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At least 685 records · Page 38

Possible quantum phase transition in partially Cu-doped ZrNi 2-x Cu x Ga Heusler alloys

We have studied the magneto-transport properties of a series of ZrNi 2- x Cu x Ga compounds. For all Cu concentration ( x ≤ 0.5) the samples exhibited the L2 1 cubic structure at room temperature and demonstrated superconductivity below 2 K. A superconducting phase transition temperature of as low as ∼1 K and a critical magnetic field of 1.78 T was observed in the system. A linear extrapolation of T C to T = 0 highly indicated that a stoichiometry-dependent superconducting quantum phase transition may occur in ZrNi 2-x Cu x Ga at x c = 0.90.

Khan, Mahmud↗

Association of Cl with C 2 H 2 by unified variable-reaction-coordinate and reaction-path variational transition-state theory

Significance A key issue in chemical kinetics is advancing the theoretical framework to handle reactions beyond the domain of textbook transition-state theory by including––for example––anharmonicity, barrierless transition states, transition states in series, and the effect of conformational flexibility on equilibrium constants. Other key issues are validating affordable electronic structure methods for direct dynamics and the direct calculation of high-pressure limiting rate constants, which are often obtainable experimentally only by extrapolation. The present article addresses all these issues for a prototype radical–molecule association reaction and thereby demonstrates how to combine improved theoretical methods to provide rate constants in cases where experimental data are uncertain or missing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Acoustic force spectroscopy reveals subtle differences in cellulose unbinding behavior of carbohydrate-binding modules

Protein adsorption to solid carbohydrate interfaces is critical to many biological processes, particularly in biomass deconstruction. To engineer more-efficient enzymes for biomass deconstruction into sugars, it is necessary to characterize the complex protein–carbohydrate interfacial interactions. A carbohydrate-binding module (CBM) is often associated with microbial surface-tethered cellulosomes or secreted cellulase enzymes to enhance substrate accessibility. However, it is not well known how CBMs recognize, bind, and dissociate from polysaccharides to facilitate efficient cellulolytic activity, due to the lack of mechanistic understanding and a suitable toolkit to study CBM–substrate interactions. Our work outlines a general approach to study the unbinding behavior of CBMs from polysaccharide surfaces using a highly multiplexed single-molecule force spectroscopy assay. Here, we apply acoustic force spectroscopy (AFS) to probe a Clostridium thermocellum cellulosomal scaffoldin protein (CBM3a) and measure its dissociation from nanocellulose surfaces at physiologically relevant, low force loading rates. An automated microfluidic setup and method for uniform deposition of insoluble polysaccharides on the AFS chip surfaces are demonstrated. The rupture forces of wild-type CBM3a, and its Y67A mutant, unbinding from nanocellulose surfaces suggests distinct multimodal CBM binding conformations, with structural mechanisms further explored using molecular dynamics simulations. Applying classical dynamic force spectroscopy theory, the single-molecule unbinding rate at zero force is extrapolated and found to agree with bulk equilibrium unbinding rates estimated independently using quartz crystal microbalance with dissipation monitoring. However, our results also highlight critical limitations of applying classical theory to explain the highly multivalent binding interactions for cellulose–CBM bond rupture forces exceeding 15 pN.

09 BIOMASS FUELS↗

Generic character of charge and spin density waves in superconducting cuprates

Charge density waves (CDWs) have been observed in nearly all families of copper-oxide superconductors. But the behavior of these phases across different families has been perplexing. In La-based cuprates, the CDW wavevector is an increasing function of doping, exhibiting the so-called Yamada behavior, while in Y- and Bi-based materials the behavior is the opposite. Here, we report a combined resonant soft X-ray scattering (RSXS) and neutron scattering study of charge and spin density waves in isotopically enriched La 1.8–x Eu 0.2 Sr x CuO 4 over a range of doping 0.07 ≤ x ≤ 0.20. In this work, we find that the CDW amplitude is temperature independent and develops well above experimentally accessible temperatures. Further, the CDW wavevector shows a nonmonotonic temperature dependence, exhibiting Yamada behavior at low temperature with a sudden change occurring near the spin ordering temperature. We describe these observations using a Landau–Ginzburg theory for an incommensurate CDW in a metallic system with a finite charge compressibility and spin-CDW coupling. Extrapolating to high temperature, where the CDW amplitude is small and spin order is absent, our analysis predicts a decreasing wavevector with doping, similar to Y and Bi cuprates. Our study suggests that CDW order in all families of cuprates forms by a common mechanism.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

A large net carbon loss attributed to anthropogenic and natural disturbances in the Amazon Arc of Deforestation

The Amazon forest contains globally important carbon stocks, but in recent years, atmospheric measurements suggest that it has been releasing more carbon than it has absorbed because of deforestation and forest degradation. Accurately attributing the sources of carbon loss to forest degradation and natural disturbances remains a challenge because of the difficulty of classifying disturbances and simultaneously estimating carbon changes. We used a unique, randomized, repeated, very high-resolution airborne laser scanning survey to provide a direct, detailed, and high-resolution partitioning of aboveground carbon gains and losses in the Brazilian Arc of Deforestation. Our analysis revealed that disturbances directly attributed to human activity impacted 4.2% of the survey area while windthrows and other disturbances affected 2.7% and 14.7%, respectively. Extrapolating the lidar-based statistics to the study area (544,300 km 2 ), we found that 24.1, 24.2, and 14.5 Tg C y −1 were lost through clearing, fires, and logging, respectively. The losses due to large windthrows (21.5 Tg C y −1 ) and other disturbances (50.3 Tg C y −1 ) were partially counterbalanced by forest growth (44.1 Tg C y −1 ). Our high-resolution estimates demonstrated a greater loss of carbon through forest degradation than through deforestation and a net loss of carbon of 90.5 ± 16.6 Tg C y −1 for the study region attributable to both anthropogenic and natural processes. This study highlights the role of forest degradation in the carbon balance for this critical region in the Earth system.

54 ENVIRONMENTAL SCIENCES↗

A tale of two planets: Disparate evolutionary models for Mars inferred from radiogenic isotope compositions of Martian meteorites

The radiogenic isotopic compositions of basaltic Martian meteorites (shergottites) and clinopyroxene/olivine cumulate meteorites (nakhlite/chassignites) are used to define the global evolution of Mars. However, the two main groups of meteorites demonstrate that their sources underwent divergent styles of magmatic evolution. The shergottites portray a planet that differentiated ~4.52 billion years ago via solidification of a magma ocean, producing incompatible element-depleted and -enriched reservoirs that remained isolated until melt production. In contrast, the reservoir from which the nakhlite/chassignites derive may have formed earlier, produced melts that fractionated Sm/Nd and Hf/W differently, was compositionally less variable, and experienced a significantly more complex history following primordial differentiation than the shergottite sources. The disparate histories recorded by these two groups of meteorites elucidate important questions that could be addressed by acquiring additional samples. Obtaining samples that shared the isotopic systematics of the shergottites would provide confidence that extrapolating the primordial differentiation history of Mars from shergottite radiogenic isotope systematics is reasonable. Returned samples from Mars will also constrain the physical locations of the meteorite source regions, providing insights into the general structure of the Martian mantle. In addition, they will help constrain the phases present in the martian mantle during melting and the conditions under which they are stable. Finally, identifying an evolved lithology that satisfies the geochemical and isotopic constraints placed on the incompatible element-enriched endmember observed in the shergottites would define the nature of magmatic evolution on Mars and whether it is more akin to processes on the Earth or the Moon.

Mars↗

Demystifying group-4 polyolefin hydrogenolysis catalysis: Gaseous propane hydrogenolysis mechanism over the same catalysts

A kinetic/mechanistic investigation of gaseous propane hydrogenolysis over the single-site heterogeneous polyolefin depolymerization catalysts AlS/ZrNp 2 and AlS/HfNp 2 (AlS = sulfated alumina, Np = neopentyl), is use to probe intrinsic catalyst properties without the complexities introduced by time- and viscosity-dependent polymer medium effects. In a polymer-free automated plug-flow catalytic reactor, propane hydrogenolysis turnover frequencies approach 3,000 h −1 at 150 °C. Both catalysts exhibit approximately linear relationships between rate and [H 2 ] at substoichiometric [H 2 ] with rate law orders of 0.66 ± 0.09 and 0.48 ± 0.07 for Hf and Zr, respectively; at higher [H 2 ], the rates approach zero-order in [H 2 ]. Reaction orders in [C 3 H 8 ] and [catalyst] are essentially zero-order under all conditions, with the former implying rapid, irreversible alkane binding/activation. This rate law, activation parameter, and DFT energy span analysis support a scenario in which [H 2 ] is pivotal in one of two plausible and competing rate-determining transition states—bimolecular metal-alkyl bond hydrogenolysis vs. unimolecular β-alkyl elimination. The Zr and Hf catalyst activation parameters, ΔH ‡ = 16.8 ± 0.2 kcal mol −1 and 18.2 ± 0.6 kcal mol −1 , respectively, track the relative turnover frequencies, while ΔS ‡ = −19.1 ± 0.8 and −16.7 ± 1.4 cal mol −1 K −1 , respectively, imply highly organized transition states. These catalysts maintain activity up to 200 °C, while time-on-stream data indicate multiday activities with an extrapolated turnover number ~92,000 at 150 °C for the Zr catalyst. This methodology is attractive for depolymerization catalyst discovery and process optimization.

03 NATURAL GAS↗

Computational investigation of water glasses using machine-learning potentials

The molecular origins of water’s anomalous properties have long been a subject of scientific inquiry. The liquid–liquid phase transition hypothesis, which posits the existence of distinct low-density and high-density liquid states separated by a first-order phase transition terminating at a critical point, has gained increasing experimental and computational support and offers a thermodynamically consistent framework for many of water’s anomalies. However, experimental challenges in avoiding crystallization near the postulated liquid–liquid critical point have focused attention to water’s canonical glassy states: low-density and high-density amorphous ice. Here, we use two Deep Potential machine-learning models, trained on the Strongly Constrained and Appropriately Normed density functional and the highly accurate Many-Body Polarizable potential, to conduct an investigation of water’s glassy phenomenology based on quantum mechanical calculations. Despite not being explicitly trained on amorphous ices, both models accurately capture the structure and transformation of the water glasses, including their interconversion along different thermodynamic paths. Isobaric quenching of liquid water at various pressures generates a continuum of intermediate amorphous ices and density fluctuations increase near the liquid–liquid critical pressure. The glass transition temperatures of the amorphous ices produced at different pressures exhibit two distinct branches, corresponding to low-density and high-density amorphous ice behaviors, consistent with experiment and the liquid–liquid transition hypothesis. Extrapolating transformation pressures from isothermal compressions to experimental compression rates brings our simulations into excellent agreement with data. Our findings demonstrate that machine-learning potentials trained on equilibrium phases can effectively model nonequilibrium glassy behavior and pave the way for studying long-timescale, out-of-equilibrium processes with quantum mechanical accuracy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Theoretical study of the HCS + –H 2 van der Waals complex: potential energy surface, rovibrational bound states, and rotationally inelastic collisional cross sections

An accurate ground-state intermolecular potential energy surface (PES) was calculated for the HCS + –H 2 complex. The surface was constructed from 3023 ab initio energies, computed with explicitly correlated coupled-cluster theory, CCSD(T)-F12b, with extrapolation to the complete basis set limit (VTZ-F12/VQZ-F12). The new 4D PES was used to compute rovibrational energies, rota- tional constants, and close-coupling quantum scattering calculations at low collision energies. A symmetry-adapted Lanczos algorithm was used to variationally compute the low-lying rovibrational bound states. Rotational constants for states of two isomers were determined from the energy levels and reported in hopes of motivating experiments. The rotationally inelastic state-to-state cross sections of HCS + by collision with para- and ortho-H 2 were computed and compared, and trends and propensities are discussed.

74 ATOMIC AND MOLECULAR PHYSICS↗

Mechanistic Multiscale Uncertainty Propagation in Support of Accelerated Fuel Qualification

Taking a nuclear fuel concept through the research, development, and qualification stages has historically taken on the order of 20 to 25 years because of extensive irradiation tests required for a variety of conditions. The concept of accelerated fuel qualification (AFQ) has been proposed to increase the innovation pace for nuclear fuels. The goal of AFQ is not to replace the traditional qualification approach but rather to reduce the total number of experiments required to ensure approval from the regulatory authority. Of the many AFQ approaches being explored, advanced modeling—and, in particular, mechanistic modeling—is in a uniquely cross-cutting position to reduce the number of required integral tests through the inclusion of separate-effects testing, while helping to extrapolate reactor performance during rare events. We make the case that propagation of uncertainty through various computational length scales helps contextualize mechanistic modeling. We will utilize UO 2 fission gas diffusion predictions from the atomistically informed cluster dynamics code Centipede to inform fuel performance rodlet simulations using the BISON finite element code as the metric for showing how multiscale mechanistic uncertainty quantification can help reduce uncertainty in fuel performance. In conclusion, by quantifying uncertainty and its reduction through multiscale modeling, the qualification process may be accelerated through the reduction of costly irradiation experiments.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Uncertainty Quantification for Data-Driven Machine Learning Models in Nuclear Engineering Applications: Where We Are and What Do We Need?

Machine learning (ML) has been leveraged to tackle a diverse range of tasks in almost all branches of nuclear engineering. Many of the successes in ML applications can be attributed to the recent performance breakthroughs in deep learning, the growing availability of computational power, data, and easy-to-use ML libraries. However, these empirical successes have often outpaced our formal understanding of the ML algorithms. An important but under-rated area is uncertainty quantification (UQ) of ML. ML-based models are subject to approximation uncertainty when they are used to make predictions, due to sources including but not limited to, data noise, data coverage, extrapolation, imperfect model architecture and the stochastic training process. The goal of this paper is to clearly explain and illustrate the importance of UQ of ML. We will elucidate the differences in the basic concepts of UQ of physics-based models and data-driven ML models. Various sources of uncertainties in physical modeling and data-driven modeling will be discussed, demonstrated, and compared. We will also present and demonstrate a few techniques to quantify the ML prediction uncertainties, including Monte Carlo dropout, deep ensemble, Bayesian neural networks, Gaussian Processes and conformal prediction. Lastly, we will discuss the need for building a verification, validation and UQ framework to establish ML credibility.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Comparative Evaluation of Spectral Methods for Robust Reactor Noise Estimation

Reactor noise analysis provides a noninvasive means to determine neutron kinetic parameters from stochastic fluctuations in detector signals. However, standard cross-power spectral density (CPSD) analyses can be sensitive to numerical processing choices, which may introduce processing-dependent systematic shifts in estimates of the prompt neutron decay constant (α) and limit reproducibility. This study uses a hybrid multitaper–Welch spectral estimator to analyze subcritical noise measurements from a fast-spectrum critical assembly. The decay constant α was extracted using three frequency-domain methods: the CPSD, the magnitude-squared coherence (MSC), and the generalized magnitude-squared coherence (GMSC). These coherence-based estimators normalize detector auto-spectral structure and are expected to reduce the sensitivity of fitted α values to processing parameters. A Sobol global sensitivity analysis identified which numerical inputs most strongly influence the fitted values of α. All estimators produced a linear dependence of α on inverse count rate, with delayed-critical extrapolations near 1.7 × 10 4 s −1 , in agreement within 8% of MCNP6.3 KOPTS benchmark calculations. Sensitivity results show that while the CPSD depends on both time-bin width and taper selection, the MSC and GMSC are dominated by time-bin width alone, indicating reduced parameter coupling and greater robustness to processing variability. These findings demonstrate the feasibility and practical value of coherence-based spectral estimators for extracting α from reactor noise and support their broader application to multi-detector and irregular datasets in subcritical system characterization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Thermophysical Properties of Ti3SiC2 MAX Phase Composites with SiC Reinforcement

In the present work, dense (∼100%) Ti 3 SiC 2 composites (TSC) are processed along with 20 vol% of SiC reinforcement (TSC20) via spark plasma sintering at 1400°C, 40 MPa, 15 min, and dynamic vacuum environment. Thermal expansion of both the composites increases from RT to 1273 K and linear fitting of data yields coefficient of thermal expansion (CTE) of 9.4 × 10 −6 K −1 for TSC which decreases to 8.3 × 10 −6 K −1 for TSC20. With increase in temperature from RT to 773 K, specific heat for both TSC and TSC20 composites is observed to increase from 598-850 J.kg −1 .K −1 , whereas thermal diffusivity and thermal conductivity values decrease with testing temperature. SiC reinforcement in Ti 3 SiC 2 resulted in improved thermal diffusivity from 12.7 to 18.7 mm 2 .s −1 and thermal conductivity from ∼57 to ∼79 W.m −1 .K −1 at RT. However, with increase in temperature (773 K), thermal diffusivity and conductivity decrease, and values get closer for both TSC and TSC20 composites. Further extrapolation of thermal conductivity data showed cross-over at ∼973 K due to domination of phonon-phonon scattering and thus lower values of thermal conductivity for TSC20 than TSC. Therefore, reduced CTE and higher thermal conductivity of TSC20 make it a viable choice for applications in high temperatures.

36 MATERIALS SCIENCE↗

A physics-constrained neural ordinary differential equations approach for robust learning of stiff chemical kinetics

The high computational cost associated with solving for detailed chemistry poses a significant challenge for predictive computational fluid dynamics (CFD) simulations of turbulent reacting flows. While deep learning techniques have been explored to develop faster surrogate models, they often fail to integrate reliably with CFD solvers. This instability arises because traditional deep learning approaches optimize for training error without ensuring compatibility with ordinary differential equation (ODE) solvers, resulting in accumulation of errors over time. Recently, neuralODE (NODE) based approaches have been shown to be a promising technique to emulate and accelerate detailed chemistry computations. Here, in the present work, we extend this NODE framework for stiff chemical kinetics by incorporating mass conservation constraints directly into the loss function during training. This ensures that the total mass as well as the individual elemental species masses are conserved in an a-posteriori manner. Proof-of-concept studies are performed with the novel physics-constrained NODE (PC-NODE) approach for homogeneous autoignition of hydrogen-air mixture over a range of composition and thermodynamic conditions. It is demonstrated that the PC-NODE framework not only improves the physical consistency of the resulting data-driven model with respect to mass conservation criteria, but also improves training efficiency. PC-NODE is shown to achieve 2–100× speedup relative to the hydrogen-air detailed chemical mechanism depending on the type of the ODE solver (implicit or explicit) used during autoregressive inference tests. Lastly, a-posteriori studies are performed wherein the trained PC-NODE model is coupled with a CFD solver. It is shown that higher accuracy is achieved with PC-NODE relative to the purely data-driven NODE approach. Moreover, PC-NODE also exhibits robustness and generalizability to unseen initial conditions from within (interpolative capability) as well as outside (extrapolative capability) the training regime.

computational combustion↗

Analysis of Design Alternatives of Actively Cooled RF Window for MPEX

The Materials Plasma Exposure eXperiment (MPEX) has been designed as a linear plasma divertor simulator in order to address plasma material interaction (PMI) science for next-generation fusion devices. It will have the capability to test neutron irradiated samples with plasma fluxes of greater than 10 24 m -2 s -1 . It is expected to operate steady state for up to 10 6 s to consider PMI affects through reactor end of life. The conceptual design of MPEX was completed in 2019, with preliminary design having begun in 2020. The plasma source for MPEX is a helicon antenna, where the energized helical antenna sits outside of the vacuum in order to minimize impurities in the plasma. It is expected to receive up to 200 kW of continuous power, and so the antenna and the window must be actively cooled. The water-cooled copper antenna has been operated at full power on the Proto-MPEX device (which is a test facility to demonstrate the plasma source and heating systems). The water-cooled window, however, is a novel component that must meet numerous competing requirements. It requires a low dielectric loss to allow the Radio Frequency (RF) power to create the plasma within the vacuum boundary. It must be structurally robust to handle the significant heat flux from the plasma and any heat from dielectric coupling. It must be compatible with the coolant (preferably water). It requires a vacuum seal that minimizes impurities into the plasma and does not compromise the structural integrity of the window. Two window designs have been tested. Results from these tests, where temperatures are measured and heat fluxes inferred from infrared camera data, have been correlated with thermal-structural simulations. When these simulations are extrapolated to the full power steady-state heat fluxes that are expected in MPEX, the designs do not appear to have the necessary structural robustness. This study explores design alternatives for the MPEX helicon antenna window, presents analysis results for several of the alternatives, and shows a viable solution that satisfies the requirements for MPEX operation.

Tipton Jr, Joseph↗

Updated Thermofluid Performance of the Simplified Flat Variant of the HEMJ

Our group has recently developed and studied “finger”-type divertors that are a simplified version of the helium-cooled modular divertor with multiple jets (HEMJ) using coupled computational fluid dynamics and thermal stress simulations. Such a simplified geometry could reduce complexity and cost given the large number of fingers required to cover the total divertor target area. Previous experimental studies for this simplified flat design reported lower heat transfer coefficients and higher pressure drops than the HEMJ, contrary to numerical predictions. Subsequent measurements determined that the original test section had significant dimensional variations in the jet exit holes. A new test section was therefore manufactured and tested in the Georgia Tech (GT) helium loop. The experimental results presented here for this test section at maximum heat flux of 7.1 MW/m 2 are in good agreement with numerical predictions. Correlations developed from these experimental data are extrapolated to predict the maximum heat flux that can be accommodated by the flat design and the coolant pumping power requirements under prototypical conditions. Lastly, numerical simulations are used to estimate the sensitivity of the flat design to geometric variations typical of manufacturing tolerances and variations in the gap width.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Bayesian Parameter Estimation of the k-ω Shear Stress Transport Model for Accurate Simulations of Impinging-Jet Heat Transfer

Given the lack of fusion-relevant component test facilities, current estimates of the thermo-fluid performance of plasma-facing components are based for the most part on numerical simulations. A major source of uncertainty in these simulations is the semiempirical turbulence (closure) models for the Reynolds stresses appearing in the governing Reynolds-averaged Navier-Stokes equations, which involve a set of constants that depend upon the flow. The objective of this study is to evaluate Bayesian parameter estimation of turbulence closure constants in ANSYS Fluent to model heat transfer in impinging jets. The Bayesian statistical calibration produces a probability distribution for these constants from experimental data; the maximum a posteriori estimates are then taken to be the calibrated constants, or parameters. The turbulence model constants are calibrated using an experimental study of a submerged jet of air impinging on a flat heated surface at Reynolds numbers Re = O(10 4 ) and impingement distance in jet diameters H/d = 2. Numerical predictions using the calibrated model parameters are then compared with those generated using the default constants. Predictions obtained with model parameters calibrated on datasets of two different sizes are compared to evaluate the effect of the number of calibration samples. Lastly, the extrapolative ability of the calibrated model is examined by predictions at a Re beyond the calibration values.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

The Sensitivity of Variational Bayesian Neural Network Performance to Hyperparameters

In scientific applications, predictive modeling is often of limited use without accurate uncertainty quantification (UQ) to indicate when a model may be extrapolating or when more data needs to be collected. Bayesian Neural Networks (BNNs) produce predictive uncertainty by propagating uncertainty in neural network (NN) weights and offer the promise of obtaining not only an accurate predictive model but also accurate UQ. However, in practice, obtaining accurate UQ with BNNs is difficult due in part to the approximations used for model training (such as those made in variational inference) and in part to the need to choose a suitable set of hyperparameters; these hyperparameters outnumber those needed for traditional NNs and often have opaque effects on the results. We aim to shed light on the effects of hyperparameter choices for variational BNNs by performing a global sensitivity analysis of variational BNN performance under varying hyperparameter settings. Our results indicate that many of the hyperparameters interact with each other to affect both predictive accuracy and UQ. For improved usage of variational BNNs in real-world applications, we suggest that thorough hyperparameter tuning, including tuning of prior hyperparameters and loss function parameters, is essential for accurate UQ in variational BNNs.

97 MATHEMATICS AND COMPUTING↗