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653 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Mechanical behavior analysis using small-size graphite disc compression testing with digital image correlation methods

The ASTM International Standard Test Method for Tensile Stress-Strain of Carbon and Graphite (ASTM C749) requires dog-bone shaped specimens. Unfortunately, specimen sizes required in ASTM C749 are much too large for irradiation within a test reactor and are incompatible with irradiation capsule volumes, molten salt degradation facilities, or oxidation apparatus. The ASTM International Standard Test Method for Tensile Strength Estimate by Disc Compression of Manufactured Graphite (ASTM D8289) was developed as an alternative that provides a means for testing tensile properties using smaller specimens when geometric constraints are present. However, the important stress–strain relation throughout the test loading history that could be recorded using ASTM C749 is lost by using ASTM D8289 because of the difficulties involved with using traditional strain gauges or extensometers on such a small specimen. In this work, a digital image correlation (DIC) method, which uses a full-field noncontact surface displacement measurement technique, was applied along with the ASTM D8289 splitting tensile test on eight small samples of graphite grade Mersen 2114 to track their deformation during compressive loading. Initial results showed that the DIC technique can measure the surface displacement/strain on these small (Ø6 × 3 mm) graphite specimens with high accuracy and good repeatability, indicating that the DIC technique, combined with the small-disc splitting tensile test in ASTM D8289, can capture the strain evolution in the test. In conclusion, further comparison of measured strains with analytical and numerical simulation results provides additional understanding of the mechanical behaviour of this nuclear graphite material in complex stress states.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Sensor Development for Liquid Water Detection in Dry Storage Casks: FY 2023 Status

Modeling efforts were undertaken in fiscal year (FY) 2023 to evaluate the feasibility and capability for sensing of liquid water inside canister-based dry cask storage systems (DCSSs). The focus was on the development of full-scale finite element models (FEMs) of ultrasound propagation through DCSS canister components. For these initial investigations, simulation of the baseplate component was targeted, envisioning water collection at the bottom of a vertically oriented canister. The effects of internal canister components (i.e., fuel basket, fuel assemblies) on ultrasound propagation in the baseplate component were evaluated in these efforts. The environment inside a DCSS confinement is intended to be inert and free of water to prevent potential corrosion of used fuel cladding or other internal hardware. Spent fuel assemblies are dried, after storage in water pools, to make sure water has been removed from assembly cavities. However, there is some uncertainty about the amount of residual water potentially left behind in a DCSS after drying processes, because water can become trapped in cavities or other small crevices in the surfaces formed by the fuel cladding, fuel assemblies, and other internal hardware components. Considering the complex space- and time-dependent temperature profiles in DCSSs, water may be in a liquid or gas phase depending on its location in the cask and how long the cask has been in storage. Evacuating most water and oxidizing agents contained within a canister is recommended by NUREG-1536 (NRC 2010), which covers DCSSs. As summarized by Salazar et al. (2020), existing guidance typically relies on achievable vacuum pumping pressures sustained over a hold period as a signal of dryness and water removal. However, time series data about water removal from full-scale commercial drying procedures are lacking (Hanson and Alsaed 2019). A review of drying specifications from several vendors led to the conclusion that if the specifications are followed correctly, the residual moisture in DCSSs should present an insignificant risk of cladding degradation (Knoll and Gilbert 1987). A more recent analysis concluded that much larger quantities of residual water could remain in DCSSs, but the amount would still not be expected to lead to significant corrosion of fuel cladding or other internal components (Jung et al. 2013). Industry drying procedures are mostly prescriptive in nature, and operational issues arising during the process could result in incomplete drying. A summary of operational issues and potential negative effects of residual water is provided by Salazar et al. (2019). Experimental efforts are ongoing to validate the extent of water removal in a DCSS based on drying procedures used at nuclear power plants through well-designed investigations of drying process efficacy and water retention (Durbin et al. 2021; Pulido et al. 2022a, Pulido et al. 2022b). This has been approached by simulating limited portions of a DCSS internal volume and fuel assemblies. So far, these efforts have focused on the effects of potential water trapping in the dashpot region of control rod guide tubes but are expected to be expanded to include the effects of other internal hardware features and failed fuel rod cladding. A method for detection and measurement of liquid water (Meyer et al. 2022; Meyer et al. 2021), in tandem with the drying information gained through experimental investigations, provides comprehensive bases for understanding the internal conditions of DCSSs and provides technical information to support licensing decisions.

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

A diffusion–deformation model with damage for polymer undergoing rapid decompression failure

Green hydrogen is emerging as one of the most promising alternatives to replace fossil fuels. While hydrogen gas has a good energy density by weight, its poor energy density by volume requires it to be stored under high pressure for commercial use. The hydrogen infrastructure developed to handle this high pressure hydrogen contains multiple components made of polymers as sealing agents. Although polymers do not react chemically with hydrogen gas, they undergo a mechanical failure when high pressure hydrogen gas is suddenly depressurized. This phenomenon known as 'rapid decompression failure' occurs due to the diffusion of hydrogen through polymer and getting trapped inside pre-existing cavities or voids. Here, in this paper, a continuum mechanics-based coupled diffusion-deformation-damage model was developed to predict the hydrogen distribution, stress distribution, and damage propagation inside the polymer while it undergoes rapid decompression failure. The polymer was modeled as a hyperelastic material because it represents the nonlinear material response observed in uniaxial tensile tests perfectly. The effects of hydrogen diffusivity, pre-existing cavity size, cavity location, applied hydrogen pressure, and depressurization rate on damage initiation were studied. It was found that the coefficient of diffusion plays an important role in damage initiation and damage was mostly concentrated in the inside areas rather than near the surface. Experiments were conducted with EPDM polymer which agreed well with the predicted trends using the given model. The effect of adding carbon black and silica filler particles and plasticizer to the pure EPDM polymer was also studied. It was found that damage during RDF decreases with the addition of fillers, but increases with the addition of the plasticizer. Finally, the damage evolution in the presence of two cavities was also studied, and was found that the interaction of stress fields around the cavities alters the damage occurring during RDF.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Unraveling the implications of finite specimen size on the interpretation of dynamic experiments for polycrystalline aluminum through direct numerical simulations

Normal and Pressure-shear plate impact (NPI and PSPI) experiments are popular experimental techniques for studying the mean-field macroscopic behavior of polycrystalline metals under high-rate dynamic loading. However, since both configurations rely upon geometry for subjecting the specimen to high strain rates, these experiments often involve a limited specimen size. Moreover, because of the inherent heterogeneities present within polycrystalline metals, it is difficult to ascertain if the size of the specimen and/or regions where measurements are made are sufficiently large for making representative inferences about the mean-field macroscopic properties from single-point velocity measurements. In the present study, we quantify the expected measurement variability on observable point measurements in NPI and PSPI experiments by carrying out direct numerical simulations (DNS) of statistically representative polycrystalline microstructures subjected to dynamic compression and compression-shear loading. In particular, we consider the role of specific material heterogeneities (e.g. the grain-to-grain difference in size, crystallographic orientation) on dispersion in the normal and transverse particle velocity records and on local fluctuations in key state variables (e.g. velocity, accumulated plastic strain) by incorporating these effects directly into a representative synthetic microstructure geometry and crystalline description of pure polycrystalline aluminum. The form of the present study is a large parametric investigation, consisting of ten ensembles of one hundred simulations. Each of the thousand simulations reflects a randomly realized synthetic microstructure in one of five cases of decreasing average grain size for the two loading configurations. Our analysis of the DNS results demonstrates that for both of these experimental configurations, the grain size directly correlates with the coefficient of variation (CV) in simulated point measurements, showing a convergent decrease in CV to zero (i.e. particle velocity record approaches the mean-field value) with decreasing grain size. Remarkably, the magnitude of variations in the particle velocity record is shown to be largest where the deviatoric stresses are most significant. In the case of NPI, this occurs at the elastic and plastic wavefront, whereas, in the case of PSPI, the magnitude of fluctuations are approximately constant throughout the experimental window time. The reasoning for the scatter in particle velocity due to the heterogeneous microstructure is demonstrated to be dependent on the mechanisms for accommodating deformation and on the interaction of reflection waves generated at sites of heterogeneities occurring at the scale of grains. Lastly, we develop a power-law description for the magnitude of scattering versus characteristic length, which provides a statistical framework for assessing the required number of grains per characteristic specimen dimension for minimizing scatter within these two experimental configurations (NPI, PSPI).

36 MATERIALS SCIENCE↗
DOE OSTI2020

LLNL Kimberlina 1.2 NUFT Simulations June 2018 (v2)

This dataset contains the output 6,000, 3-dimensional reactive multi-phase flow and transport aquifer simulations of brine and CO2 leakage into a protective aquiver in California’s San Joaquin Valley and input data files detailing the geologic mesh, aquifer physical properties and CO2 and brine injection rates. This data set was generated as an ongoing effort with the US DOE National Risk Assessment Partnership (NRAP) to evaluate the effectiveness of monitoring techniques to detect brine and CO2 leakage from legacy wells into underground sources of drinking water overlaying a CO2 storage reservoir. Each simulation contains a unique set of input parameters, generated stochastically. The outputs consist of these upper three geologic layers (from top): the Etchegoin, Macoma-Chanac, Santa Margarita-McLure formations. These simulations span the several distances (1, 3 and 6 km or wells W31-0.2, W31-0.5 and W31-1.0, respectively) from the CO2 injector, initiated from bottom hole pressure and saturation to calculate wellbore leakage from the storage reservoir, with low and high regional groundwater gradients and wellbore leakage into 5 leaky nodes. The dataset includes 1,000 unique simulations for each distance, which each contain a unique aquifer heterogeneity, aquifer and caprock permeability, and two model generations are included with a high permeability (prod07) and hybrid permeability (prod09). The range of permeability distributions is listed in Table 1. Each model generation consists of 3,000 simulations. Included in the dataset are the leakage rates determined from 2D wellbore models which utilize the pressure and CO2 saturation from LBL's reservoir simulations, NUFT mesh files with distributed lithology, NUFT rocktab files which describe the material properties for the geologic layers and the NUFT input files and post-processed output 'ntab' files. Each ntab file contains spatial (rows) and temporal (columns) model output tables for each model cell, the locations (x,y,z) and dimensions for each cells (dx, dy, dz). Table 1. Permeability distribution ranges for prod07 and prod09 model generations Geologic Layer: Permeability Range (log10 m^2) prod07 prod09 Etchegoin -12.92 to -10.92 -13.70 to -11.44 Macoma-Chanac -12.72 to -10.72 -13.50 to -11.24 Santa Margarita-McLure -12.70 to -10.70 -13.48 to -11.22 The input files used to generate the model include which are included in the dataset are: Time series of CO2 leakage input into the model (ex: Q_brn.W31-0.2.sim1000.layers123.tab) Time series of CO2 leakage input into the model (ex: Q_CO2.W31-0.2.sim1000.layers123.tab) Physical properties of the aquifer materials detailing the aquifer porosity, solid density, partitioning coefficients, permeabilities and van-Genuchten parameters detailed in a NUFT rocktab file: (ex: sim1000.usnt.rocktab) Numerical mesh and geologic data assigned to each model cell detailed in a NUFT genmsh format (ex: sim1000.mesh_k16.prod07.trans.genmsh) The primary output parameters are: pH (use absolute value) Change in TDS (mg/kg) Change in Pressure (Pa) Change CO2 gas saturation (fraction range 0.0-1.0) for example, the directory /p/lscratchh/mansoor1/nrap/kimberlina/prod09/mainfiles/sim1000/W31- 0.2 contains: sim1000.W31-0.2.trans.pH.red.ntab sim1000.W31-0.2.no_bg.trans.TDS.red.ntab sim1000.W31-0.2.usnt.P.deltabg.red.ntab sim1000.W31-0.2.usnt.CO2_sat.deltabg.red.ntab Each row in the NTAB files consist of model output per numerical grid cell. Each output file contains 33 columns (variables), including the information of numerical records, geologic location and sizes and the simulated parameter values over time. The first 13 variables are about numerical records and relative geologic information for a simulation grid: 1. index: simulation index 2. i: the ith grid of x-axis 3. j: the ith grid of y-axis 4. k: the ith grid of z-axis 5. element_ref: element reference 6. nuft_ind: nuft index 7. x: grid location in the x axis direction 8. y: grid location in the y axis direction 9. z: grid location in the z axis direction 10. dx: grid length in the x axis direction 11. dy: grid length in the y axis direction 12. dz: grid length in the z axis direction 13. volume: volume of the simulation grid The remainder (14, 15, 16...) variables are the simulated parameter values over time, take Pressure as an example, are: 14. 0.0y: initial pressure per cell. 15. 10.0y: simulated pressure at the end of the 10th year. 16. 20.0y: simulated pressure at the end of the 20th year. ... (repeated for every 10 years until 200 years)... The model extends 10,000 m, 5,000 m and 1,411 m in the x,y and z dimensions, respectively. The mesh consists of 164,832 cells with mesh dimensions of 101 x 51 x 32 (nx, ny, nz), with cell dimensions ranging from 100 m laterally (along x and y-axis) and model layers are as designated in the z-axis: Layer 1: atmosphere (1e-30 m thick) Layer 2: upper caprock (10 m thick) Layers 3-13: Etchegoin (536.23 m thck) Layers 14-27: Macoma-Chanac (679.04 m thick) Layers 28-32: Santa Margarita-McLure (185.94 m thick) The wellbore is placed along node i=51, j=26, and extends vertically along 5 nodes from the top to the bottom of the model. Special instructions when extracting files: Each Gzip archive (ex: prod07.sim1000-sim00099.tar.gz) contains 100 simulations. Gzip archives should be transferred into base directories (ie. In Linux: mkdir prod07; mv prod07.*.tar.gz prod07/.) before extracting, or files will be overwritten. Each sub-simulation tree should have the following file structure pattern (using the linux 'tree' command): |-- prod07 | |-- sim0001 | |-- W31-0.2 | | |-- Q_brn.W31-0.2.sim0001.layers123.tab | | |-- Q_co2.W31-0.2.sim0001.layers123.tab | | |-- sim0001.W31-0.2.no_bg.trans.TDS.red.ntab | | |-- sim0001.W31-0.2.trans.pH.red.ntab | | |-- sim0001.W31-0.2.usnt.CO2_sat.deltabg.red.ntab | | |-- sim0001.W31-0.2.usnt.P.deltabg.red.ntab | |-- W31-0.5 | | |-- Q_brn.W31-0.5.sim0001.layers123.tab | | |-- Q_co2.W31-0.5.sim0001.layers123.tab | | |-- sim0001.W31-0.5.no_bg.trans.TDS.red.ntab | | |-- sim0001.W31-0.5.trans.pH.red.ntab | | |-- sim0001.W31-0.5.usnt.CO2_sat.deltabg.red.ntab | | |-- sim0001.W31-0.5.usnt.P.deltabg.red.ntab | |-- W31-1.0 | | |-- Q_brn.W31-1.0.sim0001.layers123.tab | | |-- Q_co2.W31-1.0.sim0001.layers123.tab | | |-- sim0001.W31-1.0.no_bg.trans.TDS.red.ntab | | |-- sim0001.W31-1.0.trans.pH.red.ntab | | |-- sim0001.W31-1.0.usnt.CO2_sat.deltabg.red.ntab | | |-- sim0001.W31-1.0.usnt.P.deltabg.red.ntab | |-- sim0001.mesh_k16.prod07.trans.genmsh Disclaimer This document was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government nor Lawrence Livermore National Security, LLC, nor any of their employees makes any warranty, expressed or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States government or Lawrence Livermore National Security, LLC. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States government or Lawrence Livermore National Security, LLC, and shall not be used for advertising or product endorsement purposes. Lawrence Livermore National Laboratory is operated by Lawrence Livermore National Security, LLC, for the U.S. Department of Energy, National Nuclear Security Administration under Contract DE-AC52-07NA27344. This report was reviewed and released as LLNL-MI-753464.

aquifer↗