Model Of Mixture Research Articles

Joint Separation and Localization of Moving Sound Sources Based on Neural Full-Rank Spatial Covariance Analysis

This paper presents an unsupervised multichannel method that can separate moving sound sources based on an amortized variational inference (AVI) of joint separation and localization. A recently proposed blind source separation (BSS) method called neural full-rank spatial covariance analysis (FCA) trains a neural separation model based on a nonlinear generative model of multichannel mixtures and can precisely separate unseen mixture signals. This method, however, assumes that the sound sources hardly move, and thus its performance is easily degraded by the source movements. In this paper, we solve this problem by introducing time-varying spatial covariance matrices and directions of arrival of sources into the nonlinear generative model of the neural FCA. This generative model is used for training a neural network to jointly separate and localize moving sources by using only multichannel mixture signals and array geometries. The training objective is derived as a lower bound on the log-marginal posterior probability in the framework of AVI. Experimental results obtained with mixture signals of moving sources show that our method outperformed an existing joint separation and localization method and standard BSS methods.

Hyperspectral Remote Sensing Image Synthesis Based on Implicit Neural Spectral Mixing Models

Hyperspectral image (HSI) synthesis, as an emerging research topic, is of great value in overcoming sensor limitations and achieving low-cost acquisition of high-resolution remote sensing HSIs. However, the linear spectral mixing model used in recent studies oversimplifies the real-world hyperspectral imaging process, making it difficult to effectively model the imaging noise and multiple reflections of the object spectrum. As a prerequisite for hyperspectral data synthesis, accurate modeling of nonlinear spectral mixtures has long been a challenge. Considering the above difficulties, we propose a novel method for modeling nonlinear spectral mixtures based on implicit neural representations (INRs) in this article. The proposed method learns from INR and adaptively implements different mixture models for each pixel according to their spectral signature and surrounding environment. Based on the above neural mixing model, we also propose a new method for HSI synthesis. Given an RGB image as input, our method can generate an accurate and physically meaningful HSI. As a set of by-products, our method can also generate subpixel-level spectral abundance as well as the solar atmosphere signature. The whole framework is trained end-to-end in a self-supervised manner. We constructed a new dataset for HSI synthesis based on a wide range of Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data. Our method achieves a mean peak signal-to-noise ratio (MPSNR) of 52.36 dB and outperforms other state-of-the-art hyperspectral synthesis methods. Finally, our method shows great benefits to downstream data-driven applications. With the HSIs and abundance directly generated from low-cost RGB images, the proposed method improves the accuracy of HSI classification tasks by a large margin, particularly for those with limited training samples.

Synthesis, theoretical studies and viscosity correlations of ricinoleic acid based methyl ester soap solution

The use of vegetable oils for industrial applications needs chemical modification to improve its properties. For coolants and lubricant applications, the viscosity of vegetable oils plays a vital role which decides the heat dissipation and flow characteristics of vegetable oils. In this study, the viscosity prediction modeling of binary mixtures has been made using refutes empirical relations. The blend viscosity from the model depends on the blend ratios of the unsaturated fatty acids in castor oil and methanol. This blending of castor oil and methanol follows KOH assisted transesterification process to form Fatty Acid Methyl Ester (FAME) soap solution. The transesterified blend mixes have been experimentally analyzed for viscosity using Brookfield viscometer. The measured values of viscosities of the blend mix have been compared with viscosities obtained from refutes binary blend mix model. The percentage deviation between the experimental viscosities and Refutes viscosity prediction model is about 10.76%. The prepared FAME soap solution can be used as coolants and lubricants in manufacturing industries.

The P-µ-T Cubic Equation of Viscosity for Reservoir Oils

Summary Oil recovery simulation sensitivity increases with heavier oils for the existing viscosity models, driving into higher levels of difficulty when fitting viscosity data for rising oil heaviness, particularly below the saturation pressure. Keeping in view the similarity of trends of viscosity and density with isothermal pressures for reservoir oils, the P-μ-T cubic viscosity model, which was developed for pure hydrocarbon components, was extended to reservoir oils. Two parameters in the P-μ-T cubic viscosity model for mixtures with pure and pseudocomponents are identified for adjusting the viscosity data fitting: Kc for controlling the viscosity gradient with pressure and the “ε” shifting trends for increasing viscosity direction. These two parameters are treated as the adjustable parameters required for fitting the viscosity data. A total of 129 reservoir oils from different sources are used to validate the reliability of the P-μ-T viscosity model. The default model (where ε and Kc are 1 and 45, respectively), extended to 71 light oils, resulted in 31% of average absolute relative deviation (AARD) in viscosity prediction. However, separate adjusted parameters are obtained per oil for more accurate viscosity data fitting. Application of the model in this work results in (post-fitting) AARD% of 2.86% average for 36 low-viscosity oil data, 5.68% for 9 high-viscosity oil data, 9% for five oil blends, and 4.11% for bitumen blends. The model gives an AARD of 3.06% in the undersaturated region and 3.79% in the saturated region for the oil considered. The model predicts better the viscosity above saturation pressure for low-viscosity oils and below saturation pressure for high-viscosity oils. A comparative analysis of the P-μ-T cubic viscosity model vs. other models demonstrates its ability to successfully capture viscosity trends of oil and blends in all viscosity ranges. For simplicity, the P-μ-T cubic viscosity model is proposed with only two optimizing parameters even though using a third parameter improves the matching in heavy oils.

Prediction of parameters of group contribution models of mixtures by matrix completion.

Group contribution (GC) methods are widely used for predicting the thermodynamic properties of mixtures by dividing components into structural groups. These structural groups can be combined freely so that the applicability of a GC method is only limited by the availability of its parameters for the groups of interest. For describing mixtures, pairwise interaction parameters between the groups are of prime importance. Finding suitable numbers for these parameters is often impeded by a lack of suitable experimental data. Here, we address this problem by using matrix completion methods (MCMs) from machine learning to predict missing group-interaction parameters. This new approach is applied to UNIFAC, an established group contribution method for predicting activity coefficients in mixtures. The developed MCM yields a complete set of parameters for the first 50 main groups of UNIFAC, which substantially extends the scope and applicability of UNIFAC. The quality of the predicted parameter set is evaluated using vapor-liquid equilibrium data of binary mixtures from the Dortmund Data Bank. This evaluation reveals that our approach gives prediction accuracies comparable with UNIFAC for data sets to which UNIFAC was fitted, and only slightly lower accuracies for data sets to which UNIFAC is not applicable.

Formation of dissipative structures in microscopic models of mixtures with species interconversion

The separation of substances into different phases is ubiquitous in nature and important scientifically and technologically. This phenomenon may become drastically different if the species involved, whether molecules or supramolecular assemblies, interconvert. In the presence of an external force large enough to overcome energetic differences between the interconvertible species (forced interconversion), the two alternative species will be present in equal amounts, and the striking phenomenon of steady-state, restricted phase separation into mesoscales is observed. Such microphase separation is one of the simplest examples of dissipative structures in condensed matter. In this work, we investigate the formation of such mesoscale steady-state structures through Monte Carlo and molecular dynamics simulations of three physically distinct microscopic models of binary mixtures that exhibit both equilibrium (natural) interconversion and a nonequilibrium source of forced interconversion. We show that this source can be introduced through an internal imbalance of intermolecular forces or an external flux of energy that promotes molecular interconversion, possible manifestations of which could include the internal nonequilibrium environment of living cells or a flux of photons. The main trends and observations from the simulations are well captured by a nonequilibrium thermodynamic theory of phase transitions affected by interconversion. We show how a nonequilibrium bicontinuous microemulsion or a spatially modulated state may be generated depending on the interplay between diffusion, natural interconversion, and forced interconversion.

Modern Bitumen Oil Mixture Models in Ashalchinsky Field with Low-Viscosity Solvent at Various Temperatures and Solvent Concentrations

The article analyzes the modern theory and practice of pipeline transport of bituminous oil together with low-viscosity solvent. In addition, a detailed analysis of the rheological models of non-Newtonian fluids is carried out, which establishes a number of assumptions on the rheology model selection algorithm currently in use (limited number of rheological models, variability in model coefficient assignment, etc.). Ways of their elimination are proposed. Dependencies for determination of the dynamic viscosity coefficient of binary oil mixtures are investigated. Calculation of the parameters of the bituminous oil mixture with solvent is considered. Complex experimental studies on rheology mixture models of bituminous oil and solvent on the example of the Ashalchinsky field (Russia, Tatarstan) in a wide range of temperatures and concentrations of the solvent are conducted. A two-dimensional field of rheological models of the oil mixture is constructed, which makes it possible to determine the rheological model of the pumped oil mixture depending on the solvent concentration and the temperature of the mixture. Formulas for forecasting the rheological properties of the oil mixture on the basis of statistical processing of the results of experimental studies are theoretically substantiated. It is proven that the viscosity of binary oil mixtures in the Newtonian fluid field should be determined by a modified Arrhenius equation. The proposed models with a high degree of accuracy describe the rheological properties of the oil mixture. It is shown that in the case of complex mixtures, not one rheological model should be applied, but their hierarchy should be established depending on the solvent concentration and temperature.

Rheological Modeling of Bituminous Mixtures Including Polymer-Modified Binder and Fine Crumb Rubber Added through Dry Process

In recent years, both dry and wet processes to include waste rubber (WR) in bituminous mixtures have had little success in Italy due to technical and economic reasons. However, the necessity to recycle this waste material and the increasing interest in low-noise emitting pavement is encouraging researchers and stakeholders to explore new solutions. In this context, a hot-mix asphalt (HMA) manufactured with polymer-modified bitumen and fine WR added through a dry method has been recently developed. This paper deals with the rheological characterization and modeling of this innovative HMA, in comparison with two reference mixtures, with ordinary polymer-modified bitumen and with an asphalt rubber binder produced through a wet process. The complex modulus was measured through uniaxial cyclic compression tests at different temperatures and frequencies. The Huet-Sayegh rheological model was used to simulate the experimental data. The results showed that the time-temperature superposition principle is valid, despite the presence of polymers and WR. The Huet-Sayegh model allows a good fitting of the dynamic modulus and loss angle data to be achieved. The viscous response of the mixture including polymer-modified bitumen and rubber powder is higher than the other HMAs, which is mainly associated with the nature of the modifiers, the binder content in the mix and the less severe short-term aging.

Effects of kneading and impact action on the movement of aggregates in asphalt mixtures during compaction

Current studies on the movement of asphalt mixture components and the formation of internal structures during compaction are lacking. Therefore, this article focuses on the effect of compaction and compaction methods on the movement and contact of mixture components. First, models of asphalt mixtures were established based on X-ray computed tomography (X-CT) and the discrete element method (DEM). Subsequently, compaction processes of the Marshall and Superpave gyratory compactor (SGC) method were simulated, and the model was verified using monitoring results of the smart rock. Finally, the macroscopic air voids, component contacts, and coarse aggregate trajectories were recorded and analyzed. The results showed that, in the early compaction stages, the compactness of the Marshall specimen increased rapidly, which also led to more resistance. The number of contact points and the contact force inside specimens gradually increased with compaction; however, the rate of this increase was different. The trajectories of coarse aggregates were affected by their location and compaction methods, especially the distance to the impacted side and to the center of the cross-section. An edge effect, namely lower compactness at the edges may also be caused by this difference. The movement of aggregates inside the Marshall specimen mainly occurred in the direction of impact. The kneading effect of the SGC method made aggregate particles move and rotate periodically, and compaction efficiency in the later stages was higher.

Constitutive model for soil-rock mixtures in the light of an updated skeleton void ratio concept

As a type of special geological body, soil-rock mixtures (SRMs) are widely found in nature and used in civil engineering. Many structures, such as rockfill dams, highways and tunnels, have used SRMs as building materials. Proper modeling of SRMs is of great importance to capture the complex behavior of this heterogeneous material. In this manuscript, a simple constitutive model incorporating the skeleton void ratio concept is proposed for SRMs with varying soil contents (sc). A prominent feature of the model is a unified description of the behavior of SRMs with varying sc such that only model parameters of pure rock and of pure soil are required. After calibration, the model shows a good capacity to predict the stress–strain response of SRMs under a wide range of sc, void ratios, and confining pressures. In particular, it captures well the non-associated behavior of rock-dominated SRMs with different sc. Furthermore, the sc-value is shown to modify the plastic flow direction of the material without influencing its yield surface.

Numerical Simulation of the Natural Convective Heat Transfer of Nanofluids in a Square Cavity Based on Different Predictive Models for Single-Phase and Multiphase Flow Mixtures

Abstract The flow and heat transfer characteristics of nanofluids in a square cavity were simulated using single-phase and mixed-phase flow models, and the simulation results were compared with the corresponding experimental values. The effects of different prediction models for the thermal properties of nanofluids, Grashof number, and volume fraction on the Nusselt number were analyzed. The velocity and temperature distributions of the nanofluid and de-ionized water in the square cavity were compared, and the effects of the temperature and flow fields on the enhanced heat transfer were analyzed according to the field synergy theory. The results show that for the numerical simulation of convective heat transfer in water, both the single-phase flow models and multiphase flow mixing models had high prediction accuracy. For nanofluids, single-phase flow did not reflect the heat transfer characteristics well, and the simulation results of the single-phase flow model relied more strongly on a highly accurate prediction model for the physical parameters. The multiphase flow mixing model could better reflect the natural convective heat transfer properties of the nanofluids in a square cavity. The nanofluid could significantly improve the flow state in the square cavity, thereby facilitating enhanced convective heat transfer. When the concentration is 2% (Grashof number is 1 × 106), the average Nusselt number of the nanofluid is increased by 19.7% compared with the base fluid.

Metal mixtures modeling identifies birth weight-associated gene networks in the placentas of children born extremely preterm

Prenatal exposure to toxic metals is linked to numerous adverse birth and later-in-life outcomes. These outcomes are tied to disrupted biological processes in fetal-derived tissues including the placenta and umbilical cord yet the precise pathways are understudied in these target tissues. We set out to examine the relationship between metal concentrations in umbilical cord and altered gene expression networks in placental tissue. These novel relationships were investigated in a subset of the Extremely Low Gestational Age Newborn (ELGAN) cohort (n = 226). Prenatal exposure to 11 metals/metalloids was measured using inductively coupled plasma tandem-mass spectrometry (ICP-MS/MS) in cord tissue, ensuring passage through the placental barrier. RNA-sequencing was used to quantify >37,000 mRNA transcripts. Differentially expressed genes (DEGs) were identified with respect to each metal. Weighted gene co-expression analysis identified gene networks modulated by metals. Two innovative mixtures modeling techniques, namely principal components analysis and quantile-based g-computation, were employed to identify genes/gene networks associated with multi-metal exposure. Individually, lead was associated with the strongest genomic response of 191 DEGs. Joint lead and cadmium exposure was related to 657 DEGs, including DNA Methyl Transferase 1 (DNMT1). These genes were enriched for the Eukaryotic Initiation Factor 2 (EIF2) pathway. Four gene networks, each containing genes within a Nuclear Factor kappa-light-chain-enhancer of Activated B Cells (NF-kB)-mediated network, were significantly increased in average expression level in relation to increases in all metal concentrations. All four of these metal mixture-associated gene networks were negatively correlated with important predictors of neonatal health including birth weight, placenta weight, and fetal growth. Bringing together novel methodologies from epidemiological mixtures analyses and toxicogenomics, applied to a unique cohort of extremely preterm children, the present study highlighted critical genes and pathways in the placenta dysregulated by prenatal metal mixtures. These represent potential mechanisms underlying the developmental origins of metal-induced disease.

Correlation analysis of the relationship between Arrhenius viscosity parameters in Binary Liquid Mixtures

Recent linear model for pure fluids was developed by our research team. Extending and validating this model for binary fluid mixtures is the aim of this work. To achieve this, a statistical analysis of experimental data sets of 300 observations for 16 binary liquid mixtures in accordance with the families of organic liquid solvents over different temperature ranges, at atmospheric pressure, and at different molar fraction. It was found that the extended model for binary liquid mixtures facilitates the calculation of viscous behavior and engineering manipulations. In the 16 subgroups, it is possible to estimate a missing Arrhenius viscosity parameter if the second one is known. Furthermore, using statistical comparison techniques, one can accurately determine this missing parameter in specific subgroups. Those objectives help engineers to gain an in-depth understanding of pure liquids or mixtures of binary liquids to address their challenges in fluidization technology.

Numerical simulation study on the influence of scroll design parameters on helicopter intake system

Based on the numerical simulation theory, this paper establishes the two-phase flow calculation model of sand and air mixture in the sand control vortex tube, and combined with CFD numerical simulation, studies the influence rules of various design parameters of the vortex tube on the total pressure loss and sand dust separation efficiency of the helicopter intake system, optimizes the structural parameters of the vortex tube separator, and puts forward a set of vortex tube design method, it provides a basis for the design optimization of vortex tube.

New Insights Into the Petrogenesis of Voluminous Crustal‐Signature Silicic Volcanic Rocks of the Toba Eruptions (Indonesia)

AbstractThe formation of large‐volume silicic magmas in arc settings is fundamental for understanding trans‐crustal magmatic systems related to subduction zones. Here, we present an integrated study of the four Quaternary Toba eruptions and pre‐caldera Haranggaol Andesite on Sumatra, Indonesia. This rock suite has significantly enriched Sr‐Nd isotopes (87Sr/86Sr = 0.71220–0.71517, εNd = −8.9 to −10.6) compared with other volcanic rocks on Sunda‐Banda arc, but is similar to the post‐caldera (<74 ka) Sipisupisu basalts near the Toba Caldera. Thermodynamic modeling using Magma Chamber Simulator has revealed that the Toba silicic rocks can be produced by a two‐stage assimilation and fractional crystallization of mantle‐derived basaltic melts with compositions similar to the Sipisupisu basalts in the lower and upper crustal magma reservoirs. Binary modeling of Sr‐Nd isotopes suggests that the rocks near the Toba Caldera can be produced by mixing of 5%–10% of the subducting Nicobar Fan sediments (87Sr/86Sr = 0.73493, εNd = −14 on average) with depleted MORB mantle (DMM). Indeed, decompressional partial melting modeling of bulk mixtures of DMM with 7% subducting sediments using pMELTS indicates that the melts generated can have geochemical compositions similar to the Sipisupisu basalts. We therefore argue that hybridization of the subducting sediments with the mantle wedge could be an alternative scenario responsible for the enriched isotopic characteristics of the rocks near Toba. Prolonged fractionation of mantle‐derived enriched/depleted basaltic melts, accompanied by crustal assimilation and crystal‐melt segregation, could be common processes in generating large‐volume silicic magmas on continental arcs.

Tracer particle in a confined correlated medium: an adiabatic elimination method

We present a simple and systematic procedure to determine the effective dynamics of a Brownian particle coupled to a rapidly fluctuating correlated medium, modeled as a scalar Gaussian field, under spatial confinement. The method allows us, in particular, to address the case in which the fluctuations of the medium are suppressed in the vicinity of the particle, as described by a quadratic coupling in the underlying Hamiltonian. As a consequence of the confinement of the correlated medium, the resulting effective Fokker–Planck equation features spatially dependent drift and diffusion coefficients. We apply our method to simplified fluid models of binary mixtures and microemulsions near criticality containing a colloidal particle, and we analyze the corrections to the stationary distribution of the particle position and the diffusion coefficient.

Length Regulation Drives Self-Organization in Filament-Motor Mixtures.

Cytoskeletal networks form complex intracellular structures. Here we investigate a minimal model for filament-motor mixtures in which motors act as depolymerases and thereby regulate filament length. Combining agent-based simulations and hydrodynamic equations, we show that resource-limited length regulation drives the formation of filament clusters despite the absence of mechanical interactions between filaments. Even though the orientation of individual remains fixed, collective filament orientation emerges in the clusters, aligned orthogonal to their interfaces.

Internal Energy Relaxation Processes and Bulk Viscosities in Fluids

Internal energy relaxation processes in fluid models derived from the kinetic theory are revisited, as are related bulk viscosity coefficients. The apparition of bulk viscosity coefficients in relaxation regimes and the links with equilibrium one-temperature bulk viscosity coefficients are discussed. First, a two-temperature model with a single internal energy mode is investigated, then a two-temperature model with two internal energy modes and finally a state-to-state model for mixtures of gases. All these models lead to a unique physical interpretation of the apparition of bulk viscosity effects when relaxation characteristic times are smaller than fluid times. Monte Carlo numerical simulations of internal energy relaxation processes in model gases are then performed, and power spectrums of density fluctuations are computed. When the energy relaxation time is smaller than the fluid time, both the two temperature and the single-temperature model including bulk viscosity yield a satisfactory description. When the energy relaxation time is larger than the fluid time, however, only the two-temperature model is in agreement with Boltzmann equation. The quantum population of a He-H2 mixture is also simulated with detailed He-H2 cross sections, and the resulting bulk viscosity evaluated from the Green–Kubo formula is in agreement with the theory. The impact of bulk viscosity in fluid mechanics is also addressed, as well as various mathematical aspects of internal energy relaxation and Chapman–Enskog asymptotic expansion for a two-temperature fluid model.

Modeling of Diffusive Transport of Polymers Moments Using Limiting Cases of the Maxwell–Stefan Model

AbstractA polymer distribution is usually represented by its moments. Thus, to calculate transport in a polymer system, a formulation for the transport of moments of the polymer is needed. This is only possible if the moments close or if there is a suitable closing condition. To archive this, two simplifications of the Stefan–Maxwell diffusion are derived, which convert the transport equation of polymeric species to a closed set of transport equations for the polymer moments. The first approach corresponds to an infinitely diluted polymer system, whereas the second one describes a highly concentrated polymer system. Both formulations are compared with the full Stefan‐Maxwell model of a ternary mixture of a solvent and two polymer species of different chain length.

A Physiologically Based Pharmacokinetic (PBPK) Modeling Framework for Mixtures of Dioxin-like Compounds.

Humans are exposed to persistent organic pollutants, such as dioxin-like compounds (DLCs), as mixtures. Understanding and predicting the toxicokinetics and thus internal burden of major constituents of a DLC mixture is important for assessing their contributions to health risks. PBPK models, including dioxin models, traditionally focus on one or a small number of compounds; developing new or extending existing models for mixtures often requires tedious, error-prone coding work. This lack of efficiency to scale up for multi-compound exposures is a major technical barrier toward large-scale mixture PBPK simulations. Congeners in the DLC family, including 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), share similar albeit quantitatively different toxicokinetic and toxicodynamic properties. Taking advantage of these similarities, here we reported the development of a human PBPK modeling framework for DLC mixtures that can flexibly accommodate an arbitrary number of congeners. Adapted from existing TCDD models, our mixture model contains the blood and three diffusion-limited compartments-liver, fat, and rest of the body. Depending on the number of congeners in a mixture, varying-length vectors of ordinary differential equations (ODEs) are automatically generated to track the tissue concentrations of the congeners. Shared ODEs are used to account for common variables, including the aryl hydrocarbon receptor (AHR) and CYP1A2, to which the congeners compete for binding. Binary and multi-congener mixture simulations showed that the AHR-mediated cross-induction of CYP1A2 accelerates the sequestration and metabolism of DLC congeners, resulting in consistently lower tissue burdens than in single exposure, except for the liver. Using dietary intake data to simulate lifetime exposures to DLC mixtures, the model demonstrated that the relative contributions of individual congeners to blood or tissue toxic equivalency (TEQ) values are markedly different than those to intake TEQ. In summary, we developed a mixture PBPK modeling framework for DLCs that may be utilized upon further improvement as a quantitative tool to estimate tissue dosimetry and health risks of DLC mixtures.