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17 pages, 491 KiB  
Article
Kinetic Theory with Casimir Invariants—Toward Understanding of Self-Organization by Topological Constraints
by Zensho Yoshida
Entropy 2025, 27(1), 5; https://doi.org/10.3390/e27010005 - 25 Dec 2024
Viewed by 33
Abstract
A topological constraint, characterized by the Casimir invariant, imparts non-trivial structures in a complex system. We construct a kinetic theory in a constrained phase space (infinite-dimensional function space of macroscopic fields), and characterize a self-organized structure as a thermal equilibrium on a leaf [...] Read more.
A topological constraint, characterized by the Casimir invariant, imparts non-trivial structures in a complex system. We construct a kinetic theory in a constrained phase space (infinite-dimensional function space of macroscopic fields), and characterize a self-organized structure as a thermal equilibrium on a leaf of foliated phase space. By introducing a model of a grand canonical ensemble, the Casimir invariant is interpreted as the number of topological particles. Full article
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Figure 1

Figure 1
<p>(<b>A</b>) The equilibrium on the leaf defined by <math display="inline"><semantics> <mi>μ</mi> </semantics></math>. (<b>B</b>) The equilibrium on the leaf defined by <math display="inline"><semantics> <mi>μ</mi> </semantics></math> and <math display="inline"><semantics> <msub> <mi>J</mi> <mo>‖</mo> </msub> </semantics></math>. Contours: Density distribution of magnetized plasma in the neighborhood of a point dipole. Curves: Magnetic field lines (level sets of <math display="inline"><semantics> <mi>ψ</mi> </semantics></math>). See [<a href="#B14-entropy-27-00005" class="html-bibr">14</a>].</p>
Full article ">
20 pages, 1842 KiB  
Article
Explicit K-Symplectic and Symplectic-like Methods for Charged Particle System in General Magnetic Field
by Yulan Lu, Junbin Yuan, Haoyang Tian, Zhengwei Qin, Siyuan Chen and Hongji Zhou
Symmetry 2023, 15(6), 1146; https://doi.org/10.3390/sym15061146 - 25 May 2023
Cited by 1 | Viewed by 1317
Abstract
We propose explicit K-symplectic and explicit symplectic-like methods for the charged particle system in a general strong magnetic field. The K-symplectic methods are also symmetric. The charged particle system can be expressed both in a canonical and a non-canonical Hamiltonian system. If the [...] Read more.
We propose explicit K-symplectic and explicit symplectic-like methods for the charged particle system in a general strong magnetic field. The K-symplectic methods are also symmetric. The charged particle system can be expressed both in a canonical and a non-canonical Hamiltonian system. If the three components of the magnetic field can be integrated in closed forms, we construct explicit K-symplectic methods for the non-canonical charged particle system; otherwise, explicit symplectic-like methods can be constructed for the canonical charged particle system. The symplectic-like methods are constructed by extending the original phase space and obtaining the augmented separable Hamiltonian, and then by using the splitting method and the midpoint permutation. The numerical experiments have shown that compared with the higher order implicit Runge-Kutta method, the explicit K-symplectic and explicit symplectic-like methods have obvious advantages in long-term energy conservation and higher computational efficiency. It is also shown that the influence of the parameter ε in the general strong magnetic field on the Runge-Kutta method is bigger than the two kinds of symplectic methods. Full article
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Figure 1

Figure 1
<p>The phase orbit and the energy evolution obtained by the 2ndKSYM, 4thKSYM, RK3 and RK5 methods with the parameter <math display="inline"><semantics> <mrow> <mi>ϵ</mi> <mo>=</mo> <mn>1</mn> <mo>/</mo> <mn>5</mn> </mrow> </semantics></math> in Example 1. The time interval for subfigures (<b>a</b>,<b>b</b>) is <math display="inline"><semantics> <mrow> <mo>[</mo> <mn>0</mn> <mo>,</mo> <msup> <mn>10</mn> <mn>4</mn> </msup> <mi>π</mi> <mo>]</mo> </mrow> </semantics></math>, and the time stepsize is <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>π</mi> <mo>/</mo> <mn>800</mn> </mrow> </semantics></math>. In subfigure (<b>c</b>), the stepsize is <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>π</mi> <mo>/</mo> <mn>800</mn> </mrow> </semantics></math>, while in (<b>d</b>), the stepsize is <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>π</mi> <mo>/</mo> <mn>200</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 2
<p>The phase orbit projected on the <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mn>1</mn> </msub> <mo>−</mo> <msub> <mi>x</mi> <mn>2</mn> </msub> </mrow> </semantics></math> plane of the 2ndKSYM, 4thKSYM, RK3 and RK5 methods with the parameter <math display="inline"><semantics> <mrow> <mi>ϵ</mi> <mo>=</mo> <mn>1</mn> <mo>/</mo> <mn>10</mn> </mrow> </semantics></math> in Example 1. The time interval for the four subfigures is <math display="inline"><semantics> <mrow> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>2</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>4</mn> </msup> <mi>π</mi> <mo>]</mo> </mrow> </semantics></math>. In subfigure (<b>a</b>), the stepsize is <math display="inline"><semantics> <mrow> <mi>π</mi> <mo>/</mo> <mn>400</mn> </mrow> </semantics></math>, and in subfigure (<b>b</b>), the stepsize is <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>π</mi> <mo>/</mo> <mn>3200</mn> </mrow> </semantics></math>. In subfigure (<b>c</b>), the stepsize is <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>π</mi> <mo>/</mo> <mn>100</mn> </mrow> </semantics></math>, while in (<b>d</b>), the stepsize is <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>π</mi> <mo>/</mo> <mn>100</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 3
<p>The maximum energy error versus CPU time for the 2ndKSYM, 4thKSYM, RK3 and RK5 methods in Example 1. In subfigures (<b>a</b>,<b>b</b>), <math display="inline"><semantics> <mrow> <mi>ϵ</mi> <mo>=</mo> <mn>1</mn> <mo>/</mo> <mn>5</mn> </mrow> </semantics></math> while in subfigures (<b>c</b>,<b>d</b>), <math display="inline"><semantics> <mrow> <mi>ϵ</mi> <mo>=</mo> <mn>1</mn> <mo>/</mo> <mn>10</mn> </mrow> </semantics></math>. In subfigure (<b>a</b>), the stepsize is <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>π</mi> <mo>/</mo> <mn>800</mn> <mo>/</mo> <msup> <mn>2</mn> <mi>i</mi> </msup> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>3</mn> </mrow> </semantics></math>, while in (<b>b</b>), the stepsize is <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>π</mi> <mo>/</mo> <mn>100</mn> <mo>/</mo> <msup> <mn>2</mn> <mi>i</mi> </msup> <mo>,</mo> </mrow> </semantics></math><math display="inline"><semantics> <mrow> <mi>i</mi> <mo>=</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>3</mn> </mrow> </semantics></math>. In subfigure (<b>c</b>), the stepsize is <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>π</mi> <mo>/</mo> <mn>1600</mn> <mo>/</mo> <msup> <mn>2</mn> <mi>i</mi> </msup> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>3</mn> </mrow> </semantics></math>, while in (<b>d</b>), the stepsize is <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>π</mi> <mo>/</mo> <mn>100</mn> <mo>/</mo> <msup> <mn>2</mn> <mi>i</mi> </msup> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>3</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 4
<p>The energy evolution and computational efficiency for the 2ndKSYM, 4thKSYM, RK3 and RK5 methods with the parameter <math display="inline"><semantics> <mrow> <mi>ϵ</mi> <mo>=</mo> <mn>1</mn> <mo>/</mo> <mn>2</mn> </mrow> </semantics></math> in Example 2. In subfigure (<b>a</b>), the stepsize is <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>π</mi> <mo>/</mo> <mn>6400</mn> </mrow> </semantics></math>, while in subfigure (<b>b</b>), the stepsize is <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>π</mi> <mo>/</mo> <mn>800</mn> </mrow> </semantics></math>. The time interval is <math display="inline"><semantics> <mrow> <mo>[</mo> <mn>0</mn> <mo>,</mo> <msup> <mn>10</mn> <mn>4</mn> </msup> <mi>π</mi> <mo>]</mo> </mrow> </semantics></math>. In subfigure (<b>c</b>,<b>d</b>), the stepsize for the 2ndKSYM and RK3 methods is <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>π</mi> <mo>/</mo> <mn>3200</mn> <mo>/</mo> <msup> <mn>2</mn> <mi>i</mi> </msup> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>3</mn> </mrow> </semantics></math>, while that for the 4thKSYM and RK5 methods is <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>π</mi> <mo>/</mo> <mn>200</mn> <mo>/</mo> <msup> <mn>2</mn> <mi>i</mi> </msup> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>3</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 5
<p>The energy error obtained by the 2ndKSYM, 4thKSYM, RK3 and RK5 methods with the parameter <math display="inline"><semantics> <mrow> <mi>ϵ</mi> <mo>=</mo> <mn>1</mn> <mo>/</mo> <mn>5</mn> </mrow> </semantics></math> in Example 2. The stepsize for the 2ndKSYM and RK3 methods is <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>π</mi> <mo>/</mo> <mn>12</mn> <mo>,</mo> <mn>800</mn> </mrow> </semantics></math>, and the stepsize for the 4thKSYM and RK5 methods is <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>π</mi> <mo>/</mo> <mn>1600</mn> </mrow> </semantics></math>. The time interval is <math display="inline"><semantics> <mrow> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>1000</mn> <mi>π</mi> <mo>]</mo> </mrow> </semantics></math>. Subfigures (<b>a</b>,<b>b</b>) are the energy errors obtained by the 2ndKSYM and the RK3 methods. Subfigures (<b>c</b>,<b>d</b>) are the energy errors obtained by the 4thKSYM and the RK5 methods.</p>
Full article ">Figure 5 Cont.
<p>The energy error obtained by the 2ndKSYM, 4thKSYM, RK3 and RK5 methods with the parameter <math display="inline"><semantics> <mrow> <mi>ϵ</mi> <mo>=</mo> <mn>1</mn> <mo>/</mo> <mn>5</mn> </mrow> </semantics></math> in Example 2. The stepsize for the 2ndKSYM and RK3 methods is <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>π</mi> <mo>/</mo> <mn>12</mn> <mo>,</mo> <mn>800</mn> </mrow> </semantics></math>, and the stepsize for the 4thKSYM and RK5 methods is <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>π</mi> <mo>/</mo> <mn>1600</mn> </mrow> </semantics></math>. The time interval is <math display="inline"><semantics> <mrow> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>1000</mn> <mi>π</mi> <mo>]</mo> </mrow> </semantics></math>. Subfigures (<b>a</b>,<b>b</b>) are the energy errors obtained by the 2ndKSYM and the RK3 methods. Subfigures (<b>c</b>,<b>d</b>) are the energy errors obtained by the 4thKSYM and the RK5 methods.</p>
Full article ">Figure 6
<p>The phase orbit projected on the <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mn>1</mn> </msub> <mo>−</mo> <msub> <mi>x</mi> <mn>2</mn> </msub> </mrow> </semantics></math> plane and the energy evolution of the 2ndKSYM, 4thKSYM, RK3 and RK5 methods with the parameter <math display="inline"><semantics> <mrow> <mi>ϵ</mi> <mo>=</mo> <mn>1</mn> <mo>/</mo> <mn>400</mn> </mrow> </semantics></math> in Example 3. Subfigure (<b>a</b>,<b>b</b>) are the phase orbits obtained by the 2ndKSYM and the RK3 methods with the time interval being <math display="inline"><semantics> <mrow> <mo>[</mo> <mn>0</mn> <mo>,</mo> <msup> <mn>10</mn> <mn>5</mn> </msup> <mi>π</mi> <mo>]</mo> </mrow> </semantics></math> and the stepsize being <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>π</mi> <mo>/</mo> <mn>2</mn> </mrow> </semantics></math>. For subfigure (<b>c</b>), the stepsize for the two methods is <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>π</mi> <mo>/</mo> <mn>2</mn> </mrow> </semantics></math>, while for subfigure (<b>d</b>), the stepsize is <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>π</mi> </mrow> </semantics></math>.</p>
Full article ">Figure 7
<p>The computational efficiency obtained by the 2ndKSYM, 4thKSYM, RK3 and RK5 methods in Example 3. For subfigure (<b>a</b>), the value is <math display="inline"><semantics> <mrow> <mi>ϵ</mi> <mo>=</mo> <mn>1</mn> <mo>/</mo> <mn>100</mn> </mrow> </semantics></math>, and the time interval is <math display="inline"><semantics> <mrow> <mo>[</mo> <mn>0</mn> <mo>,</mo> <msup> <mn>10</mn> <mn>5</mn> </msup> <mi>π</mi> <mo>]</mo> </mrow> </semantics></math>. For subfigure (<b>b</b>), the value is <math display="inline"><semantics> <mrow> <mi>ϵ</mi> <mo>=</mo> <mn>1</mn> <mo>/</mo> <mn>200</mn> </mrow> </semantics></math>, and the time interval is <math display="inline"><semantics> <mrow> <mo>[</mo> <mn>0</mn> <mo>,</mo> <msup> <mn>10</mn> <mn>5</mn> </msup> <mi>π</mi> <mo>]</mo> </mrow> </semantics></math>. In both subfigures, the stepsize for the 2ndKSYM and RK3 methods is <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>π</mi> <mo>/</mo> <msup> <mn>2</mn> <mi>i</mi> </msup> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>3</mn> </mrow> </semantics></math>, and the stepsize for the 4thKSYM and RK5 methods is <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>2</mn> <mi>π</mi> <mo>/</mo> <msup> <mn>2</mn> <mi>i</mi> </msup> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>3</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 8
<p>The energy error and the computational efficiency obtained by the 2ndKSYM, 4thKSYM, RK3 and RK5 methods with the parameter <math display="inline"><semantics> <mrow> <mi>ϵ</mi> <mo>=</mo> <mn>1</mn> <mo>/</mo> <mn>5</mn> </mrow> </semantics></math> in Example 4. For subfigures (<b>a</b>,<b>b</b>), the time interval is <math display="inline"><semantics> <mrow> <mo>[</mo> <mn>0</mn> <mo>,</mo> <msup> <mn>10</mn> <mn>4</mn> </msup> <mi>π</mi> <mo>]</mo> </mrow> </semantics></math>. The stepsize for the 2ndKSYM and RK3 methods is <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>π</mi> <mo>/</mo> <mn>800</mn> </mrow> </semantics></math>, and the stepsize for the 4thKSYM and RK5 methods is <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>π</mi> <mo>/</mo> <mn>200</mn> </mrow> </semantics></math>. For subfigure (<b>c</b>), the time interval is <math display="inline"><semantics> <mrow> <mo>[</mo> <mn>0</mn> <mo>,</mo> <msup> <mn>10</mn> <mn>3</mn> </msup> <mi>π</mi> <mo>]</mo> </mrow> </semantics></math>. The stepsize for the 2ndKSYM and RK3 methods is <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>π</mi> <mo>/</mo> <mn>800</mn> <mo>/</mo> <msup> <mn>2</mn> <mi>i</mi> </msup> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>3</mn> </mrow> </semantics></math>, and the stepsize for the 4thKSYM and RK5 methods is <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>π</mi> <mo>/</mo> <mn>50</mn> <mo>/</mo> <msup> <mn>2</mn> <mi>i</mi> </msup> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>3</mn> </mrow> </semantics></math>.</p>
Full article ">
7 pages, 1232 KiB  
Article
A Symplectic Algorithm for Constrained Hamiltonian Systems
by Jingli Fu, Lijun Zhang, Shan Cao, Chun Xiang and Weijia Zao
Axioms 2022, 11(5), 217; https://doi.org/10.3390/axioms11050217 - 7 May 2022
Cited by 3 | Viewed by 2262
Abstract
In this paper, a symplectic algorithm is utilized to investigate constrained Hamiltonian systems. However, the symplectic method cannot be applied directly to the constrained Hamiltonian equations due to the non-canonicity. We firstly discuss the canonicalization method of the constrained Hamiltonian systems. The symplectic [...] Read more.
In this paper, a symplectic algorithm is utilized to investigate constrained Hamiltonian systems. However, the symplectic method cannot be applied directly to the constrained Hamiltonian equations due to the non-canonicity. We firstly discuss the canonicalization method of the constrained Hamiltonian systems. The symplectic method is used to constrain Hamiltonian systems on the basis of the canonicalization, and then the numerical simulation of the system is carried out. An example is presented to illustrate the application of the results. By using the symplectic method of constrained Hamiltonian systems, one can solve the singular dynamic problems of nonconservative constrained mechanical systems, nonholonomic constrained mechanical systems as well as physical problems in quantum dynamics, and also available in many electromechanical coupled systems. Full article
(This article belongs to the Special Issue 10th Anniversary of Axioms: Mathematical Physics)
Show Figures

Figure 1

Figure 1
<p>Trajectory of the canonical variable <math display="inline"><semantics> <mrow> <mtable> <mtr> <mtd> <mrow> <mover accent="true"> <mi>p</mi> <mo>˜</mo> </mover> <mo>,</mo> </mrow> </mtd> <mtd> <mover accent="true"> <mi>q</mi> <mo>˜</mo> </mover> </mtd> </mtr> </mtable> </mrow> </semantics></math>.</p>
Full article ">Figure 2
<p>Trajectory of the non-canonical variable <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>,</mo> <mi>q</mi> </mrow> </semantics></math>.</p>
Full article ">Figure 3
<p>Trajectory of the non-canonical variable.</p>
Full article ">
28 pages, 441 KiB  
Review
Quasi-Lie Brackets and the Breaking of Time-Translation Symmetry for Quantum Systems Embedded in Classical Baths
by Alessandro Sergi, Gabriel Hanna, Roberto Grimaudo and Antonino Messina
Symmetry 2018, 10(10), 518; https://doi.org/10.3390/sym10100518 - 16 Oct 2018
Cited by 16 | Viewed by 3502
Abstract
Many open quantum systems encountered in both natural and synthetic situations are embedded in classical-like baths. Often, the bath degrees of freedom may be represented in terms of canonically conjugate coordinates, but in some cases they may require a non-canonical or non-Hamiltonian representation. [...] Read more.
Many open quantum systems encountered in both natural and synthetic situations are embedded in classical-like baths. Often, the bath degrees of freedom may be represented in terms of canonically conjugate coordinates, but in some cases they may require a non-canonical or non-Hamiltonian representation. Herein, we review an approach to the dynamics and statistical mechanics of quantum subsystems embedded in either non-canonical or non-Hamiltonian classical-like baths which is based on operator-valued quasi-probability functions. These functions typically evolve through the action of quasi-Lie brackets and their associated Quantum-Classical Liouville Equations, or through quasi-Lie brackets augmented by dissipative terms. Quasi-Lie brackets possess the unique feature that, while conserving the energy (which the Noether theorem links to time-translation symmetry), they violate the time-translation symmetry of their algebra. This fact can be heuristically understood in terms of the dynamics of the open quantum subsystem. We then describe an example in which a quantum subsystem is embedded in a bath of classical spins, which are described by non-canonical coordinates. In this case, it has been shown that an off-diagonal open-bath geometric phase enters into the propagation of the quantum-classical dynamics. Next, we discuss how non-Hamiltonian dynamics may be employed to generate the constant-temperature evolution of phase space degrees of freedom coupled to the quantum subsystem. Constant-temperature dynamics may be generated by either a classical Langevin stochastic process or a Nosé–Hoover deterministic thermostat. These two approaches are not equivalent but have different advantages and drawbacks. In all cases, the calculation of the operator-valued quasi-probability function allows one to compute time-dependent statistical averages of observables. This may be accomplished in practice using a hybrid Molecular Dynamics/Monte Carlo algorithms, which we outline herein. Full article
(This article belongs to the Special Issue New Trends in Quantum Electrodynamics)
755 KiB  
Article
Metriplectic Algebra for Dissipative Fluids in Lagrangian Formulation
by Massimo Materassi
Entropy 2015, 17(3), 1329-1346; https://doi.org/10.3390/e17031329 - 16 Mar 2015
Cited by 6 | Viewed by 5092
Abstract
The dynamics of dissipative fluids in Eulerian variables may be derived from an algebra of Leibniz brackets of observables, the metriplectic algebra, that extends the Poisson algebra of the frictionless limit of the system via a symmetric semidefinite component, encoding dissipative forces. [...] Read more.
The dynamics of dissipative fluids in Eulerian variables may be derived from an algebra of Leibniz brackets of observables, the metriplectic algebra, that extends the Poisson algebra of the frictionless limit of the system via a symmetric semidefinite component, encoding dissipative forces. The metriplectic algebra includes the conserved total Hamiltonian H, generating the non-dissipative part of dynamics, and the entropy S of those microscopic degrees of freedom draining energy irreversibly, which generates dissipation. This S is a Casimir invariant of the Poisson algebra to which the metriplectic algebra reduces in the frictionless limit. The role of S is as paramount as that of H, but this fact may be underestimated in the Eulerian formulation because S is not the only Casimir of the symplectic non-canonical part of the algebra. Instead, when the dynamics of the non-ideal fluid is written through the parcel variables of the Lagrangian formulation, the fact that entropy is symplectically invariant clearly appears to be related to its dependence on the microscopic degrees of freedom of the fluid, that are themselves in involution with the position and momentum of the parcel. Full article
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