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Separation of Radionuclides from a Rare Earth-Containing Solution by Zeolite Adsorption
The increasing industrial demand for rare earths requires new or alternative sources to be found. Within this context, there have been studies validating the technical feasibility of coal and coal byproducts as alternative sources for rare earth elements. Nonetheless, radioactive materials, such as thorium and uranium, are frequently seen in the rare earths’ mineralization, and causes environmental and health concerns. Consequently, there exists an urgent need to remove these radionuclides in order to produce high purity rare earths to diversify the supply chain, as well as maintain an environmentally-favorable extraction process for the surroundings. In this study, an experimental design was generated to examine the effect of zeolite particle size, feed solution pH, zeolite amount, and contact time of solid and aqueous phases on the removal of thorium and uranium from the solution. The best separation performance was achieved using 2.50 g of 12-µm zeolite sample at a pH value of 3 with a contact time of 2 h. Under these conditions, the adsorption recovery of rare earths, thorium, and uranium into the solid phase was found to be 20.43 wt%, 99.20 wt%, and 89.60 wt%, respectively. The Freundlich adsorption isotherm was determined to be the best-fit model, and the adsorption mechanism of rare earths and thorium was identified as multilayer physisorption. Further, the separation efficiency was assessed using the response surface methodology based on the development of a statistically significant model.
Predictive modeling of Néel temperature in austenitic alloys using CALPHAD and data analytics
The Néel temperature is a crucial yet often overlooked parameter in calculating the stacking fault energy (SFE) of austenitic alloys. Several empirical equations have been proposed to estimate the Néel temperature of austenitic alloys, which are then used to calculate the SFE and explain deformation mechanisms. However, these empirical equations, typically derived using linear regression algorithms, are often simplistic and may fail to capture the complex interactions among multiple alloying elements that influence the Néel temperature. Moreover, their applicability is usually limited to specific compositional ranges. In this study, we propose a CALPHAD based approach and develop a surrogate decision tree based regression model capable of capturing the interactions among multiple alloying elements to predict the Néel temperature. Predictions from both the CALPHAD approach and the regression model show close agreement with experimental measurements reported in the literature. In conclusion, the implications of accurate Néel temperature predictions on the calculated SFE and deformation mechanisms are also discussed.
A stochastic solver based on the residence time algorithm for crystal plasticity models
Abstract The deformation of crystalline materials by dislocation motion takes place in discrete amounts determined by the Burgers vector. Dislocations may move individually or in bundles, potentially giving rise to intermittent slip. This confers plastic deformation with a certain degree of variability that can be interpreted as being caused by stochastic fluctuations in dislocation behavior. However, crystal plasticity (CP) models are almost always formulated in a continuum sense, assuming that fluctuations average out over large material volumes and/or cancel out due to multi-slip contributions. Nevertheless, plastic fluctuations are known to be important in confined volumes at or below the micron scale, at high temperatures, and under low strain rate/stress deformation conditions. Here, we develop a stochastic solver for CP models based on the residence-time algorithm that naturally captures plastic fluctuations by sampling among the set of active slip systems in the crystal. The method solves the evolution equations of explicit CP formulations, which are recast as stochastic ordinary differential equations and integrated discretely in time. The stochastic CP model is numerically stable by design and naturally breaks the symmetry of plastic slip by sampling among the active plastic shear rates with the correct probability. This can lead to phenomena such as intermittent slip or plastic localization without adding external symmetry-breaking operations to the model. The method is applied to body-centered cubic tungsten single crystals under a variety of temperatures, loading orientations, and imposed strain rates.
Application of visible and near-infrared spectroscopy to classification of Miscanthus species
Here, the feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis , M. sacchariflorus and M. fIoridulus , were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validation results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis , M. sacchariflorus and M. fIoridulus , respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.
Application of Improved Radiation Modeling to General Circulation Models
This research has accomplished its primary objectives of developing accurate and efficient radiation codes, validating them with measurements and higher resolution models, and providing these advancements to the global modeling community to enhance the treatment of cloud and radiative processes in weather and climate prediction models. A critical component of this research has been the development of the longwave and shortwave broadband radiative transfer code for general circulation model (GCM) applications, RRTMG, which is based on the single-column reference code, RRTM, also developed at AER. RRTMG is a rigorously tested radiation model that retains a considerable level of accuracy relative to higher resolution models and measurements despite the performance enhancements that have made it possible to apply this radiation code successfully to global dynamical models. This model includes the radiative effects of all significant atmospheric gases, and it treats the absorption and scattering from liquid and ice clouds and aerosols. RRTMG also includes a statistical technique for representing small-scale cloud variability, such as cloud fraction and the vertical overlap of clouds, which has been shown to improve cloud radiative forcing in global models. This development approach has provided a direct link from observations to the enhanced radiative transfer provided by RRTMG for application to GCMs. Recent comparison of existing climate model radiation codes with high resolution models has documented the improved radiative forcing capability provided by RRTMG, especially at the surface, relative to other GCM radiation models. Due to its high accuracy, its connection to observations, and its computational efficiency, RRTMG has been implemented operationally in many national and international dynamical models to provide validated radiative transfer for improving weather forecasts and enhancing the prediction of global climate change.
Surface-Modified Electrodes: Enhancing Performance Guided by Insitu Spectroscopy and Microscopy
The aim of this project is to understand the nature of active sites and degradation mechanisms in solid-oxide fuel cells cathodes. Using Co and Fe-based perovskite oxides as model systems, we developed a comprehensive understanding of the active site and degradation. In particular, we correlated cation segregation, precipitation and surface reconstruction to electrochemical activity and stability. We show that in conventional materials, the most active cathodes are not the most stable ones. A novel strategy of cation segregation buffer layer was proposed and developed to overcome this limitation. A nanoscale barrier layer was inserted a few nanometer below the surface of the cathodes which prevents the cations from diffusing from the bulk to the surface. We report a LSCF-based cathode with record performance and stability, reach an impressive 0.03 Ω cm 2 at 650 °C in air. This modification strategy is expected to lead to more active and stable solid-oxide fuel cells, and ultimately lead to lower cost in commercial systems.
Safeguards in Pyroprocessing: an Integrated Model Development and Measurement Data Analysis
Pyroprocessing is an electrochemical method based on the molten salt electrolyte, mainly the LiCl-KCl eutectic molten salt, to recycle the used nuclear fuel. For a conceptual design of commercial pyroprocessing facility, tons of special nuclear materials, namely U and Pu, may be involved, which could be used for non-peaceful purposes if they are diverted. Effective safeguards approaches have to be developed prior to the development and construction of a pyroprocessing facility. Present research focused on two main objectives, namely calculating the properties of nuclear species in LiCl-KCl molten salt and developing integrated model to safeguard a pyroprocessing facility. Understanding the characteristics of special nuclear materials in LiCl-KCl eutectic salt is extremely important to understand their behaviors in an electrorefiner. The model development for the separation processes in the pyroprocessing, including electrorefining, actinide drawdown, and rare earth drawdown benefits the understanding of material transport and separation performance of these processes under various conditions. The output signals, such as potential, current, and species concentration contribute to the material balance closure and provide safeguards signatures to detect the scenarios of diversion. U and Pu are the two main elements concerned in this study due to our interest in safeguards.
Geologic Framework Model for the Dry Alluvium Geology (DAG) Experiment Testbed Yucca Flat, Nevada National Security Site
Geologic framework models (GFMs) provide a methodology for integrating geology into, and thus geologically informing, other modeling and simulation activities. GFMs provide a threedimensional (3-D), geology-based digital framework for developing and parametrizing meshes and evaluating model and simulation results. This report describes a 3-D GFM constructed for the Source Physics Experiment Phase II Dry Alluvium Geology test series located in Yucca Flat at the Nevada National Security Site. The geology in the Yucca Flat region is complex and diverse, which creates challenges to modeling seismic wave propagation from SPE tests. The Yucca Flat GFM helps address these challenges by providing the 3-D distribution of relevant geologic features and physical properties necessary to more effectively model seismic wave propagation. The GFM includes 7 model layers and 48 faults that cut and offset the layers. An appendix is included that provides quantitative data on physical properties for each model layer.
In-process monitoring and prediction of droplet quality in droplet-on-demand liquid metal jetting additive manufacturing using machine learning
Abstract In droplet-on-demand liquid metal jetting (DoD-LMJ) additive manufacturing, complex physical interactions govern the droplet characteristics, such as size, velocity, and shape. These droplet characteristics, in turn, determine the functional quality of the printed parts. Hence, to ensure repeatable and reliable part quality it is necessary to monitor and control the droplet characteristics. Existing approaches for in-situ monitoring of droplet behavior in DoD-LMJ rely on high-speed imaging sensors. The resulting high volume of droplet images acquired is computationally demanding to analyze and hinders real-time control of the process. To overcome this challenge, the objective of this work is to use time series data acquired from an in-process millimeter-wave sensor for predicting the size, velocity, and shape characteristics of droplets in DoD-LMJ process. As opposed to high-speed imaging, this sensor produces data-efficient time series signatures that allows rapid, real-time process monitoring. We devise machine learning models that use the millimeter-wave sensor data to predict the droplet characteristics. Specifically, we developed multilayer perceptron-based non-linear autoregressive models to predict the size and velocity of droplets. Likewise, a supervised machine learning model was trained to classify the droplet shape using the frequency spectrum information contained in the millimeter-wave sensor signatures. High-speed imaging data served as ground truth for model training and validation. These models captured the droplet characteristics with a statistical fidelity exceeding 90%, and vastly outperformed conventional statistical modeling approaches. Thus, this work achieves a practically viable sensing approach for real-time quality monitoring of the DoD-LMJ process, in lieu of the existing data-intensive image-based techniques.
Multiscale Modeling of Grain Boundary Segregation and Embrittlement in Tungsten for Mechanistic Design of Alloys for Coal Fired Plants
Based on a recent discovery of premelting-like grain boundary segregation in refractory metals occurring at high temperatures and/or high alloying levels, this project investigated grain boundary segregation and embrittlement in tungsten (W) based alloys. Specifically, new interfacial thermodynamic models have been developed and quantified to predict high-temperature grain boundary segregation in the W-Ni binary alloy and W-Ni-Fe, W-Ni-Ti, W-Ni-Co, W-Ni-Cr, W-Ni-Zr and W-Ni-Nb ternary alloys. The thermodynamic modeling results have been experimentally validated for selected systems. Furthermore, multiscale modeling has been conducted at continuum, atomistic and quantum-mechanical levels to link grain boundary segregation with embrittlement. In summary, this 3-year project has successfully developed a theoretical framework in combination with a multiscale modeling strategy for predicting grain boundary segregation and embrittlement in W based alloys.
Development of a Hydrodynamic and Transport model of Bellingham Bay in Support of Nearshore Habitat Restoration
In this study, a hydrodynamic model based on the unstructured-grid finite volume coastal ocean model (FVCOM) was developed for Bellingham Bay, Washington. The model simulates water surface elevation, velocity, temperature, and salinity in a three-dimensional domain that covers the entire Bellingham Bay and adjacent water bodies, including Lummi Bay, Samish Bay, Padilla Bay, and Rosario Strait. The model was developed using Pacific Northwest National Laboratory’s high-resolution Puget Sound and Northwest Straits circulation and transport model. A sub-model grid for Bellingham Bay and adjacent coastal waters was extracted from the Puget Sound model and refined in Bellingham Bay using bathymetric light detection and ranging (LIDAR) and river channel cross-section data. The model uses tides, river inflows, and meteorological inputs to predict water surface elevations, currents, salinity, and temperature. A tidal open boundary condition was specified using standard National Oceanic and Atmospheric Administration (NOAA) predictions. Temperature and salinity open boundary conditions were specified based on observed data. Meteorological forcing (wind, solar radiation, and net surface heat flux) was obtained from NOAA real observations and National Center for Environmental Prediction North American Regional Analysis outputs. The model was run in parallel with 48 cores using a time step of 2.5 seconds. It took 18 hours of cpu time to complete 26 days of simulation. The model was calibrated with oceanographic field data for the period of 6/1/2009 to 6/26/2009. These data were collected specifically for the purpose of model development and calibration. They include time series of water-surface elevation, currents, temperature, and salinity as well as temperature and salinity profiles during instrument deployment and retrieval. Comparisons between model predictions and field observations show an overall reasonable agreement in both temporal and spatial scales. Comparisons of root mean square error values for surface elevation, velocity, temperature, and salinity time series are 0.11 m, 0.10 m/s, 1.28oC, and 1.91 ppt, respectively. The model was able to reproduce the salinity and temperature stratifications inside Bellingham Bay. Wetting and drying processes in tidal flats in Bellingham Bay, Samish Bay, and Padilla Bay were also successfully simulated. Both model results and observed data indicated that water surface elevations inside Bellingham Bay are highly correlated to tides. Circulation inside the bay is weak and complex and is affected by various forcing mechanisms, including tides, winds, freshwater inflows, and other local forcing factors. The Bellingham Bay model solution was successfully linked to the NOAA oil spill trajectory simulation model “General NOAA Operational Modeling Environment (GNOME).” Overall, the Bellingham Bay model has been calibrated reasonably well and can be used to provide detailed hydrodynamic information in the bay and adjacent water bodies. While there is room for further improvement with more available data, the calibrated hydrodynamic model provides useful hydrodynamic information in Bellingham Bay and can be used to support sediment transport and water quality modeling as well as assist in the design of nearshore restoration scenarios.
Diminishing detonator effectiveness through electromagnetic effects
An inductively coupled transmission line with distributed electromotive force source and an alternative coupling model based on empirical data and theory were developed to initiate bridge wire melt for a detonator with an open and a short circuit detonator load. In the latter technique, the model was developed to exploit incomplete knowledge of the open circuited detonator using tendencies common to all of the open circuit loads examined. Military, commercial, and improvised detonators were examined and modeled. Nichrome, copper, platinum, and tungsten are the detonator specific bridge wire materials studied. The improvised detonators were made typically made with tungsten wire and copper (.about.40 AWG wire strands) wire.
A composite position independent monitor of reactor fuel irradiation using Pu, Cs, and Ba isotope ratios
When post-irradiation materials from the nuclear fuel cycle are released to the environment, certain isotopes of actinides and fission products carry signatures of irradiation history that can potentially aid a nuclear forensic investigation into the material's provenance. In this study, combinations of Pu, Cs, and Ba isotope ratios that produce position (in the reactor core) independent monitors of irradiation history in spent light water reactor fuel are identified and explored. These position independent monitors (PIMs) are modeled for various irradiation scenarios using automated depletion codes as well as ordinary differential equation solutions to approximate nuclear physics models. Experimental validation was performed using irradiated low enriched uranium oxide fuel from a light water reactor, which was sampled at 8 axial positions from a single rod. Plutonium, barium and cesium were chemically separated and isotope ratio measurements of the separated solutions were made by quadrupole and multi-collector inductively coupled mass spectrometry (Cs and Pu, respectively) and thermal ionization mass spectrometry (Ba). The effect of axial variations in neutron fluence and energy spectrum are evident in the measured isotope ratios. Furthermore, two versions of a combined Pu and Cs based PIM are developed. A linear PIM model, which can be used to solve for irradiation time is found to work well for natural U fuel with <10% 240 Pu and known or short cooling times. A non-linear PIM model, which cannot be solved explicitly for irradiation time without additional information, can nonetheless still group samples by irradiation history, including high burnup LEU fuel with unknown cooling time. 137 Ba/ 138 Ba is also observed to act as a position independent monitor; it is nearly single valued across the sampled fuel rod, indicating that samples sharing an irradiation history (same irradiation time and cooling time) in a reactor despite experiencing different neutron fluxes will have a common 137 Ba/ 138 Ba ratio. Modeling of this Ba PIM shows it increases monotonically with irradiation and cooling time, and a confirmatory first order analytical solution is also presented.
Carbon-enhanced metal-poor star frequencies in the galaxy: corrections for the effect of evolutionary status on carbon abundances
We revisit the observed frequencies of carbon-enhanced metal-poor (CEMP) stars as a function of the metallicity in the Galaxy, using data from the literature with available high-resolution spectroscopy. Our analysis excludes stars exhibiting clear overabundances of neutron-capture elements and takes into account the expected depletion of surface carbon abundance that occurs due to CN processing on the upper red giant branch. This allows for the recovery of the initial carbon abundance of these stars, and thus for an accurate assessment of the frequencies of carbon-enhanced stars. The correction procedure we develop is based on stellar-evolution models and depends on the surface gravity, log g, of a given star. Our analysis indicates that for stars with [Fe/H] ≤–2.0, 20% exhibit [C/Fe] ≥+0.7. This fraction increases to 43% for [Fe/H] ≤–3.0 and 81% for [Fe/H] ≤–4.0, which is higher than have been previously inferred without taking the carbon abundance correction into account. These CEMP star frequencies provide important inputs for Galactic and stellar chemical evolution models, as they constrain the evolution of carbon at early times and the possible formation channels for the CEMP-no stars. We also have developed a public online tool with which carbon corrections using our procedure can be easily obtained.
Study of Data-Driven Mesh-Model Optimization in System Thermal-Hydraulic Simulation
Quantification of nuclear power plant safety risk requires a systematic and yet practical approach to identification of accident scenarios, assessment of their likelihood and consequences. Instrumental to this goal is risk-informed safety margin characterization (RISMC) framework, whose realization requires computationally robust and affordable methods for sufficiently accurate simulation of complex multi-dimensional physical phenomena, such as turbulent and multi-phase flow. The CFD-like correlation-based codes with 3D simulation capability (e.g., GOTHIC) ensure computational efficiency using coarse mesh size and the sub-grid phenomena in the boundary layer that can be captured by adequate constitutive correlations. However, the error sources and user effects on the selection of mesh size and models lead to unpredictable simulation error, while rich High-Fidelity (HF) data from experiments and numerical simulation using validated code or DNS are not fully explored. It would be useful to have a “smart” data-driven multi-scale framework in which the low-resolution models can be “taught” to emulate high-resolution models. The objective of this work is to develop and evaluate a physical-based data-driven mesh-model optimization approach (Optimized Mesh/Model Information System, OMIS) to estimate the simulation error and give advice on the optimized selection of coarse mesh size and Low-Fidelity (LF) models for System Thermal Hydraulic (STH) simulation to achieve accuracy comparable to that of HF models. This approach takes advantages of computational efficiency of coarse-mesh simulation and application of Machine Learning (ML) algorithms.
WE-AB-BRA-02: Development of Biomechanical Models to Describe Dose-Volume Response to Liver Stereotactic Body Radiation Therapy (SBRT) Patients
Purpose: Previous studies have shown that radiotherapy treatment for liver metastases causes marked liver hypertrophy in areas receiving low dose and atrophy/fibrosis in areas receiving high dose. The purpose of this work is to develop and evaluate a biomechanical model-based dose-response model to describe these liver responses to SBRT. Methods: In this retrospective study, a biomechanical model-based deformable registration algorithm, Morfeus, was expanded to include dose-based boundary conditions. Liver and tumor volumes were contoured on the planning images and CT/MR images three months post-RT and converted to finite element models. A thermal expansion-based relationship correlating the delivered dose and volume response was generated from 22 patients previously treated. This coefficient, combined with the planned dose, was applied as an additional boundary condition to describe the volumetric response of the liver of an additional cohort of metastatic liver patients treated with SBRT. The accuracy of the model was evaluated based on overall volumetric liver comparisons and the target registration error (TRE) using the average deviations in positions of identified vascular bifurcations on each set of registered images, with a target accuracy of the 2.5mm isotropic dose grid (vector dimension 4.3mm). Results: The thermal expansion coefficient models the volumetric change of the liver to within 3%. The accuracy of Morfeus with dose-expansion boundary conditions a TRE of 5.7±2.8mm compared to 11.2±3.7mm using rigid registration and 8.9±0.28mm using Morfeus with only spatial boundary conditions. Conclusion: A biomechanical model has been developed to describe the volumetric and spatial response of the liver to SBRT. This work will enable the improvement of correlating functional imaging with delivered dose, the mapping of the delivered dose from one treatment onto the planning images for a subsequent treatment, and will further provide information to assist with the biological characterization of patients’ response to radiation.
Nuclear Technology Research and Development (NTRD) Technical Monthly November FY18
Single crystals of U 3 Si 2 were grown at various crystal rotation and pull rates to establish the best parameters for growth of crystals with diameters ≥5 mm (Figure 1). Crystals were pulled from the melt using a modified Czochralski method. U 3 Si 2 single crystals will enable more fundamental property studies for this proposed high density fuel providing data to develop and inform physics-based fuel performance models. (D. Byler)