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At least 253 records · Page 14

Error quantification of the Arrhenius blending rule for viscosity of hydrocarbon mixtures

Six hundred and seventy-five measurements of dynamic viscosity and density have been used to assess the prediction error of the Arrhenius blending rule for kinematic viscosity of hydrocarbon mixtures. Major trends within the data show that mixture complexity–binary to hundreds of components—and temperature are more important determinants of prediction error than differences in molecular size or hydrogen saturation between the components of the mixtures. Over the range evaluated, no correlation between prediction error and mole fractions was observed, suggesting the log of viscosity truly is linear in mole fraction, as indicated by the Arrhenius blending rule. Mixture complexity and temperature also impact molar volume and its prediction. However, a linear regression between the two model errors explains less than 20% of the observed variation, indicating that mixture viscosity and/or molar volume are not linear with respect to temperature and/or mixture complexity. Extensive discussion of the intermolecular forces and the geometric arrangement of molecules and vacancies in liquids, which ultimately determines its viscosity, is brought into context with the implicit approximations behind the Arrhenius blending rule. The complexity of this physics is not compatible with a simple algebraic correction to the model. However, sufficient data is now available to determine confidence intervals around the prediction of fuel viscosity based on its component mole fractions and viscosities. At -40°C, when all identified components are pure molecules the modeling error is 13.2% of the predicted (nominal) viscosity times the root mean square of the component mole fractions.

10 SYNTHETIC FUELS↗

Coupled Multiphysics Modeling of Lithium-Ion Batteries for Automotive Crashworthiness Applications

Considerable advances have been made in battery safety models, but achieving predictive accuracy across a wide range of conditions continues to be challenging. Interactions between dynamically evolving mechanical, electrical, and thermal state variables make model prediction difficult during mechanical abuse scenarios. In this study, we develop a physics-based modeling approach that allows for choosing between different mechanical and electrochemical models depending on the required level of analysis. We demonstrate the use of this approach to connect cell-level abuse response to electrode-level and particle-level transport phenomena. A pseudo-two-dimensional model and simplified single-particle models are calibrated to electrical-thermal cycling data and applied to mechanically induced short-circuit scenarios to understand how the choice of electrochemical model affects the model prediction under abuse scenarios. These models are implemented using user-defined subroutines on ls-dyna finite element software and can be coupled with existing automotive crash safety models.

analysis and design of components↗

Complex concentrated alloys and chemical short-range ordering

Abstract Complex concentrated alloys (CCAs) are materials comprising three or more elements in similar proportions and possessing structural but no chemical long-range order. Fascination with CCAs has grown over the last 20 years and to date, CCAs have opened a new materials design paradigm and horizon for discovery of materials to meet the demands of applications in aggressive environments. Understanding the fundamental mechanisms controlling their response, however, is challenging due to the chemical and structural variations that wildly fluctuate over fine atomic and nanoscales. This issue focuses on the experimental, computational, and theoretical investigations that aim to uncover phenomena and processes determining the structure, kinetics, mechanics, or deformation mechanisms in CCAs at the atomic scale. At the atomic scale at which they operate, chemical short-range ordering can be influential. This issue further addresses the capabilities, as well as the debatable need, to characterize, predict, and relate short-range ordering to material performance. Collectively, the articles in this issue highlight the insights, understanding, and experimental and computational tools that attempt to create property-tunable CCAs “from the atom up” by treating short-range ordering and engineering atomic-scale mechanisms. Graphical abstract

36 MATERIALS SCIENCE↗

Linking Friction Scales from Nano to Macro via Avalanches

Abstract Steady-state fluctuations in the friction force of molybdenum disulfide (MoS 2 ), a prototypical lamellar solid, were analyzed experimentally for newton-scale forces and computationally via molecular dynamics simulations for nanonewton-scale forces. A mean field model links the statics and the dynamics of the friction behavior across these eight orders of magnitude in friction force and six orders of magnitude in friction force fluctuations (i.e., avalanches). Both the statistics and dynamics of the avalanches match model predictions, indicating that friction can be characterized as a series of avalanches with properties that are predictable over a wide range of scales.

Salners, Tyler (ORCID:0000000332647944)↗

Ab Initio Study of Stability, Local Order, and Phase Diagram For a Series of bcc-based Transition Metal Alloys

In this work, a parameter-free electronic structure approach is applied to the study of stability and chemical order in the 15 substitutional body-centered cubic (bcc)-based alloys made of the six transition metals of groups 5 (V, Nb, Ta) and 6 (Cr, Mo, W) of the periodic table. The method is based on a Green’s function description of the electronic structure of the random alloys. Configurational order is treated within the generalized perturbation method, and temperature effects are examined with a generalized mean-field approach. In contrast to the results summarized in the assessed phase diagrams, stability and ordering trends are predicted in a broad range of alloy composition for at least seven alloys, and explanation is found in their electronic structure properties. Short-range order results, thermodynamics analysis, and bcc-based phase diagrams are also presented.

36 MATERIALS SCIENCE↗

First-principles prediction of incipient order in arbitrary high-entropy alloys: exemplified in Ti 0.25 CrFeNiAl

High-temperature disordered multi-component alloys, including high-entropy alloys, experience either segregation or partially-ordered phases to reach low-temperature phases. For Ti 0.25 CrFeNiAl x (0 ≤ x ≤ 1), experiments suggest a partially-ordered B2 phase, whereas CALculation of PHAse Diagrams (CALPHAD) predicts a region of L21+B2 coexistence. We employ first-principles KKR-CPA to assess stability of phases with arbitrary order and a KKR-CPA linear-response theory to predict atomic short-range order (SRO) in the disorder phase that reveals the competing long-range ordered (LRO) phases in a given Bravais lattice. The favorable SRO provides a specific concentration-waves (site occupation probabilities and partially-ordered unit cells) and estimated energy gains that can then be assess directly by KKR-CPA formation enthalpies. Our results are in good agreement with experiments and CALPHAD in Al-poor regions (x ≤ 0.75) and with CALPHAD in Al-rich region (1≥ x >0.75). Our first-principles KKR-CPA and SRO-based concentration-wave analysis is shown to be a powerful and fast method to assess competing LRO phases in complex solid-solution alloys, and our results suggests more careful experiments in Al-rich region are needed.

36 MATERIALS SCIENCE↗

Hydrogen release from aqueous based LOHCs: The role of water in the potassium formate/bicarbonate cycle

Here, in this study, we evaluate the chemical and physical properties governing the potential round-trip efficiency of an aqueous formate/bicarbonate cycle for hydrogen storage, with a particular focus on potassium formate as a potential liquid hydrogen carrier. Using thermodynamic parameters, we predict the conversion of formate to bicarbonate across varying temperatures and pressures, revealing that hydrogen release is relatively insensitive to temperature but highly dependent on pressure. Our findings indicate that hydrogen uptake is highly efficient, whereas hydrogen release poses a greater challenge, necessitating detailed optimization to enhance round-trip efficiency. We calculate the solubility limits of bicarbonate salts influenced by the common ion effect to enable the prediction of target conversion ranges that prevent the precipitation of potassium bicarbonate in a reactor. Furthermore, the energy efficiency of hydrogen release was assessed based on the heating requirements of the aqueous solutions. This analysis maximizes the round-trip efficiency by balancing the solubility limits of bicarbonate, the heat capacity of aqueous formate solutions and conversion based on thermodynamic equilibria. Taking these factors into consideration we suggest reaction conditions that could be utilized in a systems analysis to calculate the levelized cost of storage using the bicarbonate/formate cycle at commercial scales.

common ion effect↗

Determination of single crystal thermal expansion in Uranium-6wt%Niobium shape memory alloy using in-situ diffraction and modeling of textured polycrystalline samples

In-situ neutron diffraction and ex-situ dilatometry experiments were performed on a chemically banded, quenched uranium-6wt% niobium shape memory alloy to study the impact of deformation-(detwinning-) induced texture on the evolution of thermally induced strains (and associated stresses). Thermal heating and cooling cycles between 5K and 473K were performed in-situ with neutron diffraction, and the lattice strain evolution is reported for the observed monoclinic α" phase. Comparisons between the measured diffraction strains and macroscopic dilatometry experiments reveal relationships between micro- and macro-level thermal expansion. Softening of the texture during heating suggests that twin boundary motion can accommodate the large internal thermal strains which are approximately 2% greater than that typically observed during the 175K heating interval used to age more randomly oriented polycrystalline material. Assuming weak constraint of neighboring grains in randomly textured polycrystals, the single crystal thermal expansion tensor is extracted from measurements of lattice strains over the range 5K to 473K. Predictions of polycrystalline thermal expansion, based upon this single crystal thermal expansion tensor, are shown to compare favorably with bulk thermal expansion observations of the as quenched microstructure. However, such a lower-bound estimate is insufficient to explain all aspects of the behavior of the textured material, where the matrix is not isotropic. In conclusion, it is hypothesized that relaxation processes which occur within the quenched microstructure during heating are responsible for the distinct thermal expansion behavior observed during the initial heating cycle compared to cooling and subsequent cycling.

36 MATERIALS SCIENCE↗

Estimating snow cover from high-resolution satellite imagery by thresholding blue wavelengths

We report the extent and duration of snow cover, a critical component of the hydrologic cycle and the global climate system, is expected to shift dramatically under climate change. Therefore, developing high-resolution assessments of snow cover change is crucial for estimating the impact of changing snow cover on watershed and ecosystems processes in cold regions. Remote sensing tools provide a powerful method for mapping snow-covered area (SCA) across a landscape. The most common method for estimating SCA utilizes the normalized difference snow index (NDSI), which relies on spectral measurements in the shortwave-infrared wavelengths (SWIR). NDSI can effectively estimate catchment- to regional-scale SCA, but it cannot be used to assess fine-scale SCA because of current limitations on the spatial resolution of satellite-derived SWIR measurements. Here, we map SCA using a threshold of blue wavelengths and high-resolution satellite imagery. The thresholding method, which we call the Blue Snow Threshold algorithm (BST), has previously been used with digital camera imagery. We refine and automate the algorithm for use with cloud-free high-resolution satellite imagery and find that the BST can be used to assess fine-scale SCA. For validation, we compared BST-derived estimates of SCA to a) airborne lidar surveys, b) Landsat fractional SCA, and c) snow disappearance dates from Snow Telemetry (SNOTEL) stations. When compared to airborne lidar surveys of SCA, the BST predicted SCA had a range of F-scores between 0.81 and 0.94 in four study areas in California and Colorado. We also found general agreement between SCA and snow disappearance at multiple SNOTEL sites across the western United States. Given the relatively recent availability of high-resolution satellite imagery with spectral measurements in the visible wavelengths but lacking in SWIR, the BST offers a reliable and easy-to-apply tool for examining fine-scale snow-related processes.

54 ENVIRONMENTAL SCIENCES↗

Multiple Operando Fields Can Identify a Predictive Mass Transport Theory in Electrolytes

An electrolyte transport theory connects its transport properties, evolution of spatiotemporal fields (e.g., concentration), and corresponding macroscopic current and voltage responses. Given this interconnection, the transport properties are typically inferred by analyzing the macroscopic response through the lens of a chosen electrolyte transport theory. Unfortunately, the same macroscopic measurements can be analyzed by using different theories to arrive at seemingly dissimilar properties that are inconsistent with each other. Here, we offer a resolution to this dilemma by analyzing multiple spatiotemporal (i.e., operando) fields for a given electrolyte. We show that predictive transport theory can analyze operando fields to estimate underlying transport properties and subsequently predict another operando field. A theory passing this test identifies meaningful transport properties such that the electrolyte behavior can be accurately predicted over a wide range of excitations, and is critical to property-based electrolyte screening and electrolyte discovery efforts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Subseasonal Great Plains Rainfall via Remote Extratropical Teleconnections: Regional Application of Theory‐Guided Causal Networks

Abstract Long‐range U.S. summer rainfall prediction skill is low. Monsoon variability, especially over the West North Pacific Monsoon (WNPM) and/or East Asian Monsoon (EAM) region, can influence U.S. Great Plains hydroclimate variability via a forced Rossby wave response. Here, we explored subseasonal monsoon variability as a source of predictability for Great Plains rainfall. The boreal summer intraseasonal oscillation (BSISO) is related to Great Plains convection and Great Plains low‐level jet (LLJ) anomalies as well as a cross‐Pacific wave train. Using a causal effect network, we found that the time between BSISO‐related geopotential height anomalies and Great Plains rainfall anomalies is about 2 weeks; therefore, BSISO convection may be a valuable forecast of opportunity for subseasonal prediction of Great Plains convection anomalies. More specifically, causal link patterns/maps revealed that the above‐normal weekly EAM rainfall, rather than WNPM rainfall or general geopotential height activity over the East Asia, was causally linked to Great Plains LLJ strengthening and active Great Plains convection the following week.

Meteorology & Atmospheric Sciences↗

High-intensity X-ray beams can influence the kinetics in a time-resolved experiment

X-ray induced structural damage is well known, but the potential for changes in the kinetics of physical and chemical processes is rarely recognized or considered. These can happen over a wide intensity range, are difficult to predict and often escape detection. Subsequently, the problem deserves more attention from experimentalists.

47 OTHER INSTRUMENTATION↗

The integrated miniaturized electrostatic analyzer: A space plasma environment sensor

The integrated Miniaturized Electrostatic Analyzer (iMESA) was a satellite-based ionospheric sensor that operated on NASA’s Space Test Program Satellite (STPSat-3) from December 2013 to July 2019. The instrument’s scientific objective was to (1) measure the plasma density in low Earth orbit, (2) measure the plasma temperature in low Earth orbit, and (3) quantify the spacecraft potential with respect to the ambient plasma potential in the ionosphere. iMESA sampled the ionosphere every 10 s by measuring the ion current density through the ESA as a result of the motion of the spacecraft through the plasma. Current density spectra were transmitted to the ground where they were post-processed into ion density spectra and then analyzed numerically to determine the ion density, ion temperature, and spacecraft potential. This article discusses the instrument design and simulation, the determination of a geometric factor, and data processing procedures and evaluates the final data product with regard to the mission success criteria. Here, the ion density and ion temperature captured by the iMESA instrument are on the same order and range as the values predicted in the literature. The spacecraft potential was also quantified. The conclusion after the evaluation of the instrument’s data product is that the scientific mission is successful on all three points.

47 OTHER INSTRUMENTATION↗

Machine learning guided optimal composition selection of niobium alloys for high temperature applications

Nickel- and cobalt-based superalloys are commonly used as turbine materials for high-temperature applications. However, their maximum operating temperature is limited to about 1100 °C. Therefore, to improve turbine efficiency, current research is focused on designing materials that can withstand higher temperatures. Niobium-based alloys can be considered as promising candidates because of their exceptional properties at elevated temperatures. The conventional approach to alloy design relies on phase diagrams and structure–property data of limited alloys and extrapolates this information into unexplored compositional space. In this work, we harness machine learning and provide an efficient design strategy for finding promising niobium-based alloy compositions with high yield and ultimate tensile strength. Unlike standard composition-based features, we use domain knowledge-based custom features and achieve higher prediction accuracy. We apply Bayesian optimization to screen out novel Nb-based quaternary and quinary alloy compositions and find these compositions have superior predicted strength over a range of temperatures. We develop a detailed design flow and include Python programming code, which could be helpful for accelerating alloy design in a limited alloy data regime.

Mohanty, Trupti (ORCID:0000000342701430)↗

Cement sensors with acoustic bandgaps using carbon nanotubes

Cement is widely used in wellbores to stabilize the steel casing used in wellbore operations for oil and gas production, enhanced geothermal systems and carbon sequestration, and to limit fluid movement between sub-surface strata. Flaws such as microcracks in wellbore cement can lead to leakage along the wellbore compromising wellbore integrity. There is an increasing need for methods to monitor cement crack propagation in wellbore environments. In this study, we develop and report the first cementitious sensors capable of exhibiting high frequency acoustic bandgaps (ABGs) using carbon nanotubes (CNTs). Computational simulations of a sensor unit cell are used to design cement-multi walled carbon nanotubes (MWCNTs) sensors that show a wide bandgap. When the cement-MWCNTs sensors is embedded in cement specimens, bandgaps were measured experimentally under 300 kHz and under 600 kHz, consistent with the computationally predicted bandgaps in the range of 290–360 kHz, 410–460 kHz and 515–585 kHz. These bandgap features were absent in homogeneous cement specimens. X-ray tomographic reconstructions showed microscopic debonding at cement-MWCNTs sensor interface. Frequency response analysis of a three-dimensional computational model indicated a shift of frequency of minimum transmission due to the interface debonding, but no perturbation of bandgap response was observed. Here, the cement-MWCNTs sensors developed in this study show the potential of a packed CNT inclusion material in cementitious matrix to create ABGs in a cement matrix.

36 MATERIALS SCIENCE↗

Increasing the density in Wendelstein 7-X: benefits and limitations

In stellarators, increasing the density is beneficial for the energy confinement. While there is no single reason for this observation, it is still very robust across different devices and this is reflected in the empirical energy confinement time scaling for stellarators, ISS04. In order to study whether this is also true for Wendelstein 7-X, here the density scaling of the energy confinement time is analyzed and compared to ISS04 for the first divertor experiments. When the density is increased beyond a critical density, however, radiative collapses are frequently observed. Existing analytical models for the critical density are revisited to assess whether they can predict the accessible density range. Furthermore, since close to the collapse the radiation losses increase substantially, the impact on the global energy confinement is investigated. It is found that in plasmas with high radiation the density scaling of the energy confinement time becomes weaker, the reason for this observation is not yet clear. In the second half of the first divertor campaign, boronization was applied to W7-X for the first time. This broadened the operational window, allowing for operation at higher density and, hence, higher stored energy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Quantum Monte Carlo studies of a trimer scaling function with microscopic two- and three-body interactions

Here, we present an energy scaling function to predict, in a specific range, the energy of bosonic trimers with large scattering lengths and finite range interactions, which is validated by quantum Monte Carlo calculations using microscopic Hamiltonians with two- and three-body potentials. The proposed scaling function depends on the scattering length, effective range, and a reference energy, which we chose as the trimer energy at unitarity. We obtained the scaling function as a limit cycle from the solution of the renormalized zero-range model with effective range corrections. We proposed a simple parametrization of the energy scaling function. Besides the intrinsic interest in theoretical and experimental investigations, this scaling function allows one to probe Efimov physics with only the trimer ground states, which may open opportunities to identify Efimov trimers whenever access to excited states is limited.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Dynamical model of φ meson photoproduction on the nucleon and 4 He

We investigate φ meson photoproduction on the nucleon and the 4 He targets within a dynamical model approach based on a Hamiltonian which describes the production mechanisms by the Pomeron-exchange, meson-exchanges, φ radiations, and nucleon resonance excitations mechanisms. The final φN interactions are included being described by the gluon-exchange, direct φN couplings, and the box-diagrams arising from the couplings with πN, ρN, KΛ, and KΣ channels. The parameters of the Hamiltonian are determined by the experimental data of γp → φp from the CLAS Collaboration. The resulting Hamiltonian is then used to predict the coherent φ-meson production on the 4 He targets by using the distorted-wave impulse approximation. For the proton target, the final φN rescattering effects, as required by the unitarity condition, are found to be very weak, which supports the earlier calculations in the literature. For the 4 He targets, the predicted differential cross sections are in good agreement with the data obtained by the LEPS Collaboration. Furthermore, the role of each mechanism in this reaction is discussed and predictions for a wide range of scattering angles are presented, which can be tested in future experiments.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗