73 research outputs found

    Software for evaluating probability-based integrity of reinforced concrete structures

    Get PDF
    In recent years, much research work has been carried out in order to obtain a more controlled durability and long-term performance of concrete structures in chloride containing environment. In particular, the development of new procedures for probability-based durability design has proved to give a more realistic basis for the analysis. Although there is still a lack of relevant data, this approach has been successfully applied to several new concrete structures, where requirements to a more controlled durability and service life have been specified. A probability-based durability analysis has also become an important and integral part of condition assessment of existing concrete structures in chloride containing environment. In order to facilitate the probability-based durability analysis, a software named DURACON has been developed, where the probabilistic approach is based on a Monte Carlo simulation. In the present paper, the software for the probability-based durability analysis is briefly described and used in order to demonstrate the importance of the various durability parameters affecting the durability of concrete structures in chloride containing environment

    On the wave propagation of the multi-scale hybrid nanocomposite doubly curved viscoelastic panel

    Get PDF
    In this paper, wave propagation analysis of multi-hybrid nanocomposite (MHC) reinforced doubly curved panel embedded in the viscoelastic foundation is carried out. Higher-order shear deformable theory (HSDT) is utilized to express the displacement kinematics. The rule of mixture and modified Halpin–Tsai model are engaged to provide the effective material constant of the MHC reinforced doubly curved panel. By employing Hamilton’s principle, the governing equations of the structure are derived and solved with the aid of an analytical method. Afterward, a parametric study is carried out to investigate the effects of the viscoelastic foundation, carbon nanotubes’ (CNTs’) weight fraction, various MHC patterns, radius to total thickness ratio, and carbon fibers angel on the phase velocity of the MHC reinforced doubly curved panel in the viscoelastic medium. The results show that, by considering the viscous parameter, the relation between wavenumber and phase velocity changes from exponential increase to logarithmic boost. A useful suggestion of this research is that the effects of fiber angel and damping parameter on the phase velocity of a doubly curved panel are hardly dependent on the wavenumber. The presented study outputs can be used in ultrasonic inspection techniques and structural health monitoring.publishe

    Plasmonic nanoantenna based coupler for telecom range

    Get PDF

    Maxwell Equations without a Polarization Field using a paradigm from biophysics

    Full text link
    Electrodynamics is usually written with a polarization vector field to describe the response of matter to electric fields, or more specifically, to describe changes in distribution of charge as an electric field is changed. This approach does not allow unique specification of a polarization field from measurements of electric and magnetic fields. Many polarization fields produce the same electric and magnetic fields, because only the divergence of the polarization enters Maxwell's first equation, relating charge and electric field. The curl of any function can be added to a polarization field without changing the electric field at all. The divergence of the curl is always zero. Models of structures that produce polarization cannot be uniquely determined from electrical measurements for the same reason. Models must describe charge distribution not just distribution of polarization to be unique. I propose a different paradigm to describe field dependent charge, i.e., to describe the phenomena of polarization. I propose an operational definition of polarization that has worked well in biophysics where a field dependent, time dependent polarization provides the gating current that makes neuronal sodium and potassium channels respond to voltage. The operational definition has been applied successfully to experiments for nearly fifty years. Estimates of polarization have been computed from simulations, models, and theories using this definition and they fit experimental data quite well. I propose that the same operational definition be used to define polarization charge in experiments, models, computations, theories, and simulations of other systems. Charge movement needs to be computed from a combination of electrodynamics and mechanics because 'everything interacts with everything else'. The classical polarization field need not enter into that treatment at all.Comment: Typos correcte

    Integral equations applied to electromagnetic scattering in the resonance region.

    Get PDF
    In the application of an integral method to the problem of electromagnetic scattering by three-dimensional objects, the electromagnetic problem is formulated in terms of an electric field integral equation for conducting bodies and a combined field integral equation for dielectric or composite objects. The electric and magnetic fields are related to the unknown surface currents by the Green's functions for the scalar and vector potentials. Triangular patches are used to model the scatterer's surface and the basis functions proposed by Rao, Wilton and Glisson, represent the surface current on the scatterer's surface. The application of the method of moments for the solution of the integral equations results in double surface integrals, which are computationally very expensive. Rao, Wilton and Glisson avoided the computation of a double surface integral by approximating the surface integral over the observation triangle by evaluating the integral at the centre of each observation point using a one point Gaussian quadrature scheme. This approach has also been adopted by other workers as it is relatively straightforward to implement since it only requires the field evaluation over the source triangle. In addition, the edge lengths of the triangle patches should be of the order of one-tenth of a wavelength if good results are to be obtained. This simplifies the computational task and it was believed that it decreases the computation time. For electrically large objects, many patches are needed and the order of the system matrices derived from the discretisation of the integral equations becomes large. This thesis investigates whether the approximation used to compute the impedance terms in the reported schemes lead to a computationally efficient scheme. In this thesis, a comparison is made between the use of the EFIE and the CFIE with the full double surface integrals and the original EFIE and the CFIE schemes with the associated approximation. The integrals over the observation and source triangles are both evaluated. The equations of the discretised integral equations for conducting, dielectric and composite objects are derived to enable the impedance terms to be computed efficiently. A method is described of how to minimise the computing time for the evaluation of the double surface integrals and a criterion is presented for obtaining a good compromise between accuracy and total computing time. The proposed formulation has been developed for the EFIE, for scattering by perfect electric conductors only; for the CFIE, for both dielectric/magnetic materials only and also the CFIE for mixed perfect electric conductors and dielectric materials. The scheme has been used to calculate the radar cross- section of conducting, dielectric and mixed objects and the results compared with those based on the RWG formulation and from the literature. The basis of comparison with the RWG formulation is based on accuracy, total computation time and computer memory required. The proposed formulation's results for conducting objects compare well with results from the literature and clearly demonstrate significant computational advantage over the original RWG formulation. For dielectric objects, the proposed formulation shows only some computational advantage over the RWG formulation whereas there is a no Improvement with the mixed objects

    Discontinuous Galerkin Finite Element Methods for Maxwell\u27s Equations in Dispersive and Metamaterials Media

    Full text link
    Discontinuous Galerkin Finite Element Method (DG-FEM) has been further developed in this dissertation. We give a complete proof of stability and error estimate for the DG-FEM combined with Runge Kutta which is commonly used in different fields. The proved error estimate matches those numerical results seen in technical papers. Numerical simulations of metamaterials play a very important role in the design of invisibility cloak, and sub-wavelength imaging. We propose a leap-frog discontinuous Galerkin Finite Element Method to solve the time-dependent Maxwell\u27s equations in metamaterials. The stability and error estimate are proved for this scheme. The proposed algorithm is implemented and numerical results supporting the analysis are provided. The wave propagation simulation in the double negative index metamaterials supplemented with perfectly matched layer (PML) boundary is given with one discontinuous Galerkin time difference method (DGTD), of which the stability and error estimate are proved as well in this dissertation. To illustrate the effectiveness of this DGTD, we present some numerical result tables which show the consistent convergence rate and the simulation of PML in metamaterials is tested in this dissertation as well. Also the wave propagation simulation in metamaterals by this DGTD scheme is consistent with those seen in other papers. Several techniques have appeared for solving the time-dependent Maxwell\u27s equations with periodically varying coefficients. For the first time, I apply the discontinuous Galerkin (DG) method to this homogenization problem in dispersive media. For simplicity, my focus is on obtaining a solution in two-dimensions (2D) using 2D corrector equations. my numerical results show the DG method to be both convergent and efficient. Furthermore, the solution is consistent with previous treatments and theoretical expectations

    Compact support wavelet representations for solution of quantum and electromagnetic equations: Eigenvalues and dynamics

    Get PDF
    Wavelet-based algorithms are developed for solution of quantum and electromagnetic differential equations. Wavelets offer orthonormal localized bases with built-in multiscale properties for the representation of functions, differential operators, and multiplicative operators. The work described here is part of a series of tools for use in the ultimate goal of general, efficient, accurate and automated wavelet-based algorithms for solution of differential equations. The most recent work, and the focus here, is the elimination of operator matrices in wavelet bases. For molecular quantum eigenvalue and dynamics calculations in multiple dimensions, it is the coupled potential energy matrices that generally dominate storage requirements. A Coefficient Product Approximation (CPA) for the potential operator and wave function wavelet expansions dispenses with the matrix, reducing storage and coding complexity. New developments are required, however. It is determined that the CPA is most accurate for specific choices of wavelet families, and these are given here. They have relatively low approximation order (number of vanishing wavelet function moments), which would ordinarily be thought to compromise both wavelet reconstruction and differentiation accuracy. Higher-order convolutional coefficient filters are determined that overcome both apparent problems. The result is a practical wavelet method where the effect of applying the Hamiltonian matrix to a coefficient vector can be calculated accurately without constructing the matrix. The long-familiar Lanczos propagation algorithm, wherein one constructs and diagonalizes a symmetric tridiagonal matrix, uses both eigenvalues and eigenvectors. We show here that time-reversal-invariance for Hermitian Hamiltonians allows a new algorithm that avoids the usual need to keep a number Lanczos vectors around. The resulting Conjugate Symmetric Lanczos (CSL) method, which will apply for wavelets or other choices of basis or grid discretization, is simultaneously low-operation-count and low-storage. A modified CSL algorithm is used for solution of Maxwell's time-domain equations in Hamiltonian form for non-lossy media. The matrix-free algorithm is expected to complement previous work and to decrease both storage and computational overhead. It is expected- that near-field electromagnetic solutions around nanoparticles will benefit from these wavelet-based tools. Such systems are of importance in plasmon-enhanced spectroscopies

    Generalized averaged Gaussian quadrature and applications

    Get PDF
    A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal

    MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

    Get PDF
    Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described

    Advanced physics-based and data-driven strategies

    Get PDF
    Simulation Based Engineering Science (SBES) has brought major improvements in optimization, control and inverse analysis, all leading to a deeper understanding in many processes occuring in the real world. These noticeable breakthroughts are present in a vast variety of sectors such as aeronautic or automotive industries, mobile telecommunications or healthcare among many other fields. Nevertheless, SBES is currently confronting several difficulties to provide accurate results in complex industrial problems. Apart from the high computational costs associated with industrial applications, the errors introduced by constitutive modeling become more and more important when dealing with new materials. Concurrently, an unceasingly growing interest in concepts such as Big-Data, Machine Learning or Data-Analytics has been experienced. Indeed, this interest is intrinsically motivated by an exhaustive development in both data-acquisition and data-storage systems. For instance, an aircraft may produce over 500 GB of data during a single flight. This panorama brings a perfect opportunity to the so-called Dynamic Data Driven Application Systems (DDDAS), whose main objective is to merge classical simulation algorithms with data coming from experimental measures in a dynamic way. Within this scenario, data and simulations would no longer be uncoupled but rather a symbiosis that is to be exploited would achieve milestones which were inconceivable until these days. Indeed, data will no longer be understood as a static calibration of a given constitutive model but rather the model will be corrected dynamicly as soon as experimental data and simulations tend to diverge. Several numerical algorithms will be presented throughout this manuscript whose main objective is to strengthen the link between data and computational mechanics. The first part of the thesis is mainly focused on parameter identification, data-driven and data completion techniques. The second part is focused on Model Order Reduction (MOR) techniques, since they constitute a fundamental ally to achieve real time constraints arising from DDDAS framework.La Ciencia de la Ingeniería Basada en la Simulación (SBES) ha aportado importantes mejoras en la optimización, el control y el análisis inverso, todo lo cual ha llevado a una comprensión más profunda de muchos de los procesos que ocurren en el mundo real. Estos notables avances están presentes en una gran variedad de sectores como la industria aeronáutica o automotriz, las telecomunicaciones móviles o la salud, entre muchos otros campos. Sin embargo, SBES se enfrenta actualmente a varias dificultades para proporcionar resultados precisos en problemas industriales complejos. Aparte de los altos costes computacionales asociados a las aplicaciones industriales, los errores introducidos por el modelado constitutivo son cada vez más importantes a la hora de tratar con nuevos materiales. Al mismo tiempo, se ha experimentado un interés cada vez mayor en conceptos como Big-Data, Machine Learning o Data-Analytics. Ciertamente, este interés está intrínsecamente motivado por un desarrollo exhaustivo de los sistemas de adquisición y almacenamiento de datos. Por ejemplo, una aeronave puede producir más de 500 GB de datos durante un solo vuelo. Este panorama brinda una oportunidad perfecta a los denominados Sistemas de Aplicación Dinámicos Impulsados por Datos (DDDAS), cuyo objetivo principal es fusionar de forma dinámica los algoritmos clásicos de simulación con los datos procedentes de medidas experimentales. En este escenario, los datos y las simulaciones ya no se desacoplarían, sino que aprovechando una simbiosis se alcanzaría hitos que hasta ahora eran inconcebibles. Mas en detalle, los datos ya no se entenderán como una calibración estática de un modelo constitutivo dado, sino que el modelo se corregirá dinámicamente tan pronto como los datos experimentales y las simulaciones tiendan a diverger. A lo largo de este manuscrito se presentarán varios algoritmos numéricos cuyo objetivo principal es fortalecer el vínculo entre los datos y la mecánica computacional. La primera parte de la tesis se centra principalmente en técnicas de identificación de parámetros, basadas en datos y de compleción de datos. La segunda parte se centra en las técnicas de Reducción de Modelo (MOR), ya que constituyen un aliado fundamental para conseguir las restricciones de tiempo real derivadas del marco DDDAS.Les sciences de l'ingénieur basées sur la simulation (Simulation Based Engineering Science, SBES) ont apporté des améliorations majeures dans l'optimisation, le contrôle et l'analyse inverse, menant toutes à une meilleure compréhension de nombreux processus se produisant dans le monde réel. Ces percées notables sont présentes dans une grande variété de secteurs tels que l'aéronautique ou l'automobile, les télécommunications mobiles ou la santé, entre autres. Néanmoins, les SBES sont actuellement confrontées à plusieurs dificultés pour fournir des résultats précis dans des problèmes industriels complexes. Outre les coûts de calcul élevés associés aux applications industrielles, les erreurs introduites par la modélisation constitutive deviennent de plus en plus importantes lorsqu'il s'agit de nouveaux matériaux. Parallèlement, un intérêt sans cesse croissant pour des concepts tels que les données massives (big data), l'apprentissage machine ou l'analyse de données a été constaté. En effet, cet intérêt est intrinsèquement motivé par un développement exhaustif des systèmes d'acquisition et de stockage de données. Par exemple, un avion peut produire plus de 500 Go de données au cours d'un seul vol. Ce panorama apporte une opportunité parfaite aux systèmes d'application dynamiques pilotés par les données (Dynamic Data Driven Application Systems, DDDAS), dont l'objectif principal est de fusionner de manière dynamique des algorithmes de simulation classiques avec des données provenant de mesures expérimentales. Dans ce scénario, les données et les simulations ne seraient plus découplées, mais une symbiose à exploiter permettrait d'envisager des situations jusqu'alors inconcevables. En effet, les données ne seront plus comprises comme un étalonnage statique d'un modèle constitutif donné mais plutôt comme une correction dynamique du modèle dès que les données expérimentales et les simulations auront tendance à diverger. Plusieurs algorithmes numériques seront présentés tout au long de ce manuscrit dont l'objectif principal est de renforcer le lien entre les données et la mécanique computationnelle. La première partie de la thèse est principalement axée sur l'identification des paramètres, les techniques d'analyse des données et les techniques de complétion de données. La deuxième partie est axée sur les techniques de réduction de modèle (MOR), car elles constituent un allié fondamental pour satisfaire les contraintes temps réel découlant du cadre DDDAS
    • …
    corecore