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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 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Development of an interatomic potential for the W–Ta system

A physics-inspired, data-driven interatomic potential framework for multi-element system is presented. This potential is based on the generalization of the embedded atom method potential and systematically incorporates two-, three- and many-body effects. Different atomic environments are described by atom-centered Gaussian basis sets which provides sufficient flexibility to capture diverse atomic environments and allows for easy optimization of the free parameters. An interatomic potential model for the tungsten–tantalum (W–Ta) system is developed using this framework by training on data from ab initio density functional theory (DFT) calculations. The potential is thoroughly tested at various compositions by comparing bulk and defect properties from experimental and DFT data. It is shown that the potential predicts the elastic constants, defect properties, such as vacancy and interstitial formation energies, core structures of dislocations, and melting points for both pure and tantalum and tungsten with an accuracy comparable to other single element machine learning based potentials. In conclusion, the potential model is used to investigate the formation energies of ordered alloys and vacancy formation energies for chemically random W–Ta alloys.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Experimental verification of a crystal plasticity-based simulation framework for predicting microstructure and geometric shape changes: Application to bending and Taylor impact testing of Zr

This paper is concerned with experimental verification of a recently developed multi-scale simulation framework for plastic deformation of metallic materials from quasi-static to impact deformation conditions. The framework is a visco-plastic self-consistent (VPSC) polycrystalline model embedded in an implicit finite element method (FE-VPSC) to provide a microstructure-sensitive constitutive response at each material point. Each material point of the FEM model is a polycrystalline aggregate with crystallographic deformation mechanisms operating at the single crystal scale with their evolving activity based on a dislocation density-based hardening law and texture. Four beams and three cylinders machined in different orientations from a textured plate of high-purity zirconium are tested quasi-statically in 4-point bending and at speeds of 100 m/s, 170 m/s and 243 m/s during Taylor impact tests, respectively. The variation in dimensional changes resulting from different sample orientations in the plate with respect to loading directions is measured for each sample. Moreover, texture and twinning characterization is performed using electron backscattered diffraction (EBSD). The deformation processes and underlying evolution of microstructure are successfully simulated using the FE-VPSC framework. In doing so, the model parameters are optimized and validated across a broad range of strain rates and temperatures. Simulation results in terms of geometrical changes and microstructural evolution are compared with the experimental measurements. The model predicts anisotropic material flow resulting from the hard-to-deform crystallographic directions, the development of gradients in texture and twinning through the geometries, tension–compression asymmetry, as well as the extent of plasticity under impact.

42 ENGINEERING↗
DOE OSTI2024

Highly Permeable Rubbery Thin Film Composite Membranes for CO2 Capture from Steel Mills

For presentation at the 2024 AIChE Annual Meeting, San Diego, CA, October 27-31, 2024. High-permeance and CO2-selective membranes are needed to make membrane technology economically viable for large-scale deployment of carbon capture from various industrial point sources such as steel mills. Thin film composite (TFC) membranes are necessary for this practical implementation because they can provide high permeance by forming a thin selective layer on top of a porous support layer. This presentation reports the rational design and fabrication of National Energy Technology Laboratory’s highly permeable non-aging TFC membranes achieved by: (1) synthesizing a high-performance rubbery selective material; (2) developing a high-porosity membrane support; (3) optimizing coating methods to assemble the two materials into scalable membranes; and (4) scaling up membrane supports and TFCs via a roll-to-roll process. This talk will also cover the design, computational fluid dynamic simulation, 3D printing, construction, and permeation testing of plate-and-frame membrane modules for an upcoming field demonstration at U. S. Steel’s Edgar Thomson Plant in Braddock, PA.

Zhu, Lingxiang↗
DOE OSTI2022

Contextual Active Online Model Selection with Expert Advice

How can we collect the most useful labels to learn a model selection policy, when presented with arbitrary heterogeneous data streams? In this paper, we formulate this task as a contextual active model selection problem, where at each round the learner receives an unlabeled data point along with a context. The goal is to output the best model for any given context without obtaining an excessive amount of labels. In particular, we focus on the task of selecting pre-trained classifiers, and propose a contextual active model selection algorithm (CAMS), which relies on a novel uncertainty sampling query criterion defined on a given policy class for adaptive model selection. In comparison to prior art, our algorithm does not assume a globally optimal model. We provide rigorous theoretical analysis for the regret and query complexity under both adversarial and stochastic settings. Our experiments on several benchmark classification datasets demonstrate the algorithm’s effectiveness in terms of both regret and query complexity. Notably, to achieve the same accuracy, CAMS incurs less than 10% of the label cost when compared to the best online model selection baselines on CIFAR10.

Liu, Xuefeng↗
DOE OSTI2025

Design and performance validation of a high-temperature downhole permanent magnet generator used for an electro-pulse boring system in geothermal energy applications

Novel, direct-energy drilling technologies such as electro-pulse boring, have the potential to significantly increase the speed and depth of geothermal drilling but have not achieved widespread adoption due to several economic and technical barriers. One major challenge is that these drilling systems require electric power downhole. The current practice is to supply power to the drill string components by running electric cables down the geothermal well, but at the targeted well depths, this practice is cost-inhibitive, inefficient in terms of power consumption, and adds an additional failure point with the long cabling going through a highly corrosive, high-temperature environment. A solution to this problem is to develop high-temperature electric generator technology that can generate the required power downhole. Such a generator must also operate with high efficiency at the target downhole ambient temperature of 250 °C. In this paper, we investigate the various design considerations for this concept and subsequently design the downhole electric generator using a multi-objective design optimization approach. Through electromagnetic-, thermal- and short-circuit fault condition analysis, it is demonstrated that the optimized downhole electric generator concept presented in this paper can meet the performance requirements within this extreme drilling environment. Most remarkably, it is shown that a generator efficiency of 90% is achieved. In conclusion, to validate the results presented in this paper, a prototype generator is built and its performance is measured at 250 °C using a test bench uniquely developed for this application.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

CH 3 Radical Generation in Microplasmas for Up-Conversion of Methane

The conversion of methane, CH 4 , into higher value chemicals using low temperature plasmas is challenged by both improving efficiency and selectivity. One path towards selectivity is capturing plasma produced methyl radicals, CH 3 , in a solvent for aqueous processing. Due to the rapid reactions of methyl radicals in the gas phase, the transport distance from production of the CH 3 to its solvation should be short, which then motivates the use of microplasmas. The generation of CH 3 in Ar/CH 4 /H 2 O plasmas produced in nanosecond pulsed dielectric barrier discharge microplasmas is discussed using results from a computational investigation. The microplasma is sustained in the channel of a microfluidic chip in which the solvent flows along one wall or in droplets. CH 3 is primarily produced by electron-impact of and dissociative excitation transfer to CH 4 , as well as CH 2 reacting with CH 4 . CH 3 is rapidly consumed to form C 2 H 6 which, in spite of being subject to these same dissociative processes, accumulates over time, as do other stable products including C 3 H 8 and CH 3 OH. The gas mixture and electrical properties were varied to assess their effects on CH 3 production. CH 3 production is largest with 5% CH 4 in the Ar/CH 4 /H 2 O mixture due to an optimal balance of electron-impact dissociation, which increases with CH 4 percentage, and dissociative excitation transfer and CH 2 reacting with CH 4 , which decrease with CH 4 percentage. Design parameters of the microchannels were also investigated. Increasing the permittivity of the dielectrics in contact with the plasma increased the ionization wave intensity which increased CH 3 production. Increased energy deposition per pulse generally increased CH 3 production as does lengthening pulse length up to a certain point. The arrangement of the solvent flow in the microchannel can also affect the CH 3 density and fluence to the solvent. The fluence of CH 3 to the liquid solvent is increased if the liquid is immersed in the plasma as a droplet or is a layer on the wall where the ionization wave terminates. The solvation dynamics of CH 3 with varying numbers of droplets was also examined. Here, the maximum density of solvated methyl radicals CH 3aq occurs with a large number of droplets in the plasma. However, the solvated CH 3aq density can rapidly decrease due to desolvation, emphasizing the need to quickly react the solvated species in the solvent.

03 NATURAL GAS↗
DOE OSTI2020

Back-Surface Passivation of CdTe Solar Cells Using Solution-Processed Oxidized Aluminum

Although back-surface passivation plays an important role in high-efficiency photovoltaics, it has not yet been definitively demonstrated for CdTe. Here, we present a solution-based process, which achieves passivation and improved electrical performance when very small amounts of oxidized Al 3+ species are deposited at the back surface of CdTe devices. The open circuit voltage (V oc ) is increased and the fill factor (FF) and photoconversion efficiency (PCE) are optimized when the total amount added corresponds to ~1 monolayer, suggesting that the passivation is surface specific. Addition of further Al 3+ species, present in a sparse alumina-like layer, causes the FF and PCE to drop as the interface layer becomes blocking to current flow. The optimized deposit increases the average baseline PCE for both Cu-free devices and devices where Cu is present as a dopant. The greatest improvement is found when the Al 3+ species are deposited prior to the CdCl 2 activation step and Cu is employed. In this case, the best-cell efficiency was improved from 12.6 to 14.4%. Furthermore, time-resolved photoluminescence measurements at the back surface and quantum efficiency measurements performed at the maximum power point indicate that the performance enhancement is due to a reduction in the interface recombination current at the back surface.

14 SOLAR ENERGY↗
DOE OSTI2023

Securely Aggregated Coded Matrix Inversion

Coded computing is a method for mitigating straggling workers in a centralized computing network, by using erasure-coding techniques. Federated learning is a decentralized model for training data distributed across client devices. In this work we propose approximating the inverse of an aggregated data matrix, where the data is generated by clients; similar to the federated learning paradigm, while also being resilient to stragglers. To do so, we propose a coded computing method based on gradient coding. We modify this method so that the coordinator does not access the local data at any point; while the clients access the aggregated matrix in order to complete their tasks. Here, the network we consider is not centrally administrated, and the communications which take place are secure against potential eavesdroppers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

High-dimensional Data-driven Energy optimization for Multi-Modal Transit Agencies (HD-EMMA) (Final Technical Report)

Public bus transit services in the U.S. are responsible for at least 19.7 million metric tons of CO 2 emission annually. Electric vehicles (EVs) can have a much lower environmental impact than comparable internal combustion engine vehicles (ICEVs), especially in urban areas. Unfortunately, EVs are also much more expensive than ICEVs. As a result, many public transit agencies can afford only mixed fleets of transit vehicles, consisting of EVs, hybrids (HEVs), and ICEVs. Transit agencies that operate such mixed fleets of vehicles face a challenging optimization problem: these agencies need to decide which vehicles are assigned to serving which transit trips. Since the advantage of EVs over ICEVs varies depending on the route and time of day (e.g., the benefit of EVs is higher in slower traffic with frequent stops and lower on highways), the assignment can have a significant effect on energy use and, hence, environmental impact. Through this project, we have developed reference data about energy collections and constructed a set of machine learning models that can accurately predict the energy consumption for the whole fleet at the level of each trip. We have used these models to develop a scheduling and assignment strategy that can rotate the different vehicle types across the transit agencies’ routes. The optimization algorithm ensures that the vehicles are matched to trips considering weather patterns, expected congestion, and road gradients to minimize the overall energy usage. We list the key observations from our project for other practitioners below. Details are available in the report, and the list of source code and our publications are included in the appendix. 1. We have demonstrated the feasibility of collecting, merging and analyzing large volumes of high-resolution real-world telemetry data from a mixed vehicle fleet. To mitigate the inherent noise of the recorded GPS points, the team developed an algorithm that filters data and maps the points onto a street. The algorithm considers previous and subsequent location measurements and different characteristics of nearby streets to determine how likely the vehicle travels on them. Then, the team segmented the time series into disjoint contiguous samples based on adjacent road segments and repeated the outlier detection and removal. For each data point, the team added features corresponding to elevation changes within the samples, weather features, such as temperature, and traffic data, such as speed ratio between actual speed and free-flow speed. 2. We have developed two forms of machine learning models that be used to understand and analyze the energy operations of a mixed vehicle transit fleet. The micro prediction model provides estimates of instantaneous energy prediction for all types of buses (diesel, hybrid, and electric). Such a model is important in evaluating the energy impacts of real-time bus operation strategies, but it is challenging due to diversified driving cycles of transit buses. The model can help the drivers understand the impact of their driving behaviors and short-term congestions. The macro prediction models estimate average energy consumption across the whole trip considering the features: distance traveled, various road-type features, elevation change, day of the week, time of day, various weather features (temperature, humidity, etc.), and traffic features (speed ratio and jam factor). 3. We have demonstrated that it is possible to transfer the machine learning models we have developed in this project to other teams and cities by using inductive transfer learning. We also showed that the performance of the macro energy prediction models can be improved using a multi-task learning approach where the learning parameters are shared between the models being developed for different vehicle types. The advantage of this approach is improved learning performance as the models can exploit common spatio-temporal and environmental characteristics. 4. Finally, we have developed trip and vehicle assignment and scheduling algorithms that use the energy prediction models and develop a trip to vehicle type (diesel, electric, hybrid) assignment for the whole operation to reduce overall emissions and cost. We have shown through simulations that the proposed algorithms can save $\$$ 48,910 in energy costs and 175 metric tons of CO 2 emission annually for CARTA.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Autonomous alloy composition optimization using molecular dynamics guided by a large language model

Here, we present an autonomous materials discovery framework that couples a large language model (LLM) with molecular dynamics (MD) simulations to optimize Fe–Cr–Mn alloy compositions for tensile strength. Starting from six distinct compositions, the LLM operated as an intelligent agent, iteratively proposing changes based on prior simulation results and constraints. Over 50 iterations per case, the LLM adaptively explored the composition space, identifying high-strength regions, not easily accessible by conventional methods. The highest strength, 18.7 GPa, was achieved with Fe 71 Cr 25 Mn 4 composition, identified from a Fe 75 Cr 20 Mn 5 starting point. The LLM autonomously adjusted its strategy in real time, demonstrating closed-loop decision-making using commodity hardware. This approach showcases the potential of LLMs as scientific co-pilots, capable of accelerating materials discovery and generalizable to other domains like biology and drug design.

Autonomy↗
DOE OSTI2023

Integral Field Spectroscopy of the cometary starburst galaxy NGC 4861

ABSTRACT Using the PMAS Integral Field Unit on the Calar Alto 3.5 m telescope, we observed the southern component (Markarian 59) of the ‘cometary’ starburst galaxy NGC 4861. Mrk 59 is centred on a giant nebula and concentration of stars 1 kpc in diameter. Strong $\rm H\,\alpha$ emission points to a star-formation rate (SFR) at least 0.47 $\rm M_{\odot }\, yr^{-1}$. Mrk 59 has a very high [O iii] $\rm \lambda 5007/H\,\beta$ ratio, reaching 7.35 in the central nebula, with a second peak at a star-forming hotspot further north. Fast outflows are not detected but nebular motion and galaxy rotation produce relative velocities up to 40 km $\rm s^{-1}$. Spectral analysis of different regions with ‘Fitting Analysis using Differential evolution Optimization’ (FADO) finds that the stars in the central and ‘spur’ nebulae are very young, $\rm \le 125~Myr$ with a large $\rm \lt 10~Myr$ contribution. Older stars ($\rm \sim 1~Gyr$) make up the northern disk component, while the other regions show mixtures of 1 Gyr age with very young stars. This and the high specific SFR $\rm \sim 3.5~Gyr^{-1}$ imply a bimodal star formation history, with Mrk 59 formed in ongoing starbursts fuelled by a huge gas inflow, turning the galaxy into an asymmetric ‘green pea’ or blue compact dwarf. We map the He ii λ4686 emission, and identify a broad component from the central nebula, consistent with the emission of ∼300 Wolf–Rayet stars. About a third of the He ii λ4686 flux is a narrow line emitted from a more extended area covering the central and spur nebulae, and may have a different origin.

Roche, Nathan (ORCID:0000000319363058)↗
DOE OSTI2020

Novel Geometric Operations for Linear Programming

This report summarizes the work performed under the project "Linear Programming in Strongly Polynomial Time." Linear programming (LP) is a classic combinatorial optimization problem heavily used directly and as an enabling subroutine in integer programming (IP). Specifically IP is the same as LP except that some solution variables must take integer values (e.g. to represent yes/no decisions). Together LP and IP have many applications in resource allocation including general logistics, and infrastructure design and vulnerability analysis. The project was motivated by the PI's recent success developing methods to efficiently sample Voronoi vertices (essentially finding nearest neighbors in high-dimensional point sets) in arbitrary dimension. His method seems applicable to exploring the high-dimensional convex feasible space of an LP problem. Although the project did not provably find a strongly-polynomial algorithm, it explored multiple algorithm classes. The new medial simplex algorithms may still lead to solvers with improved provable complexity. We describe medial simplex algorithms and some relevant structural/complexity results. We also designed a novel parallel LP algorithm based on our geometric insights and implemented it in the Spoke-LP code. A major part of the computational step is many independent vector dot products. Our parallel algorithm distributes the problem constraints across processors. Current commercial and high-quality free LP solvers require all problem details to fit onto a single processor or multicore. Our new algorithm might enable the solution of problems too large for any current LP solvers. We describe our new algorithm, give preliminary proof-of-concept experiments, and describe a new generator for arbitrarily large LP instances.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Centimeter human skeleton pose estimation

A method, apparatus and system for human skeleton pose estimation includes synchronously capturing images of a human moving through an area from a plurality of different points of view, for each of the plurality of captured images, determining a bounding box that bounds the human in the captured image and identifying pixel locations of the bounding box in the image, for each of the plurality of captured images, determining 2D and single-view 3D skeletons from the pixel locations of the bounding box, determining a first, multi-view 3D skeleton using a combination of the 2D and single-view 3D skeletons, and optimizing the first, multi-view 3D skeleton to determine a final 3D skeleton pose for the human. The method, apparatus and system can further include illuminating the area with structured light during the capturing of the images of the human moving through the area.

Brookshire, Jonathan D.↗
DOE OSTI2023

Phase Trombone Design in the Beam Transfer line for the Project PIP-II at Fermilab

PIP-II beam transfer line (BTL) to transport the beam from PIP-II Linac to the Booster ring at Fermilab. One crucial aspect of the BTL design involved the implementation of collimators. These collimators play a vital role in removing large amplitude particles from the beamline that might otherwise miss the horizontal and vertical edge of the foil at the point of the Booster injection. To ensure the effectiveness of these collimators, simulation was conducted to determine their optical placement within the BTL. The simulation revealed that the precise control of accumulated phase advances between the collimators and the foil was crucial. To achieve this control, a phase trombone is needed within the BTL. The phase trombone serves as an adjustment mechanism, allowing for the fine-tuning of the phase advance between the collimators and the foil, thus optimizing the performance of the collimation process. This paper discusses and presents the details of the design and implementation of the phase trombone within the PIP-II BTL.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Parametric study of an acid baking process for rare earth element recovery from a bituminous-coal source

Acid baking treatment is widely used to extract rare earth elements (REEs) from refractory rare earth bearing minerals such as monazite and xenotime. Since these REE minerals have been identified in coal-based sources, a parametric study was conducted to evaluate the impact and optimize the parametric values associated with the acid-baking process when treating a bituminous coal source. The parameters studied using a three-level statistical experimental program were acid baking time, acid solution concentration, baking temperature, and acid solution-to-solids ratio and each were found to significantly impact REE and contaminant element recovery. An increase in baking temperature up to around 250°C improved the light and heavy REE recovery values by more than 50 absolute percentage points relative to performances achieved when direct leaching. Acid baking was needed to dehydroxylate the clays and liberate the REE minerals, which allowed access for the acid to solubilize the REEs. Acid concentration of the solution used for acid baking was studied as a means of minimizing the amount of acid needed to achieve a target REE recovery. However, thermo-gravimetric and differential scanning calorimetry analysis (TGA-DSC) of sulfuric acid under oxidizing atmosphere revealed that the addition of water decreased the evaporation temperature, which explains the lower REE recovery values obtained when using lower acid concentrations. Using pure sulfuric acid at an acid-to-solid ratio of 0.8:1 resulted in recovery values of around 70% for both LREEs and HREEs. The decomposition reaction time was relatively quick with 65% of the TREEs recovered within the first 10 minutes. Water leaching experiments performed on the acid-baked products under a temperature of 25°C instead of 75°C revealed an increase in REE recovery by 10 absolute percentage points, which was likely due to the high solubility of REE-sulfates at room temperatures.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

Breaking Rayleigh’s Law with Spatially Correlated Disorder to Control Phonon Transport

Controlling thermal transport in insulators and semiconductors is crucial for many technological fields such as thermoelectrics and thermal insulation, for which a low thermal conductivity (κ) is desirable. A major obstacle for realizing low κ materials is Rayleigh’s law, which implies that acoustic phonons, which carry most of the heat, are insensitive to scattering by point defects at low energy. We demonstrate, with large scale simulations on tens of millions of atoms, that isotropic long-range spatial correlations in the defect distribution can dramatically reduce phonon lifetimes of important low-frequency heat-carrying modes, leading to a large reduction of κ—potentially an order of magnitude at room temperature. We propose a general and quantitative framework for controlling thermal transport in complex functional materials through structural spatial correlations, and we establish the optimal functional form of spatial correlations that minimize κ. Finally, we end by briefly discussing experimental realizations of various correlated structures.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Comprehensive Efficiency Analysis of Current Source Inverter Based on CSI-Type Double Pulse Test and Genetic Algorithm

A current-source inverter (CSI) has a natural output voltage boost feature that can be advantageous for traction applications. Switching frequency is one of the easily-controlled variables that can be adjusted to improve the CSI efficiency at different operating points when the boost function is used. This paper investigates a CSI-based double-pulse test (DPT) measurement that mimics normal operation of the CSI. A loss model of the CSI is developed based on the CSI-type DPT experimental results. The impacts of the switching frequency and voltage boost ratio on the CSI efficiency and output voltage ripple are investigated. Based on the loss model, a genetic algorithm has been introduced that makes it possible to optimize the CSI's switching frequency and modulation index to maximize its efficiency under any desired operating condition.

current-source inverter, genetic algorithm, SPM ma↗
DOE OSTI2024

Atomistic Simulations for Thermophysical Properties of Uranium-Containing Halide Molten Salts

Characterizing the thermophysical properties in both fuel and coolant salts are critical in modeling, developing, process optimizing and utilizing molten salt reactors (MSRs), as these properties directly relate to operation metrics and can inform on the selection of candidate salts. The demand for consistent, accurate and publicly available thermophysical property data has become more apparent in recent years as interests have increased from molten salt reactor developers. There are a number of challenges in experimentally measuring properties such as thermal conductivity, viscosity, density and heat capacity , which have led to sparse and often times conflicting data points or molten salts in general. Additionally, there are a number of hazards to consider when synthesizing, storing, using, treating and disposing of molten salts. With the advances in computational capabilities over the last 10 years, the use of atomistic simulations can be implemented to support these efforts. The primary objective of this work is characterize the thermophysical transport properties in a number of molten chloride salts, and in particular NaCl-UCl 3 using ab-initio molecular dynamic (AIMD) simulations. In this binary salt the UCl 3 acts as the primary fissile material and NaCl acts as a carrier salt due with its’ high solubility for actinides A number of studies on the thermophysical properties of NaCl-UCl 3 have been published but there is not a vast amount of viscosity data for this system. In 1975, Desyatnik, et al published a study reporting dynamic viscosities that were calculated from kinematic viscosity measurements, and using the coefficients provided the viscosity in a 70:30 NaCl:UCl 3 mixture is 2.29 cP and 2.88 for a 60:40 mixture. Termini et al. recently reported viscosities in the range of 2.75 – 3 cP for the 63:37 NaCl-UCl 3 mixture in the same temperature range using rolling ball viscosity measurements. Computational viscosity of a similar mixture (64:36) can be obtained from the work Andersson et al. using the reported diffusion coefficients, and the hydrodynamic radius from the pair-radial distribution functions (RDFs). Using Eq (1) (vida infra), the viscosity would be 2.50 cP at 1100K. This is not to say that these values are incorrect due to the varying reported values, but aims to highlight the necessity of this work. The data reported in this ongoing work are computations on a 64:36 mixture of NaCl-UCl 3 at 987K. This work is likely to be expanded into varying concentrations of this mixture along with the inclusion of other salt candidate mixtures.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗