP; Des Plaines, IL) as product IONSIV® R9140-B (Na form). The Zheng Anthony Miller (ZAM) isotherm model (Zheng et al. 1997) is a multicomponent ion exchange model used to predict the exchange of Group I metals onto CST. The ZAM isotherm has historically been used to predict Cs distribution values from Hanford and Savannah River Site (SRS) tank waste simulants. Figure S.1 summarizes model predictions from the ZAM isotherm that indicate poor prediction of Cs distribution values for simple and complex simulants with the engineered form of CST, IONSIV® R9140-B, and IONSIV® R9120-B where the solid line indicates a perfect fit by the model. The dotted lines indicate ±20% error. Batch contact testing with Hanford tank waste complex and simple simulants was used in conjunction with SRS simulants to experimentally determine Cs distribution values using a modification to the original isotherm model. The experimentally determined maximum Cs capacity for IONSIV® R9140-B CST in both the simple and complex matrices was found to be 0.53±0.3 mmoles Cs/g of CST. This value is not drastically different from the maximum Cs capacity of 0.58 mmoles Cs/g TAM-5 reported by Zheng et al. (1997). However, it is important to note that TAM-5 (commercially IONSIV® IE-910) is a powder. Hamm et al. (2002) determined that a dilution factor was needed to account for the Zr(OH)2 binder in the engineered form of CST. Hamm et al. determined that a dilution factor of 0.68 was appropriate to account for binder contribution and correct overprediction of Cs exchange on the engineered form of CST in ZAM calculations. This reduced the total capacity from 0.58 mmol/g with TAM-5 to 0.39 mmol/g for the engineered form of CST (Hamm et al. 2002). Despite substituting the experimentally determined maximum Cs capacity of 0.55 mmoles Cs/g for the literature-reported capacity of 0.39 mmoles Cs/g, it was determined that additional modifications to the model’s equilibrium rate constants were necessary in refining the isotherm model. The modified model overpredicted K+ uptake by the CST when compared to digested CST results described by Campbell et al. (2019). The modified model was further revised to omit three of the five K+ exchange equilibrium reactions described by ZAM to reduce the additional K+ loading seen by the model. Figure S.2 summarizes the revised model isotherm predictions plotted against measured Kd values.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

AGR-5/6/7 Capsule 1 Temperature Re-analysis_Grant Hawkes_Final

Slides for Gas-Cooled Reactor AGR-5/6/7 Thermal Model with Non-Uniform Gas Gaps. Includes investigation of offset through modeling and experimental results, ABAQUS finite element model created for the entire test train, Capsule 1 information, Three irradiation dates to compare the thermal model with the working thermocouples (TCs), Offset distances and direction of offset for the top and bottom of the graphite holder with computer runs, determination of offset by the root mean square error (RMSE) between the TCs and model predtictions, and the results.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

ab initio Sub-Mechanism Development for Cyclopentene Oxidation

To accurately predict low-temperature oxidation behavior, chemical kinetics mechanisms must contain complete reaction networks that include detailed consumption reactions of intermediates produced directly from hydroperoxyalkyl radicals, Q̇OOH, which undergo competing unimolecular reactions and bimolecular reactions with O2. Rates of chain-branching are governed by the flux between the two competing pathways, and inherently depend on temperature, pressure, and oxygen concentration. Neglect of consumption pathways for major oxidation intermediates leads to mechanism truncation error that is ameliorated by expanding the level of detail included in sub-mechanisms and employing ab initio methods for computing rates of elementary reactions and thermochemical properties of species involved. In the present work, an ab initio-derived sub-mechanism is developed using AutoMech to model the chemical kinetics of cyclopentene, a major product of cyclopentane oxidation. The ab initio sub-mechanism builds on a detailed mechanism developed using Reaction Mechanism Generator (RMG) for the specific purpose of determining the extent to which replacing cyclopentene-specific reactions and species with quantum chemical computations reduces model inaccuracies resulting from mechanism truncation error. In an effort to minimize interference from other reactions present during the formation of cyclopentene from cyclopentyl + O2, providing a narrower experimental scope, the model is compared against speciation measurements from jet-stirred reactor (JSR) experiments on cyclopentene oxidation. The experiments utilize vacuum ultraviolet-absorption spectroscopy and mass spectrometry for isomer-resolved speciation of intermediates at 835 Torr from 700 – 950 K. [O2]-dependent experiments were also conducted from 0.057 – 2.01 · 1018 molecules cm–3 at 825 K to examine the influence of oxygen on species profiles. Model predictions using the ab initio-revised mechanism yielded significant improvements in species profiles for both the temperature- and [O2]-dependent measurements, owing in part to increased rates of HOȮ and H2O2 production, which underscores the influence of theoretical calculations of reaction rates involving species produced from Ṙ + O2 such as cyclopentene.

AutoMech↗
DOE OSTI2024

Dissipation Scaled Internal Wave Drag in a Global Heterogeneously Coupled Internal/External Mode Total Water Level Model

This study showcases a global, heterogeneously coupled total water level system wherein salinity and temperature outputs from a coarser-resolution (~12 km) ocean general circulation model are used to calculate density-driven terms within a global, higher-resolution (~2.5 km) depth-averaged total water level model. We demonstrate that the inclusion of baroclinic forcing in the barotropic model requires modification of the internal wave drag term to prevent excess degradation of tidal results compared to the barotropic model. By scaling the internal tide dissipation by an easy to calculate dissipation ratio, the resulting heterogeneously coupled model has complex root mean square errors (RMSE) of 2.27 cm in the deep ocean and 12.16 cm in shallow waters for the M 2 tidal constituent. While this represents a 10%–20% deterioration as compared to the barotropic model, the improvements in total water level prediction more than offset this degradation. Global median RMSE compared to observations of total water levels, 30-day sea levels, and non-tidal residuals improve by 1.86 (18.5%), 2.55 (42.5%), and 0.36 (5.3%) cm respectively. The drastic improvement in model performance highlights the importance of including density-driven effects within global hydrodynamic models and will help to improve the results of both hindcasts and forecasts in modeling extreme and nuisance flooding. With only an 11% increase in model run time compared to the fully barotropic total water level model, this approach paves the way for high resolution coastal water level and flood models to be used alongside climate models, improving operational forecasting of total water levels.

Blakely, Coleman Peter [University of Notre Dame, ↗
DOE OSTI2026

Resilience Measurement Framework For Post-deployment Artificial Intelligence (ai) Integrated Systems

Resilience is largely defined as the ability to adapt or recover from adverse conditions, stresses, attacks, or compromises on systems that use or are enabled by digital resources. In Artificial Intelligence Management and Research for Advanced Networked Testbed Hub (AMARANTH), resilience is measured in the amount of time it took from the beginning of a testing period for the model to reach predictions outside of the original 95% confidence interval or using the Kullback-Leibler (KL) divergence theorem, the Population Stability Index (PSI), and traditional methods such as root mean squared error (RMSE) threshold. Artificial Intelligence (AI) model drift is of significant concern when deploying AI-integrated systems into critical and/or secure environments. Drift can impact resilience of the AI-integrated system post-deployment and requires consistent maintenance and upkeep to ensure the model is accurate and precise. To quantify model drift and predict the point when a model's drift becomes unacceptable, we describe using Kullback-Leibler (KL) divergence, Population Stability Index (PSI) and/or confidence interval width estimations to determine the point of failure and time to failure of a model post-deployment. Through simple code functions, the KL-divergence, PSI, confidence interval, and root mean squared (RMSE) point of failures can be used to derive when a model needs to be maintained as well as the impact of adversarial action through statistical means.

Yockey, Patience [Idaho National Laboratory (INL),↗
DOE OSTI2022

Finding predictive models for singlet fission by machine learning

Singlet fission (SF), the conversion of one singlet exciton into two triplet excitons, could significantly enhance solar cell efficiency. Molecular crystals that undergo SF are scarce. Computational exploration may accelerate the discovery of SF materials. However, many-body perturbation theory (MBPT) calculations of the excitonic properties of molecular crystals are impractical for large-scale materials screening. We use the sure-independence-screening-and-sparsifying-operator (SISSO) machine-learning algorithm to generate computationally efficient models that can predict the MBPT thermodynamic driving force for SF for a dataset of 101 polycyclic aromatic hydrocarbons (PAH101). SISSO generates models by iteratively combining physical primary features. The best models are selected by linear regression with cross-validation. The SISSO models successfully predict the SF driving force with errors below 0.2 eV. Based on the cost, accuracy, and classification performance of SISSO models, we propose a hierarchical materials screening workflow. Three potential SF candidates are found in the PAH101 set.

36 MATERIALS SCIENCE↗
DOE OSTI2026

MCP-enabled agentic AI workflow for building energy modelling: framework and use cases

Traditional building energy modelling workflows remain labor-intensive and error-prone, requiring specialized expertise that limits broader adoption. This paper introduces a novel Model Context Protocol (MCP)-enabled framework that connects AI assistants to EnergyPlus through MCP, a standardized interface for tool invocation and context management. Two complementary integration paradigms are presented and compared: conversational integration, where users interact through natural language while an AI assistant orchestrates MCP tools on demand, and agentic workflow integration, where specialized agents coordinate autonomously to complete multi-step tasks. Using an experimental testbed for residential buildings, the end-to-end workflows are demonstrated. The conversational approach reduced typical inspection and modification tasks from 1-2 h to under 15 min, while maintaining full transparency through visible tool invocations. The agentic approach automated parametric analysis. These demonstrations establish MCP as a foundational layer for AI-assisted building energy modelling, enabling natural language interactions with simulation tools while preserving professional oversight and decision-making authority.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Machine Learning-Enabled Image Classification for Automated Electron Microscopy

Abstract Traditionally, materials discovery has been driven more by evidence and intuition than by systematic design. However, the advent of “big data” and an exponential increase in computational power have reshaped the landscape. Today, we use simulations, artificial intelligence (AI), and machine learning (ML) to predict materials characteristics, which dramatically accelerates the discovery of novel materials. For instance, combinatorial megalibraries, where millions of distinct nanoparticles are created on a single chip, have spurred the need for automated characterization tools. This paper presents an ML model specifically developed to perform real-time binary classification of grayscale high-angle annular dark-field images of nanoparticles sourced from these megalibraries. Given the high costs associated with downstream processing errors, a primary requirement for our model was to minimize false positives while maintaining efficacy on unseen images. We elaborate on the computational challenges and our solutions, including managing memory constraints, optimizing training time, and utilizing Neural Architecture Search tools. The final model outperformed our expectations, achieving over 95% precision and a weighted F-score of more than 90% on our test data set. This paper discusses the development, challenges, and successful outcomes of this significant advancement in the application of AI and ML to materials discovery.

Materials Science↗
DOE OSTI2021

HAIMOS Ensemble Forecasts for Intra-day and Day- Ahead GHI, DNI and Ramps

The objective of this research is to develop a hybrid physics-based/data-driven forecast model to improve direct normal and global horizontal irradiance (DNI and GHI) prediction for horizons ranging from 1 to 72 hours. Project objectives also address key gaps in state-of-the-art solar forecasting: accurate probabilistic solar forecasts and the forecasting of large irradiance ramps (ramp onset and magnitude). The proposed model ensembles Numerical Weather Prediction (NWP) forecasts, determinist physics-based algorithms, and new-generation cloud cover products (high-resolution rapid refresh satellite images and Large Eddy Simulations). The result is the Hybrid Adaptive Input Model Objective Selection (HAIMOS) ensemble model. HAIMOS blends state of the art machine learning methodologies with physics-based models for cloud cover and cloud optical depth forecasts. The technical activities followed a two-pronged strategy. First, the preprocessing of data, the selection of inputs to the nonlinear approximators, the type of approximator and objective functions, and post-processing ensembling techniques included in HAIMOS were all optimized adaptively to find the best model for a specific goal (reduce DNI/GHI forecast error, improve the prediction of ramp onset, etc.). Second, a large effort was put in improving cloud identification and the forecast of cloud cover and cloud optical depth. To this end, new-generation cloud parametrization products were developed in this work. These include improved algorithms to assist in cloud identification, cloud classification and cloud parametrization from satellite images – three key factors in the accuracy of 1 to 6-hours irradiance forecasts and prediction of ramp onset. Furthermore, we also included cloud information extracted from high resolution rapid refresh satellite images (GOES-16) and Large Eddy Simulations (LES). LES was used to model the atmosphere in detail over locations of interest and produce cloud optical depth forecasts. Once these data streams were validated, they were used as input data to the HAIMOS forecast. The model was developed using data from several climatologically distinct locations with potential for high solar penetration. In the last year of the project, we conducted a validation campaign according to the guidelines stipulated by the Topic Area 1 project as described in the FOA. This effort brings, for the first time, proven machine-learning methodologies for generating state-of-the-art solar forecasts interweaved with detailed physics-based models for cloud detection, and cloud optical depth forecasts. HAIMOS will generate accurate irradiance probabilistic forecast to assist in reducing solar generation prediction error. Globally optimized solar forecast models are more likely to impact solar energy stakeholders. The goal of this project was to increase the state-of-the-art forecast skill from their present values of 10 to 35%. At the end of the project, we achieved between 30% and 50% forecast skill across a wide range of horizons for both GHI and DNI.

14 SOLAR ENERGY↗
DOE OSTI2024

A model to separate conduction and radiation in high temperature laser flash measurements for semi-transparent materials

Laser flash analysis (LFA) is a widely used technique in the scientific and industrial communities for measuring the thermal diffusivity and conductivity of materials. However, traditional fitting methods based solely on heat conduction do not account for radiation heat transfer in transparent and semi-transparent materials, which becomes significant at high temperatures. In this study, we have developed a coupled radiation-conduction model that considers the optical properties of materials to accurately extract thermal diffusivity from LFA measurements. We conducted LFA measurements on fused silica glass samples of varying thicknesses and within the temperature range of 25 to 1000 °C and fitted the data with the model. Since the coupled model requires optical properties as input parameters, which are often unavailable for new materials, we have also proposed a modified transparent model. Compared to the coupled model, results from the modified transparent model generally overestimate thermal conductivity, but the errors are acceptable in silica glass with small thickness. Here, we also investigated the applicability of the modified transparent model with materials of different optical properties.

14 SOLAR ENERGY↗
DOE OSTI2024

Manufactured solutions for an electromagnetic slot model

The accurate modeling of electromagnetic penetration is an important topic in computational electromagnetics. Electromagnetic penetration occurs through intentional or inadvertent openings in an otherwise closed electromagnetic scatterer, which prevent the contents from being fully shielded from external fields. To efficiently model electromagnetic penetration, aperture or slot models can be used with surface integral equations to solve Maxwell's equations. A necessary step towards establishing the credibility of these models is to assess the correctness of the implementation of the underlying numerical methods through code verification. Surface integral equations and slot models yield multiple interacting sources of numerical error and other challenges, which render traditional code-verification approaches ineffective. In this paper, we provide approaches to separately measure the numerical errors arising from these different error sources for the method-of-moments implementation of the electric-field integral equation with a slot model. Finally, we demonstrate the effectiveness of these approaches for a variety of cases.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Machine learning prediction of self-diffusion in Lennard-Jones fluids

In this work, different machine learning (ML) methods were explored for the prediction of self-diffusion in Lennard-Jones (LJ) fluids. Using a database of diffusion constants obtained from the molecular dynamics simulation literature, multiple Random Forest (RF) and Artificial Neural Net (ANN) regression models were developed and characterized. The role and improved performance of feature engineering coupled to the RF model development was also addressed. The performance of these different ML models was evaluated by comparing the prediction error to an existing empirical relationship used to describe LJ fluid diffusion. It was found that the ANN regression models provided superior prediction of diffusion in comparison to the existing empirical relationships.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Collaborative Distribution System Restoration Planning and Real-Time Dispatch Considering Behind-the-Meter DERs

Modern distribution systems are becoming more resilient against power outages thanks to the flexible generation capacity provided by the increasing integration of distributed energy resources (DERs). Compared to utility-scale DERs, which can be directly controlled by system operators, small-scale, behind-the-meter DERs are much more difficult to coordinate despite being the vast majority. Two major obstacles to the implementation of DERs for service restoration are the coordination of behind-the-meter DERs and the management of uncertainty. In this paper, a novel distribution system restoration strategy is proposed to overcome these two obstacles. The flexibility of be-hind-the-meter DERs is quantified and integrated into the restoration planning models within a hierarchical structure. The model predictive control technique is employed to enable adaptive restoration planning solution adjustments to accommodate fault scenarios and forecast errors. Besides, real-time power dispatch models are proposed to maintain power balance using available DER capability. Comprehensive integration of adaptive restoration planning schemes and real-time dispatch results are achieved through a collaborative restoration framework. Finally, the effectiveness of the proposed restoration strategy is verified through a modified IEEE 123-bus system and a real-world utility system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Athena-I CUBIT Journal Files

The Monte Carlo N-Particle (MCNP)1 transport code version 6 (also known as MCNP6) has the capability for tracking particles on unstructured mesh (UM) geometry models embedded into con structive solid geometry (CSG) cells. This feature has been developed for performing calculations of complex geometry models because manually creating CSG models is a time-consuming and error prone process as the complexities of geometries increase. The UM capability was originally designed to work with UM models created with the Abaqus software and ASCII input files that it generates. The MCNP code version 6.0 and later can process UM models formatted as Abaqus input files. Starting with version 6.3, MCNP can also process HDF5 mesh input files. External codes must be used to generate Abaqus input files for MCNP UM calculations. CUBIT, the Sandia National Laboratory automated mesh generation toolkit, can generate a UM model formatted as an Abaqus input file. However, the Abaqus input files exported from CUBIT cannot be used for MCNP simulations because it lacks the proper syntax. A Python script was developed to convert an Abaqus file created by CUBIT to an Abaqus file format that MCNP can process. Creating UM models for complex geometries is not an easy task. The process of creating UM models in CUBIT for MCNP simulations is detailed in. CUBIT provides several user interface options including a graphical user interface (GUI) and a command line interface. A GUI provides an easy way to use CUBIT without learning the CUBIT command syntax. When using CUBIT with either interface option, command lines are written into an ASCII file known as a journal file; this journal file can be edited and archived so that it can be played back in CUBIT to automatically generate a UM model. This report describes the CUBIT journal files of the UM models developed for Athena-I. The Athena platform, an energy-tuning assembly, was developed to spectrally shape the National Ignition Facility (NIF) deuterium-tritium fusion neutron source to a thermonuclear (fusion) plus prompt fission neutron spectrum with capability to act as a short-pulse neutron source. MCNP6 was used for the Athena experiment design analysis.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗