Empirical Fit Research Articles

A tractable methodology for assessing the pressure scaling of sooting processes in a counterflow diffusion flame from 1 to 6 bar

A parametric study using ethylene as fuel was undertaken which examines the efficacy of concentration based and soot production rate (SPR) based parameters for assessing the pressure scaling of sooting processes in a counterflow diffusion flame. Two experimental designs pertaining to constant flow residence times (Case A), and constant carbon mass flux (Case B) were respectively implemented for pressures between 1 to 6 bars and across peak temperatures ranging from ∼2300−2600 K for Yf=Yox=0.35, ∼2100−2400 K for Yf=Yox=0.30, and ∼1800−2100 K for Yf=Yox=0.25 flames. A diffuse line-of-sight based extinction imaging diagnostic was employed for the measurement of soot concentrations. The evaluation of SPR follow a transport based coupling of inferred gaseous precursors, temperatures and velocity fields from detailed 1D OpenSMOKE++ simulations and soot concentrations from extinction measurements. The use of concentration based parameters, namely peak (∼p2.5−4.5) and integrated soot volume fractions (∼p2.1−3.9), is noted to be dependent on the Case A or B with little information discernible about the pressure effects in an experimental setting. The use of production rate based parameters, namely mean SPR (∼p2.2−3.4) removes the disparity of pressure scaling exponents across cases A and B but inherits the dependency to peak flame temperature, and gaseous precursor concentrations. An empirical fit of local SPR to acetylene and pyrene concentrations reveals a universal Arrhenius activation energy parameter in the high-temperature region (1300–2000 K) of the studied flames. The global activation energy is noted to remain roughly constant across varying peak flame temperatures, fuel flux, flow residence times and pressure. Consequently, we propose a soot yield parameter (Ω), calculated as the mean soot production rate normalized to acetylene and pyrene mass fractions which is noted to universally scale with pressure as Ω∼p1.7.

Impact of calmodulin missense variants associated with congenital arrhythmia on the thermal stability and the degree of unfolding.

Thermal denaturation profiles of proteins that bind several ligands may deviate from the single transition, making their thermodynamic description challenging. We report an empirical method that estimates melting temperatures (Tm) from multi-transition thermal denaturation profiles of 16 variants of calmodulin (CaM) associated with congenital arrhythmia. Differences in Tm estimated by empirical fitting correlate (for apo CaM variants) with those obtained by thermodynamic models. Most CaM variants were more stable than the wild type (WT) in the absence of Ca2+, but less stable in the presence of Ca2+, and displayed either WT-like or higher unfolding percentages in their apo-form, as evaluated by circular dichroism spectroscopy.

Exploring Physically Motivated Models to Fit Gamma-Ray Burst Spectra

We explore fitting gamma-ray burst (GRB) spectra with three physically motivated models, and thus revisit the viability of synchrotron radiation as the primary source of GRB prompt emission. We pick a sample of 100 bright GRBs observed by the Fermi Gamma-ray Burst Monitor (GBM), based on their energy flux values. In addition to the standard empirical spectral models used in previous GBM spectroscopy catalogs, we also consider three physically motivated models; (a) a thermal synchrotron model, (b) a Band model with a high-energy cutoff, and (c) a smoothly broken power-law (SBPL) model with a multiplicative broken power law (MBPL). We then adopt the Bayesian information criterion to compare the fits obtained and choose the best model. We find that 42% of the GRBs from the fluence spectra and 23% of GRBs from the peak-flux spectra have one of the three physically motivated models as their preferred one. From the peak-flux spectral fits, we find that the low-energy index distributions from the empirical model fits for long GRBs peak around the synchrotron value of −2/3, while the two low-energy indices from the SBPL+MBPL fits of long GRBs peak close to the −2/3 and −3/2 values expected for a synchrotron spectrum from marginally fast-cooling electrons.

A General Evaluation System for Optimal Selection Performance of Radar Clutter Model

The optimal selection of radar clutter model is the premise of target detection, tracking, recognition, and cognitive waveform design in clutter background. Clutter characterization models are usually derived by mathematical simplification or empirical data fitting. However, the lack of standard model labels is a challenge in the optimal selection process. To solve this problem, a general three-level evaluation system for the model selection performance is proposed, including model selection accuracy index based on simulation data, fit goodness indexs based on the optimally selected model, and evaluation index based on the supporting performance to its third-party. The three-level evaluation system can more comprehensively and accurately describe the selection performance of the radar clutter model in different ways, and can be popularized and applied to the evaluation of other similar characterization model selection.

Predicting sulfide precipitation in magma oceans on Earth, Mars and the Moon using machine learning

The sulfur content at sulfide saturation (SCSS) of a silicate melt can regulate the stability of sulfides and, therefore, chalcophile elements’ behaviors in planetary magma oceans. Many studies have reported high-pressure experiments to determine SCSS using either linear or exponential regressions to parameterize the thermodynamics of the system. Although these empirical equations describe the effects of different parameters on SCSS, they perform poorly when predicting laboratory measurements. Here, we compiled 542 published analyses of experiments performed on a range of sulfide and silicate compositions at varying P-T conditions (<24 GPa, <2673 K). Using empirical equations, linear regression, Random Forest algorithms, and a hybrid algorithm employing empirical fits to P-T conditions and the Random Forest algorithm for compositions, we developed several SCSS models and compared them to laboratory measurements. The Random Forest and hybrid models (R2 = 0.82–0.91, mean average error [MAE] < 746 ppmw S, residual mean standard error [RMSE] < 972 ppmw S), significantly outperform previous empirical models (R2 = 0.28–0.69, MAE = 622–1,170 ppmw S, RMSE = 1,070–1,744 ppmw S), whereas linear regression performs moderately well, i.e., between the classic and machine learning models. We applied our hybrid model to predict SCSS during magma ocean solidification on Earth, Mars, and the Moon, and we compared our model results to expected S contents in the residual magma oceans calculated by mass balance. Our results confirm that during early accretion, sulfides precipitated from magma oceans and into the outer cores of Earth and Mars, but not the Moon. Subsequently, once the respective magma oceans began precipitating minerals with increasingly FeO-rich and SiO2-, Al2O3-, and MgO-depleted compositions, the increasing S concentration in the residual magma was offset by temperature and compositional effects on SCSS, preventing sulfide precipitation during intermediate stages of crystallization. Sulfides precipitated late during magma ocean crystallization, but failed to percolate through the underlying crystalline mantle, significantly contributing to the modern bulk-silicate sulfur abundances of Earth, Mars, and the Moon. Our calculations suggest that late-stage sulfide precipitation occurred at shallow depths of 120–220 km, 40–320 km, and < 10 km in the magma oceans of Earth, Mars, and the Moon, respectively.

Numerical investigations on interactions between 2D/3D conical shock wave and axisymmetric boundary layer at Ma=2.2

Numerical simulation and analysis are carried out on interactions between a 2D/3D conical shock wave and an axisymmetric boundary layer with reference to the experiment by Kussoy et al. [AIAA J, 1980, 18(12): 1477–1487], in which the shock was generated by a 15° half-angle cone in a tube at 15° angle of attack (AOA). Based on the Reynolds-averaged Navier–Stokes equations and Menter's SST turbulence model, the present study uses the newly developed WENO3−PRM1,12 scheme and the PHengLEI CFD platform for the computations. First, computations are performed for the 3D interaction corresponding to the conditions of the experiment by Kussoy et al., and these are then extended to cases with AOA = 10° and 5° For comparison, 2D axisymmetric counterparts of the 3D interactions are investigated for cones coaxial with the tube and having half-cone angles of 27.35°, 24.81°, and 20.96° The shock wave structure, vortex structure, variable distributions, and wall separation topology of the interaction are computed. The results show that in 2D/3D interactions, a new Mach reflection-like event occurs and a Mach stem-like structure is generated above the front of the separation bubble, which differs from the model of Delery for 2D planar shock wave/boundary layer interaction. A new interaction model is established to describe this behavior. The relationship between the length of the circumferentially unseparated region in the tube and the AOA of the cone indicates the existence of a critical AOA at which the length is zero, and a prediction of this angle is obtained using an empirical fit, which is verified by computation. The occurrence of side overflow in the windward meridional plane is analyzed, and a quantitative knowledge is obtained. To further elucidate the characteristics of the 3D interaction, the scale and structure of the vortex and the pressure and friction force distributions are presented and compared with those of the 2D interaction.

Forest Aboveground Biomass Estimation in Subtropical Mountain Areas Based on Improved Water Cloud Model and PolSAR Decomposition Using L-Band PolSAR Data

Forest aboveground biomass (AGB) retrieval using synthetic aperture radar (SAR) backscatter has received extensive attention. The water cloud model (WCM), because of its simplicity and physical significance, has been one of the most commonly used models for estimating forest AGB using SAR backscatter. Nevertheless, forest AGB estimation using the WCM is usually based on simplified assumptions and empirical fitting, leading to results that tend to overestimate or underestimate. Moreover, the physical connection between the model and the polarimetric synthetic aperture radar (PolSAR) is not established, which leads to the limitation of the inversion scale. In this paper, based on the fully polarimetric SAR data from the Advanced Land Observing Satellite-2 (ALOS-2) Phased Array-type L-band Synthetic Aperture Radar (PALSAR-2), the relative contributions of the three major scattering mechanisms were first analyzed in a hilly area of southern China. On this basis, the traditional WCM was extended by considering the secondary scattering mechanism. Then, to establish the direct relationship between the vegetation scattering mechanism and forest AGB, a new relationship equation between the PolSAR decomposition model and the improved water cloud model (I-WCM) was constructed without the help of external data. Finally, a nonlinear iterative method was used to estimate the forest AGB. The results show that volume scattering is the dominant mechanism, accounting for more than 60%. Double-bounce scattering accounts for the smallest fraction, but still about 10%, which means that the contribution of the double-bounce scattering component is not negligible in forested areas because of the strong penetration capability of the long-wave SAR. The modified method provides a correlation coefficient R2 of 0.665 and a root mean square error (RMSE) of 21.902, which is an improvement of 36.42% compared to the traditional fitting method. Moreover, it enables the extraction of forest parameters at the pix scale using PolSAR data without the need for low-resolution external data and is thus helpful for high-resolution mapping of forest AGB.

Molecular dynamics simulations of several linear homopolymers: Assessment and comparison of shock properties

Understanding the behavior of polymers under shock loading is essential for their applications in car equipment, aircraft, space structure and plastic bonded explosives. Despite much effort, both experimentally and numerically over the last twenty years, there are specificities of the shock behavior of polymers that are still unclear. These include the relation of the shocked state to the dynamic glass transition and the deviation of the Hugoniot locus from the usual linear relation us=c+Sup, with us the shock velocity, c, the bulk sound velocity, up the particle velocity and S an empirical fitting parameter. To address these questions this work studies direct shock simulations in molecular dynamics (MD) of three different polymers, cis-1,4-polybutadiene, polystyrene and phenoxy resin. After thoroughly assessing that the polymer configurations created in this work are correct with respect to mass density, structure factor, chain dimensions and Hugoniot locus, this work focuses on two aspects of the behavior under shock loading. (i) The deviation of the Hugoniot locus from the linear relation us=c+Sup is related to a change in the relative contribution to the shock energy of the bonding and non-bonding potential energies as the shock strength increases. (ii) The shear stress relaxations behind the shock front are compared for the three polymers. It is found that the polymers with the highest glass transition temperature have the most slowly relaxing shear stress behind the shock front.

Optimal binary gratings for multi-wavelength magneto-optical traps.

Grating magneto-optical traps are an enabling quantum technology for portable metrological devices with ultracold atoms. However, beam diffraction efficiency and angle are affected by wavelength, creating a single-optic design challenge for laser cooling in two stages at two distinct wavelengths - as commonly used for loading, e.g., Sr or Yb atoms into optical lattice or tweezer clocks. Here, we optically characterize a wide variety of binary gratings at different wavelengths to find a simple empirical fit to experimental grating diffraction efficiency data in terms of dimensionless etch depth and period for various duty cycles. The model avoids complex 3D light-grating surface calculations, yet still yields results accurate to a few percent across a broad range of parameters. Gratings optimized for two (or more) wavelengths can now be designed in an informed manner suitable for a wide class of atomic species enabling advanced quantum technologies.

Determining the parent and associated fragment formulae in mass spectrometry via the parent subformula graph

BackgroundIdentifying the molecular formula and fragmentation reactions of an unknown compound from its mass spectrum is crucial in areas such as natural product chemistry and metabolomics. We propose a method for identifying the correct candidate formula of an unidentified natural product from its mass spectrum. The method involves scoring the plausibility of parent candidate formulae based on a parent subformula graph (PSG), and two possible metrics relating to the number of edges in the PSG. This method is applicable to both electron-impact mass spectrometry (EI-MS) and tandem mass spectrometry (MS/MS) data. Additionally, this work introduces the two-dimensional fragmentation plot (2DFP) for visualizing PSGs.ResultsOur results suggest that incorporating information regarding the edges of the PSG results in enhanced performance in correctly identifying parent formulae, in comparison to the more well-accepted “MS/MS score”, on the 2016 Computational Assessment of Small Molecule Identification (CASMI 2016) data set (76.3 vs 58.9% correct formula identification) and the Research Centre for Toxic Compounds in the Environment (RECETOX) data set (66.2% vs 59.4% correct formula identification). In the extension of our method to identify the correct candidate formula from complex EI-MS data of semiochemicals, our method again performed better (correct formula appearing in the top 4 candidates in 20/23 vs 7/23 cases) than the MS/MS score, and enables the rapid identification of both the correct parent ion mass and the correct parent formula with minimal expert intervention.ConclusionOur method reliably identifies the correct parent formula even when the mass information is ambiguous. Furthermore, should parent formula identification be successful, the majority of associated fragment formulae can also be correctly identified. Our method can also identify the parent ion and its associated fragments in EI-MS spectra where the identity of the parent ion is unclear due to low quantities and overlapping compounds. Finally, our method does not inherently require empirical fitting of parameters or statistical learning, meaning it is easy to implement and extend upon.Scientific contributionDeveloped, implemented and tested new metrics for assessing plausibility of candidate molecular formulae obtained from HR-MS data.

Influence of PPD and Mass Scaling Parameter on the Goodness of Fit of Dry Ice Compaction Curve Obtained in Numerical Simulations Utilizing Smoothed Particle Method (SPH) for Improving the Energy Efficiency of Dry Ice Compaction Process

The urgent need to reduce industrial electricity consumption due to diminishing fossil fuels and environmental concerns drives the pursuit of energy-efficient production processes. This study addresses this challenge by investigating the Smoothed Particle Method (SPH) for simulating dry ice compaction, an intricate process poorly addressed by conventional methods. The Finite Element Method (FEM) and SPH have been dealt with by researchers, yet a gap persists regarding SPH mesh parameters’ influence on the empirical curve fit. This research systematically explores Particle Packing Density (PPD) and Mass Scaling (MS) effects on the agreement between simulation and experimental outputs. The Sum of Squared Errors (SSE) method was used for this assessment. By comparing the obtained FEM and SPH results under diverse PPD and MS settings, this study sheds light on the SPH method’s potential in optimizing the dry ice compaction process’s efficiency. The SSE based analyses showed that the goodness of fit did not vary considerably for PDD values of 4 and up. In the case of MS, a better fit was obtained for its lower values. In turn, for the ultimate compression force FC, an empirical curve fit was obtained for PDD values of 4 and up. That said, the value of MS had no significant bearing on the ultimate compression force FC. The insights gleaned from this research can largely improve the existing sustainability practices and process design in various energy-conscious industries.

New Insights into the Behaviour of Commercial Silicon Electrode MaterialsviaEmpirical Fitting of Galvanostatic Charge‐Discharge Curves

AbstractSilicon (Si) materials for use in Lithium ion batteries (LIBs) are of continued interest to battery manufacturers. With an increasing number of commercially available Si materials, evaluating their performance becomes a challenge. Here, we use an empirical fitting function presented earlier to aid in the analysis of galvanostatic charge‐discharge data of commercial Si half‐cells with relatively high loading. We find that the fitting procedure is capable of detecting dynamic changes in the cell, such as reversible capacity fade of the Si electrode. This fading is found to be due to the highly lithiated Li2SiLi3.5Si phase and that the behaviour is strongly dependent on the potential of this phase. EIS reveals that the Si electrode is responsible for the reversible behaviour due to progressive loss of Li+leading to increasing resistance. SEM/EDX and XPS characterization are also employed to determine the origin of the irreversible resistance growth on the Si electrodes.

Atomic collisional data for neutral beam modeling in fusion plasmas

The injection of energetic neutral particles into the plasma of magnetic confinement fusion reactors is a widely-accepted method for heating such plasmas; various types of neutral beam are also used for diagnostic purposes. Accurate atomic data are required to properly model beam penetration into the plasma and to interpret photoemission spectra from both the beam particles themselves (e.g. beam emission spectroscopy) and from plasma impurities with which they interact (e.g. charge exchange recombination spectroscopy). This paper reviews and compares theoretical methods for calculating ionization, excitation and charge exchange cross sections applied to several important processes relevant to neutral hydrogen beams, including H + Be4+ and H + H+. In particular, a new cross section for the proton-impact ionization of H (1s) is recommended which is significantly larger than that previously accepted at fusion-relevant energies. Coefficients for an empirical fit function to this cross section and to that of the first excited states of H are provided and uncertainties estimated. The propagation of uncertainties in this cross section in modeling codes under JET-like conditions has been studied and the newly-recommended values determined to have a significant effect on the predicted beam attenuation. In addition to accurate calculations of collisional atomic data, the use of these data in codes modeling beam penetration and photoemission for fusion-relevant plasma density and temperature profiles is discussed. In particular, the discrepancies in the modeling of impurities are reported. The present paper originates from a Coordinated Research Project (CRP) on the topic of fundamental atomic data for neutral beam modeling that the International Atomic Energy Agency (IAEA) ran from 2017 to 2022; this project brought together ten research groups in the fields of fusion plasma modeling and collisional cross section calculations. Data calculated during the CRP is summarized in an and is available online in the IAEA’s atomic database, CollisionDB.

Evaluation of the reliability of buried gas pipelines exposed to ground movement in the perspective of their use for hydrogen transportation: A state-of-art review

Hydrogen (H2) has been recognized as having the potential to become the cornerstone of a low-carbon energy system. As a result, it has garnered much interest from policymakers and industry as a focus for future infrastructure.&#x0D; In its race towards climate neutrality by 2050, the European Union seems to be betting on green hydrogen as a high-potential energy source. Europe envisages to develop more than 11,000 km of pipelines transporting hydrogen in 2030 [2]. The French government seems to prioritize the reuse of gas networks for the transport and storage of H2 [4]. On the other hand, the substitution of gas by hydrogen leads to an evolution of the mechanical characteristics of steel pipelines, and consequently, increases their vulnerability vis-à-vis external stresses (loads, ground movements, natural hazards, etc.). It has been shown that pipeline steel could lose up to 40% of its ductility after exposure to 100 bar H2 [3]. Thus, the reuse of the existing gas networks for the hydrogen transport raises today a set of scientific and technical questions. The problematic of pipelines vulnerability is getting even more serious by both the climate change and the evolution of human activities associated with the ecological transition.&#x0D; In the framework of a thesis that we recently started (October 2022), we aim to understand the interaction between buried networks (gas networks reused for hydrogen transport) and ground movements, with a purpose to enhance risk prevention and to reduce the associated consequences. The aim is to establish a reliable analytical model enabling sensitivity studies to be performed and the influence of the uncertainties on the results to be assessed. The outcomes will be also valued by developing fragility curves that constitute operational approaches when assessing damages in an uncertain context.&#x0D; The aim of this study is to synthesize the existing research work available in the literature addressing the behaviour of buried pipelines subjected to various ground motion phenomena. This review covers the various modelling approaches and simulation techniques for the soil-pipe system found in the literature. The main limitations of the existing methods are also stated.These studies encompass a diversity of ground motion sources including, but not limited to: Fault movements (strike-slip, normal-slip), soil settlements due to tunnelling, differential ground settlements related to groundwater lowering, and soil liquefaction. The main research methodologies employed are analytical analysis, numerical simulation and experimental testing.&#x0D; One way to study the behaviour of the buried pipelines is the analytical modelling based on theoretical analysis [6, 7, 8]. For this approach, reasonable assumptions and simplification are necessary when simulating the pipe-soil system but also the ground movement. Soil-pipe interaction models found in previous study are mainly based on Winkler model, Pasternak model and others. The pipeline material behaviour was considered to have either elastic, bilinear or tri-linear stress-strain response. The adopted simplifying assumptions in the abovementioned studies can be prominent in certain cases of practical interest.&#x0D; Analytical models based upon empirical curve fits have also been employed in some studies. The fitted functions can be derived from a numerical parametric analysis, by performing regression analyses on data obtained from a large number of configurations covering different values of the input parameters, such as pipe diameter, burial depth, soil subgrade modulus, pipe elasticity, and maximum displacement of the soil [11]. The derived empirical expressions can be used to directly estimate pipe response induced by a soil settlement.&#x0D; Another major way to investigate this topic is the numerical simulation. Numerous studies were carried out using numerical finite element models [10, 12]. The results of these computational methods are becoming increasingly accurate as currently available numerical analysis techniques allow this problem to be solved in a rigorous manner, minimizing the number of required approximations. However, the non-linear behaviour of the pipeline steel, the soil-pipeline interaction and the second-order effects induced by large displacements make these analyses quite challenging.&#x0D; Apart from the above analytical and numerical methodologies, there have been a growing number of papers involving the experimental simulation of buried pipes under ground motion. These investigations were based on centrifuge tests [1, 5] and other designed testing apparatus [9].&#x0D; However, despite the advancement of modelling techniques, there are still some limitations when examining the behaviour of buried pipelines subjected to ground settlement such as the need for accurate soil and pipe parameters and the lack of consideration of external loads.

Unveiling the magnetic structure and phase transition of Cr2CoAl using neutron diffraction

We report the detailed analysis of temperature dependent neutron diffraction pattern of the Cr2CoAl inverse Heusler alloy and unveil the magnetic structure up to the phase transition as well as its fully compensated ferrimagnetic nature. The Rietveld refinement of the diffraction pattern using the space group I4¯m2 confirms the inverse tetragonal structure over the large temperature range from 100 to 900 K. The refinement of the magnetic phase considering the wave vector k= (0, 0, 0) reveals the ferrimagnetic nature of the sample below 730±5 K. This transition temperature is obtained from empirical power law fitting of the variation in the ordered net magnetic moment variation in intensity of (110) peak as a function of temperature. The spin configuration of the microscopic magnetic structure suggests the nearly fully compensated ferrimagnetic behavior where the magnetic moments of Cr2 are antiparallel with respect to the Cr1 and Co moments. Moreover, the observed anomaly in the thermal expansion and lattice parameters at 730±5 K suggests that the distortion in the crystal structure may play an important role in the magnetic phase transition.

Modeling aqueous association constants and mineral solubilities at subcritical and supercritical temperatures

The need for sustainable power generation has increased interest in the use of hydrothermal fluids for industrial applications. New high-enthalpy geothermal systems and biowaste-to-fuel processes are two relevant examples that employ supercritical fluids which require an in-depth understanding of complex chemical reactions occurring near the supercritical temperature of water (374 °C). As these processes operate in thermodynamic regimes that are not currently covered by a standard molar Gibbs energy of formation model, only empirical fits for single reaction systems are available which limit the use of multi-component phase equilibria calculations that are standard practice for less extreme environments. Here, we advance a standard molar Gibbs energy of formation model able to operate in these otherwise inaccessible thermodynamic states to include species needed for key mineral solubility systems and ion association reactions. This work extends a model based on molecular statistical thermodynamics (MST) into four new systems (Na3PO4-H2O, LiOH-H2O, KOH-H2O, and BaSO4-H2O) by extending the model to cover 10 new species. For each of these systems, model predictions were consistently within the experimental uncertainties for the new systems covered. A breakdown of MST contributions to the model revealed that electrostatic and hard sphere contributions were key to reproducing density dependencies of standard molar Gibbs energy of formation values around the critical point of water.

Glacier Change in the West Kunlun Main Peak Area from 2000 to 2020

Glaciers are sensitive indicators of climate change, and investigation of their dynamics is crucial for ensuring regional ecological security as well as disaster prevention and mitigation measures. Based on Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+)/Operational Land Imager (OLI) imagery, the outlines and length of glaciers in the West Kunlun Main Peak Area (WKMPA) during 2000–2020 were obtained by combining a band ratio method with manual interpretation and an automatic extraction method for the glacier centerline, respectively. There were 440 glaciers in the WKMPA in 2020, covering an area of 2964.59 ± 54.87 km2, with an average length of 2916 ± 60 m. The glacier count increased due to division, while the area and length all exhibited a declining trend from 2000 to 2020, at rates of −0.04%·a−1 (24.83 km2) and −0.11%·a−1 (66 m), respectively. Glacier retreat was primarily observed during the early period (2000–2005). Except for glaciers located above an elevation of 6250 m, the glacier area decreased with each altitude interval from 2000 to 2020, and the rate of relative change in glacier area generally decreased with increasing altitude. Moreover, except for a slight increase in north-facing glaciers, the area of glaciers facing other orientations decreased during 2000–2020. The accuracy of the empirical formula fit for glacier length was highly dependent on glacier class, with greater precision observed for smaller glaciers and lower precision for larger valley-basin glaciers due to their complex morphological structures being neglected and only a single quantitative relationship being considered. There was a time lag of 12 years between temperature changes and glacier area response in this region. The mechanism by which glacier division affects glacier change is complex, requiring dissection of multiple factors such as area, length, and terminal elevation before and after division.

Multiphase condensation in cluster haloes: interplay of cooling, buoyancy, and mixing

ABSTRACT Gas in the central regions of cool-core clusters and other massive haloes has a short cooling time (≲1 Gyr). Theoretical models predict that this gas is susceptible to multiphase condensation, in which cold gas is expected to condense out of the hot phase if the ratio of the thermal instability growth time-scale (tti) to the free-fall time (tff) is tti/tff ≲ 10. The turbulent mixing time tmix is another important time-scale: if tmix is short enough, the fluctuations are mixed before they can cool. In this study, we perform high-resolution (5122 × 768–10242 × 1536 resolution elements) hydrodynamic simulations of turbulence in a stratified medium, including radiative cooling of the gas. We explore the parameter space of tti/tff and tti/tmix relevant to galaxy and cluster haloes. We also study the effect of the steepness of the entropy profile, the strength of turbulent forcing and the nature of turbulent forcing (natural mixture versus compressive modes) on multiphase gas condensation. We find that larger values of tti/tff or tti/tmix generally imply stability against multiphase gas condensation, whereas larger density fluctuations (e.g. due to compressible turbulence) promote multiphase gas condensation. We propose a new criterion min (tti/min (tmix, tff)) ≲ c2 × exp (c1σs) for when the halo becomes multiphase, where σs denotes the amplitude of logarithmic density fluctuations and c1 ≃ 6, c2 ≃ 1.8 from an empirical fit to our results.

Visualizing Penetration of Fluorescent Dye through Polymer Coatings.

Understanding how small molecules penetrate and contaminate polymer films is of vital importance for developing protective coatings for a wide range of applications. To this end, rhodamine B fluorescent dye is visualized diffusing through polystyrene-polydimethylsiloxane block copolymer (BCP) coatings using confocal microscopy. The intensity of dye inside the coatings grows and decays non-monotonically, which is likely due to a combination of dye molecule transport occurring concurrently in different directions. An empirical fitting equation allows for comparing the contamination rates between copolymers, demonstrating that dye penetration is related to the chemical makeup and configuration of the BCPs. This work shows that confocal microscopy can be a useful tool to visualize the transport of a fluorophore in space and time through a coating.

Prediction of hemiwicking dynamics in micropillar arrays

Dynamic hemiwicking behavior is observable in both nature and a wide range of industrial applications ranging from biomedical devices to thermal management. We present a semi-analytical modeling framework (without empirical fitting coefficients) to predict transient capillary-driven hemiwicking behavior of a liquid through a nano/microstructured surface, specifically a micropillar array. In our model framework, the liquid domain is discretized into micropillar unit cells to enable the time marching of the hemiwicking front. A simplified linear pressure drop is assumed along the hemiwicking length such that the local meniscus curvature, contact angle, and effective liquid height are determined at each time step in our transient model. This semi-analytical model is validated with experimental data from our own experiments and from published literature for different fluids. Our model predicts hemiwicking dynamics with &amp;lt;20% error over a broad range of micropillar geometries with height-to-pitch ratio ranging between ≈0.34 and 6.7 and diameter-to-pitch ratio in the range of ≈0.25–0.7 and without any fitting parameters. For lower diameter-to-pitch ratio data points related to sparse micropillar array arrangements, we suggest modifications to the semi-analytical model. This work sheds light on complex and dynamic solid–liquid–vapor interfacial interactions which could serve as a guide for the design of textured surfaces for wicking enhancement in multi-phase thermal and mass transport technologies and applications.