THERMODYNAMIC MODELING, ENERGY EQUIPARTITION, AND NONCONSERVATION OF ENTROPY FOR DISCRETE-TIME pptx

NERSESOV, AND VIJAYSEKHAR CHELLABOINA Received 19 November 2004 We develop thermodynamic models for discrete-time large-scale dynamical systems.Specifically, using compartmental dynamica

AND NONCONSERVATION OF ENTROPY

FOR DISCRETE-TIME DYNAMICAL SYSTEMS

WASSIM M HADDAD, QING HUI, SERGEY G NERSESOV,

AND VIJAYSEKHAR CHELLABOINA

Received 19 November 2004

We develop thermodynamic models for discrete-time large-scale dynamical systems.Specifically, using compartmental dynamical system theory, we develop energy flow mod-els possessing energy conservation, energy equipartition, temperature equipartition, andentropy nonconservation principles for discrete-time, large-scale dynamical systems Fur-

thermore, we introduce a new and dual notion to entropy; namely, ectropy, as a measure

of the tendency of a dynamical system to do useful work and grow more organized, andshow that conservation of energy in an isolated thermodynamic system necessarily leads

to nonconservation of ectropy and entropy In addition, using the system ectropy as a punov function candidate, we show that our discrete-time, large-scale thermodynamicenergy flow model has convergent trajectories to Lyapunov stable equilibria determined

Lya-by the system initial subsystem energies

1 Introduction

Thermodynamic principles have been repeatedly used in continuous-time dynamical tem theory as well as in information theory for developing models that capture the ex-change of nonnegative quantities (e.g., mass and energy) between coupled subsystems[5,6,8,11,20,23,24] In particular, conservation laws (e.g., mass and energy) are used

sys-to capture the exchange of material between coupled macroscopic subsystems known ascompartments Each compartment is assumed to be kinetically homogeneous; that is,any material entering the compartment is instantaneously mixed with the material in the

compartment These models are known as compartmental models and are widespread in

engineering systems as well as in biological and ecological sciences [1,7,9,16,17,22].Even though the compartmental models developed in the literature are based on the firstlaw of thermodynamics involving conservation of energy principles, they do not tell uswhether any particular process can actually occur; that is, they do not address the secondlaw of thermodynamics involving entropy notions in the energy flow between subsys-tems

The goal of the present paper is directed towards developing nonlinear discrete-timecompartmental models that are consistent with thermodynamic principles Specifically,

Copyright©2005 Hindawi Publishing Corporation

Advances in Di fference Equations 2005:3 (2005) 275–318

DOI: 10.1155/ADE.2005.275

276 Thermodynamic modeling for discrete-time systems

since thermodynamic models are concerned with energy flow among subsystems, wedevelop a nonlinear compartmental dynamical system model that is characterized by en-ergy conservation laws capturing the exchange of energy between coupled macroscopicsubsystems Furthermore, using graph-theoretic notions, we state three thermodynamicaxioms consistent with the zeroth and second laws of thermodynamics that ensure thatour large-scale dynamical system model gives rise to a thermodynamically consistent en-ergy flow model Specifically, using a large-scale dynamical systems theory perspective,

we show that our compartmental dynamical system model leads to a precise tion of the equivalence between work energy and heat in a large-scale dynamical sys-tem

formula-Next, we give a deterministic definition of entropy for a large-scale dynamical tem that is consistent with the classical thermodynamic definition of entropy and showthat it satisfies a Clausius-type inequality leading to the law of entropy nonconservation

sys-Furthermore, we introduce a new and dual notion to entropy; namely, ectropy, as a

mea-sure of the tendency of a large-scale dynamical system to do useful work and grow moreorganized, and show that conservation of energy in an isolated thermodynamically con-sistent system necessarily leads to nonconservation of ectropy and entropy Then, usingthe system ectropy as a Lyapunov function candidate, we show that our thermodynami-cally consistent large-scale nonlinear dynamical system model possesses a continuum of

equilibria and is semistable; that is, it has convergent subsystem energies to Lyapunov

sta-ble energy equilibria determined by the large-scale system initial subsystem energies Inaddition, we show that the steady-state distribution of the large-scale system energies isuniform leading to system energy equipartitioning corresponding to a minimum ectropyand a maximum entropy equilibrium state In the case where the subsystem energiesare proportional to subsystem temperatures, we show that our dynamical system modelleads to temperature equipartition, wherein all the system energy is transferred into heat

at a uniform temperature Furthermore, we show that our system-theoretic definition

of entropy and the newly proposed notion of ectropy are consistent with Boltzmann’skinetic theory of gases involving ann-body theory of ideal gases divided by diathermal

walls

The contents of the paper are as follows In Section 2, we establish notation, nitions, and review some basic results on nonnegative and compartmental dynamicalsystems InSection 3, we use a large-scale dynamical systems perspective to develop anonlinear compartmental dynamical system model characterized by energy conservationlaws that is consistent with basic thermodynamic principles Then we turn our attention

defi-to stability and convergence In particular, using the defi-total subsystem energies as a date system energy storage function, we show that our thermodynamic system is losslessand hence can deliver to its surroundings all of its stored subsystem energies and can storeall of the work done to all of its subsystems Next, using the system ectropy as a Lyapunovfunction candidate, we show that the proposed thermodynamic model is semistable with

candi-a uniform energy distribution corresponding to candi-a minimum ectropy candi-and candi-a mcandi-aximum tropy InSection 4, we generalize the results ofSection 3to the case where the subsystemenergies in large-scale dynamical system model are proportional to subsystem tempera-tures and arrive at temperature equipartition for the proposed thermodynamic model

en-Furthermore, we provide an interpretation of the steady-state expressions for entropyand ectropy that is consistent with kinetic theory InSection 5, we specialize the results of

con-clusions inSection 6

2 Mathematical preliminaries

In this section, we introduce notation, several definitions, and some key results neededfor developing the main results of this paper LetRdenote the set of real numbers, letZ +

denote the set of nonnegative integers, letRndenote the set ofn ×1 column vectors, let

Rm × ndenote the set ofm × n real matrices, let ( ·)Tdenote transpose, and letI norI denote

then × n identity matrix For v ∈ R q, we writev ≥≥0 (resp.,v 0) to indicate that everycomponent ofv is nonnegative (resp., positive) In this case, we say that v is nonnegative

or positive, respectively LetR +q andRq+denote the nonnegative and positive orthants of

Rq; that is, ifv ∈ R q, thenv ∈ R q+andv ∈ R q+are equivalent, respectively, tov ≥≥0 and

v 0 Finally, we write · for the Euclidean vector norm,᏾(M) and ᏺ(M) for the

range space and the null space of a matrixM, respectively, spec(M) for the spectrum of

the square matrixM, rank(M) for the rank of the matrix M, ind(M) for the index of M;

that is, min{ k ∈ Z+: rank(M k)=rank(M k+1)},M# for the group generalized inverse of

M, where ind(M) ≤1,∆E(x(k)) for E(x(k + 1)) − E(x(k)), Ꮾ ε(α), α ∈ R n,ε > 0, for the

open ball centered atα with radius ε, and M ≥0 (resp.,M > 0) to denote the fact that the

Hermitian matrixM is nonnegative (resp., positive) definite.

The following definition introduces the notion ofZ-, M-, nonnegative, and

compart-mental matrices

Definition 2.1 [2,5,12] LetW ∈ R q × q.W is a Z-matrix if W(i, j) ≤0,i, j =1, ,q, i = j.

W is an M-matrix (resp., a nonsingular M-matrix) if W is a Z-matrix and all the principal

minors ofW are nonnegative (resp., positive) W is nonnegative (resp., positive) if W(i, j) ≥

0 (resp.,W(i, j) > 0), i, j =1, ,q Finally, W is compartmental if W is nonnegative and

q

i =1W(i, j) ≤1,j =1, ,q.

In this paper, it is important to distinguish between a square nonnegative (resp., tive) matrix and a nonnegative-definite (resp., positive-definite) matrix

posi-The following definition introduces the notion of nonnegative functions [12]

Definition 2.2 Let w =[w1, ,w q]T:ᐂ→ R q, whereᐂ is an open subset ofRqthat tainsRq+ Thenw is nonnegative if w i(z) ≥0 for alli =1, ,q and z ∈ R q+

con-Note that ifw(z) = Wz, where W ∈ R q × q, thenw( ·) is nonnegative if and only ifW is

278 Thermodynamic modeling for discrete-time systems

The following definition introduces several types of stability for the discrete-time

nonnegative dynamical system (2.1)

Definition 2.4 The equilibrium solution z(k) ≡ zeof (2.1) is Lyapunov stable if, for every

ε > 0, there exists δ = δ(ε) > 0 such that if z0∈Ꮾδ(ze) ∩ R q+, thenz(k) ∈Ꮾε(ze) ∩

Rq+,k ∈ Z+ The equilibrium solution z(k) ≡ ze of (2.1) is semistable if it is Lyapunov

stable and there existsδ > 0 such that if z0∈Ꮾδ(ze) ∩ R q+, then limk →∞ z(k) exists and

corresponds to a Lyapunov stable equilibrium point The equilibrium solutionz(k) ≡ ze

of (2.1) is asymptotically stable if it is Lyapunov stable and there exists δ > 0 such that if

z0∈Ꮾδ(ze) ∩ R q+, then limk →∞ z(k) = ze Finally, the equilibrium solutionz(k) ≡ zeof(2.1) is globally asymptotically stable if the previous statement holds for all z0∈ R q+.Finally, recall that a matrixW ∈ R q × q is semistable if and only if lim k →∞ W kexists [12],whileW is asymptotically stable if and only if lim k →∞ W k =0

3 Thermodynamic modeling for discrete-time systems

3.1 Conservation of energy and the first law of thermodynamics The fundamental

and unifying concept in the analysis of complex (large-scale) dynamical systems is theconcept of energy The energy of a state of a dynamical system is the measure of its abil-ity to produce changes (motion) in its own system state as well as changes in the systemstates of its surroundings These changes occur as a direct consequence of the energy flowbetween different subsystems within the dynamical system Since heat (energy) is a funda-mental concept of thermodynamics involving the capacity of hot bodies (more energeticsubsystems) to produce work, thermodynamics is a theory of large-scale dynamical sys-tems [13] As in thermodynamic systems, dynamical systems can exhibit energy (due tofriction) that becomes unavailable to do useful work This is in turn contributes to anincrease in system entropy; a measure of the tendency of a system to lose the ability to douseful work

To develop discrete-time compartmental models that are consistent with namic principles, consider the discrete-time large-scale dynamical systemᏳ shown in

(and hence a nonnegative quantity) of theith subsystem, let S i:Z +→ Rdenote the ternal energy supplied to (or extracted from) theith subsystem, let σ i j:Rq+→ R+,i = j,

ex-i, j =1, ,q, denote the exchange of energy from the jth subsystem to the ith subsystem,

and letσ ii:Rq+→ R+,i =1, ,q, denote the energy loss from the ith subsystem An energy balance equation for the ith subsystem yields

.

Figure 3.1 Large-scale dynamical system Ᏻ.

where E(k) =[E1(k), ,E q(k)]T, S(k) =[S1(k), ,S q(k)]T, d(E(k)) =[σ11(E(k)), ,

σ qq(E(k))]T,k ≥ k0, andw =[w1, ,w q]T:Rq+→ R qis such that

from) theith subsystem plus the energy gained by the ith subsystem from all other

sub-systems due to subsystem coupling minus the energy dissipated from theith subsystem.

Note that (3.2) or, equivalently, (3.1) is a statement reminiscent of the first law of dynamics for each of the subsystems, with E i(·),S i(·),σ i j(·),i = j, and σ ii(·),i =1, ,q,

thermo-playing the role of the ith subsystem internal energy, energy supplied to (or extracted

from) theith subsystem, the energy exchange between subsystems due to coupling, and

the energy dissipated to the environment, respectively

To further elucidate that (3.2) is essentially the statement of the principle of the servation of energy, let the total energy in the discrete-time large-scale dynamical system

con-Ᏻ be given by U
eTE, E ∈ R q+, where eT
[1, ,1], and let the energy received by

the discrete-time large-scale dynamical systemᏳ (in forms other than work) over thediscrete-time interval{ k1, ,k2}be given byQ

k2

k = k1eT[S(k) − d(E(k))], where E(k),

k ≥ k0, is the solution to (3.2) Then, premultiplying (3.2) by eTand using the fact that

eTw(E) ≡eTE, it follows that

280 Thermodynamic modeling for discrete-time systems

where∆U
U(k2)− U(k1) denotes the variation in the total energy of the discrete-timelarge-scale dynamical systemᏳ over the discrete-time interval{ k1, ,k2} This is a state-ment of the first law of thermodynamics for the discrete-time large-scale dynamical sys-temᏳ and gives a precise formulation of the equivalence between variation in systeminternal energy and heat

It is important to note that our discrete-time large-scale dynamical system model doesnot consider work done by the system on the environment nor work done by the envi-ronment on the system Hence,Q can be interpreted physically as the amount of energy

that is received by the system in forms other than work The extension of addressing workperformed by and on the system can be easily handled by including an additional stateequation, coupled to the energy balance equation (3.2), involving volume states for eachsubsystem [13] Since this slight extension does not alter any of the results of the paper, it

is not considered here for simplicity of exposition

For our large-scale dynamical system modelᏳ, we assume that σi j(E) =0,E ∈ R q+,wheneverE j =0,i, j =1, ,q This constraint implies that if the energy of the jth sub-

system ofᏳ is zero, then this subsystem cannot supply any energy to its surroundings nordissipate energy to the environment Furthermore, for the remainder of this paper, we as-sume thatE i ≥ σ ii(E) − S i −
q j =1,j = i[σ i j(E) − σ ji(E)] = − ∆Ei,E ∈ R q+,S ∈ R q,i =1, ,q.

This constraint implies that the energy that can be dissipated, extracted, or exchanged bytheith subsystem cannot exceed the current energy in the subsystem Note that this as-

sumption implies thatE(k) ≥≥0 for allk ≥ k0

Next, premultiplying (3.2) by eTand using the fact that eTw(E) ≡eTE, it follows that

Now, for the discrete-time large-scale dynamical systemᏳ, define the input u(k)
S(k)

and the outputy(k)
d(E(k)) Hence, it follows from (3.5) that the discrete-time scale dynamical systemᏳ is lossless [23] with respect to the energy supply rate r(u, y) =

large-eTu −eTy and with the energy storage function U(E)
eTE, E ∈ R q+ This implies that (see[23] for details)

andE0= E(k0)∈ R q+ SinceUa(E0) is the maximum amount of stored energy which can

be extracted from the discrete-time large-scale dynamical systemᏳ at any discrete-timeinstant K, and U(E) is the minimum amount of energy which can be delivered to

the discrete-time large-scale dynamical systemᏳ to transfer it from a state of minimumpotentialE( − K) =0 to a given stateE(k0)= E0, it follows from (3.6) that the discrete-time large-scale dynamical systemᏳ can deliver to its surroundings all of its stored sub-system energies and can store all of the work done to all of its subsystems In the casewhereS(k) ≡0, it follows from (3.5) and the fact thatσ ii(E) ≥0,E ∈ R q+,i =1, ,q, that

the zero solutionE(k) ≡0 of the discrete-time large-scale dynamical systemᏳ with theenergy balance equation (3.2) is Lyapunov stable with Lyapunov functionU(E) corre-

sponding to the total energy in the system

The next result shows that the large-scale dynamical systemᏳ is locally controllable

Proposition 3.1 Consider the discrete-time large-scale dynamical system Ᏻ with energy balance equation ( 3.2 ) Then for every equilibrium state Ee∈ R q+and every ε > 0 and T ∈

Z +, there exist Se∈ R q , α > 0, and T∈ {0, ,T } such that for every E∈ R q

sinceM(α,T) →0 asα →0+.) Now, letE∈ R q

+be such that   E − Ee ≤ αT With T

  E − Ee /α T, where x denotes the smallest integer greater than or equal tox, and

282 Thermodynamic modeling for discrete-time systems

Rq+ Recall that the discrete-time large-scale dynamical systemᏳ with the energy balanceequation (3.2) is reachable from the origin inRq+if, for allE0= E(k0)∈ R q+, there exist afinite timeki≤ k0and an inputS(k) defined on { ki, ,k0}such that the stateE(k), k ≥ ki,can be driven fromE(ki)=0 toE(k0)= E0 Alternatively,Ᏻ is controllable to the origin in

Rq+if, for allE0= E(k0)∈ R q+, there exist a finite timekf≥ k0and an inputS(k) defined on { k0, ,kf}such that the stateE(k), k ≥ k0, can be driven fromE(k0)= E0toE(kf)=0

We letᐁr denote the set of all admissible bounded energy inputs to the discrete-timelarge-scale dynamical systemᏳ such that for any K ≥ − k0, the system energy state can

be driven fromE( − K) =0 toE(k0)= E0∈ R+q byS( ·)∈ᐁr, and we letᐁc denote theset of all admissible bounded energy inputs to the discrete-time large-scale dynamicalsystemᏳ such that for any K ≥ k0, the system energy state can be driven fromE(k0)=

E0∈ R q+toE(K) =0 byS( ·)∈ᐁc Furthermore, letᐁ be an input space that is a subset ofbounded continuousRq-valued functions onZ The spacesᐁr,ᐁc, andᐁ are assumed to

be closed under the shift operator; that is, ifS( ·)∈ᐁ (resp., ᐁcorᐁr), then the function

S Kdefined byS K(k) = S(k + K) is contained in ᐁ (resp., ᐁcorᐁr) for allK ≥0

3.2 Nonconservation of entropy and the second law of thermodynamics The

non-linear energy balance equation (3.2) can exhibit a full range of nonlinear behavior cluding bifurcations, limit cycles, and even chaos However, a thermodynamically consis-tent energy flow model should ensure that the evolution of the system energy is diffusive(parabolic) in character with convergent subsystem energies Hence, to ensure a ther-modynamically consistent energy flow model, we require the following axioms For thestatement of these axioms, we first recall the following graph-theoretic notions

in-Definition 3.2 [2] A directed graph G(Ꮿ) associated with the connectivity matrix Ꮿ ∈ R q × q has vertices {1, 2, ,q } and an arc from vertex i to vertex j, i = j, if and only if Ꮿ(j,i) =0

A graph G(Ꮿ) associated with the connectivity matrix Ꮿ ∈ R q × q is a directed graph for

which the arc set is symmetric; that is,Ꮿ=ᏯT It is said thatG(Ꮿ) is strongly connected

if for any ordered pair of vertices (i, j), i = j, there exists a path (i.e., sequence of arcs)

leading fromi to j.

Recall thatᏯ∈ R q × q is irreducible; that is, there does not exist a permutation matrix

such thatᏯ is cogredient to a lower-block triangular matrix, if and only if G(Ꮿ) is strongly

connected (see [2, Theorem 2.7]) Letφ i j(E)
σ i j(E) − σ ji(E), E ∈ R q+, denote the netenergy exchange between subsystemsᏳiandᏳjof the discrete-time large-scale dynamicalsystemᏳ

Axiom 1 For the connectivity matrixᏯ∈ R q × q associated with the large-scale dynamical system Ᏻ defined by

Axiom 2 For i, j =1, ,q, (E i − E j)φ i j(E) ≤ 0, E ∈ R+q

Axiom 3 For i, j =1, ,q, (∆E i − ∆E j)/(E i − E j)≥ − 1, E i = E j

The fact thatφ i j(E) =0 if and only ifE i = E j,i = j, implies that subsystems Ᏻ i and

Ᏻj ofᏳ are connected; alternatively, φi j(E) ≡0 implies thatᏳi andᏳj are disconnected.

energy exchange between these subsystems is not possible This is a statement consistent

with the zeroth law of thermodynamics which postulates that temperature equality is a

necessary and sufficient condition for thermal equilibrium Furthermore, it follows fromthe fact thatᏯ=ᏯT and rankᏯ= q −1 that the connectivity matrixᏯ is irreduciblewhich implies that for any pair of subsystemsᏳiandᏳj,i = j, of Ᏻ, there exists a sequence

of connected subsystems ofᏳ that connect Ᏻi andᏳj.Axiom 2implies that energy isexchanged from more energetic subsystems to less energetic subsystems and is consistent

with the second law of thermodynamics which states that heat (energy) must flow in the

direction of lower temperatures Furthermore, note thatφ i j(E) = − φ ji(E), E ∈ R q+,i = j,

i, j =1, ,q, which implies conservation of energy between lossless subsystems With S(k) ≡0, Axioms1and2along with the fact thatφ i j(E) = − φ ji(E), E ∈ R q+,i = j, i, j =

1, ,q, imply that at a given instant of time, energy can only be transported, stored, or

dissipated but not created and the maximum amount of energy that can be transportedand/or dissipated from a subsystem cannot exceed the energy in the subsystem Finally,

difference between consecutive time instants is monotonic; that is, [Ei(k + 1) − E j(k +

re-i =1, ,q, and E i(k) = E j(k), i, j =1, ,q, i = j, k ∈ { k0, ,kf−1}

Proof Since E(k) ≥≥0,k ≥ k0, andφ i j(E) = − φ ji(E), E ∈ R q+,i = j, i, j =1, ,q, it

fol-lows from (3.2), Axioms 2 and 3, and the fact that x/(x + 1) ≤loge(1 +x), x > −1

284 Thermodynamic modeling for discrete-time systems

k = k0(∆Ei(k)/(c + E i(k + 1))) =0 is equivalent to∆Ei(k) =0,i =1, ,q, k ∈ { k0, ,kf−

1} Hence,φ i j(E(k))(E j(k + 1) − E i(k + 1)) = φ i j(E(k))(E j(k) − E i(k)) =0,i, j =1, ,q,

i = j, k ≥ k0 Thus, it follows from Axioms1,2, and3that equality holds in (3.13) if andonly if∆Ei =0,i =1, ,q, and E j = E i,i, j =1, ,q, i = j. Inequality (3.13) is analogous to Clausius’ inequality for reversible and irreversiblethermodynamics as applied to discrete-time large-scale dynamical systems It followsfromAxiom 1and (3.2) that for the isolated discrete-time large-scale dynamical systemᏳ;that is,S(k) ≡0 andd(E(k)) ≡0, the energy states given byEe= αe, α ≥0, correspond tothe equilibrium energy states ofᏳ Thus, we can define an equilibrium process as a process

where the trajectory of the discrete-time large-scale dynamical systemᏳ stays at the librium point of the isolated systemᏳ The input that can generate such a trajectory can

equi-be given byS(k) = d(E(k)), k ≥ k0 Alternatively, a nonequilibrium process is a process that

is not an equilibrium one Hence, it follows fromAxiom 1that for an equilibrium cess,φ i j(E(k)) ≡0,k ≥ k0,i = j, i, j =1, ,q, and thus, byProposition 3.3and∆Ei =0,

pro-i =1, ,q, inequality (3.13) is satisfied as an equality Alternatively, for a nonequilibriumprocess, it follows from Axioms1,2, and3that (3.13) is satisfied as a strict inequality.Next, we give a deterministic definition of entropy for the discrete-time large-scaledynamical system Ᏻ that is consistent with the classical thermodynamic definition ofentropy

Definition 3.4 For the discrete-time large-scale dynamical systemᏳ with energy balanceequation (3.2), a function᏿ :Rq+→ Rsatisfying

for anyk2≥ k1≥ k0andS( ·)∈ ᐁ, is called the entropy of Ᏻ.

Next, we show that (3.13) guarantees the existence of an entropy function forᏳ For

this result, define, the available entropy of the large-scale dynamical systemᏳ by

whereE(k0)= E0∈ R q+andE(K) = 0, and define the required entropy supply of the

large-scale dynamical systemᏳ by

E(k0)= E0

Theorem 3.5 Consider the discrete-time large-scale dynamical system Ᏻ with energy ance equation ( 3.2 ) and assume that Axioms 2 and 3 hold Then there exists an entropy function for Ᏻ Moreover, ᏿a(E), E ∈ R q+, and᏿r(E), E ∈ R q+, are possible entropy functions for Ᏻ with ᏿a(0)=᏿r(0)= 0 Finally, all entropy functions ᏿(E), E ∈ R q+, for Ᏻ satisfy

bal-᏿r(E) ≤ ᏿(E) −᏿(0)≤᏿a(E), E ∈ R q+. (3.18)

Proof Since, byProposition 3.1,Ᏻ is controllable to and reachable from the origin inRq+,

it follows from (3.16) and (3.17) that᏿a(E0)< ∞,E0∈ R q+, and᏿r(E0)> −∞,E0∈ R q+,respectively Next, letE0∈ R+qand letS( ·)∈ ᐁ be such that E(ki)= E(kf)=0 andE(k0)=

E0, whereki≤ k0≤ kf In this case, it follows from (3.13) that

286 Thermodynamic modeling for discrete-time systems

Now, taking the supremum on both sides of (3.20) over allS( ·)∈ᐁrandki+ 1≤ k0, weobtain

Next, taking the infimum on both sides of (3.21) over allS( ·)∈ᐁcandkf≥ k0, we obtain

᏿r(E0)≤᏿a(E0),E0∈ R q+, which implies that−∞ < ᏿r(E0)≤᏿a(E0)< + ∞,E0∈ R q+.Hence, the function᏿a(·) and᏿r(·) are well defined

Next, it follows from the definition of᏿a(·) that, for anyK ≥ k1andS( ·)∈ᐁc suchthatE(k1)∈ R q+andE(K) =0,

Next, suppose that there exists an entropy function᏿ :Rq+→ RforᏳ and let E(k2)=0

in (3.15) Then it follows from (3.15) that

for allk2≥ k1andS( ·)∈ᐁc, which implies that

SinceE(k1) is arbitrary, it follows that᏿(E) −᏿(0)≤᏿a(E), E ∈ R q+ Alternatively, let

E(k1)=0 in (3.15) Then it follows from (3.15) that

which, sinceE(k2) is arbitrary, implies that᏿r(E) ≤ ᏿(E) − ᏿(0), E ∈ R q+ Thus, all

Remark 3.6 It is important to note that inequality (3.13) is equivalent to the existence

of an entropy function for Ᏻ Sufficiency is simply a statement ofTheorem 3.5 whilenecessity follows from (3.15) withE(k2)= E(k1) For nonequilibrium process with energybalance equation (3.2),Definition 3.4does not provide enough information to define theentropy uniquely This difficulty has long been pointed out in [19] for thermodynamicsystems A similar remark holds for the definition of ectropy introduced below

The next proposition gives a closed-form expression for the entropy ofᏳ

Proposition 3.7 Consider the discrete-time large-scale dynamical system Ᏻ with energy balance equation ( 3.2 ) and assume that Axioms 2 and 3 hold Then the function᏿ :Rq+→ R given by

᏿(E) =eTloge

ce + E

− q log e c, E ∈ R+q, (3.28)

where c > 0 and log e(ce + E) denotes the vector natural logarithm given by [log e(c + E1), ,

loge(c + E q)]T , is an entropy function of Ᏻ.

288 Thermodynamic modeling for discrete-time systems

Proof Since E(k) ≥≥0,k ≥ k0, andφ i j(E) = − φ ji(E), E ∈ R q+,i = j, i, j =1, ,q, it

The entropy expression given by (3.28) is identical in form to the Boltzmann entropyfor statistical thermodynamics Due to the fact that the entropy is indeterminate to theextent of an additive constant, we can place the constantq log e c to zero by taking c =1.Since᏿(E) given by (3.28) achieves a maximum when all the subsystem energiesE i,i =

1, ,q, are equal, entropy can be thought of as a measure of the tendency of a system to

lose the ability to do useful work, lose order, and to settle to a more homogenous state

3.3 Nonconservation of ectropy In this subsection, we introduce a new and dual

no-tion to entropy; namely ectropy, describing the status quo of the discrete-time large-scaledynamical systemᏳ First, however, we present a dual inequality to inequality (3.13) thatholds for our thermodynamically consistent energy flow model

Proposition 3.9 Consider the discrete-time large-scale dynamical system Ᏻ with energy balance equation ( 3.2 ) and assume that Axioms 1 , 2 , and 3 hold Then for all E0∈ R q+,

kf≥ k0, and S( ·)∈ ᐁ such that E(kf)= E(k0)= E0,

Proof Since E(k) ≥≥0,k ≥ k0, andφ i j(E) = − φ ji(E), E ∈ R q+,i = j, i, j =1, ,q, it

fol-lows from (3.2) and Axioms2and3that

290 Thermodynamic modeling for discrete-time systems

Note that inequality (3.30) is satisfied as an equality for an equilibrium process and as

a strict inequality for a nonequilibrium process Next, we present the definition of ectropyfor the discrete-time large-scale dynamical systemᏳ

Definition 3.10 For the discrete-time large-scale dynamical systemᏳ with energy balanceequation (3.2), a functionᏱ :Rq+→ Rsatisfying

for anyk2≥ k1≥ k0andS( ·)∈ ᐁ, is called the ectropy of Ᏻ.

For the next result, define the available ectropy of the large-scale dynamical systemᏳby

whereE(k0)= E0∈ R q+andE(K) = 0, and define the required ectropy supply of the

large-scale dynamical systemᏳ by

Theorem 3.11 Consider the discrete-time large-scale dynamical system Ᏻ with energy ance equation ( 3.2 ) and assume that Axioms 2 and 3 hold Then there exists an ectropy function for Ᏻ Moreover, Ᏹa(E), E ∈ R q+, andᏱr(E), E ∈ R q+, are possible ectropy functions for Ᏻ with Ᏹa(0)=Ᏹr(0)= 0 Finally, all ectropy functions Ᏹ(E), E ∈ R q+, for Ᏻ satisfy

bal-Ᏹa(E) ≤ Ᏹ(E) −Ᏹ(0)≤Ᏹr(E), E ∈ R q+. (3.35)

Proof Since, byProposition 3.1,Ᏻ is controllable to and reachable from the origin inRq+,

it follows from (3.33) and (3.34) thatᏱa(E0)> −∞,E0∈ R q+, andᏱr(E0)< ∞,E0∈ R q+,respectively Next, letE0∈ R+qand letS( ·)∈ ᐁ be such that E(ki)= E(kf)=0 andE(k0)=

E0, whereki≤ k0≤ kf In this case, it follows from (3.30) that

Rq+ Hence, the functionsᏱa(·) andᏱr(·) are well defined.

Next, it follows from the definition ofᏱa(·) that, for anyK ≥ k1andS( ·)∈ᐁc suchthatE(k1)∈ R q+andE(K) =0,

which implies thatᏱa(E), E ∈ R q+, satisfies (3.32) Thus,Ᏹa(E), E ∈ R q+, is a possible tropy function for the systemᏳ Note that with E(k0)= E(K) =0, it follows from (3.30)that the infimum in (3.33) is taken over the set of nonnegative values with one of thevalues being zero forS(k) ≡0 Thus,Ᏹa(0)=0 Similarly, it can be shown thatᏱr(E),

ec-E ∈ R q+, given by (3.34) satisfies (3.32), and hence is a possible ectropy function for thesystemᏳ with Ᏹr(0)=0

292 Thermodynamic modeling for discrete-time systems

Next, suppose that there exists an ectropy functionᏱ :Rq+→ RforᏳ and let E(k2)=0

in (3.32) Then it follows from (3.32) that

Since E(k1) is arbitrary, it follows that Ᏹ(E) −Ᏹ(0)≥Ᏹa(E), E ∈ R q+ Alternatively, let

E(k1)=0 in (3.32) Then it follows from (3.32) that

which, sinceE(k2) is arbitrary, implies thatᏱr(E) ≥ Ᏹ(E) − Ᏹ(0), E ∈ R q+ Thus, all

The next proposition gives a closed-form expression for the ectropy ofᏳ

Proposition 3.12 Consider the discrete-time large-scale dynamical system Ᏻ with energy balance equation ( 3.2 ) and assume that Axioms 2 and 3 hold Then the functionᏱ :Rq+→ R given by

Ᏹ(E) =1

is an ectropy function of Ᏻ.

Proof Since E(k) ≥≥0,k ≥ k0, andφ i j(E) = − φ ji(E), E ∈ R q+,i = j, i, j =1, ,q, it

Now, summing (3.46) over{ k1, ,k2−1}yields (3.32) 

Remark 3.13 Note that it follows from the last equality in (3.46) that the ectropy functiongiven by (3.45) satisfies (3.32) as an equality for an equilibrium process and as a strictinequality for a nonequilibrium process

It follows from (3.45) that ectropy is a measure of the extent to which the systemenergy deviates from a homogeneous state Thus, ectropy is the dual of entropy and is ameasure of the tendency of the discrete-time large-scale dynamical systemᏳ to do usefulwork and grow more organized

3.4 Semistability of thermodynamic models Inequality (3.15) is analogous to sius’ inequality for equilibrium and nonequilibrium thermodynamics as applied todiscrete-time large-scale dynamical systems; while inequality (3.32) is an anti-Clausius’inequality Moreover, for the ectropy function defined by (3.45), inequality (3.46) shows

Clau-that a thermodynamically consistent discrete-time large-scale dynamical system is pative [23] with respect to the supply rateETS and with storage function corresponding to

dissi-the system ectropyᏱ(E) For the entropy function given by (3.28), note that᏿(0)=0, or,

294 Thermodynamic modeling for discrete-time systems

equivalently, limE →0᏿(E) = 0, which is consistent with the third law of thermodynamics

(Nernst’s theorem) which states that the entropy of every system at absolute zero canalways be taken to be equal to zero

For the isolated discrete-time large-scale dynamical systemᏳ, (3.15) yields the mental inequality

Inequality (3.47) implies that, for any dynamical change in an isolated (i.e.,S(k) ≡0 and

d(E(k)) ≡0) discrete-time large-scale system, the entropy of the final state can never beless than the entropy of the initial state It is important to stress that this result holds for anisolated dynamical system It is however possible with energy supplied from an externaldynamical system (e.g., a controller) to reduce the entropy of the discrete-time large-scaledynamical system The entropy of both systems taken together however cannot decrease.The above observations imply that when an isolated discrete-time large-scale dynamicalsystem with thermodynamically consistent energy flow characteristics (i.e., Axioms1,2,and3 hold) is at a state of maximum entropy consistent with its energy, it cannot besubject to any further dynamical change since any such change would result in a decrease

of entropy This of course implies that the state of maximum entropy is the stable state of

an isolated system and this state has to be semistable

Analogously, it follows from (3.32) that for an isolated discrete-time large-scale namical systemᏳ, the fundamental inequality

minimum ectropy is the stable state of an isolated system and this equilibrium state has to

be semistable The next theorem concretizes the above observations

Theorem 3.14 Consider the discrete-time large-scale dynamical system Ᏻ with energy balance equation ( 3.2 ) with S(k) ≡ 0 and d(E) ≡ 0, and assume that Axioms 1 , 2 , and

3 hold Then for every α ≥ 0, αe is a Lyapunov equilibrium state of ( 3.2 ) Furthermore, E(k) →(1/q)eeTE(k0) as k → ∞ and (1/q)eeTE(k0) is a semistable equilibrium state Fi- nally, if for some m ∈ {1, ,q } , σ mm(E) ≥ 0, E ∈ R q+, and σ mm(E) = 0 if and only if E m = 0, then the zero solution E(k) ≡ 0 to ( 3.2 ) is a globally asymptotically stable equilibrium state

of ( 3.2 ).

Proof It follows fromAxiom 1thatαe ∈ R q+,α ≥0, is an equilibrium state for (3.2) Toshow Lyapunov stability of the equilibrium stateαe, consider the system shifted ectropy

Ᏹs(E) =(1/2)(E − αe)T(E − αe) as a Lyapunov function candidate Now, since φ i j(E) =

− φ ji(E), E ∈ R q+,i = j, i, j =1, ,q, and eTE(k + 1) =eTE(k), k ≥ k0, it follows from

To show thatαe is semistable, note that

where᏷i
ᏺi\ ∪ i −1

l =1{ l }andᏺi
{ j ∈ {1, ,q }: φ i j(E) =0 if and only ifE i = E j },i =

1, ,q.

Next, we show that∆Ᏹs(E) =0 if and only if (E i − E j)φ i j(E) =0,i =1, ,q, j ∈᏷i.First, assume that (E i − E j)φ i j(E) =0,i =1, ,q, j ∈᏷i Then it follows from (3.50)that∆Ᏹs(E) ≥0 However, it follows from (3.49) that∆Ᏹs(E) ≤0 Hence,∆Ᏹs(E) =0.Conversely, assume that∆Ᏹs(E) =0 In this case, it follows from (3.49) that (E i(k + 1) −

᏾= { E ∈ R q+: E1= ··· = E q } Since the set᏾ consists of the equilibrium states of (3.2),

it follows that the largest invariant setᏹ contained in ᏾ is given by ᏹ=᏾ Hence, it

296 Thermodynamic modeling for discrete-time systems

follows from LaSalle’s invariant set theorem that for any initial condition E(k0)∈ R q+,

E(k) → ᏹ as k → ∞, and henceαe is a semistable equilibrium state of (3.2) Next, note

that since eTE(k) =eTE(k0) andE(k) → ᏹ as k → ∞, it follows thatE(k) →(1/q)eeTE(k0)

ask → ∞ Hence, withα =(1/q)eTE(k0),αe =(1/q)eeTE(k0) is a semistable equilibriumstate of (3.2)

Finally, to show that in the case where for somem ∈ {1, ,q },σ mm(E) ≥0,E ∈ R q+,andσ mm(E) =0 if and only ifE m =0, the zero solutionE(k) ≡0 to (3.2) is globally asymp-totically stable, consider the system ectropyᏱ(E) =(1/2)ETE as a candidate Lyapunov

function Note thatᏱ(0)=0,Ᏹ(E) > 0, E ∈ R q+,E =0, andᏱ(E) is radially unbounded.

Now, the Lyapunov difference is given by

which shows that the zero solutionE(k) ≡0 to (3.2) is Lyapunov stable

To show global asymptotic stability of the zero equilibrium state, note that

Next, we show that ∆Ᏹ(E) =0 if and only if (E i − E j)φ i j(E) =0 and σ mm(E) =0,i =

1, ,q, j ∈᏷i, m ∈ {1, ,q } First, assume that (E i − E j)φ i j(E) =0 andσ mm(E) =0,

i =1, ,q, j ∈᏷i,m ∈ {1, ,q } Then it follows from (3.53) that∆Ᏹ(E) ≥0 However,

3.4 Semistability of thermodynamic. .. ᐁcorᐁr) for allK ≥0

3.2 Nonconservation of entropy and the second law of thermodynamics The

non-linear energy balance equation (3.2)... Clausius’ inequality for reversible and irreversiblethermodynamics as applied to discrete-time large-scale dynamical systems It followsfromAxiom 1and (3.2) that for the isolated discrete-time large-scale