Digital Storytelling, comics and new technologies in education: review, research and perspectives

This work reviews the current application of one of the most widely used techniques in education around the world: Digital Storytelling (DS), along with comic and animation tools, and presents a study about the Greek educational system as well as posing questions concerning the form of a new study, design, implementation and assessment of educational project across all educational levels. Nowadays, people and students at all educational levels in the developed world are surrounded by multiple electronic media and are familiar with a variety of pictures, video and information from early childhood. The educational process, as it proceeds in parallel with fast technological and societal evolution, tries to smoothly adjust new educational methods without abandoning traditional teaching and moving away from its main aim: the establishment of knowledge

Current Trends in Fluid Research in the Era of Artificial Intelligence: A Review

Computational methods in fluid research have been progressing during the past few years, driven by the incorporation of massive amounts of data, either in textual or graphical form, generated from multi-scale simulations, laboratory experiments, and real data from the field. Artificial Intelligence (AI) and its adjacent field, Machine Learning (ML), are about to reach standardization in most fields of computational science and engineering, as they provide multiple ways for extracting information from data that turn into knowledge, with the aid of portable software implementations that are easy to adopt. There is ample information on the historical and mathematical background of all aspects of AI/ML in the literature. Thus, this review article focuses mainly on their impact on fluid research at present, highlighting advances and opportunities, recognizing techniques and methods having been proposed, tabulating, and testing some of the most popular algorithms that have shown significant accuracy and performance on fluid applications. We also investigate algorithmic accuracy on several fluid datasets that correspond to simulation results for the transport properties of fluids and suggest that non-linear, decision tree-based methods have shown remarkable performance on reproducing fluid properties

Fluid flows at the nanoscale: arithmetic simulation by molecular dynamics methods

Computer simulations for atomic and molecular systems at the nanoscale aim at computing properties concerning the system’s structure and dynamics (there are indications that continuum theory breaks down at the nanoscale). The theoretical basis for this approach is statistical thermodynamics. Molecular Dynamics (MD) is a useful tool that can be used to reveal phenomena occurring at the nanoscale. As a simulation method, it is based on calculating interactions between system particles and solving Newton’s equations for each particle in order to extract thermodynamic and transport properties. In this work, the MD method is applied to study flows in nanochannels. After presenting the method and the aim of our work, a detailed report is done on how we calculate quantities concerning fluid properties at the nanoscale (i.e., density profiles, velocity profiles, strain rate profiles, temperature profiles, slip length) and transport properties (i.e., diffusion coefficient, shear viscosity, thermal conductivity). The effect of various parameters that can affect the above properties is examined, such as temperature, external force, channel walls geometry characteristics, wall/fluid atoms interaction, mean fluid density and various simulation model characteristics. To sum up, a categorization of all the parameters that could affect nano-flow characteristics is made, along with dimension effects, which are the dominant effects. In conclusion, we observe that dimension effects are very important for fluid behavior in nano-channels. The fluid is strongly inhomogeneous in dimensions close to 1-3 nm, it presents non-parabolic velocity profiles, small diffusion coefficient, large value for shear viscosity and small thermal conductivity (all values are compared to bulk values). Moreover, we observed significant effect in all fluid properties at walls with protrusions, since we proved that there are fluid atoms that are trapped inside the cavities. As a result of trapping, velocity values are minimized inside the cavities, diffusion coefficient decreases and shear viscosity increases.Οι προσομοιώσεις με ηλεκτρονικό υπολογιστή σε ατομικά και μοριακά συστήματα στις νανο-διαστάσεις στοχεύουν στον υπολογισμό ιδιοτήτων που αφορούν στη δομή και τη δυναμική του συστήματος (στη νανοκλίμακα υπάρχουν ενδείξεις ότι δεν ισχύουν οι εξισώσεις της συνεχούς θεωρίας). Η θεωρητική βάση αυτής της προσέγγισης είναι η στατιστική θερμοδυναμική. Η Μοριακή Δυναμική αποτελεί ένα χρήσιμο εργαλείο για την αποκάλυψη των φαινομένων που λαμβάνουν χώρα στη νανοκλίμακα. Ως μέθοδος προσομοίωσης, βασίζεται στον υπολογισμό των αλληλεπιδράσεων μεταξύ των ατόμων του συστήματος και την επίλυση των εξισώσεων του Νεύτωνα για κάθε σωματίδιο έτσι ώστε να εξαχθούν οι θερμοδυναμικές ιδιότητες και οι ιδιότητες μεταφοράς. Στην παρούσα εργασία εφαρμόζεται η μέθοδος της Μοριακή Δυναμικής (MD - Molecular Dynamics) για τη μελέτη ροών σε νανο-αγωγούς. Μετά από ανάλυση της μεθόδου και οριοθέτηση των στόχων της διατριβής γίνεται εκτενής αναφορά στον τρόπο υπολογισμού ποσοτήτων που σχετίζονται με τις ιδιότητες του υγρού στους νανο-αγωγούς (π.χ., προφίλ πυκνότητας, προφίλ ταχύτητας, προφίλ ρυθμού παραμόρφωσης, προφίλ θερμοκρασίας, μήκος ολίσθησης) αλλά και στις ιδιότητες μεταφοράς (π.χ., συντελεστής διάχυσης, ιξώδες και θερμική αγωγιμότητα). Εξετάζεται η επίδραση διαφόρων παραμέτρων οι οποίες μπορεί να επηρεάσουν τις παραπάνω ιδιότητες, όπως η θερμοκρασία, η εξωτερική δύναμη, χαρακτηριστικά της γεωμετρίας των τοιχωμάτων των αγωγών, η αλληλεπίδραση ατόμων τοίχου και υγρού, η μέση πυκνότητα του υγρού και διάφορα χαρακτηριστικά του μοντέλου της προσομοίωσης. Συνοψίζοντας, επιχειρείται μια κατηγοριοποίηση όλων των παραμέτρων που δύνανται να επηρεάσουν τα χαρακτηριστικά των ροών σε νανο-αγωγούς, σε σχέση πάντα με την επίδραση της διάστασης των αγωγών η οποία αποτελεί και την πρωταρχική παράμετρο. Συμπερασματικά, παρατηρούμε ότι η επίδραση της διάστασης είναι καθοριστική για τη συμπεριφορά του υγρού σε νανο-αγωγούς. Το υγρό παρουσιάζει ισχυρή ανομοιογένεια σε διαστάσεις κοντά στα 1-3 nm, προφίλ ταχύτητας που δεν έχει παραβολική συμπεριφορά, μικρό συντελεστή διάχυσης, μεγάλο ιξώδες και μικρή θερμική αγωγιμότητα (όλες οι ποσότητες συγκρίνονται με τις τιμές τους σε ισορροπία). Επιπλέον, είναι γεγονός ότι σε τοίχους με προεξοχές παρατηρήσαμε σημαντική επίδραση σε όλες τις ιδιότητες του υγρού, καθώς αποδείξαμε ότι υπάρχουν άτομα υγρού τα οποία παγιδεύονται στο εσωτερικό των εσοχών του τοίχου. Αποτέλεσμα της παγίδευσης των ατόμων είναι η μείωση της μέσης ταχύτητας του υγρού στις εσοχές, η μείωση του συντελεστή διάχυσης αλλά και η αύξηση του τοπικού ιξώδους

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

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

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

Fiber-Reinforced Polymer Confined Concrete: Data-Driven Predictions of Compressive Strength Utilizing Machine Learning Techniques

Accurate estimation of the mechanical properties of concrete is important for the development of new materials to lead construction applications. Experimental research, aided by empirical and statistical models, has been commonly employed to establish a connection between concrete properties and the resulting compressive strength. However, these methods can be labor-intensive to develop and may not always produce accurate results when the relationships between concrete properties, mixture composition, and curing conditions are complex. In this paper, an experimental dataset based on uniaxial compression experiments conducted on concrete specimens, confined using fiber-reinforced polymer jackets, is incorporated to predict the compressive strength of confined specimens. Experimental measurements are bound to the mechanical and physical properties of the material and fed into a machine learning platform. Novel data science techniques are exploited at first to prepare the experimental dataset before entering the machine learning procedure. Twelve machine learning algorithms are employed to predict the compressive strength, with tree-based methods yielding the highest accuracy scores, achieving coefficients of determination close to unity. Eventually, it is shown that, by carefully manipulating experimental datasets and selecting the appropriate algorithm, a fast and accurate computational platform is created, which can be generalized to bypass expensive, time-consuming, and susceptible-to-errors experiments, and serve as a solution to practical problems in science and engineering

How wall properties control diffusion in grooved nanochannels: a molecular dynamics study

The effect of a geometrically-rough wall, amplified by its degree of wettability and stiffness on diffusion coefficient in cases of fluid flow in nanochannels is studied by non-equilibrium molecular dynamics. Diffusion coefficient values, either inside the grooves or as average channel values are affected by the rough wall characteristics. A significant anisotropy along the directions parallel and normal to the flow is observed inside the grooves, while a critical value of groove length below which this anisotropy is enhanced exists. Wall wettability is the property that affects diffusion the most and could be a means of controlling its behavior