# 耗散结构

A dissipative system is a thermodynamically open system which is operating out of, and often far from, thermodynamic equilibrium in an environment with which it exchanges energy and matter. A tornado may be thought of as a dissipative system.

A dissipative system is a thermodynamically open system which is operating out of, and often far from, thermodynamic equilibrium in an environment with which it exchanges energy and matter. A tornado may be thought of as a dissipative system.

Dissipative system 是一个热力学上的开放系统，在一个与热力学平衡交换能量和物质的环境中运行。龙卷风可以被认为是 Dissipative system。

A dissipative structure is a dissipative system that has a dynamical régime that is in some sense in a reproducible steady state. This reproducible steady state may be reached by natural evolution of the system, by artifice, or by a combination of these two.

A dissipative structure is a dissipative system that has a dynamical régime that is in some sense in a reproducible steady state. This reproducible steady state may be reached by natural evolution of the system, by artifice, or by a combination of these two.

## Overview

A dissipative structure is characterized by the spontaneous appearance of symmetry breaking (anisotropy) and the formation of complex, sometimes chaotic, structures where interacting particles exhibit long range correlations. Examples in everyday life include convection, turbulent flow, cyclones, hurricanes and living organisms. Less common examples include lasers, Bénard cells, droplet cluster, and the Belousov–Zhabotinsky reaction.[1]

A dissipative structure is characterized by the spontaneous appearance of symmetry breaking (anisotropy) and the formation of complex, sometimes chaotic, structures where interacting particles exhibit long range correlations. Examples in everyday life include convection, turbulent flow, cyclones, hurricanes and living organisms. Less common examples include lasers, Bénard cells, droplet cluster, and the Belousov–Zhabotinsky reaction.

One way of mathematically modeling a dissipative system is given in the article on wandering sets: it involves the action of a group on a measurable set.

One way of mathematically modeling a dissipative system is given in the article on wandering sets: it involves the action of a group on a measurable set.

Dissipative systems can also be used as a tool to study economic systems and complex systems.[2] For example, a dissipative system involving self-assembly of nanowires has been used as a model to understand the relationship between entropy generation and the robustness of biological systems.[3]

Dissipative systems can also be used as a tool to study economic systems and complex systems. For example, a dissipative system involving self-assembly of nanowires has been used as a model to understand the relationship between entropy generation and the robustness of biological systems.

## Dissipative structures in thermodynamics

The term dissipative structure was coined by Russian-Belgian physical chemist Ilya Prigogine, who was awarded the Nobel Prize in Chemistry in 1977 for his pioneering work on these structures. The dissipative structures considered by Prigogine have dynamical regimes that can be regarded as thermodynamic steady states, and sometimes at least can be described by suitable extremal principles in non-equilibrium thermodynamics.

The term dissipative structure was coined by Russian-Belgian physical chemist Ilya Prigogine, who was awarded the Nobel Prize in Chemistry in 1977 for his pioneering work on these structures. The dissipative structures considered by Prigogine have dynamical regimes that can be regarded as thermodynamic steady states, and sometimes at least can be described by suitable extremal principles in non-equilibrium thermodynamics.

In his Nobel lecture,[4] Prigogine explains how thermodynamic systems far from equilibrium can have drastically different behavior from systems close to equilibrium. Near equilibrium, the local equilibrium hypothesis applies and typical thermodynamic quantities such as free energy and entropy can be defined locally. One can assume linear relations between the (generalized) flux and forces of the system. Two celebrated results from linear thermodynamics are the Onsager reciprocal relations and the principle of minimum entropy production.[5] After efforts to extend such results to systems far from equilibrium, it was found that they do not hold in this regime and opposite results were obtained.

In his Nobel lecture, Prigogine explains how thermodynamic systems far from equilibrium can have drastically different behavior from systems close to equilibrium. Near equilibrium, the local equilibrium hypothesis applies and typical thermodynamic quantities such as free energy and entropy can be defined locally. One can assume linear relations between the (generalized) flux and forces of the system. Two celebrated results from linear thermodynamics are the Onsager reciprocal relations and the principle of minimum entropy production. After efforts to extend such results to systems far from equilibrium, it was found that they do not hold in this regime and opposite results were obtained.

One way to rigorously analyze such systems is by studying the stability of the system far from equilibrium. Close to equilibrium, one can show the existence of a Lyapunov function which ensures that the entropy tends to a stable maximum. Fluctuations are damped in the neighborhood of the fixed point and a macroscopic description suffices. However, far from equilibrium stability is no longer a universal property and can be broken. In chemical systems, this occurs with the presence of autocatalytic reactions, such as in the example of the Brusselator. If the system is driven beyond a certain threshold, oscillations are no longer damped out, but may be amplified. Mathematically, this corresponds to a Hopf bifurcation where increasing one of the parameters beyond a certain value leads to limit cycle behavior. If spatial effects are taken into account through a reaction-diffusion equation, long-range correlations and spatially ordered patterns arise,[6] such as in the case of the Belousov–Zhabotinsky reaction. Systems with such dynamic states of matter that arise as the result of irreversible processes are dissipative structures.

One way to rigorously analyze such systems is by studying the stability of the system far from equilibrium. Close to equilibrium, one can show the existence of a Lyapunov function which ensures that the entropy tends to a stable maximum. Fluctuations are damped in the neighborhood of the fixed point and a macroscopic description suffices. However, far from equilibrium stability is no longer a universal property and can be broken. In chemical systems, this occurs with the presence of autocatalytic reactions, such as in the example of the Brusselator. If the system is driven beyond a certain threshold, oscillations are no longer damped out, but may be amplified. Mathematically, this corresponds to a Hopf bifurcation where increasing one of the parameters beyond a certain value leads to limit cycle behavior. If spatial effects are taken into account through a reaction-diffusion equation, long-range correlations and spatially ordered patterns arise, such as in the case of the Belousov–Zhabotinsky reaction. Systems with such dynamic states of matter that arise as the result of irreversible processes are dissipative structures.

Recent research has seen reconsideration of Prigogine's ideas of dissipative structures in relation to biological systems.[7]

Recent research has seen reconsideration of Prigogine's ideas of dissipative structures in relation to biological systems.

## Dissipative systems in control theory

In systems theory the concept of dissipativity was first introduced by Willems,[8] which describes dynamical systems by input-output properties. Considering a dynamical system described by its state $\displaystyle{ x(t) }$, its input $\displaystyle{ u(t) }$ and its output $\displaystyle{ y(t) }$, the input-output correlation is given a supply rate $\displaystyle{ w(u(t),y(t)) }$. A system is said to be dissipative with respect to a supply rate if there exists a continuously differentiable storage function $\displaystyle{ V(x(t)) }$ such that $\displaystyle{ V(0)=0 }$, $\displaystyle{ V(x(t))\ge 0 }$ and

In systems theory the concept of dissipativity was first introduced by Willems, which describes dynamical systems by input-output properties. Considering a dynamical system described by its state $\displaystyle{ x(t) }$, its input $\displaystyle{ u(t) }$ and its output $\displaystyle{ y(t) }$, the input-output correlation is given a supply rate $\displaystyle{ w(u(t),y(t)) }$. A system is said to be dissipative with respect to a supply rate if there exists a continuously differentiable storage function $\displaystyle{ V(x(t)) }$ such that $\displaystyle{ V(0)=0 }$, $\displaystyle{ V(x(t))\ge 0 }$ and

$\displaystyle{ \dot{V}(x(t)) \le w(u(t),y(t)) }$.[9]

$\displaystyle{ \dot{V}(x(t)) \le w(u(t),y(t)) }$.

Math dot { v }(x (t)) le w (u (t) ，y (t)) / math.

As a special case of dissipativity, a system is said to be passive if the above dissipativity inequality holds with respect to the passivity supply rate $\displaystyle{ w(u(t),y(t)) = u(t)^Ty(t) }$.

As a special case of dissipativity, a system is said to be passive if the above dissipativity inequality holds with respect to the passivity supply rate $\displaystyle{ w(u(t),y(t)) = u(t)^Ty(t) }$.

The physical interpretation is that $\displaystyle{ V(x) }$ is the energy stored in the system, whereas $\displaystyle{ w(u(t),y(t)) }$ is the energy that is supplied to the system.

The physical interpretation is that $\displaystyle{ V(x) }$ is the energy stored in the system, whereas $\displaystyle{ w(u(t),y(t)) }$ is the energy that is supplied to the system.

This notion has a strong connection with Lyapunov stability, where the storage functions may play, under certain conditions of controllability and observability of the dynamical system, the role of Lyapunov functions.

This notion has a strong connection with Lyapunov stability, where the storage functions may play, under certain conditions of controllability and observability of the dynamical system, the role of Lyapunov functions.

Roughly speaking, dissipativity theory is useful for the design of feedback control laws for linear and nonlinear systems. Dissipative systems theory has been discussed by V.M. Popov, J.C. Willems, D.J. Hill, and P. Moylan. In the case of linear invariant systems模板:Clarify, this is known as positive real transfer functions, and a fundamental tool is the so-called Kalman–Yakubovich–Popov lemma which relates the state space and the frequency domain properties of positive real systems模板:Clarify.[10] Dissipative systems are still an active field of research in systems and control, due to their important applications.

Roughly speaking, dissipativity theory is useful for the design of feedback control laws for linear and nonlinear systems. Dissipative systems theory has been discussed by V.M. Popov, J.C. Willems, D.J. Hill, and P. Moylan. In the case of linear invariant systems, this is known as positive real transfer functions, and a fundamental tool is the so-called Kalman–Yakubovich–Popov lemma which relates the state space and the frequency domain properties of positive real systems. Dissipative systems are still an active field of research in systems and control, due to their important applications.

## Quantum dissipative systems

As quantum mechanics, and any classical dynamical system, relies heavily on Hamiltonian mechanics for which time is reversible, these approximations are not intrinsically able to describe dissipative systems. It has been proposed that in principle, one can couple weakly the system – say, an oscillator – to a bath, i.e., an assembly of many oscillators in thermal equilibrium with a broad band spectrum, and trace (average) over the bath. This yields a master equation which is a special case of a more general setting called the Lindblad equation that is the quantum equivalent of the classical Liouville equation. The well-known form of this equation and its quantum counterpart takes time as a reversible variable over which to integrate, but the very foundations of dissipative structures imposes an irreversible and constructive role for time.

As quantum mechanics, and any classical dynamical system, relies heavily on Hamiltonian mechanics for which time is reversible, these approximations are not intrinsically able to describe dissipative systems. It has been proposed that in principle, one can couple weakly the system – say, an oscillator – to a bath, i.e., an assembly of many oscillators in thermal equilibrium with a broad band spectrum, and trace (average) over the bath. This yields a master equation which is a special case of a more general setting called the Lindblad equation that is the quantum equivalent of the classical Liouville equation. The well-known form of this equation and its quantum counterpart takes time as a reversible variable over which to integrate, but the very foundations of dissipative structures imposes an irreversible and constructive role for time.

## Applications on dissipative systems of dissipative structure concept

The framework of dissipative structures as a mechanism to understand the behavior of systems in constant interexchange of energy has been successfully applied on different science fields and applications, as in optics[11][12], population dynamics and growth [13] [14][15] and chemomechanical structures[16][17][18]

The framework of dissipative structures as a mechanism to understand the behavior of systems in constant interexchange of energy has been successfully applied on different science fields and applications, as in optics, population dynamics and growth and chemomechanical structures

## Notes

1. Li, HP (February 2014). "Dissipative Belousov–Zhabotinsky reaction in unstable micropyretic synthesis". Current Opinion in Chemical Engineering. 3: 1–6. doi:10.1016/j.coche.2013.08.007.
2. Chen, Jing (2015). The Unity of Science and Economics: A New Foundation of Economic Theory. https://www.springer.com/us/book/9781493934645: Springer.
3. Hubler, Alfred; Belkin, Andrey; Bezryadin, Alexey (2 January 2015). "Noise induced phase transition between maximum entropy production structures and minimum entropy production structures?". Complexity. 20 (3): 8–11. Bibcode:2015Cmplx..20c...8H. doi:10.1002/cplx.21639.
4. Prigogine, Ilya. "Time, Structure and Fluctuations". Nobelprize.org. PMID 17738519.
5. Prigogine, Ilya (1945). "Modération et transformations irréversibles des systèmes ouverts". Bulletin de la Classe des Sciences, Académie Royale de Belgique. 31: 600–606.
6. Lemarchand, H.; Nicolis, G. (1976). "Long range correlations and the onset of chemical instabilities". Physica. 82A (4): 521–542. Bibcode:1976PhyA...82..521L. doi:10.1016/0378-4371(76)90079-0.
7. England, Jeremy L. (4 November 2015). "Dissipative adaptation in driven self-assembly". Nature Nanotechnology. 10 (11): 919–923. Bibcode:2015NatNa..10..919E. doi:10.1038/NNANO.2015.250. PMID 26530021.
8. Willems, J.C. (1972). "Dissipative dynamical systems part 1: General theory" (PDF). Arch. Rational Mech. Anal. 45 (5): 321. Bibcode:1972ArRMA..45..321W. doi:10.1007/BF00276493. hdl:10338.dmlcz/135639.
9. Arcak, Murat; Meissen, Chris; Packard, Andrew (2016). Networks of Dissipative Systems. Springer International Publishing. ISBN 978-3-319-29928-0.
10. Lugiato, L. A.; Prati, F.; Gorodetsky, M. L.; Kippenberg, T. J. (28 December 2018). "From the Lugiato–Lefever equation to microresonator-based soliton Kerr frequency combs". Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 376 (2135): 20180113. arXiv:1811.10685. Bibcode:2018RSPTA.37680113L. doi:10.1098/rsta.2018.0113. PMID 30420551.
11. Andrade-Silva, I.; Bortolozzo, U.; Castillo-Pinto, C.; Clerc, M. G.; González-Cortés, G.; Residori, S.; Wilson, M. (28 December 2018). "Dissipative structures induced by photoisomerization in a dye-doped nematic liquid crystal layer". Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 376 (2135): 20170382. Bibcode:2018RSPTA.37670382A. doi:10.1098/rsta.2017.0382. PMC 6232603. PMID 30420545.
12. Zykov, V. S. (28 December 2018). "Spiral wave initiation in excitable media". Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 376 (2135): 20170379. Bibcode:2018RSPTA.37670379Z. doi:10.1098/rsta.2017.0379. PMID 30420544.
13. Tlidi, M.; Clerc, M. G.; Escaff, D.; Couteron, P.; Messaoudi, M.; Khaffou, M.; Makhoute, A. (28 December 2018). "Observation and modelling of vegetation spirals and arcs in isotropic environmental conditions: dissipative structures in arid landscapes". Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 376 (2135): 20180026. Bibcode:2018RSPTA.37680026T. doi:10.1098/rsta.2018.0026. PMID 30420548.
14. Gunji, Yukio-Pegio; Murakami, Hisashi; Tomaru, Takenori; Basios, Vasileios (28 December 2018). "Inverse Bayesian inference in swarming behaviour of soldier crabs". Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 376 (2135): 20170370. Bibcode:2018RSPTA.37670370G. doi:10.1098/rsta.2017.0370. PMC 6232598. PMID 30420541.
15. Bullara, D.; De Decker, Y.; Epstein, I. R. (28 December 2018). "On the possibility of spontaneous chemomechanical oscillations in adsorptive porous media". Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 376 (2135): 20170374. Bibcode:2018RSPTA.37670374B. doi:10.1098/rsta.2017.0374. PMC 6232597. PMID 30420542.
16. Gandhi, Punit; Zelnik, Yuval R.; Knobloch, Edgar (28 December 2018). "Spatially localized structures in the Gray–Scott model". Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 376 (2135): 20170375. Bibcode:2018RSPTA.37670375G. doi:10.1098/rsta.2017.0375. PMID 30420543.
17. Kostet, B.; Tlidi, M.; Tabbert, F.; Frohoff-Hülsmann, T.; Gurevich, S. V.; Averlant, E.; Rojas, R.; Sonnino, G.; Panajotov, K. (28 December 2018). "Stationary localized structures and the effect of the delayed feedback in the Brusselator model". Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 376 (2135): 20170385. arXiv:1810.05072. Bibcode:2018RSPTA.37670385K. doi:10.1098/rsta.2017.0385. PMID 30420547.

## References

• B. Brogliato, R. Lozano, B. Maschke, O. Egeland, Dissipative Systems Analysis and Control. Theory and Applications. Springer Verlag, London, 2nd Ed., 2007.

• Philipson, Schuster, Modeling by Nonlinear Differential Equations: Dissipative and Conservative Processes, World Scientific Publishing Company 2009.

• J.C. Willems. Dissipative dynamical systems, part I: General theory; part II: Linear systems with quadratic supply rates. Archive for Rationale mechanics Analysis, vol.45, pp. 321–393, 1972.