Muhammad Ibrahim

Bio: Muhammad Ibrahim is an academic researcher from University of Science and Technology Beijing. The author has contributed to research in topics: Nanofluid & Nusselt number. The author has an hindex of 6, co-authored 38 publications receiving 203 citations. Previous affiliations of Muhammad Ibrahim include University of Tabuk & Khalifa University.

Fast parallel algorithms for short-range molecular dynamics

Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Modeling of Cattaneo-Christov double diffusions (CCDD) in Williamson nanomaterial slip flow subject to porous medium

Abstract: Here magnetohydrodynamics (MHD) nanomaterial slip flow of Williamson fluid is addressed. The flow is discussed over a porous medium and generated via nonlinear stretching phenomenon. The behavior of heat and mass transport are discussed subject to Cattaneo-Christov double diffusions (CCDD). Mathematical modeling for both CCDD is performed under the basic concept of Fourier's and Fick's laws. The energy equation for the consider flow problem is developed using Brownian motion, dissipation and thermophoretic diffusion. Relevant transformations variables are utilized to convert the partial differential equations into ordinary ones. Flow parameters are discussed on the velocity, temperature and concentration through built-in-Shooting method. Skin friction is computed and discussed through bar chart versus Weissenberg number and slip parameter. It is concluded that the skin friction coefficient is decreased for higher values of Weissenberg number and slip parameter. Furthermore, velocity field decays against Weissenberg number, slip parameter and porosity parameter.

A Review on Battery Modelling Techniques

Abstract: The growing demand for electrical energy and the impact of global warming leads to a paradigm shift in the power sector. This has led to the increased usage of renewable energy sources. Due to the intermittent nature of the renewable sources of energy, devices capable of storing electrical energy are required to increase its reliability. The most common means of storing electrical energy is battery systems. Battery usage is increasing in the modern days, since all mobile systems such as electric vehicles, smart phones, laptops, etc., rely on the energy stored within the device to operate. The increased penetration rate of the battery system requires accurate modelling of charging profiles to optimise performance. This paper presents an extensive study of various battery models such as electrochemical models, mathematical models, circuit-oriented models and combined models for different types of batteries. It also discusses the advantages and drawbacks of these types of modelling. With AI emerging and accelerating all over the world, there is a scope for researchers to explore its application in multiple fields. Hence, this work discusses the application of several machine learning and meta heuristic algorithms for battery management systems. This work details the charging and discharging characteristics using the black box and grey box techniques for modelling the lithium-ion battery. The approaches, advantages and disadvantages of black box and grey box type battery modelling are analysed. In addition, analysis has been carried out for extracting parameters of a lithium-ion battery model using evolutionary algorithms.