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DOE OSTI2023

Density Measurement of NaCl-MgCl 2 -PuCl 3 and NaCl-UCl 3 -PuCl 3 Molten Salt Systems by Neutron Radiographic Dilatometry

A novel neutron radiographic technique was developed and implemented to measure the change in volume as a function of temperature of select plutonium-containing liquid chloride mixtures from melt to 1250 K. These measurements were performed at Los Alamos National Laboratory by members of the molten salt research group under support from Gateway for Accelerated Innovation in Nuclear (GAIN) voucher NE-21-25117: “Density Measurements of Plutonium Bearing Salts via Neutron Beam Dilatometry”. This technical report contains a description of the experimental method and a summary of the results. Initial characterization of the {NaCl, MgCl 2 , UCl 3 , PuCl 3 } salts used in this experiment includes a combination of x-ray diffraction, differential scanning calorimetry, gamma ray spectroscopy, and inductively coupled plasma mass spectrometry. Four samples of NaCl + MgCl 2 + x mol% PuCl 3 and four samples of NaCl + UCl 3 + x mol% PuCl 3 were prepared at the Plutonium Facility, shipped to the Los Alamos Neutron Science Center (LANSCE), and radiographed with the density via neutron radiography (DvNR) apparatus. Density data in the form of meniscus heights were recorded as a function of temperature. The results show a linear dependence of density with temperature and a third order polynomial dependence of density with composition as a function of PuCl 3 addition in the corresponding halide salt mixture. Additions of PuCl 3 result in a significant increase in density of the salt mixture and a slight reduction in the coefficient of volumetric expansion. These results represent a first measurement of the density of plutonium chloride salt mixtures and, in combination with additional measurements, may lead to a generalized description of the density of such salt systems for arbitrary compositions and temperature.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Low temperature recovery of OFF-state stress induced degradation of AlGaN/GaN high electron mobility transistors

Thermal annealing is a widely used strategy to enhance semiconductor device performance. However, the process is complex for multi-material multi-layered semiconductor devices, where thermoelastic stresses from lattice constant and thermal expansion coefficient mismatch may create more defects than those annealed. We propose an alternate low temperature annealing technique, which utilizes the electron wind force (EWF) induced by small duty cycle high density pulsed current. To demonstrate its effectiveness, we intentionally degrade AlGaN/GaN high electron mobility transistors (HEMTs) with accelerated OFF-state stressing to increase ON-resistance ∼182.08% and reduce drain saturation current ∼85.82% of pristine condition at a gate voltage of 0 V. We then performed the EWF annealing to recover the corresponding values back to ∼122.21% and ∼93.10%, respectively. The peak transconductance, degraded to ∼76.58% of pristine at the drain voltage of 3 V, was also recovered back to ∼92.38%. This recovery of previously degraded transport properties is attributed to approximately 80% recovery of carrier mobility, which occurs during EWF annealing. We performed synchrotron differential aperture x-ray microscopy measurements to correlate these annealing effects with the lattice structural changes. We found a reduction of lattice plane spacing of (001) planes and stress within the GaN layer under the gate region after EWF annealing, suggesting a corresponding decrease in defect density. Application of this low-temperature annealing technique for in-operando recovery of degraded electronic devices is discussed.

Physics↗

DOE OSTI2022

Ion Transport in 2D Nanostructured $\pi$-Conjugated Thieno[3,2- b ]thiophene-Based Liquid Crystal

Leveraging the self-assembling behavior of liquid crystals designed for controlling ion transport is of both fundamental and technological significance. Here, we have designed and prepared a liquid crystal that contains (2,5-bis(5-(2,5,8,11-tetraoxatridecan-13-yl)thiophen-2yl)thieno[3,2-b]thiophene (BTTT) as mesogenic core and conjugated segment and symmetric tetra(ethylene oxide) (EO4) as polar side chains for ionic conducting regions. Driven by the crystallization of the BTTT cores, BTTT/dEO4 exhibits well-ordered smectic phases below 71.5 °C as confirmed by differential scanning calorimetry, polarized optical microscope, temperature dependent wide-angle X-ray scattering and grazing incidence wide-angle X-ray scattering (GIWAXS). We adopted a combination of experimental GIWAXS and molecular dynamics (MD) simulations to better understand the molecular packing of BTTT/dEO4 films, particularly when loaded with the ion conducting salt, lithium bis(trifluoromethanesulfonyl)imide (LiTFSI). Ionic conduction of BTTT/dEO4 is realized by the addition of LiTFSI, with the material able to maintain smectic phases up to r = [Li + ]/[EO] = 0.1. In this work, The highest ionic conductivity of 8 × 10 -3 S/cm was attained at an intermedium salt concentration of r = 0.05. It was also found that ion conduction in BTTT/dEO4 is enhanced by forming a smectic layered structure with irregular interfaces between the BTTT and EO4 layers and by the lateral film expansion upon salt addition. This can be explained by the enhancement of the misalignment and configurational entropy of the side chains, which increase their local mobility and that of the solvated ions. Our molecular design thus illustrates how, beyond the favorable energetic interactions that drive the assembly and ion solvating domains, modulation of entropic effects can also be favorably harnessed to improve ion conduction.

thin film electrochemical impedance spectroscopy↗

DOE OSTI2026

Topography and functional traits shape the distribution of key shrub plant functional types in low-Arctic tundra

The expansion of shrubs in the Arctic tundra fundamentally modifies land-atmosphere interactions. However, it remains unclear how shrub distribution and expansion differ across key species due to challenges with discriminating tundra plant species at regional scales. Here, we combined multi-scale, multi-platform remote sensing and in situ trait measurements to elucidate the distribution patterns and primary controls of two representative deciduous-tall-shrub (DTS) genera, Alnus and Salix, in low-Arctic tundra. We show that topographic features were a key control on DTSs, creating heterogeneous, but predictable distributions of Alnus and Salix fractional cover (fCover). Alnus was more tolerant of elevation and slope and was found on hilly uplands (slope >10°) within a specific elevational band (200–400 m above sea level [MSL]). In contrast, Salix occurred at lower elevations (50–300 m MSL) on gentler slopes (3-10°) and required adequate soil moisture associated with its profligate water use. We also show that niche differentiation between Alnus and Salix changed with patch size, where larger patches were more specialized in resource requirements than individual plants of Alnus and Salix. To understand what constrains the growth of DTSs at locations with low fCover, we developed environmental limiting factor models, which showed that topography limits the upper bound of Alnus and Salix fCover in 69.2% and 48.7% of the landscape, respectively. These findings highlight a critical need to better understand and represent topography-controlled processes and functional traits in regulating shrub distribution, as well as a need for more detailed species classification to predict shrubification in the Arctic.

alder↗

DOE OSTI2022

Physics-Informed Sparse Gaussian Process for Probabilistic Stability Analysis of Large-Scale Power System with Dynamic PVs and Loads

This work proposes a physics-informed sparse Gaussian process (SGP) for probabilistic stability assessment of large-scale power systems in the presence of uncertain dynamic PVs and loads. The differential and algebraic equations considering uncertainties from dynamic PVs and loads are reformulated to a nonlinear mapping relationship that allows the application of SGP. Thanks to the nonparametric characteristic of Gaussian process, the proposed framework does not require distributions of uncertain inputs and this distinguishes it from existing approaches. As the original Gaussian process is not scalable to large-scale systems with high dimensional uncertain inputs, this paper develops the SGP with a stochastic variational inference technique. It leads to approximately two orders of complex reduction. A data pre-processing step is also introduced to tackle the coexistence of stable and unstable cases by sample clustering and constructing separate SGPs. The probabilistic transient stability index is analyzed to assess system stability under different uncertain dynamics loads and PVs. Comparisons are performed with the sampling-based, the polynomial chaos expansion-based, and traditional Gaussian process-based methods on the modified IEEE 118-bus and Texas 2000-bus systems under various scenarios, including different levels of uncertainties and the existence of nonlinear correlations among dynamic PVs. The impacts of data quality and quantity issues are also investigated. It is shown that the proposed SGP achieves significantly improved computational efficiency while maintaining high accuracy with a limited number of data.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

CFT correlators, $\mathcal{W}$-algebras and generalized Catalan numbers

In two spacetime dimensions the Virasoro heavy-heavy-light-light (HHLL) vacuum block in a certain limit is governed by the Catalan numbers. The equation for their generating function can be generalized to a differential equation which the logarithm of the block satisfies. We show that a similar story holds for the HHLL $\mathcal{W}_N$ vacuum blocks, where a suitable generalization of the Catalan numbers plays the main role. Moreover, the $\mathcal{W}_N$ blocks have the same form as the stress tensor sector of HHLL near lightcone conformal correlators in 2(N – 1) spacetime dimensions. In the latter case the Catalan numbers are generalized to the numbers of linear extensions of certain partially ordered sets.

1/N expansion↗

DOE OSTI2019

Toward the implementation of self-powered, wireless, real-time reactor power sensing

The successful development of real-time, self-powered, wireless reactor power sensing technology would enable key operating parameters of every fuel assembly in a commercial reactor core to be continuously monitored, and potentially be utilized for applications such as closed-loop reactor control. This paper discusses the expansion of successful work on the application of Self Powered Neutron Detectors (SPND) for reactor power monitoring and the wireless transmission of these detector signals using advanced vacuum micro-electronic (VME) transmitter technology developed by Westinghouse in collaboration with the Penn State University Radiation Science and Engineering Center. Results demonstrate that this technology may be successfully applied via long-term testing in the Penn State TRIGA reactor. SPND are inherently slower than, for example, ion chambers due to their dependence on radioactive decay to generate a signal. Additional research developed delay compensation algorithms to accelerate the response of these detectors for use in near-real-time closed-loop reactor control. Initial results indicate that practical signal acceleration can be obtained digitally or with an equivalent analog circuit. Future research will apply an inverse model of the sensor’s balance differential equations to more-effectively accelerate the SPND signal. Use of inverse models will also prove to be adaptive in the sense that time-dependent burn-up of these detectors, due to neutron activation of the detector material, can be modeled and applied in the acceleration algorithm.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

Evaluation of Hybrid FPOG Applications in Regulated and Deregulated Markets Using HERON

Recent changes in the U.S. energy market, such as low natural gas prices and increased electricity production for variable renewable energy (VRE) sources, have led to an economic crisis for existing light-water reactor (LWR) nuclear power plants (NPP). Many owners and operators of LWRs have elected to decommission these plants rather than continue using them as consistent sources of clean baseload power. This has led to exploration of various possibilities to increase the economic viability of these units, including market restructuring to monetize benefits LWRs already provide to the grid through ancillary markets, load following and economic dispatch, and possible integration of secondary systems directly to the NPP for production of additional products through technologies such as hydrogen electrolysis or water desalination. Previous studies have considered the technologies associated with these Integrated Energy Systems (IES) activities, and the analysis of markets for these secondary products. To analyze the economic viability of various system configurations including IES, especially given the uncertainty surrounding load demand, electricity prices, and the availability of VRE resources, the stochastic technoeconomic analysis package HERON (Heuristic Energy Resource Optimization Network) was released earlier this year as an extension of the risk analysis framework RAVEN (Risk Analysis Virtual Environment). HERON focuses foremost on making the complex uncertainty quantification analysis tools approachable for energy systems analysts, also providing general dispatch optimization algorithms for those workflows. HERON continues to be improved and tested as a significant part of the IES viability analyses performed in this work. HERON is not a capacity expansion model. To consider market and grid energy system development in a variety of scenarios, HERON is best used in coupling with modelling tools such as US-REGEN, which sacrifice some of the uncertainty analysis and resolution of HERON's modelling for the ability to efficiently predict the change in the grid energy system's profile due to economic drivers over decades. HERON can then use this information to explore the economic viability of introducing changes to the predicted outcomes, such as the introduction of an IES. In this work, experts at EPRI using US-REGEN provide six projection scenarios for use in HERON stochastic technoeconomic analysis (STEA) in considering the options available for increasing LWR economic viability through introduction of a hydrogen-centric IES using a high-temperature steam electrolysis plant (HTSE), hydrogen storage, and a constant-rate contracted hydrogen consumer. The results obtained are differential in nature; they do not report expected profits for any configuration, but rather report on the possible increase in the NPV of a configuration with respect to a baseline no-IES configuration. Due to the uncertainty captured in the variable net load of the systems, there is likewise uncertainty in the mean values reported. We consider this viability both in terms of a regulated market, where the energy producers and IES are owned and operated by single entity, as well as a deregulated market, where the IES chooses its bid for electricity generation and is then dispatched by the grid system operator. Results indicate that for deregulated markets, the inclusion of the IES is often statistically beneficial. This is especially true in policies that are not favorable towards nuclear, as nuclear is less often dispatched and is forced to deal with frequent idle capacity. In the nominal case as well as the case of carbon tax policies, inclusion of the IES clearly benefited the economic performance of the NPP. In the regulated case, however, there was a trend towards minimizing the IES, likely due to the optimal sizing performed by US-REGEN of the NPP within the system as well as the lack of penalty for idle capacity at the NPP in the regulated market analyses.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2020

LA17-V-InverseRadTransportProblem-PD2Kb. Final Project Report

In the inverse radiation transport problem, a radiation signal emanating from an unknown object is used to reconstruct geometric and material parameters of the object , e.g., the mass of special nuclear material (SNM), the isotopic composition of the SNM (e.g., the enrichment of 235U), the thickness of shielding, etc. The inverse radiation transport problem is one of the most fundamental and important in the emergency response mission, and has been addressed in previous NA-22 and NA-42/84 investments by using derivative-free optimization methods. Our project was aimed at (i) consolidating previous work by applying polynomial chaos expansion (PCE) for reducing significantly the number of forward model simulations required by derivative-free optimization methods; (ii) developing a general-purpose framework and stand-alone software for solving the inverse radiation transport problem by applying a new “predictive modeling” methodology (predictive modeling of coupled multi-physics systems, PM_CMPS) which enables the fusing of experimental and computational information; and (iii) developing several benchmarks (for unscattered gamma rays) for demonstrating the quantification of non-Gaussian features (e.g., skewness and kurtosis, which characterize asymmetries and long tails) of response distributions by deterministically and efficiently propagating parameter uncertainties (covariances and higher-order moments, where available) using high-order sensitivities computed by the recently developed comprehensive adjoint sensitivity analysis methodology (C-ASAM). With regard to (i), application of PCE, the most significant accomplishment of this project was an implementation of PCE into INVERSE that significantly reduced computation time for the DiffeRential Evolution Adaptive Metropolis (DREAM) and Differential Evolution (DE) methods. For inverse transport problems with measurements of neutron leakage, neutron multiplication, and neutron-induced gamma-rays, the solution times for the DREAM and DE methods were reduced by factors of 30-300, reducing run times from hours to minutes while maintaining the same accuracy as the standard (non-PCE) DREAM and DE. Speedups were further improved by exploring sparse grid quadrature sets using the Toolkit for Adaptive Stochastic Modeling and Non-Intrusive ApproximatioN (Tasmanian) toolset. An initial exploration of sparse grids indicated that further speedups factors of 5-10 are obtainable. The implementation of PCE in INVERSE is not in a form that users can easily access. With regard to (ii), solving the inverse problem using a predictive modeling paradigm, the most significant accomplishment of this project were the derivation and implementation of adjoint-based second derivatives in neutron and photon transport theory. There are two codes that compute these derivatives. SENSPG computes them for uncollided gamma rays and SENSMG computes them for neutron leakage. SENSMG is open-source code available on github. Other significant accomplishments included a stand-alone code, MULTI-PRED, that implements the predictive modeling paradigm, along with a detailed manual. The code was implemented in the existing INVERSE code for inverse problems, but a head-to-head comparison of inverse methods was not achieved. We also derived and implemented the capability to compute sensitivities for multiplicity measurements into the SENSMG code, and this capability was used in a data assimilation application, but it was not used for solving inverse problems. With regard to (iii), developing benchmarks to demonstrate the importance of high-order sensitivity and uncertainty analysis, the most significant accomplishment of this project were two test problems that showed how important second-order sensitivities are.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2020

Ceramic encapsulated metal phase change material for high temperature thermal energy storage

Thermal energy storage (TES) is a broad-based technology for reducing CO₂ emissions and advancing concentrating solar, fossil, and nuclear power through improvements in efficiency and economics. Phase change materials (PCMs) are of interest as TES media because of their ability to store large amounts of heat in relatively small volumes. The volume expansion during a phase change, typically between a solid and liquid, can cause breakage of protective coatings. This paper reports on the fabrication of a ceramic encapsulated metal (CEM) high temperature TES technology using a rotary calcining furnace and a fluidized bed chemical vapor deposition coating technique. Aluminum beads were chosen as the PCM because Al has a high melting point (660 °C), low cost, high heat of fusion, and an ability to form a thin, strong alumina layer capable of supporting the Al melt for subsequent processing. Quite remarkably, this study shows that 1 mm diameter Al can be fluidized up to at least 1500 °C in an appropriate atmosphere while maintaining a spheroid geometry. This allowed for producing a first of a kind CEM whereby Al particles were encapsulated in pyro-carbon (PyC) and high purity, dense chemical vapor deposited SiC. The CEM with a PyC only coating was exposed to thermal cycling to test the performance with a differential scanning calorimeter; the melting point and latent heat were measured to be 648.4 ± 2.8 °C and 293.3 ± 6.2 J/g respectively. It was demonstrated that the CEM design is possible to produce, laying the foundation for manufacturing of high temperature, tunable, TES media.

25 ENERGY STORAGE↗

DOE OSTI2023

DSPS: Differentiable stellar population synthesis

ABSTRACT Models of stellar population synthesis (SPS) are the fundamental tool that relates the physical properties of a galaxy to its spectral energy distribution (SED). In this paper, we present DSPS: a python package for SPS. All of the functionality in DSPS is implemented natively in the JAX library for automatic differentiation, and so our predictions for galaxy photometry are fully differentiable, and directly inherit the performance benefits of JAX, including portability onto GPUs. DSPS also implements several novel features, such as i) a flexible empirical model for stellar metallicity that incorporates correlations with stellar age, ii) support for the Diffstar model that provides a physically-motivated connection between the star formation history of a galaxy (SFH) and the mass assembly of its underlying dark matter halo. We detail a set of theoretical techniques for using autodiff to calculate gradients of predictions for galaxy SEDs with respect to SPS parameters that control a range of physical effects, including SFH, stellar metallicity, nebular emission, and dust attenuation. When forward modelling the colours of a synthetic galaxy population, we find that DSPS can provide a factor of 5 speed-up over standard SPS codes on a CPU, and a factor of 300-400 on a modern GPU. When coupled with gradient-based techniques for optimization and inference, DSPS makes it practical to conduct expansive likelihood analyses of simulation-based models of the galaxy–halo connection that fully forward model galaxy spectra and photometry.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Learning and meta-learning of stochastic advection–diffusion–reaction systems from sparse measurements

Physics-informed neural networks (PINNs) were recently proposed in [18] as an alternative way to solve partial differential equations (PDEs). A neural network (NN) represents the solution, while a PDE-induced NN is coupled to the solution NN, and all differential operators are treated using automatic differentiation. Here, we first employ the standard PINN and a stochastic version, sPINN, to solve forward and inverse problems governed by a non-linear advection–diffusion–reaction (ADR) equation, assuming we have some sparse measurements of the concentration field at random or pre-selected locations. Subsequently, we attempt to optimise the hyper-parameters of sPINN by using the Bayesian optimisation method (meta-learning) and compare the results with the empirically selected hyper-parameters of sPINN. In particular, for the first part in solving the inverse deterministic ADR, we assume that we only have a few high-fidelity measurements, whereas the rest of the data is of lower fidelity. Hence, the PINN is trained using a composite multi-fidelity network, first introduced in [12], that learns the correlations between the multi-fidelity data and predicts the unknown values of diffusivity, transport velocity and two reaction constants as well as the concentration field. For the stochastic ADR, we employ a Karhunen–Loève (KL) expansion to represent the stochastic diffusivity, and arbitrary polynomial chaos (aPC) to represent the stochastic solution. Correspondingly, we design multiple NNs to represent the mean of the solution and learn each aPC mode separately, whereas we employ a separate NN to represent the mean of diffusivity and another NN to learn all modes of the KL expansion. For the inverse problem, in addition to stochastic diffusivity and concentration fields, we also aim to obtain the (unknown) deterministic values of transport velocity and reaction constants. The available data correspond to 7spatial points for the diffusivity and 20 space–time points for the solution, both sampled 2000 times. We obtain good accuracy for the deterministic parameters of the order of 1–2% and excellent accuracy for the mean and variance of the stochastic fields, better than three digits of accuracy. In the second part, we consider the previous stochastic inverse problem, and we use Bayesian optimisation to find five hyper-parameters of sPINN, namely the width, depth and learning rate of two NNs for learning the modes. Here, we obtain much deeper and wider optimal NNs compared to the manual tuning, leading to even better accuracy, i.e., errors less than 1% for the deterministic values, and about an order of magnitude less for the stochastic fields.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Lagrangian particle simulation of hydrogen pellets and SPI into runaway electron beam in ITER

Numerical studies of the ablation of pellets and shattered pellet injection (SPI) fragments into a runaway electron beam in ITER have been performed using a time-dependent pellet ablation code [Samulyak et al., Nucl. Fusion, 61(4), 046007 (2021)]. The code resolves detailed ablation physics near pellet fragments and large-scale expansion of ablated clouds. The study of a single-fragment ablation quantifies the influence of various factors, in particular, the impact ionization by runaway electrons and cross-field transport models, on the dynamics of ablated plasma and its penetration into the runaway beam. Simulations of SPI performed using different numbers of pellet fragments study the formation and evolution of the ablation clouds and their large-scale dynamics in ITER. In conclusion, the penetration depth of the ablation clouds is found to be of the order of 50 cm.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

Identification of conserved skeletal enhancers associated with craniosynostosis risk genes

Abstract Craniosynostosis, defined by premature fusion of one or multiple cranial sutures, is a common congenital defect affecting more than 1/2000 infants and results in restricted brain expansion. Single gene mutations account for 15%–20% of cases, largely as part of a syndrome, but the majority are nonsyndromic with complex underlying genetics. We hypothesized that the two noncoding genomic regions identified by a GWAS for craniosynostosis contain distal regulatory elements for the risk genes BMPER and BMP2. To identify such regulatory elements, we surveyed conserved noncoding sequences from both risk loci for enhancer activity in transgenic Danio rerio. We identified enhancers from both regions that direct expression to skeletal tissues, consistent with the endogenous expression of bmper and bmp2. For each locus, we also found a skeletal enhancer that also contains a sequence variant associated with craniosynostosis risk. We examined the activity of each enhancer during craniofacial development and found that the BMPER-associated enhancer is active in the restricted region of cartilage closely associated with frontal bone initiation. The same enhancer is active in mouse skeletal tissues, demonstrating evolutionarily conserved activity. Using enhanced yeast one-hybrid assays, we identified transcription factors that bind each enhancer and observed differential binding between alleles, implicating multiple signaling pathways. Our findings help unveil the genetic mechanism of the two craniosynostosis risk loci. More broadly, our combined in vivo approach is applicable to many complex genetic diseases to build a link between association studies and specific genetic mechanisms.

He(何璇), Xuan Anita (ORCID:0000000313221972)↗

DOE OSTI2023

Spark Channel Dynamics of Electrostatic Discharges

When two differently-charged objects are brought in close proximity to each other, the resulting high electric fields can cause electron avalanche breakdown of the air gap separating the objects, a process known as electrostatic discharge (ESD). If enough initial charge is stored on the objects, the electrical breakdown can proceed to ionize the air to such a degree that a highly conductive filament of plasma forms in the gap, known as a spark channel. The spark electrically bridges the air gap, resulting in a rapid pulse of current that neutralizes the charge difference. The current pulse produces significant heating of the gas in the spark, resulting in dissociation, ionization, thermal radiation, and hydrodynamic expansion. ESD presents a hazard to electrically-sensitive devices, with consequences such as economic losses (e.g. damaged electronics) or unsafe response (e.g. unintended ignition of flammable gas mixtures, initiation of detonators, etc.). For this thesis, the ESD spark is taken to occur between two conducting electrodes, with the spark channel being axisymmetric in a cylindrical coordinate system centered on the channel. An RLC-type circuit is used for the discharge model of the ESD event. The spark is treated as a time-dependent resistance that is in series with a capacitance, an inductance, and (optionally) a load resistance representing a “victim” component under threat from the ESD event. The primary motivation of this work is to use a numerical hydrodynamic model to understand the energy dissipation and transport processes in the spark. The model consists of the compressible Euler equations of mass, momentum, and energy conservation together with an Eddington/P1 approximation for thermal radiation transport. To close the hydrodynamic system, an equation of state (EOS) was fitted from tabular data for air that accounts for the dissociation and ionization of air species. The hydrodynamic equations are solved using a conservative Lagrangian finite volume method. These partial differential equations are coupled to the circuit equations by calculation of the spark resistance via numerical integration of the electrical conductivity of the channel. Computational results are compared against experimental measurements of discharge current and radial density of the spark channel.

42 ENGINEERING↗

DOE OSTI2023

Snekmer: a scalable pipeline for protein sequence fingerprinting based on amino acid recoding

Abstract Motivation The vast expansion of sequence data generated from single organisms and microbiomes has precipitated the need for faster and more sensitive methods to assess evolutionary and functional relationships between proteins. Representing proteins as sets of short peptide sequences (kmers) has been used for rapid, accurate classification of proteins into functional categories; however, this approach employs an exact-match methodology and thus may be limited in terms of sensitivity and coverage. We have previously used similarity groupings, based on the chemical properties of amino acids, to form reduced character sets and recode proteins. This amino acid recoding (AAR) approach simplifies the construction of protein representations in the form of kmer vectors, which can link sequences with distant sequence similarity and provide accurate classification of problematic protein families. Results Here, we describe Snekmer, a software tool for recoding proteins into AAR kmer vectors and performing either (i) construction of supervised classification models trained on input protein families or (ii) clustering for de novo determination of protein families. We provide examples of the operation of the tool against a set of nitrogen cycling families originally collected using both standard hidden Markov models and a larger set of proteins from Uniprot and demonstrate that our method accurately differentiates these sequences in both operation modes. Availability and implementation Snekmer is written in Python using Snakemake. Code and data used in this article, along with tutorial notebooks, are available at http://github.com/PNNL-CompBio/Snekmer under an open-source BSD-3 license. Supplementary information Supplementary data are available at Bioinformatics Advances online.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Expression of blue pigment synthetase a from Streptomyces lavenduale reveals insights on the effects of refactoring biosynthetic megasynthases for heterologous expression in Escherichia coli .

High GC bacteria from the genus Streptomyces harbor expansive secondary metabolism. The expression of biosynthetic proteins and the characterization and identification of biological "parts" for synthetic biology purposes from such pathways are of interest. However, the high GC content of proteins from actinomycetes in addition to the large size and multi-domain architecture of many biosynthetic proteins (such as non-ribosomal peptide synthetases; NRPSs, and polyketide synthases; PKSs often called "megasynthases") often presents issues with full-length translation and folding. Here we evaluate a non-ribosomal peptide synthetase (NRPS) from Streptomyces lavenduale, a multidomain "megasynthase" gene that comes from a high GC (72.5%) genome. While a preliminary step in revealing differences, to our knowledge this presents the first head-to-head comparison of codon-optimized sequences versus a native sequence of proteins of streptomycete origin heterologously expressed in E. coli. We found that any disruption in co-translational folding from codon mismatch that reduces the titer of indigoidine is explainable via the formation of more inclusion bodies as opposed to compromising folding or posttranslational modification in the soluble fraction. In conclusion, this result supports that one could apply any refactoring strategies that improve soluble expression in E. coli without concern that the protein that reaches the soluble fraction is differentially folded.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Property Measurements of NaCl-UCl 3 and LiF-NaF-KF Molten Salts Doped with Surrogate Fission Products

Knowing thermophysical and thermochemical property values of salts over the expected range of operating temperatures is essential to modeling and simulation efforts that support the commercialization of molten salt reactor (MSR) technologies. Salt properties being measured at Argonne include thermal transitions, phase behavior, heat capacity, density, surface tension, volumetric thermal expansion, thermal diffusivity, thermal conductivity, and viscosity. The properties of several salts of interest including NaCl-UCl 3 and LiF-NaF-KF (FLiNaK) have been measured at Argonne and reported previously. These property measurements are suitable for use in evaluating reactor performance during startup and the early operating life of the reactor. The physical and chemical behavior of the fuel salt is altered by the buildup of fission products and the resulting changes in heat transfer and other properties must be understood by reactor developers. This report summarizes melting behavior, heat capacity, and thermal diffusivity values that were measured for mixtures of FLiNaK and NaCl-UCl 3 salts that have been doped with surrogate fission products. Additional heat capacity measurements were also performed on a separate mixture of FLiNaK without dopants. Measured properties are suitable for incorporation into the Molten Salt Thermal Properties Database–Thermochemical (MSTDB–TC) and the Molten Salt Thermal Properties Database–Thermophysical (MSTDB–TP) to support the development of MSRs. Salts were prepared by doping eutectic mixtures of NaCl-UCl 3 and FLiNaK with surrogate fission products. These salts were prepared for use in salt spill experiments being performed at Argonne and were subsequently used for thermal property measurements. Thermal transitions and heat capacities were measured by using differential scanning calorimetry (DSC) and thermal diffusivity was measured by using laser flash analysis (LFA) at temperatures spanning the typical MSR operating range. The measured property values of the doped salts were compared to measured property values of salts without fission products to quantify the effects of the doped fission products.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗