Algirdas Maknickas

Parallel discrete element simulation of poly-dispersed granular material

The paper presents parallel 3D DEM simulation of poly-dispersed material described by the normal size distribution. Static domain decomposition and message passing inter-processor communication have been implemented in the DEM code. A novel algorithm for moving particles that exchange processors has been incorporated in the domain decomposition framework. Parallel performance of the developed algorithm and software has been investigated by a series of benchmark tests conducting tri-axial compaction of material with different numbers of particles, heterogeneity ratios and compaction durations. The speed-up equal to 8.81 has been obtained on 10 processors of the distributed memory PC cluster. It has been shown that a drastic increase of computational expenses of simulation for the poly-dispersed material in terms of CPU time is associated with the increase of its heterogeneity. A contribution of the temporal evolution of microscopic behaviour has also been illustrated.

Investigation of performance of programming approaches and languages used for numerical simulation of granular material by the discrete element method

Abstract Performance of programming approaches and languages used for the development of software codes for numerical simulation of granular material dynamics by the discrete element method (DEM) is investigated. The granular material considered represents a space filled with discrete spherical visco-elastic particles, and the behaviour of material under imposed conditions is simulated using the DEM. The object-oriented programming approach (implemented via C++) was compared with the procedural approach (using FORTRAN 90 and OBJECT PASCAL) in order to test their efficiency. The identical neighbour-searching algorithm, contact forces model and time integration method were implemented in all versions of codes. Two identical representative examples of the dynamic behaviour of granular material on a personal computer (compatible with IBM PC) were solved. The results show that software based on procedural approach runs faster in compare with software based on OOP, and software developed by FORTRAN 90 runs faster in compare with software developed by OBJECT PASCAL.

Financial Market Prediction System with Evolino Neural Network and Delphi Method

Use of artificial intelligence systems in forecasting financial markets requires a reliable and simple model that would ensure profitable growth. The model presented in the paper combines Evolino recurrent neural networks with orthogonal data inputs and the Delphi expert evaluation method for its investment portfolio decision making process. A statistical study demonstrates the reliability of the model and describes its accuracy. Capabilities of the model are demonstrated using a trading simulation.

Computation and visualization of discrete particle systems on gLite-based grid

Three-dimensional simulation of discrete particle systems is performed by the discrete element method (DEM) software on the gLite-based BalticGrid infrastructure. The performance of a parallel algorithm for particles exchanging processors is investigated by using a number of benchmarks. Polydispersed particle systems are visualized by a novel grid e-service VizLitG designed for convenient access and interactive visualization of remote data files located on the grid. Partial dataset transfer from the storage element is implemented in the visualization e-service. The efficiency tests of VizLitG are performed on the datasets of different sizes. Two granular problems associated with triaxial compaction and hopper discharge are solved.

Discrete element simulating the hydrodynamic effects in acoustic agglomeration of micron-sized particles

ABSTRACT Numerical simulation of the acoustic agglomeration of micron-sized aerosol particles by a discrete element method (DEM) is demonstrated. The conventional DEM technique used in granular dynamics is modified for simulation of the acoustically induced attractive motion of particles in an incompressible fluid. The problem-specific orthokinetic collision, acoustic wake, and mutual radiation pressure effects yielding binary attraction and sticking of the particles are considered within the DEM approach. The acoustically induced agglomeration of two aerosol particles and 3D particles’ system is illustrated by numerical results. Numerical values of the agglomeration time of two particles obtained for a wide range of acoustic frequencies are analyzed. Comparison of various hydrodynamic effects with available experimental data indicates an overestimated contribution of the mutual radiation pressure model. The performance of the DEM technique and specific features concerning long-range interactions between particles are demonstrated by simulating 3D particles’ systems. The obtained numerical results illustrating the variation of number concentration with time are compared to available experimental data of coal-fired fly ash particles’ agglomeration; a relatively good agreement with the acoustic wake mechanism is observed.

Development of cloud services for patient-specific simulations of blood flows through aortic valves

The paper presents the development of cloud software services for patient-specific computational analysis of blood flows through the aortic valve on a private university cloud. The main focus is on the software service level at the top of the provided computational platform. Blood flow through the aortic valve was considered as a pilot application of the OpenStack cloud infrastructure. A modelling software environment based on ANSYS Fluent was developed as a software service (SaaS) for the numerical analysis of low flow, low pressure gradient aortic stenosis. Segmentation software services were designed to deal with the patient-specific issues of the computational analysis. User-friendly management tools were developed using Apache jclouds API to enhance the management of OpenStack cloud infrastructure and to increase the accessibility of the required software. The performance of the cloud infrastructure was assessed by testing CPU, memory bandwidth, disk I/O and the developed software service for medical computations. The performance measured on Xen hardware virtual machines, KVM virtual machines and Docker containers were compared with the performance obtained by using the native hardware.

Biefeld-Brown Effect and Space Curvature of Electromagnetic Field

With applying of new proposed electromagnetic gravity Lagrangian together with Einstein-Hilbert equation not zero space curvature was derived. The curvature gives “a priory” postulate of equivalence of mass and electromagnetic field gravity properties. The non zero trace of energy-stress tensor of electrical field changes space curvature of gravity mass, which yields to prediction of dependence of capacitor gravity mass from capacitor capacitance and voltage values, observed in Biefeld-Brown effect. The other, not observed prediction could be applied to coil gravity mass dependence from coil inductance and current values. New physical constant, electromagnetic field gravity constant αg, was introduced to conform with theoretical and experimental data.

Adapting the discrete element method to simulation of acoustic agglomeration of aerosol particles

Adaptation of the Discrete Element Method (DEM) for simulation of acoustic agglomeration of aerosol particles is considered. The aerosol is assumed to be a system of dispersed spherical particles embedded in incompressible medium. The modelled processes are considered on the scale of the oscillatory motion of particles. Under the action of a monochromatic sound wave agglomeration of smaller particles, which are sequentially formed into larger particles, is initiated. The effect of the orthokinetic and acoustic wake mechanisms is taken into account. This report presents some theoretical aspects of acoustic agglomeration, as well as the overall computational framework and evaluation of the acoustic forces on the particles resulting from oscillatory fluid motion. Theoretical models are illustrated by the results obtained in numerical simulation of a multiparticle system.

Recognition of normal–abnormal phonocardiographic signals using deep convolutional neural networks and mel-frequency spectral coefficients

Intensive care unit patients are heavily monitored, and several clinically-relevant parameters are routinely extracted from high resolution signals. OBJECTIVE The goal of the 2016 PhysioNet/CinC Challenge was to encourage the creation of an intelligent system that fused information from different phonocardiographic signals to create a robust set of normal/abnormal signal detections. APPROACH Deep convolutional neural networks and mel-frequency spectral coefficients were used for recognition of normal-abnormal phonocardiographic signals of the human heart. This technique was developed using the PhysioNet.org Heart Sound database and was submitted for scoring on the challenge test set. MAIN RESULTS The current entry for the proposed approach obtained an overall score of 84.15% in the last phase of the challenge, which provided the sixth official score and differs from the best score of 86.02% by just 1.87%.

On Efficiency of Parallel Solvers for the Blood Flow through Aortic Valve

Mathematical modelling of cardiac haemodynamics presents a great challenge to the computational scientists due to numerous numerical issues and required computational resources. In this paper, we study the parallel performance of 3D simulation software for the blood flow through the aortic valve. The fluid flow problem with the open aortic valve leaflets is formulated and solved in parallel. The choice between the segregated and coupled numerical schemes is discussed and investigated. We present and compare the parallel performance results of both types of parallel solvers. We investigate their strong and weak scalability.