Simulation and Modeling of Nanotechnology Aircraft Using MATLAB

Simulation and Modeling of Nanotechnology Aircraft Using MATLAB

Indradeep Kumar (Vels Institute of Science, Technology & Advanced Studies, India)
Copyright: © 2019 |Pages: 34
DOI: 10.4018/978-1-5225-7921-2.ch008

Abstract

The design methods based on aerospace model have been widely used in aircraft conceptual design for decades and proven very effective when restricted to simple problems with very approximate analyses. These monolithic, large, design and analysis codes are genuinely multidisciplinary, but as analyses become more complex, such codes have grown so large as to be incomprehensible and hence difficult to maintain. This chapter deals with the computational modeling of nanoparticles. Nanomaterials constitute a prominent sub-discipline in the materials and chemical sciences. Conventional materials like glass, ceramic, metals, polymers, or semiconductors can be acquired with nanoscale proportions. Nanomaterials have various microstructural distinctive attributes such as nanodiscs, nanotubes, nanocoatings, quantum dots, nanocomposites, and nanowires. The unique properties of nanoparticle-based materials and devices depend directly on size and structure dependent properties.
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Introduction

This chapter deals with the computational modeling of nanoparticles. Nanomaterials constitute a prominent sub-discipline in the materials and chemical sciences. Conventional materials like glass, ceramic, metals, polymers or semiconductors can be acquired with nanoscale proportions. Nanomaterials have various micro structural distinctive attributes such as nanodiscs, nanotubes, nanocoatings, quantum dots, nanocomposites, and nanowires. The unique properties of nanoparticle based materials and devices depend directly on size and structure dependent properties. Nanoparticle size must be firmly controlled to take all advantage of effects of quantum size in technological applications, and agglomeration must be averted. It can only be done if the flock is controlled, which requires, the rate of new particle formation is quantitatively resolute.

In Real-time measurements, distributions of particle size and particle structure are, thus, validate techniques for the evolution of nanotechnology. Precise and reliable models for simulating transport, coagulation, deposition, and dispersion of nanoparticles and their cluster are needed for the development of design tools for applications in technological, including nanoparticle instrumentation, sensing, dilution, sampling, and focusing nanoparticle behavior in the chemically reactive framework. Computational techniques allow us to validate and explore hypotheses about the experimentally observation that may otherwise not be approachable through conventional experimental techniques. Additionally, simulations with computer allow for the theory to propose areas of interest in which experimental techniques can be applied. The most common computer simulation methods are Monte Carlo (MC) and molecular dynamics (MD), have been extensively applied in a variety of model framework.

Computer science offers more opportunities for nanotechnology. Soft computing techniques such as cellular automata, genetic algorithms and swarm intelligence, can impart required emergent properties like self-repair, growth, and complex networks to the framework. Many books have successfully applied such techniques to real-world problems, including complex control systems in manufacturing and control in aircraft system. Dealing in nanoscale systems involves the understanding and development of nanotechnology computing techniques as well as the application of these techniques in real-world tasks, often to the problems of other Chapter areas. The techniques in nanotechnology systems comprise algorithms in machine learning, artificial intelligence (AI), knowledge representation reasoning, and natural computing. With some improvement in nanotechnology characteristics, these techniques can be applied to control a flock of a trillion nano assemblers. It is expected that normal computing methods such as these will overcome concerns about harmful implications of nanotechnology and prevent the notorious scenario of self-replicating nanorobots multiplying uncontrollably.

Presents micromagnetics: finite element analysis of nano-sized magnetic materials using MATLAB. Fabrication techniques developed over the past decades have allowed engineers and scientists to form magnetic materials in the nanometer regime. Such materials exhibit very interesting magnetic properties that are significantly different from bulk materials. At such scales, the behaviors of the magnetic materials are inadequately described by Maxwell’s equations alone. The theory of micromagnetics deals with the behaviors of these nano-sized magnetic materials by including the quantum mechanical effects that are significant at this scale. Micromagnetics theory has been successful in predicting the formation of domain walls in magnetic materials and also the formation of interesting magnetic states such as the vortex and leaf states. It also finds its application in many engineering aspects such as the digital data storage technology. Chapter 3 will introduce the basic theory of micromagnetics, show how the finite element method can be applied to the governing equation of micromagnetics (Landau–Lifshitz–Gilbert equation), and finally, show how all these can be done using MATLAB.

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