Understanding Of Fluid Properties Research Articles

Using Computationally-Determined Properties for Machine Learning Prediction of Self-Diffusion Coefficients in Pure Liquids.

The ability to predict transport properties of liquids quickly and accurately will greatly improve our understanding of fluid properties both in bulk and complex mixtures, as well as in confined environments. Such information could then be used in the design of materials and processes for applications ranging from energy production and storage to manufacturing processes. As a first step, we consider the use of machine learning (ML) methods to predict the diffusion properties of pure liquids. Recent results have shown that Artificial Neural Networks (ANNs) can effectively predict the diffusion of pure compounds based on the use of experimental properties as the model inputs. In the current study, a similar ANN approach is applied to modeling diffusion of pure liquids using fluid properties obtained exclusively from molecular simulations. A diverse set of 102 pure liquids is considered, ranging from small polar molecules (e.g., water) to large nonpolar molecules (e.g., octane). Self-diffusion coefficients were obtained from classical molecular dynamics (MD) simulations. Since nearly all the molecules are organic compounds, a general set of force field parameters for organic molecules was used. The MD methods are validated by comparing physical and thermodynamic properties with experiment. Computational input features for the ANN include physical properties obtained from the MD simulations as well as molecular properties from quantum calculations of individual molecules. Fluid properties describing the local liquid structure were obtained from center of mass radial distribution functions (COM-RDFs). Feature sensitivity analysis revealed that isothermal compressibility, heat of vaporization, and the thermal expansion coefficient were the most impactful properties used as input for the ANN model to predict the MD simulated self-diffusion coefficients. The MD-based ANN successfully predicts the MD self-diffusion coefficients with only a subset (2 to 3) of the available computationally determined input features required. A separate ANN model was developed using literature experimental self-diffusion coefficients as model targets. Although this second ML model was not as successful due to a limited number of data points, a good correlation is still observed between experimental and ML predicted self-diffusion coefficients.

The liquid of aloe extract with shear-thinning Carreau fluids

In the analysis of plant extracts, rheological and heat transfer characteristics are of great significance for one to study regarding applications, such as in the biorheology or pharmaceutical industries. In this paper, we examine the aloe extract experimentally and test its rheological properties. To gain a better understanding of the extract's fluid properties, numerical simulations using FEM are performed on a square channel. Results show that, under a large shear rate, such a fluid behavior closely to the shear-thinning power-law fluid. Combining with numerical simulations, we analyze the influence of the Pr number and the material coefficient λ on the motion of the fluid. We also discuss the effects of other parameter values. • We studied the aloe extract experimentally and tested its rheological properties. • Results show that, under the large shear rate, it is close to the shear-thinning power law fluid. • To gain a better understanding of these fluid properties, a numerical simulation is performed on a square channel. • The influence of Pr number and material coefficient on the motion state of fluid are analyzed.

A new in situ mass-loss approach for determining mineral solubility at high pressures and temperatures: crystal volume computation method

Data for the solubility of minerals in aqueous solutions at high pressures and temperatures are essential for our understanding of fluid properties, mass transport and growth/dissolution processes of minerals in Earth’s crust and upper mantle. Almost all available data on mineral solubility above 0.5 GPa have been obtained by ex situ quench methods in piston-cylinder-type apparatus. To exploit the obvious advantages of direct in situ solubility determinations in diamond anvil cells, we have developed simple in situ mass-loss approaches to calculate the mass of the observed crystal at any given pressure and temperature condition. This strategy combines the advantages of in situ and ex situ experiments while circumventing their disadvantages. In the present paper we describe a method that takes advantage of the tendency of crystals to approach an idiomorphic habit during hydrothermal diamond-anvil-cell runs. It can also be used for cleavage fragments. Using a modified version of the Kristall 2000 software designed for crystal drawing, the three-dimensional habit of a crystal can be modelled based on a two-dimensional digital image. This three-dimensional model allows the crystal volume and therefore its mass to be determined by measuring the exact length of just one crystal edge. The accuracy of this method is demonstrated by modelling the mass of various mineral grains of different shapes, morphologies, masses, densities and complexities, and comparing the modelled values to their actual weight. Modelled mass calculations are within 1σ = 0.38 % based on the Gaussian Error propagation for crystals varying from 0.2 to 38 mg. This method can be used not only for solubility experiments, but also in all situations where the crystalline sample is accessible only by optical methods.