
Deterministic and Stochastic Modeling in Computational Electromagnetics
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Deterministic computational models are those for which all inputs are precisely known, whereas stochastic modeling reflects uncertainty or randomness in one or more of the data inputs. Many problems in computational engineering therefore require both deterministic and stochastic modeling to be used in parallel, allowing for different degrees of confidence and incorporating datasets of different kinds. In particular, non-intrusive stochastic methods can be easily combined with widely used deterministic approaches, enabling this more robust form of data analysis to be applied to a range of computational challenges.
Deterministic and Stochastic Modeling in Computational Electromagnetics provides a rare treatment of parallel deterministic-stochastic computational modeling and its beneficial applications. Unlike other works of its kind, which generally treat deterministic and stochastic modeling in isolation from one another, it aims to demonstrate the usefulness of a combined approach and present particular use-cases in which such an approach is clearly required. It offers a non-intrusive stochastic approach which can be incorporated with minimal effort into virtually all existing computational models.
Readers will also find:
* A range of specific examples demonstrating the efficiency of deterministic-stochastic modeling
* Computational examples of successful applications including ground penetrating radars (GPR), radiation from 5G systems, transcranial magnetic and electric stimulation (TMS and TES), and more
* Introduction to fundamental principles in field theory to ground the discussion of computational modeling
Deterministic and Stochastic Modeling in Computational Electromagnetics is a valuable reference for researchers, including graduate and undergraduate students, in computational electromagnetics, as well as to multidisciplinary researchers, engineers, physicists, and mathematicians.
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Persons
DRAGAN POLJAK, PH.D., is Professor in the Department of Electronics and Computing Technology, University of Split, Croatia. He is a Senior Member of the IEEE and author of three books and more than 150 articles on subjects related to computational electromagnetics.
ANNA sUsNJARA, PH.D., is a Postdoctoral Researcher in the Department of Electronics and Computing Technology, University of Split, Croatia. She is a member of the IEEE and has authored or co-authored more than 40 journal and conference papers on subjects related to computational electromagnetics.
Content
About the Authors xv
Preface xvii
Part I Some Fundamental Principles in Field Theory 1
1 Least Action Principle in Electromagnetics 3
1.1 Hamilton Principle 4
1.2 Newton's Equation of Motion from Lagrangian 7
1.3 Noether's Theorem and Conservation Laws 8
1.4 Equation of Continuity from Lagrangian 12
1.5 Lorentz Force from Gauge Invariance 16
2 Fundamental Equations of Engineering Electromagnetics 21
2.1 Derivation of Two-Canonical Maxwell's Equation 21
2.2 Derivation of Two-Dynamical Maxwell's Equation 22
2.3 Integral Form of Maxwell's Equations, Continuity Equations, and Lorentz Force 25
2.4 Phasor Form of Maxwell's Equations 27
2.5 Continuity (Interface) Conditions 29
2.6 Poynting Theorem 30
2.7 Electromagnetic Wave Equations 32
2.8 Plane Wave Propagation 35
2.9 Hertz Dipole as a Simple Radiation Source 37
2.10 Wire Antennas of Finite Length 41
3 Variational Methods in Electromagnetics 47
3.1 Analytical Methods 47
3.2 Variational Basis for Numerical Methods 51
4 Outline of Numerical Methods 57
4.1 Variational Basis for Numerical Methods 60
4.2 The Finite Element Method 61
4.3 The Boundary Element Method 77
Part II Deterministic Modeling 87
5 Wire Configurations - Frequency Domain Analysis 89
5.1 Single Wire in the Presence of a Lossy Half-Space 89
5.2 Horizontal Dipole Above a Multi-layered Lossy Half-Space 100
5.3 Wire Array Above a Multilayer 125
5.4 Wires of Arbitrary Shape Radiating Over a Layered Medium 150
5.5 Complex Power of Arbitrarily Shaped Thin Wire Radiating Above a Lossy Half-Space 186
6 Wire Configurations - Time Domain Analysis 207
6.1 Single Wire Above a Lossy Ground 208
6.2 Numerical Solution of Hallen Equation via the Galerkin-Bubnov Indirect Boundary Element Method (GB-IBEM) 222
6.3 Application to Ground-Penetrating Radar 228
6.4 Simplified Calculation of Specific Absorption in Human Tissue 246
6.5 Time Domain Energy Measures 255
6.6 Time Domain Analysis of Multiple Straight Wires above a Half-Space by Means of Various Time Domain Measures 260
7 Bioelectromagnetics - Exposure of Humans in GHz Frequency Range 285
7.1 Assessment of Sab in a Planar Single Layer Tissue 286
7.2 Assessment of Transmitted Power Density in a Single Layer Tissue 295
7.3 Assessment of Sab in a Multilayer Tissue Model 318
7.4 Assessment of Transmitted Power Density in the Planar Multilayer Tissue Model 325
8 Multiphysics Phenomena 339
8.1 Electromagnetic-Thermal Modeling of Human Exposure to HF Radiation 340
8.2 Magnetohydrodynamics (MHD) Models for Plasma Confinement 348
8.3 Modeling of the Schrodinger Equation 370
Part III Stochastic Modeling 385
9 Methods for Stochastic Analysis 387
9.1 Uncertainty Quantification Framework 388
9.2 Stochastic Collocation Method 393
9.3 Sensitivity Analysis 402
10 Stochastic-Deterministic Electromagnetic Dosimetry 407
10.1 Internal Stochastic Dosimetry for a Simple Body Model Exposed to Low-Frequency Field 408
10.2 Internal Stochastic Dosimetry for a Simple Body Model Exposed to Electromagnetic Pulse 413
10.3 Internal Stochastic Dosimetry for a Realistic Three-Compartment Human Head Exposed to High-Frequency Plane Wave 417
10.4 Incident Field Stochastic Dosimetry for Base Station Antenna Radiation 423
11 Stochastic-Deterministic Thermal Dosimetry 433
11.1 Stochastic Sensitivity Analysis of Bioheat Transfer Equation 434
11.2 Stochastic Thermal Dosimetry for Homogeneous Human Brain 437
11.3 Stochastic Thermal Dosimetry for Three-Compartment Human Head 447
11.4 Stochastic Thermal Dosimetry below 6 GHz for 5G Mobile Communication Systems 450
12 Stochastic-Deterministic Modeling in Biomedical Applications of Electromagnetic Fields 459
12.1 Transcranial Magnetic Stimulation 460
12.2 Transcranial Electric Stimulation 466
12.3 Neuron's Action Potential Dynamics 481
12.4 Radiation Efficiency of Implantable Antennas 488
13 Stochastic-Deterministic Modeling of Wire Configurations in Frequency and Time Domain 503
13.1 Ground-Penetrating Radar 503
13.2 Grounding Systems 515
13.3 Air Traffic Control Systems 523
14 A Note on Stochastic Modeling of Plasma Physics Phenomena 535
14.1 Tokamak Current Diffusion Equation 535
References 543
Index 545
1
Least Action Principle in Electromagnetics
Laws of nature are governed by following fundamental principles - the action principle, locality, Lorentz invariance, and gauge invariance [1]. Hamilton's principle, or the least action principle, is originally developed for classical mechanics stating that a particle, among all of the trajectories between fixed time instants t1 and t2, follows the path which minimizes the action. Action is defined as time integral of the difference between the kinetic energy and potential energy, respectively. Thus, Hamilton's principle somehow requires the time averages of the kinetic energy and potential energy to distribute as equally as possible (equipartition) [2]. In classical mechanics, Hamilton's principle and Newton's second law represent equivalent formulations.
An extension of Hamilton's principle from classical mechanics to classical electromagnetics can be undertaken starting with the analysis of the motion of single charged particle [3]. Next step is to construct a Lagrangian for the electromagnetic field by extending the Lagrangian pertaining to classical mechanics. From the corresponding Lagrangians, featuring Noether's theorem and gauge invariance, it is possible to derive equation of continuity for the charge, Lorentz force, and Maxwell's equations, which can be found elsewhere, e.g. [2-5].
Generally, when a functional is extremal, Noether's theorem yields the conservation law. Thus, invariance of the system under a time translation results in the energy conservation. It is also worth noting that space translation invariance corresponds to the conservation of linear momentum, rotation invariance corresponds to the conservation of angular momentum, while gauge invariance yields the charge conservation [1, 2].
These derivations are recently reviewed in [6-8].
This chapter first deals with derivation of continuity equation and Lorentz force, and a derivation of Maxwell's equations from the electromagnetic field functional is carried out. Finally, a variational basis of numerical solution methods in electromagnetics is discussed.
1.1 Hamilton Principle
Hamilton variational principle represents not only the basis of modern analytical dynamics but also of universal physical laws, i.e. fundamental laws of classical physics can be understood in terms of action.
This section first deals with Hamilton's variational principle in mechanics, Newton's equation of motion, and Noether's theorem. Then the variational principle in electromagnetics is discussed.
For simplicity, a system with one degree of freedom represented by a generalized coordinate q is considered together with related function of position, velocity, and time where denotes the time derivative.
The task is to determine how a point particle should move in this one-dimensional space so that the time integral of L is minimized compared with the integral over the conceivable paths between the same starting and end points, as depicted in Fig. 1.1. The solution is given by stating q as a function of time q = q(t).
To compare all paths having the same starting and end points, the variation of function q is zero at both ends [1-4]
(1.1)i.e. all alternatives start at instant t1 and arrive together at instant t2.
The minimum condition is then given by a functional F expressed in terms of the integral [1-4]
(1.2)Figure 1.1 The varied function q(t).
In classical mechanics, function L is referred to as Lagrangian and is expressed as
(1.3)where Wkin and Wpot are the kinetic energy and potential energy, respectively.
According to the calculus of variation, the functional approaches minimum value
(1.4)when its variation vanishes, i.e.:
(1.5)which can also be written as
(1.6)Therefore, function q(t) minimizes functional (1.2) or (1.4), respectively, and it follows
(1.7)For the simplest case given by the variation of function L is given by
(1.8)And by performing some further mathematical manipulation, one readily obtains
(1.9)As dq = 0 at the ends of the path, the second term at the right-hand side automatically vanishes.
Furthermore, according to the fundamental lemma of variational calculus [4] the first integral term at the right-hand side of (1.9) vanishes if the following condition is satisfied
(1.10)It is worth noting that the second order differential Eq. (1.10) relates the position q for the time t, and also determines the true path of the system when two end positions and times are given. This equation is known as Lagrange-Euler equation of motion [1, 2].
In the case of a single function of multiple variables, (1.10) becomes
(1.11)Finally, if few functions of multiple independent variables are considered, it follows
(1.12)which, for example, pertains to three-dimensional problems in electromagnetics.
Hamilton variational principle can be considered a general law not only for particle dynamics but also for the dynamics of continuous materials.
An extension to three-dimensional problems in continuous materials, i.e. for physical fields, the variational principle corresponding to Eq. (1.6) is given by
(1.13)where is the so-called Lagrange density defined as
(1.14)and has a unit of energy per volume.
It is worth noting that the variational principle is an invariant for coordinate transformations [1].
1.2 Newton's Equation of Motion from Lagrangian
Lagrangian in classical mechanics for a particle with mass m with displacement at time t is of the form
(1.15)where is the particle velocity and stands for potential energy (not due to electromagnetic field).
The corresponding action F0 related to Lagrangian (1.15) is given by integral
(1.16)Now varying the action (1.16)
(1.17)It can be written as:
(1.18) (1.19)and it follows:
(1.20)which simply gives:
(1.21)If mass m is regarded as constant quantity, right-hand side of (1.21) represents the components of mechanical force:
(1.22)As the mechanical force is defined as a negative gradient of the potential energy
(1.23)one simply obtains a set of Newton's equations of motion
(1.24)where the right-hand side of (1.24) represents a force acting on the particle.
1.3 Noether's Theorem and Conservation Laws
There are physical quantities that do not change throughout the time development of physical systems. These quantities are stated to be conserved under certain conditions which are governed by conservation laws. It can be shown that conservation laws are a consequence of the symmetry properties of a physical system (invariance properties of a system under a group of transformations [2, 9]). The symmetry properties of the system and conservation laws are connected with Noether's theorem, e.g. [2, 9].
Let uk(x) (k = 1,2,.n) be a set of differentiable functions of the independent variable x and let vk(x) be the first derivatives, i.e. it can be written as
(1.25)Now Lagrangian L is defined as a function of x, and n functions of uk and n functions of vk.
(1.26)If one considers an infinitesimal transformation T
(1.27)Furthermore, under T, one consequently has:
(1.28) (1.29)Now assuming the functional
(1.30)to be invariant under T so that the transformation (1.30) which maps the interval O into O´ does not change, it follows
(1.31)Performing some mathematical manipulations, one obtains the following expression
(1.32)Finally, Noether's theorem states that if functional S is invariant under the infinitesimal one-parameter group of transformations T, then the set of n equations
(1.33)simply gives
(1.34)i.e. it can be written as
(1.35)namely, the expression is...
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