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Results for “modeling and performance 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.

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DOE OSTI2024

Risk-Aware Framework Development for Disruption Prediction: Alcator C-Mod and DIII-D Survival Analysis

Abstract Survival regression models can achieve longer warning times at similar receiver operating characteristic performance than previously investigated models. Survival regression models are also shown to predict the time until a disruption will occur with lower error than other predictors. Time-to-event predictions from time-series data can be obtained with a survival analysis statistical framework, and there have been many tools developed for this task which we aim to apply to disruption prediction. Using the open-source Auton-Survival package we have implemented disruption predictors with the survival regression models Cox Proportional Hazards, Deep Cox Proportional Hazards, and Deep Survival Machines. To compare with previous work, we also include predictors using a Random Forest binary classifier, and a conditional Kaplan-Meier formalism. We benchmarked the performance of these five predictors using experimental data from the Alcator C-Mod and DIII-D tokamaks by simulating alarms on each individual shot. We find that developing machine-relevant metrics to evaluate models is an important area for future work. While this study finds cases where disruptive conditions are not predicted, there are instances where the desired outcome is produced. Giving the plasma control system the expected time-to-disruption will allow it to determine the optimal actuator response in real time to minimize risk of damage to the device.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

The Applicability of Unit Systems to High-Performance Computing Applications

Dimensional analysis is a key technique used to verify the soundness of scientific models. Most experts agree that engineering and scientific software would be made more reliable by integrating dimensional analysis in their type system. We explored how High Performance Computing (HPC) applications could integrate compile-time dimensional analysis. We started by investigating various implementation of unit systems for C++. Eventually, selecting the latest (and most advanced) one to apply to our test codes. We worked with code of increasing complexity, from a projectile trajectory calculation to the proxy-application Lulesh. This included our code, Springs-3D, which focuses on demonstrating language features while performing simple physic computations. Finally, our main contribution is a source-code analysis which extracts constraints on the dimension of all variables, functions, and constants in an application. This resulting system of equations is solved using the dimensions of a few of these objects. This analysis has the potential to greatly reduce the time spent performing dimensional analysis when refactoring application to use a representation of units.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

SITCOMTN-161: PSF assessment in the field of Abell 360 and shapeHSM shear profile using LSSTComCam data

The Rubin LSSTComCam on-sky campaign performed at the end of 2024 provided observations of the Abell 360 galaxy cluster; these data allow a preliminary study of cluster weak lensing analysis using Rubin Data Preview 1 (DP1) data. Among all the steps required for such analyses, accurate modeling of the PSF is essential. This work uses several diagnostics, mostly based on the residuals between the second moments of stars and the PSF model, to characterize the accuracy of the PSF modeling in the A360 field. We find the level of the residuals to be sufficiently low not to hinder the measurement of the tangential shear profile around A360. With a simple source selection process, we demonstrate that outputs of the LSST Science Pipelines can be used to detect the tangential shear profile in Abell 360 at the 3.6σ level, and our analysis indicates that contamination from PSF modeling systematics is negligible.

Dell'Antonio, Ian [Brown University]↗

DOE OSTI2020

Summary of Analytical Results from Samples Supporting Tank Closure Cesium Removal (TCCR) Batch 3 and Modeling Results for Cs Loading on CST

Savannah River Remediation (SRR) is currently operating the Tank Closure Cesium Removal (TCCR) process to remove 137 Cs from tank waste supernate using an ion exchange process. As part of that process, Savannah River National Laboratory (SRNL) receives and analyzes samples in support of the qualification of each batch to be processed. SRNL recently received supernate samples retrieved from Tank 10H as well as in-tank batch contact samples for characterization in support of qualifying Batch 3 for processing through the TCCR unit. Some results from analysis of those samples have been previously reported. This report documents the remaining analyses of the in-tank batch contact samples as well as the results of ZAM (Zheng, Anthony, Miller) isotherm modeling performed for comparison to the measured results. Results of the additional analyses include analysis of the loading of other radionuclides besides 137 Cs on the crystalline silicotitanate (CST) contained within the in-tank batch contact test samples. Results from those analyses revealed the next highest contributor to the activity on the CST was 90 Sr with an average loading of 2.76E+08 dpm/g CST compared to 3.56E+10 dpm/g CST for the 137Cs. Isotopes of plutonium were also detected on the samples. ZAM modeling was performed using the measured composition of the Tank 10H Batch 3 qualification samples. The modeling predicted a maximum Cs loading approximately 2.2x higher than the measured result. This is a slightly lower ratio (expected/measured) compared to what was observed for the prior TCCR in-tank batch contact testing performed for Batches 1A and 2 where the ZAM results were 2.7-2.8x higher than the measured values.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

DECOVALEX-2023: An international collaboration for advancing the understanding and modeling of coupled thermo-hydro-mechanical-chemical (THMC) processes in geological systems

The DECOVALEX initiative is an international research collaboration (www.decovalex.org), initiated in 1992, for advancing the understanding and modeling of coupled thermo-hydro-mechanical-chemical (THMC) processes in geological systems. DECOVALEX stands for “DEvelopment of COupled Models and VALidation against EXperiments”. The creation of this international initiative was motivated by the recognition that prediction of these coupled effects is an essential part of the performance and safety assessment of geologic disposal systems for radioactive waste and spent nuclear fuel. DECOVALEX emphasizes joint analysis and comparative modeling of the complex perturbations and coupled processes in geologic repositories and how these impact long-term performance predictions. The most recent phase of the DECOVALEX Project, here referred to as DECOVALEX-2023, started in early 2020 and ended in late 2023. More than fifty research teams associated with 17 international DECOVALEX partner organizations participated in the comparative evaluation of eight modeling tasks covering a wide range of spatial and temporal scales, geological formations, and coupled processes. This Virtual Special Issue on DECOVALEX-2023 provides an in-depth overview of these collaborative research efforts and how these have advanced the state-of-the-art of understanding and modeling coupled THMC processes. While primarily focused on radioactive waste, much of the work included here has wider application to many geoengineering topics.

Coupled processes↗

DOE OSTI2022

Sensitivity Study of Multiscale and Phenomenological Elasto-Viscoplastic Grade 91 Material Models for Component-Scale Response

Many advanced nuclear reactor concepts currently being developed are targeting higher operating temperatures relative to the current fleet of light water nuclear reactors, for efficiency gains and other operational considerations. The design of high temperature structural components with reliable long-term operational performance will depend on material models that accurately capture the inelastic deformation mechanisms active in these environments. In this work, we perform a detailed parameter sensitivity analysis of two unified elasto-viscoplastic Grade 91 material models capable of capturing long term high temperature creep deformation. The first model is a phenomelogical material model from the Nuclear Engineering Material Library (NEML) developed at Argonne National Lab. The NEML model parameters and their uncertainty were fit to a range of Grade 91 experimental data using Bayesian Markov Chain Monte Carlo analysis. The second model is a LAROMance data-driven surrogate material model developed at Los Alamos National Lab. The LAROMance model is fit to a large database of responses produced by a mechanistic crystal plasticity based polycrystal model. Parameters for the LAROMance surrogate material model reflect the pedigree of the Grade 91 microstructure. Both material models have been integrated into the Grizzly code, based on the open-source MOOSE multiphysics simulation framework, to simulate both the progression of aging mechanisms and the effects of that aging on nuclear power plant structures. Grizzly is used analyze a three-dimensional Grade 91 piping system to compare the long-term inelastic response predicted by these two fundamentally different models and assess the sensitivity of the material model input parameters on this quantity of interest.

42 ENGINEERING↗

DOE OSTI2022

Analysis of the ECH effect on EPM/AE stability in Heliotron J plasma using a Landau closure model

The aim of the present study is to analyze the effect of the electron cyclotron heating (ECH) on the linear stability of Alfvén eigenmodes (AEs) and energetic particle modes (EPMs) triggered by energetic ions in Heliotron J plasma. The analysis is performed using the FAR3d code that solves a reduced MHD model to describe the thermal plasma coupled with a gyrofluid model for the energetic particle (EP) species. The simulations reproduce the AE/EPM stability trends observed in the experiments as the electron temperature (T e ) increases, modifying the thermal plasma β, EP β and EP slowing-down time. Particularly, the $n/m = 1/2$ EPM and $2/4$ Global AE are stabilized in the low-bumpiness (LB) configuration due to an enhancement of the continuum, finite Larmor radius and e-i Landau damping effects as the thermal β increases. On the other hand, a larger ECH injection power cannot stabilize the AE/EPM in medium-bumpiness and high-bumpiness (HB) configurations because the damping effects are weaker compared to the LB case, unable to balance the further destabilization induced by an enhanced EP resonance as the EP slowing-down time and EP β increases with T e .

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2026

Modeling and Experimental Validation of a Direct-Contact Counter-Flow Fluidized Bed Heat Exchanger for Thermal Energy Storage (TES) Applications

Particle-based thermal energy storage (TES) systems are an emerging energy storage technology. The technological advances have reduced costs, making TES more competitive and reliable in the marketplace but an efficient and reliable operation is heavily dependent on coherent heat transfer between air to particles or vice versa. The particle-based TES technologies provide an intermediate system that can store energy for short (0-10 h), long (10-200 h) and seasonal (> 200 h) timescales. The TES systems store energy by converting electricity to thermal energy; electricity can be directly sourced intermittent generation technologies and/or the grid, helping manage peak loads and other mismatches in supply and demand. The overall efficiency of the TES system depends on the performance of system components (particle storage silos and particle transfer mechanism etc.). The particle heat exchanger is one of the key system components that affects the system efficiency. The pressurized fluidized bed heat exchanger (PFB HX) performance is challenging to predict due to the chaotic behavior of particle and fluid interaction. This research presents a computational study of a novel direct-contact, counter-flow and air-to-particles PFB HX, that contributes in advancing the particle-based long-duration TES technologies. For the current analysis an unsteady Eulerian-Eulerian CFD model was developed and validated against experiments performed at the National Laboratory of the Rockies for two particle sizes (600 ..mu..m and 825 ..mu..m ). Following validation, parametric simulations were conducted to evaluate the effects of interphase drag models (Syamlal-O'Brien and Gidaspow), particle size, bed height and the influence of a frictional-viscosity term on hydrodynamics and heat transfer between the air & particles. The key findings from the analysis are: (1) for the studied operating window Syamlal-O'Brien provides superior agreement with measured gas temperatures (errors generally < 10%) while Gidaspow shows large deviations for the coarse particle case; (2) model predictions are most sensitive in the lower 0.2 m above the air distributor where bubble initiation and local mixing dominate interphase heat transfer; (3) representation of the distributor (number of inlet ports) materially affects predicted local mixing and temperature stratification; and (4) the Eulerian-Eulerian framework reproduces bulk thermal trends but shows regime dependent limitations for coarse particles, motivating mesoscale informed closures for scale-up analysis for future studies. These results provide validated guidance for drag selection and distributor design in particle-based thermal energy storage applications. Collectively, the validated model and parametric results quantify key drivers of PHB-HX performance and provide practical guidance for design and optimization. The results provide confidence in the model predictability and provide a step forward to improve on heat exchange performance. The demonstrated performance and modeling approach support the deployment and further development of this novel PHB-HX concept for robust, particle-based long-duration thermal energy storage systems.

25 ENERGY STORAGE↗

DOE OSTI2024

Steam Generator Model Design Parameter Sensitivity Study Using Advanced Optimization Tools

This study focuses on design parameter sensitivity studies pertaining to a steam generator (SG) model, using both Python and machine-learning tools. The SG model is a mathematical representation (including fluid flow and heat transfer equations/models/correlations) of a steam-generating unit in a pressurized water reactor (PWR)-type small modular reactor (SMR) system. Design studies involve changing the model’s input design parameters (e.g., temperature, pressure, mass flow rate) to observe the resulting effects on the output of the system (e.g., heat transfer coefficient [HTC], Nusselt number, heat transfer performance). Sensitivity studies analyze the degree to which system output and/or desired parameters (e.g., HTC or heat transfer performance) are sensitive to changes in input parameters. By using machine-learning tools such as the Risk Analysis Virtual Environment (RAVEN) developed at Idaho National Laboratory (INL), detailed design parametric sensitivity studies and model optimization were performed. Six input parameters—namely, the pressure, temperature, and mass flow rate for the inlet of the primary-side (hot fluid) and secondary-side (cold fluid) of the SG—were randomly perturbed via RAVEN’s Monte Carlo Sampler module, using uniform distributions (±1% relative changes). The analysis results give valuable insights into SG system performance and optimization, and provide justification for researching optimized sensor placement to effectively monitor and obtain experimental data.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2024

Steam Generator Model Design Parameter Sensitivity Study Using Advanced Optimization Tools

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2025

Burst pressure models and validations for thick-walled pipelines containing corrosion defects

Corrosion is one major threat to pipeline integrity. Over the past decades, many corrosion models have been developed for determining the remaining strength of corroded pipelines, including ASME B31.G, Modified B31.G, LPC, PCORRC and their modified models. All these corrosion models are applicable only to large diameter, thin-walled pipelines with a diameter to wall thickness ratio D/t ≥ 20. In practice, many pipelines have a small diameter and thick wall with a D/t ratio < 20, and thus an adequate corrosion model is needed for assessing remaining strength for corroded thick-walled pipelines. This paper briefly reviews the theoretical burst pressure models for defect-free thin and thick-walled pipelines and four representative corrosion assessment models for thin-walled corroded pipelines. On this basis, two modified corrosion models are proposed to thick-walled pipelines in terms of the average shear stress yield theory. To verify the proposed corrosion models, comprehensive validations are performed. Numerical validations include the elastic-plastic finite element analysis to determine burst pressure for pipelines without and with corrosion defects and the model evaluation using a large dataset of available FEA results of burst pressure for machined defects. Experimental validations include a set of burst pressure tests for defect-free thick-walled pipes with different thicknesses and the model evaluation using one large burst dataset for machined defects with flat bottoms and another large dataset for real corrosion defects with curved river bottom profiles. Both numerical and experimental validations show that the proposed corrosion models can more accurately predict the remaining strength for corroded thin and thick-walled pipelines.

Pipeline↗

DOE OSTI2021

Antiferromagnetic character of the quantum phase transition in the Hubbard model on the honeycomb lattice

In this work we provide a unified, comprehensive treatment of all operators that contribute to the antiferromagnetic, ferromagnetic, and charge-density-wave structure factors and order parameters of the hexagonal Hubbard Model. We use the Hybrid Monte Carlo algorithm to perform a systematic, carefully controlled analysis in the temporal Trotter error and of the thermodynamic limit. We expect our findings to improve the consistency of Monte Carlo determinations of critical exponents. We perform a data collapse analysis and determine the critical exponent β = 0.898 (37) for the semimetal-Mott insulator transition in the hexagonal Hubbard Model. Our methods are applicable to a wide range of lattice theories of strongly correlated electrons.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Manipulation and statistical analysis of the fluid flow of polymer semiconductor solutions during meniscus-guided coating

Recent work in structure–processing relationships of polymer semiconductors have demonstrated the versatility and control of thin-film microstructure offered by meniscusguided coating (MGC) techniques. Here, we analyze the qualitative and quantitative aspects of solution shearing, a model MGC method, using coating blades augmented with arrays of pillars. The pillars induce local regions of high strain rates—both shear and extensional—not otherwise possible with unmodified blades, and we use fluid mechanical simulations to model and study a variety of pillar spacings and densities. We then perform a statistical analysis of 130 simulation variables to find correlations with three dependent variables of interest: thin-film degree of crystallinity and transistor field-effect mobilities for charge-transport parallel (Μ para ) and perpendicular (Μ perp ) to the coating direction. Our study suggests that simple fluid mechanical models can reproduce substantive correlations between the induced fluid flow and important performance metrics, providing a methodology for optimizing blade design.

36 MATERIALS SCIENCE↗

DOE OSTI2025

From Machine Learning to Machine Reasoning: A Model-based Approach to Analyze Equipment Reliability Data

In current nuclear power plants (NPPs) a large amount of condition-based data which can be used to assess and monitor component health and performance. Assessing component health from such data can be performed with a large variety of methods. While the analysis of numeric data can be performed with several methods, the extraction of information from textual data remains a challenge. Currently employed natural language processing (NLP) methods do not really provide quantitative information that might be contained in IRs. In addition, the integration of numeric and textual data to identify possible causal relationships between data elements is still an unresolved challenge. This paper presents an approach to extract information from textual (e.g., incident or maintenance reports) and numeric data that relies on model based system engineer (MBSE) models. MBSE are diagrams designed to represent system and component dependencies (from both a form and functional point of view). In our approach, MBSE models emulate system engineer knowledge about component/system architecture. NLP methods are employed to perform syntactic and semantic analyses. Syntactic analysis analyzes the grammatical structure of a sentence while semantic analysis is designed to analyze the logic structure of a sentence. An innovative element of our approach is that semantic analysis uses MBSE models to identify links between textual elements. Similarly, numeric data is directly linked to elements of the MBSE models in order to map which functions are being monitored.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Modeling Molten Salt Spreading and Heat Transfer using MELTSPREAD – Model Development Updates

This report summarizes spreading and heat transfer results calculated using MELTSPREAD for a scenario of molten FLiNaK spilling onto a flat stainless steel substrate that was inspired by the “maximum credible accident” scenario of the Molten Salt Reactor Experiment (MSRE). MELTSPREAD was developed at Argonne to model the one-dimensional flowing and freezing of molten corium and was applied to model molten salts for the first time during FY21. The molten salt MELTSPREAD model that was developed during FY21 was updated as part of this work using a corrected heat of fusion value of FLiNaK and to accommodate a larger salt spill volume. The model was run with and without the inclusion of contributions from decay heat and a sensitivity analysis of initial spill conditions was performed to determine the importance of those factors on model outcome. The results provide insight into the expected spreading and heat transfer behavior of simulated and irradiated fuel salt that has been spilled and indicate aspects of the model to be revised in future versions of the MELTSPREAD model for applications to molten salt.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2022

Modeling Isothermal Reduction of Iron Ore Pellet Using Finite Element Analysis Method: Experiments & Validation

Iron ore pellet reduction experiments were performed with pure hydrogen (H2) and mixtures with carbon monoxide (CO) at different ratios. For direct reduction processes that switch dynamically between reformed natural gas and hydrogen as the reductant, it is important to understand the effects of the transition on the oxide reduction kinetics to optimize the residence time of iron ore pellets in a shaft reactor. Hence, the reduction rates were studied by varying experimental parameters such as the temperature (800, 850 & 900 °C), reactant gas flow rate (100, 150 & 200 cm3/min), pellet size and composition of the reactant gas mixture. The rate of reduction was observed to increase with an increase in temperature and reactant gas flow rate, but it decreased with an increase in pellet size. SEM greyscale analysis was performed to analyze the porosity and phase composition of partially reduced pellets. The porosity of the pellets was observed to increase from 0.3 for unreacted pellet to 0.42 for a completely reduced pellet. Energy-dispersive X-ray spectroscopy (EDAX) analysis was performed to identify the phases observed in the SEM images. The fraction of iron phase was observed to increase from the shell region of the pellet to the core region with an increase in the degree of reduction. A 2D-axisymmetric numerical model was developed on COMSOL Multiphysics, and it was validated using the conversion (X) vs. time curves obtained from each experiment. The model was able to accurately predict the total time needed for the complete conversion of a single iron ore pellet for multiple experiments. Effects of changes in the porosity and tortuosity of the pellet on the model were also studied and the rate of reduction was observed to be sensitive to changes in both porosity and tortuosity. The SEM analysis and the model results show that tortuosity is higher for pellets reduced with H2 than for pellets reduced with H2-CO gas mixtures.

08 HYDROGEN↗

DOE OSTI2022

Global Sensitivity Analysis Using the Ultra‐Low Resolution Energy Exascale Earth System Model

Abstract For decades, Arctic temperatures have increased twice as fast as average global temperatures. As a first step toward quantifying parametric uncertainty in Arctic climate, we performed a variance‐based global sensitivity analysis (GSA) using a fully coupled, ultra‐low resolution (ULR) configuration of version 1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SMv1). Specifically, we quantified the sensitivity of six quantities of interests (QOIs), which characterize changes in Arctic climate over a 75 year period, to uncertainties in nine model parameters spanning the sea ice, atmosphere, and ocean components of E3SMv1. Sensitivity indices for each QOI were computed with a Gaussian process emulator using 139 random realizations of the random parameters and fixed preindustrial forcing. Uncertainties in the atmospheric parameters in the Cloud Layers Unified by Binormals (CLUBB) scheme were found to have the most impact on sea ice status and the larger Arctic climate. Our results demonstrate the importance of conducting sensitivity analyses with fully coupled climate models. The ULR configuration makes such studies computationally feasible today due to its low computational cost. When advances in computational power and modeling algorithms enable the tractable use of higher‐resolution models, our results will provide a baseline that can quantify the impact of model resolution on the accuracy of sensitivity indices. Moreover, the confidence intervals provided by our study, which we used to quantify the impact of the number of model evaluations on the accuracy of sensitivity estimates, have the potential to inform the computational resources needed for future sensitivity studies.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Sixteen multiple-amplifier sensing charge-coupled devices and characterization techniques targeting the next generation of astronomical instruments

We present a candidate sensor for future spectroscopic applications, such as a Stage-5 Spectroscopic Survey Experiment or the Habitable Worlds Observatory. This type of charge-coupled device (CCD) sensor features multiple in-line amplifiers at its output stage allowing multiple measurements of the same charge packet, either in each amplifier or in the different amplifiers. Recently, the operation of an eight-amplifier sensor has been experimentally demonstrated, and we present the operation of a 16-amplifier sensor. This new sensor enables a noise level of ∼1 erms− with a single sample per amplifier. In addition, it is shown that sub-electron noise can be achieved using multiple samples per amplifier. In addition to demonstrating the performance of the 16-amplifier sensor, we aim to create a framework for future analysis and performance optimization of this type of detectors. New models and techniques are presented to characterize specific parameters, which are absent in conventional CCDs and Skipper CCDs: charge transfer between amplifiers and independent and common noise in the amplifiers and their processing.

16 multiple-amplifer sensing CCD (MAS-CCD)↗