Water Purification in Micromagnetofluidic Devices: Mixing in MHD Micromixers

AbstractThis contribution addresses a possible solution for water purification from heavy metals by magnetic nanoparticles in microfluidic water flow systems. In this technique, the most important component is the micromixer while efficient mixing and particle driving is achieved by external magnetic fields. For the simulation of water flow and nanoparticles, Computational Fluid Dynamics methods are used. The 2D and 3D Navier-Stokes equations are solved for the flow field while trajectories of the magnetic nanoparticles are simulated by the use of a Lagrangian method. Compared to traditional techniques, this method is expected to succeed chemical speed and increased water purification times

Nanoscale slip length prediction with machine learning tools

Abstract This work incorporates machine learning (ML) techniques, such as multivariate regression, the multi-layer perceptron, and random forest to predict the slip length at the nanoscale. Data points are collected both from our simulation data and data from the literature, and comprise Molecular Dynamics simulations of simple monoatomic, polar, and molecular liquids. Training and test points cover a wide range of input parameters which have been found to affect the slip length value, concerning dynamical and geometrical characteristics of the model, along with simulation parameters that constitute the simulation conditions. The aim of this work is to suggest an accurate and efficient procedure capable of reproducing physical properties, such as the slip length, acting parallel to simulation methods. Non-linear models, based on neural networks and decision trees, have been found to achieve better performance compared to linear regression methods. After the model is trained on representative simulation data, it is capable of accurately predicting the slip length values in regions between or in close proximity to the input data range, at the nanoscale. Results also reveal that, as channel dimensions increase, the slip length turns into a size-independent material property, affected mainly by wall roughness and wettability

Molecular Dynamics Simulations of Ion Drift in Nanochannel Water Flow

The present paper employs Molecular Dynamics (MD) simulations to reveal nanoscale ion separation from water/ion flows under an external electric field in Poiseuille-like nanochannels. Ions are drifted to the sidewalls due to the effect of wall-normal applied electric fields while flowing inside the channel. Fresh water is obtained from the channel centerline, while ions are rejected near the walls, similar to the Capacitive DeIonization (CDI) principles. Parameters affecting the separation process, i.e., simulation duration, percentage of the removal, volumetric flow rate, and the length of the nanochannel incorporated, are affected by the electric field magnitude, ion correlations, and channel height. For the range of channels investigated here, an ion removal percentage near 100% is achieved in most cases in less than 20 ns for an electric field magnitude of E = 2.0 V/Å. In the nutshell, the ion drift is found satisfactory in the proposed nanoscale method, and it is exploited in a practical, small-scale system. Theoretical investigation from this work can be projected for systems at larger scales to perform fundamental yet elusive studies on water/ion separation issues at the nanoscale and, one step further, for designing real devices as well. The advantages over existing methods refer to the ease of implementation, low cost, and energy consumption, without the need to confront membrane fouling problems and complex electrode material fabrication employed in CDI

The Electrical Conductivity of Ionic Liquids: Numerical and Analytical Machine Learning Approaches

In this paper, we incorporate experimental measurements from high-quality databases to construct a machine learning model that is capable of reproducing and predicting the properties of ionic liquids, such as electrical conductivity. Empirical relations traditionally determine the electrical conductivity with the temperature as the main component, and investigations only focus on specific ionic liquids every time. In addition to this, our proposed method takes into account environmental conditions, such as temperature and pressure, and supports generalization by further considering the liquid atomic weight in the prediction procedure. The electrical conductivity parameter is extracted through both numerical machine learning methods and symbolic regression, which provides an analytical equation with the aid of genetic programming techniques. The suggested platform is capable of providing either a fast, numerical prediction mechanism or an analytical expression, both purely data-driven, that can be generalized and exploited in similar property prediction projects, overcoming expensive experimental procedures and computationally intensive molecular simulations

The Electrical Conductivity of Ionic Liquids: Numerical and Analytical Machine Learning Approaches

In this paper, we incorporate experimental measurements from high-quality databases to construct a machine learning model that is capable of reproducing and predicting the properties of ionic liquids, such as electrical conductivity. Empirical relations traditionally determine the electrical conductivity with the temperature as the main component, and investigations only focus on specific ionic liquids every time. In addition to this, our proposed method takes into account environmental conditions, such as temperature and pressure, and supports generalization by further considering the liquid atomic weight in the prediction procedure. The electrical conductivity parameter is extracted through both numerical machine learning methods and symbolic regression, which provides an analytical equation with the aid of genetic programming techniques. The suggested platform is capable of providing either a fast, numerical prediction mechanism or an analytical expression, both purely data-driven, that can be generalized and exploited in similar property prediction projects, overcoming expensive experimental procedures and computationally intensive molecular simulations

The Impact of Reduced Gravity on Oscillatory Mixed Convective Heat Transfer around a Non-Conducting Heated Circular Cylinder

The present analysis addresses the impact of reduced gravity and magnetohydrodynamics on oscillating mixed-convective electricallyconducting fluid flow over a thermal, non-conducting horizontal circular cylinder. In reduced gravity, buoyancy forces may induce fluid motion due to a weak gravitational field but in non-gravity forces, fluid motion can be induced by a variety of factors, including surface tension and density variations. The fluid motion is governed by connected nonlinear partial differential equations which are converted into convenient equations by applying a finite-difference scheme with the primitive transformation and a Gaussian elimination technique. The numerical solutions of the connected dimensionalized equations were obtained for various emerging dimensionless parameters, reduced gravity parameter Rg, Prandtl number Pr, and some other fixed parameters. First, the fluid velocity, temperature distribution and magnetic-field profiles were obtained and then these profiles were used to examine the oscillating quantities of skinfriction, oscillating heat transfer and oscillating rate of currentdensity. The FORTRAN software was used for the numerical results and these results were displayed on Tech Plot. The fluid velocity and magnetic profile were increased at the π/2 station as reduced gravity increased but the dimensionless temperature of the fluid attained a maximum magnitude as reduced gravity was decreased. The larger amplitude of the oscillating coefficients of τt and τm was concluded with a prominent variation for each λ in the presence of reduced gravity. Physically, this could be because an increase in the decreased gravity parameter impacts the fluid flow’s driving potential along a thermal, non-conducting horizontalcylinder

Nonlinear dynamics in Divisia monetary aggregates: an application of recurrence quantification analysis

Abstract We construct recurrence plots (RPs) and conduct recurrence quantification analysis (RQA) to investigate the dynamic properties of the new Center for Financial Stability (CFS) Divisia monetary aggregates for the United States. In this study, we use the latest vintage of Divisia aggregates, maintained within CFS. We use monthly data, from January 1967 to December 2020, which is a sample period that includes the extreme economic events of the 2007–2009 global financial crisis. We then make comparisons between narrow and broad Divisia money measures and find evidence of a nonlinear but reserved possible chaotic explanation of their origin. The application of RPs to broad Divisia monetary aggregates encompasses an additional drift structure around the global financial crisis in 2008. Applying the moving window RQA to the growth rates of narrow and broad Divisia monetary aggregates, we identify periods of changes in data-generating processes and associate such changes to monetary policy regimes and financial innovations that occurred during those times