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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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At least 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A multiscale phase field fracture approach based on the non-affine microsphere model for rubber-like materials

Rubber-like materials have a broad scope of applications due to their unique properties like high stretchability and increased toughness. Hence, computational models for simulating their fracture behavior are paramount for designing them against failures. In this study, the phase field fracture approach is integrated with a multiscale polymer model for predicting the fracture behavior in elastomers. At the microscale, damaged polymer chains are modeled to be made up of a number of elastic chain segments pinned together. Using the phase field approach, the damage in the chains is represented using a continuous variable. Both the bond stretch internal energy and the entropic free energy of the chain are assumed to drive the damage, and the advantages of this assumption are expounded. A framework for utilizing the non-affine microsphere model for damaged systems is proposed here by considering the minimization of a hypothetical undamaged free energy, ultimately connecting the chain stretch to the macroscale deformation gradient. At the macroscale, a thermodynamically consistent formulation is derived in which the total dissipation is assumed to be mainly due to the rupture of molecular bonds. Using a monolithic scheme, the proposed model is numerically implemented and the resulting three-dimensional simulation predictions are compared with existing experimental data. The capability of the model to qualitatively predict the propagation of complex crack paths and quantitatively estimate the overall fracture behavior is verified. Additionally, the effect of the length scale parameter on the predicted fracture behavior is studied for an inhomogeneous system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Computational Fluid Dynamics Analysis of the Molten Salt Tritium Transport Experiment Test Section

Tritium, a radionuclide produced through neutron capture by lithium and other elements (beryllium and fluoride) in molten salts, presents unique challenges to radionuclide release. This is true for both fusion energy breeder blankets and molten salt fission reactors. The fundamental understanding of tritium transport is crucial to the safe design and operation of these reactors. The Molten Salt Tritium Transport Experiment (MSTTE), currently under construction at Idaho National Laboratory, aims to investigate tritium transport phenomena using a forced-convection fluoride salt loop. This loop is designed to study various transport mechanisms, such as permeation through metals and gas-liquid interactions, and is intended to support future research on tritium extraction units. A critical aspect of the MSTTE loop design is ensuring a fully developed velocity profile before the fluid reaches the permeation test section where measurements are made. This study employs computational fluid dynamics to model the salt flow behavior within the MSTTE permeation test section. A realizable k-ε turbulent model with enhanced wall treatment is used to simulate the single-phase, vertical upward flow of molten salt FLiNaK under isothermal conditions. The simulation results indicated flow distortion and underdeveloped profiles at all planned flow rates within the test section due to the 85-deg sharp bend. To address this issue, a reduced diameter with a reducer and expander and a flow conditioner are investigated to achieve fully developed flow. The analysis showed that the flow conditioner successfully corrected the flow profile, achieving fully developed behavior at a flow rate of 50 liters per minute (LPM). In conclusion, this research enhances our understanding of flow dynamics in molten salt systems and contributes to optimizing tritium transport control technologies.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Numerical Simulation and Experimental Comparison of System Analysis Module 1D Mixing Model for Cold Shock Transients in the Gallium Thermal-Hydraulic Mixing Facility

Abstract Liquid metals are being investigated as coolants in many advanced reactor designs because of their high thermal conductivity and effectiveness at high temperatures. However, they often pose challenges to reactor operation and safety because of the complex thermal mixing and stratification in the plenum of pool-type reactor designs. The advanced system analysis code System Analysis Module (SAM) currently under development at Argonne National Laboratory aims to develop and implement thermal mixing models to accurately capture these complex thermal fluid behaviors. In this study, the SAM thermal mixing model was compared against experimental data from the Gallium Thermal-Hydraulic Experiment facility, a scaled liquid metal test facility that uses gallium as a surrogate fluid to investigate the stratification and thermal mixing of low-Prandtl-number fluids in the upper plenum of a liquid metal-cooled reactor. Two cold shock transient cases were used: one with stable stratified flow (Ri = 32) and one with stronger thermal mixing (Ri = 0.5). The resultant temperatures were then compared with the experimental temperatures over the entire plenum to assess the ability of the mixing models to capture the thermal behavior and to better correspond mixing parameters to various flow scenarios. Generally, the zero-dimensional mixing model was more capable of capturing the bulk temperature of the component modeled assuming that an accurate mass flow rate was provided, but it was inherently unable to capture thermal gradients in space. The one-dimensional mixing model was capable of capturing that the thermal gradients provided accurate selection of the mixing coefficients. Further, the temperature at the outlet junction was compared over time for each of the mixing models with the recorded experimental temperature. The implemented mixing models demonstrated the ability to effectively capture the overall thermal behavior for stronger mixing scenarios but struggled with more stably stratified flows. It was found that a system analysis code's covering of the entire range of different operating conditions still remains a challenging task, and it is suggested that further model and closure improvements are necessary to accurately capture complex thermal mixing and stratification phenomena.

stratification↗
DOE OSTI2020

Three-Dimensional Evaluation of Sand Particle Fracture Using Discrete-Element Method and Synchrotron Microcomputed Tomography Images

Recent research showed that fracture of sand particles plays a significant role in determining the plastic bulk volumetric changes of granular materials under different loading conditions. One of the major tools used to better understand the influence of particle fracture on the behavior of granular materials is discrete-element modeling (DEM). This paper employed the bonded block model (BBM) to simulate the fracture behavior of sand. Each sand particle is modeled as an agglomerate of rigid blocks bonded at their contacts using the linear-parallel contact model, which can transmit both moment and force. DEM simulated particles closely matched the actual three-dimensional (3D) shape of sand particles acquired using high-resolution 3D synchrotron microcomputed tomography (SMT). Results from unconfined one-dimensional (1D) compression of a single synthetic silica cube were used to calibrate the model parameters. Particle fracture was investigated for specimens composed of three sand particles that were loaded under confined 1D compression. Breakage energy measured from DEM models matched well with that measured experimentally. The paper studied the effects of contact loading condition and particle interaction on the fracture mode of particles using BBM that can closely capture the 3D shape of real sand particles.

58 GEOSCIENCES↗
DOE OSTI2022

Diffusion-limited hydrolysis in polymeric materials

Polymer degradation under aggressive environmental stressors often develops heterogeneities due to diffusion-limited reaction phenomena. This is well established for diffusion-limited oxidation (DLO), which is known to occur for most polymeric materials at elevated temperatures but has been less summarized for the conditions of diffusion-limited hydrolysis (DLH). Here, an overview of hydrolysis for several materials and a computational model, analogous to the underlying equations for DLO, able to define this diffusion-reaction system is presented. A systematic study of the influence of various parameters, such as water diffusivity, reaction rate and order, and a more in-depth focus on residual isocyanate hydrolysis in a polymeric methylene diphenyl diisocyanate (pMDI) based polyurethane (PU) foam is given. For this system, we present experimental data for model ‘input’ parameters and discuss predictions for different conditions. We conceptually compare the behavior of diffusion-limited oxidation to that of diffusion-limited hydrolysis (DLH). With the mathematical framework and key material properties presented herein, any DLH phenomena following Fickian diffusion behavior can be understood, modeled, and predicted.

36 MATERIALS SCIENCE↗
DOE OSTI2020

In situ characterization of ceramic cold sintering by small-angle scattering

The first in situ characterization of the pore morphology evolution during the cold sintering process (CSP) is presented using small-angle X-ray scattering methods. For practical reasons, measurements have been made on a model system, KH 2 PO 4 (KDP). The scattering signal revealed a striking behavior that could be modeled with nanoscale structural features associated with the dissolution and reprecipitation of KDP close to the grain/pore interface during CSP. The prospects for future more quantitative experiments under a range of temperature and pressure conditions, as well as for studies of more technologically important materials such as ZnO are considered.

36 MATERIALS SCIENCE↗
DOE OSTI2021

White paper on Verification and Validation for Cyber Emulation Models

All disciplines that use models to predict the behavior of real-world systems need to determine the accuracy of the models’ results. Techniques for verification, validation, and uncertainty quantification (VVUQ) focus on improving the credibility of computational models and assessing their predictive capability. VVUQ emphasizes rigorous evaluation of models and how they are applied to improve understanding of model limitations and quantify the accuracy of model predictions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Flue Gas Desulfurization Wastewater Composition and Implications for Regulatory and Treatment Train Design

The U.S. Environmental Protection Agency is currently revising its regulations on trace element discharges from flue gas desulfurization (FGD) wastewater.<br>In this work, we expand a predictive model of trace element behavior at coal-fired power plants (CFPPs) to estimate the trace element concentration of FGD wastewater at the plant level. We demonstrate that variation in trace element concentrations in FGD wastewater can span several orders of magnitude and is a function of both coal rank and installed air pollution control devices. This conclusion suggests that the benefits and costs of FGD wastewater treatment for the median plant will poorly describe the actual benefits and costs over the full range of existing CFPPs.

Mauter, Meagan↗
DOE OSTI2025

Mid-latitude clouds contribute to Arctic amplification via interactions with other climate feedbacks

Abstract Traditional feedback analyses, which assume that individual climate feedback mechanisms act independently and add linearly, suggest that clouds do not contribute to Arctic amplification. However, feedback locking experiments, in which the cloud feedback is disabled, suggest that clouds, particularly outside of the Arctic, do contribute to Arctic amplification. Here, we reconcile these two perspectives by introducing a framework that quantifies the interactions between radiative feedbacks, radiative forcing, ocean heat uptake, and atmospheric heat transport. We show that including the cloud feedback in a comprehensive climate model can result in Arctic amplification because of interactions with other radiative feedbacks. The surface temperature change associated with including the cloud feedback is amplified in the Arctic by the surface-albedo, Planck, and lapse-rate feedbacks. A moist energy balance model with a locked cloud feedback exhibits similar behavior as the comprehensive climate model with a disabled cloud feedback and further indicates that the mid-latitude cloud feedback contributes to Arctic amplification via feedback interactions. Feedback locking in the moist energy balance model also suggests that the mid-latitude cloud feedback contributes substantially to the intermodel spread in Arctic amplification across comprehensive climate models. These results imply that constraining the mid-latitude cloud feedback will greatly reduce the intermodel spread in Arctic amplification. Furthermore, these results highlight a previously unrecognized non-local pathway for Arctic amplification.

58 GEOSCIENCES↗
DOE OSTI2023

Effectiveness and predictability of in-network storage cache for Scientific Workflows

Large scientific collaborations often have multiple scientists accessing the same set of files while doing different analyses, which create repeated accesses to the large amounts of shared data located far away. These data accesses have long latency due to distance and occupy the limited bandwidth available over the wide-area network. To reduce the wide-area network traffic and the data access latency, regional data storage caches have been installed as a new networking service. To study the effectiveness of such a cache system in scientific applications, we examine the Southern California Petabyte Scale Cache for a high-energy physics experiment. By examining about 3TB of operational logs, we show that this cache removed 67.6% of file requests from the wide-area network and reduced the traffic volume on wide-area network by 12. 3TB (or 35.4%) an average day. The reduction in the traffic volume (35.4%) is less than the reduction in file counts (67.6%) because the larger files are less likely to be reused. Due to this difference in data access patterns, the cache system has implemented a policy to avoid evicting smaller files when processing larger files. We also build a machine learning model to study the predictability of the cache behavior. Tests show that this model is able to accurately predict the cache accesses, cache misses, and network throughput, making the model useful for future studies on resource provisioning and planning.

Sim, Caitlin↗
DOE OSTI2020

Variability and Diversity Load Model Tool [SWR-20-03]

The motivation for the development of this tool and the underlying algorithms and methods was to enable the development of high-temporal resolution, realistic time-series data for quasi-static time-series (QSTS) analysis of distribution systems. Often, aggregated load profile data for a distribution circuit is available (e.g. feeder loading data collected via SCADA at the utility substation) and, while this data is typically accurate it masks the considerable variability of the 100’s or 1000’s of individual loads connected on the circuit. This tool was developed to model both the increased variability expected for these individual loads (e.g. the load of a single distribution transformer connected to 8-12 houses) and the expected diversity between loads on the circuit. It is important to note that the difference in variability and diversity, in the context of this tool, is that variability modeling only adds representative variability due to disaggregated load characteristics (e.g. the presence in the load profile of loads turning off and on like an air conditioner/oven) while the average energy profile remains the same as the user supplied power profile. Diversity modeling generates multiple individual load profiles which, in aggregate, sum to the user supplied power profile. Diversity is effectively variability in the energy usage over longer periods of time than seen in the variability model. Put another way, variability modeling supplies the expected variability due to the operation of various end-use loads and diversity modeling supplies the usage differences due to human behavior, schedules, etc. This load modeling tool was developed for use in generating data for distribution systems. Modeling is summarized by two major functions: 1) taking low resolution load profiles and adding intra-seconds variability onto the profiles, and 2) taking a user supplied load profile and distribution factors and adding both diversity and variability to the user supplied profile.

Zhu, Xiangqi↗
DOE OSTI2021

Comparing Global Spray Combustion Characteristics and Local Shot-to-Shot Variations in a Reacting n -Heptane Spray

In support of efforts to develop improved models of turbulent spray behavior and combustion in diesel engines, experimental data and analysis must be obtained for guidance and validation. For Reynolds-averaged Navier–Stokes (RANS)-based Computational fluid dynamics (CFD) modeling approaches, representative ensemble average experimental results are important. For high-fidelity models such as large eddy simulations (LES)-based CFD, precise individual experimental results are desirable. However, making comparisons between a given experiment and LES is a challenge since local parameters cannot be directly compared. In this work, an optically accessible constant pressure flow rig (CPFR) is utilized to acquire diesel-like fuel injection and reaction behavior simultaneously with three optical diagnostic techniques: rainbow Schlieren deflectometry (RSD), OH* chemiluminescence (OH*), and two-color pyrometry (2CP). The CPFR allows a large number of repeated injection experiments to be performed for statistical analysis and convergence using ensemble-averaging techniques, while maintaining highly repeatable test conditions. Even for stable test conditions, variations in local turbulent fuel–air mixing introduce variability, which manifests as significant differences in OH* and 2CP results. Experimental measurements of characteristic parameters including liquid and vapor jet penetration, liftoff length, soot temperature and concentration, and turbulent flame speed, along with the shot-to-shot variability of each dataset, are presented and discussed. Finally, a statistical method is utilized to analyze the extent of this variability, and to identify superlative injections within the dataset for discussion and analysis of shot-to-shot variations.

42 ENGINEERING↗
DOE OSTI2025

Modeling and Simulation of Fuel Dispersal During the Loss-of-Coolant Accident

This document is the compilation of the milestone portion to a larger end of project NEUP report. The executive summary of the modeling portion is provided below: In the event of cladding rupture during a postulated LOCA in a pressurized water reactor, fuel particles, along with fission gases, can be expelled into the reactor core from the fractured fuel rod, a phenomenon referred to as fuel dispersal. The initial stage of fuel dispersal is strongly influenced by the high-pressure ejection of fuel fragments, the size and geometry of the ruptured cladding, and the depressurization history of the fuel rod during the postulated LOCA transient. Depending on the location of the burst orifice relative to the quench front, the dispersal event represents an intricate three-phase flow and heat transfer phenomenon, where high-temperature fuel particles carried by the fission gases interact with the coolant within the narrow subchannels of the fuel assemblies, inducing localized phase change. Given the unique multiphysics nature of this phenomena, the current study develops a dedicated computational framework to predict the mass distribution and cooling of dispersing fuel particles, facilitating post-accident assessment and management of the fuel assemblies. Considering the scale of nuclear reactor applications, a continuum three-fluid model is proposed for simulating the transport of solids within the reactor core. With high-temperature fuel fragments within the liquid media, nucleation sites inducing phase changes are dispersed within the flow domain. Coupled with the fact that the transient dispersal event occurs on different time scales than other three-phase flow applications, this study derives a time-averaged three-fluid flow model without losing generality. The assumptions regarding the continuum treatment of the solid phase and the modeling of fuel dispersal behavior are incorporated to simplify the governing equations and derive applicable closure relations. The computational validation of the model was conducted using adiabatic experimental results obtained from ongoing research at Oregon State University, focusing on characterizing fuel dispersal behavior during simulated LOCA conditions. Settlement characteristics of the solids, quantified by the probability distribution of equivalent particles, closely matched the probability density functions reported in experimental studies. The transport of fuel particles within a scaled 5 × 5 lattice of a pressurized-water reactor rod bundle geometry was modeled through a two-fluid Eulerian framework. The required boundary conditions were evaluated from the fuel performance code BISON in a postulated large-break LOCA scenario. The modeling framework considered solid fuel particles as granular matter, interacting with the gaseous dry steam phase and fission gases through the governing interfacial momentum exchange between the participating fluids. The simulation results provided the volume fraction of the solids obtained at the bottom surface of the enclosing tank geometry. Postulated LOCA leading to fuel dispersal phenomena involves the strong coupling between fuel thermomechanics, cladding deformation, thermal-hydraulics, and fuel particle transport. Incorporation of such a strong coupling in numerical simulation is performed by coupling the multiphysics solvers. In the case of fuel dispersal, a strong coupled simulation can be performed by coupling the BISON code for fuel performance, the TRACE code for system-level thermal hydraulics, and fuel particle transport in Multiphysics Object-Oriented Simulation Environment (MOOSE). For such intricate infrastructure, the MOOSE Framework eases the data transfer between codes. The recent version of MOOSE has incorporated the Navier-Stokes module for the fluid flow. An exploratory exercise was done to gain familiarity with finite volume capabilities in the MOOSE framework to incorporate the Spalart-Allmaras (SA) turbulence model. New finite-volume and auxiliary kernels were introduced to assemble the SA transport equation, compute turbulent viscosity, and evaluate wall distance and diagnostic turbulence terms, fully integrated with existing Navier-Stokes modules. A turbulent lid-driven cavity at a Reynolds number of approximately 10,000 is used for verification. MOOSE shows the robust solver convergence and produces the turbulent features. But it underpredicts the velocity profile and turbulent quantities, emphasizing the need to develop improved SA near-wall treatments (e.g., low-Re corrections or wall functions) as a key direction for future work.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

A two-dimensional analytical unit cell model for redox flow battery evaluation and optimization

Cell performance optimization is important for improving the overall system efficiency of a redox flow battery. To gain better insights into key controlling factors of system efficiency, this work first proposed a theoretical model for a unit flow battery cell by extending a two-dimensional analytic model to a full battery cell. Such a model is then used for cell performance optimization after validating it with experimental and numerical modeling data. With the model results, the activation, equilibrium, and pump energy losses are identified as the dominant sources of battery energy losses. Further, a guideline for reducing these sources is also proposed. Following the guideline, the mass transport coefficient is shown as a key control factor of the equilibrium energy loss and Coulombic efficiency (CE). Approaches are then proposed to improve CE and the overall system efficiency. The mass transport is also revealed as the mechanism of pump rate optimization where an optimal pump rate significantly reduces the equilibrium energy loss. With both low equilibrium and pump energy losses, an optimal electrode porosity or specific area design can further improve a battery's system efficiency based on an optimal porosity predicted by the present model. The model also demonstrates distinct behaviors and overestimation in the system efficiency when reduced to a zero-dimensional model with neglected mass transport resistance. With the new model, the guideline, and new insights, this work provides a reliable and efficient tool for the evaluation and optimization of redox flow battery design in practical applications.

25 ENERGY STORAGE↗
DOE OSTI2022

International Photovoltaic Modeling Intercomparison [Slides]

Irradiance transposition models seem to perform well, except the Isotropic with -11.25 W/m 2 underestimation. Most temperature models could not capture behavior when ΔΤ between module and ambient is negative. Uncertainties due to derate factors: modelers overbudgeted resulting in significant power underestimation; maybe ~10% is appropriate for commercial systems but not lab-scale? Most software and models cluster together showing good reproducibility among participants. Modeler’s skills seem to be more important than the PV model itself (flat efficiency with irradiance, positive power temperature coefficients, etc.). Results and best practices will be communicated in a journal article.

14 SOLAR ENERGY↗
DOE OSTI2021

Mining and Validating Social Media Data for COVID-19–Related Human Behaviors Between January and July 2020: Infodemiology Study

Background: Health authorities can minimize the impact of an emergent infectious disease outbreak through effective and timely risk communication, which can build trust and adherence to subsequent behavioral messaging. Monitoring the psychological impacts of an outbreak, as well as public adherence to such messaging, is also important for minimizing long-term effects of an outbreak. Objective: We used social media data from Twitter to identify human behaviors relevant to COVID-19 transmission, as well as the perceived impacts of COVID-19 on individuals, as a first step toward real-time monitoring of public perceptions to inform public health communications. Methods: We developed a coding schema for 6 categories and 11 subcategories, which included both a wide number of behaviors as well codes focused on the impacts of the pandemic (eg, economic and mental health impacts). We used this to develop training data and develop supervised learning classifiers for classes with sufficient labels. Classifiers that performed adequately were applied to our remaining corpus, and temporal and geospatial trends were assessed. We compared the classified patterns to ground truth mobility data and actual COVID-19 confirmed cases to assess the signal achieved here. Results: We applied our labeling schema to approximately 7200 tweets. The worst-performing classifiers had F1 scores of only 0.18 to 0.28 when trying to identify tweets about monitoring symptoms and testing. Classifiers about social distancing, however, were much stronger, with F1 scores of 0.64 to 0.66. We applied the social distancing classifiers to over 228 million tweets. We showed temporal patterns consistent with real-world events, and we showed correlations of up to –0.5 between social distancing signals on Twitter and ground truth mobility throughout the United States. Conclusions: Behaviors discussed on Twitter are exceptionally varied. Twitter can provide useful information for parameterizing models that incorporate human behavior, as well as for informing public health communication strategies by describing awareness of and compliance with suggested behaviors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

A Perspective on Traditional and Data Driven Electrochemical Modeling and Analysis

To understand the behavior of electrochemical systems, we need to reduce the dimensionality of the measured current-voltage-time (I-V-t) data by fitting models, thus enabling us to analyze and compare the governing physics. Traditionally, the process for this is an 'expert first' approach: defining the model and its explicit assumptions based on inductive reasoning or empirical observation, fitting small portions of the I-V-t data where assumptions are most valid or carefully designing experiments to enforce key assumptions, and then interpreting the model parameters. However, modern data-driven methods enable a new paradigm: a 'data first' approach, where the latent behaviors governing the system's measured response are identified directly using machine-learning models that optimize both model structure and parameters from the I-V-t data, guaranteeing that the learned model explains as much of the observed system response as possible. After model identification, the model can then be interrogated by an expert to connect observed behaviors with underlying physics. This talk will review several different types of electrochemical analysis (electrochemical impedance, differential voltage-capacity, electrochemical kinetics) and compare the traditional and data-driven methods for analyzing the data.

42 ENGINEERING↗
DOE OSTI2025

Identifying Adversarial Cyber-Activity in Operational Technology Environments Using Bayesian Networks

Critical infrastructure and other operational technology (OT) environments face increasing cybersecurity risks from adversarial behavior. This paper describes the development of a risk model using a Bayesian network to enhance the comprehension of observable cyber events caused by malicious activity in OT environments. The core of the Bayesian network is a process model that describes the stages of adversary behavior. The remainder of the model is based on the MITRE ATT&CK® for Industrial Control Systems (ICS) taxonomy, which includes tactics and techniques that may be used by the adversary. The observables provide evidence for adversary behavior through the intermediary technique and tactic nodes. One challenge in constructing this model is a lack of open-source data from cyber-attacks on OT systems. This paper discusses learning from limited data, the elicitation of expert opinion to construct the conditional probability tables when data is scarce, and the refinement of the most difficult conditional probabilities tables using several forms of sensitivity analyses. Finally, the Bayesian network is demonstrated using two historical case studies: the DarkSide ransomware attack on the Colonial Pipeline and the destructive cyberattack targeting the ThyssenKrupp blast furnace. Index Terms—Cybersecurity, industrial control systems, operational technology

97 - MATHEMATICS AND COMPUTING↗