** Applied Mathematics ** Vol. 3 No. 11 (2012) , Article ID: 24521 , 17 pages DOI:10.4236/am.2012.311236

The Discrete Agglomeration Model: Equivalent Problems

West Virginia University, Morgantown, USA

Email: moseley@math.wvu.edu

Received July 22, 2011; revised October 11, 2012; accepted October 18, 2012

**Keywords:** Agglomeration; Coagulation; Smoluchowski; Differential Equations

ABSTRACT

In this paper we develop equivalent problems for the Discrete Agglomeration Model in the continuous context.

1. Introduction

Agglomeration of particles in a fluid environment (e.g., a chemical reactor or the atmosphere) is an integral part of many industrial processes (e.g., Goldberger [1]) and has been the subject of scientific investigation (e.g., Siegell [2]). A fundamental mathematical problem is the determination of the number of particles of each particle-type as a function of time for a system of particles that may agglutinate during two particle collisions. Little analytical work has been done for systems where particle-type requires several variables. Efforts have focused on particle size (or mass). This allows use of what is often called the coagulation equation which has been well studied in aerosol research (Drake [3]). Original work on this equation was done by Smoluchowski [4]) and it is also referred to as Smoluchowski’s equation. The agglomeration equation is perhaps more descriptive since the term coagulation implies a process carried out until solidification whereas we focus on the agglomeration process; that is, on the determination of a time-varying particle-size distribution even if coagulation is never reached.

In his original work Smoluchowski considered the agglomeration equation in a discrete form. Later it was considered in a continuous form by Mller [5]). In either case, an initial particle-size distribution to specify the initial number of particles for each particle size is needed to complete the initial value problem (IVP). We refer to these as the Discrete Agglomeration Model and the Continuum Agglomeration Model respectively. Solution of either model yields an updated particle-size distribution giving number densities as time progresses. For various conditions, studies of these and more general models include Morganstern [6], Melzak [7], Mcleod [8], Marcus [9], White [10], Spouge [11], Treat [12], McLaughlin, Lamb, and McBride [13], Moseley [14], and Moseley [15].

Let R be the real numbers, I is a finite, infinite, or semi-infinite open interval}, and for,

If A is a subspace of a vector space B we write. These function spaces are vector spaces and

To develop the discrete model, assume that all particles are a multiple of a particle of smallest size (volume), say. Thus a particle made up of i smallest-sized particles has size. In polymer chemistry, the particle is called an i-mer. The initial time is where I_{0} is the largest time interval of interest. We indicate this by the extended interval notation. We also let and. Unless otherwise specified, we assume. Now for each let be a real-valued function (either in) that approximates the number of i-mers in the reactor at time t. Since there are an infinite number of sizes, initially, we take the state (or phase) space to be. Assume the initial number density is known.

As time passes, particles collide, agglutinations occur, and larger particles result. The net rate of increase in n_{i}(t) with time, dn_{i}/dt, is the rate of formation minus the rate of depletion (conservation of mass). For we consider as a possible Σ space (i.e., the designated space where we look for solutions) either for the analytic context or for the continuous context where

Functions in are continuous, but functions in are not as we have not established a topology on R^{∞}. They are componentwise continuous.

For we may define . The derivatives dn_{i}/dt exist and are in C(I,R). However, we can not assert that as we have no topology on R^{∞}.

Let be the set of “infinite matrices”. The kernel (which measures adhesion or “stickiness”), , is a doubly infinite array of real-valued functions of time either in

(analytic context) or in

(continuous context). As with, we establish no topology on.

The resultant Discrete Agglomeration Model or Discrete Agglomeration Problem (DAP) is an IVP consisting of an infinite system of Ordinary Differential Equations (ODE’s) each with an Initial Condition (IC) that may be written in scalar (componentwise) form as:

(1)

IVP

(2)

where for i = 1 the empty sum on the right hand side of (1) is assumed to be zero. The first sum in the scalar (componentwise) discrete agglomeration Equation (1) is the (average) rate of formation of i-mers by agglutinations of with j-mers. The 1/2 avoids double counting. The second sum is the (average) rate of depletion of i-mers by the agglutinations of i-mers with all particle sizes. We model a stochastic process as deterministic. The physical system is often stationary so that each is time independent and the model is said to be autonomous. In a physical context, we require. However, we will address DAP as a mathematical problem where we allow the initial number of particles, the components of the kernel, and the components of the solution, , to be negative. The physical context will be a special case.

Smoluchowski found in the physical context that when is a constant, that

(3)

where

(4)

uniquely satisfies DAP on its interval of validity

. If we assume

, then.

The requirement on in (4) and the infinite sum in (1.1) motivate consideration of the Banach spaces

where

(Martin [16, p. 3]) with norm

(and hence a metric and a topology). Equality of two vectors in requires the metric (the norm of their difference) to be zero. This is equivalent to both vectors being in and being componentwise equal. If , then defines a norm on (Naylor and Sell [17, p. 58]). To insure that exists (even for negative initial conditions)we will require so that.

We are particularly interested in the time-varying kernel which depends on time, but not on particle size. In the continuous context where

the problem parameters are

. In the analytic context where

the problem parameters are

. For any kernel, solution requires that both sides of (1) are continuous in the continuous context and analytic in the analytic context.

The i^{th} depletion coefficient associated with and the distribution is defined formally by the infinite series

(5)

The only direct dependence of on t is through. If (5) converges for all, then maps to. We may view as a function of an infinite number of real variables or as a function of time and a size distribution. Regardless, if, and we have convergence, the composition maps I to R.

Implicit in (1) is that for solution in the continuous context, we must have for all, that . That is, DAP requires us to first find such that for all and, exists (i.e., converges) and defines a function in. If, in addition, (the Σ space) and satisfies (1) on I and (2), then it solves DAP on I. This formulation of DAP does not require mathematics beyond calculus and is often used by engineers and scientists.

For DAP with a time varying kernel, , in the analytic context, Moseley [14] established that the more general formula

(6)

where, satisfies DAP uniquely on its interval of validity or the physical context where, again we have and require . The formula (6) satisfies (1) on I and (2) in the continuous context as well where we now allow However, since (6) was not derived using equivalent equation operations, uniqueness has not been proved rigorously for . Unless otherwise stated, for the rest of the paper, we focus on the continuous context.

Moseley [14] divided DAP into several problems which could be considered separately. Under certain conditions, a reasonably complicated change of (both the independent and dependent) variables transforms DAP with a time varying kernel (Moseley, [14]) into another IVP which Moseley later referred to as the Fundamental Agglomeration Problem (FAP). The solution process for FAP is fully documented in Moseley [15]. For FAP, Moseley established existence and uniqueness for both the analytic and continuous contexts by using a sequential solution. To facilitate further progress, in this paper we develop equivalent problems for DAP in the continuous context. Analogs for the analytic context can be obtained.

To rearrange terms in infinite series we will need

(7)

If all sums exist, we add all of the elements in in two different ways. Since we use them often, we will use to mean “for all” and to mean “there exists” (with apologies to the logicians). If y = n(t), we use any of n, n(t), y(t) and n() to denote the function. Also, we denote the restriction of a function to a smaller domain by the same symbol. The context will make it clear.

2. Mathematical Problem Solving

Often, a mathematical problem is specified by giving a condition (or conditions) (e.g., an algebraic equation or an ODE with an initial condition) on elements in a Σ set (the designated set where we look for solutions, e.g.,). If the set is a vector space, we say Σ space. A problem is (set-theoretically) well-posed if it has exactly one solution in its set. (In this paper, we will not consider continuity with respect to problem parameters.) A well-developed model of dynamics using an IVP is well-posed (exactly one event happens). As modelers, we expect our models to be well-posed. As mathematicians, we require rigorous proof. Often, we solve equations by using equivalent equation operations to isolate the unknown(s). This yields uniqueness, and, as all steps are reversible, existence. (Squaring both sides of an equation is not an equivalent equation operation and may lead to extraneous roots.) For linear ODE’s, we may guess the form of a solution and prove existence and uniqueness by using the linear theory. For nonlinear problems, we may prove existence by substituting back into the equation. Uniqueness then becomes an issue.

Let. If a solution is unique in B, and it is in A, then it is unique in A. If A is the Σ set for the problem and contains only one solution, then the solution is unique in B. Being in A is a requirement for existence. In the continuous context, for, we look for solutions to

(8)

in the space. Thus, as is usually done, we require solutions to (8) to not only exist, but to also have continuous derivatives. We also require where and the range of y(t) is in U for y(t) in the space. Placing these additional constraints avoids dealing with pathology, but narrows the space where a known solution is to be shown to be unique. There may be (pathological) solutions to (8) where the derivative exists, but is not continuous.

Also, as is usually done, we allow I to vary. If we show that there exists a solution for some I, then we say that we have local existence on I. The largest where a solution exists is the interval of validity for the solution (i.e., the domain). We say that we have shown global existence on I if, given , we prove that there exists a solution on I (i.e., a solution in). Suppose a solution on goes through the point where. It is said to be locally unique at if there exists such that it is the only solution on I_{1}. It is locally unique on I if it is locally unique at every point in I. Obviously, if a solution exists globally on , and is locally unique on I, then it is globally unique on I. That is, it is the only solution in the space).

For DAP in the continuous context we start with the large space and say that

satisfies (1) on I if, the composition exists (converges) and is in and n_{i}(t) satisfies (1) on I. Since composition of continuous functions is continuous, we expect if in some sense from given by (5) is continuous. But we do not have a topology on and hence not one on. Instead of requiring, as a separate condition for solution, we may incorporate it into the Σ space. We refer to DAP with the Σ space

as the Scalar Discrete Agglomeration Problem (SDAP). Obviously, this may be formulated in an analytic context as well.

Recalling the constraint , instead of, we may choose the state space as which has a norm (and hence a metric and a topology). A solution on I is then a time-varying infinite-dimensional “state vector”. Later we will choose an appropriate space and write DAP in vector form. We refer to this formulation of DAP as the Vector Discrete Agglomeration Problem (VDAP). As with SDAP, VDAP may be in the continuous or analytic context. If SDAP is well-posed, and its solution is in the (smaller) space for VDAP, then SDAP and VDAP are equivalent except for the space where local uniqueness is proved. That is, by choosing a smaller space, VDAP requires proving local uniqueness in a smaller space than does SDAP. If we do not worry about pathology, and redefine the space for SDAP to be the same as for VDAP, the two problems are equivalent. The question is: How do we choose an appropriate (smaller) space? But first we consider an equivalent scalar problem and spaces.

2.1. Equivalent Scalar Problems

Again assume for that converges where. Now define the functions

(9)

(10)

(11)

which also map. For these functions, as with the only explicit dependence on t is through. For we may now write (1) as the system of ODE’s

(12)

If the restriction of to (which we denote by the same symbol) converges and is continuous on with respect to the norm topology, we write. That is, .

Initially, we assume and investigate , and. Note is just a finite sum involving K_{i,j}(t) and components of, is just the product of with a component of, and is just the difference of and.

Theorem 2.1. Let and . Then, , and are all in.

Proof. Sums, products, and compositions of continuous functions involving ℓ^{1} are continuous. ■

Detailed ε-δ proofs follow proofs in an elementary real analysis course. All functions map to R. We must choose sufficiently small so that. For example, if , then the projection function is continuous since if, then satisfies a Lipschitz condition (Bartle [18, p. 161]) and hence is continuous on ℓ^{1} i.e., is in. Since it is a constant function of t, it is in. We investigate continuity and differentiability in ℓ^{p} in more detail in the next section.

Let. If the composition converges and is continuous on I, we write. Previewing the next section, we define the function spaces and as the componentwise continuous functions that have codomain ℓ^{1}, and claim that .

Corollary 2.2. Let and . If, then the compositions and are all in.

Proof. Sums, products, and compositions of continuous functions involving are continuous. ■

We now show that in the continuous context if , then SDAP given by (12) and

(2) with the Σ space is equivalent to the infinite system of scalar (componentwise) Voltera integral equations

(13)

where is a solution to (13) if it is in the space

and, satisfies (6). (We require

and not just that the integral in (6)

exists.) We refer to this problem as the Integral Scalar Discrete Agglomeration Problem (ISDAP) in the continuous context. A formulation in the analytic context can also be established.

Theorem 2.3. In the continuous context, a distribution

is a solution of SDAP in if and only if it is a solution of ISDAP in.

Proof. First assume that is a solution of SDAP in. We have by the definition of a solution of SDAP, that

, that

, that (13) is satisfied on I, and that (2) is satisfied. Since both sides of (13) are continuous, we may integrate from t_{0} to to obtain

(14)

Applying the initial condition we obtain (13). Similarly, let us assume that is a solution of (13) in. Substituting in t_{0} we obtain (2).

Since, we have that

so that the integrand,

, is continuous. Since n_{i}(t) is written as an integral, it is differentiable so that. Differentiating we see that (11) is satisfied. ■

For the scalar Equation (2.1), it is the integral formulation that is used to obtain existence (Picard iterations) and uniqueness using a Lipschitz condition. If we choose to specify as the space for both problems, the problems remain equivalent as any solution to (13) in is in fact in. That is, there are no solutions to (13) in

. These results can also be established in the analytic context.

2.2. Continuity and Differentiability for ℓ^{p} Spaces

Since has a norm (and hence a metric) we have a topology on the subspace of. Many of the limit laws can be extended to. For example, if

and, then

. We also have if

and, then

Definition 2.1. A function is continuous at with respect to the norm topology if

in; that is, given ε > 0,

such that implies

. If it is continuous, it is continuous on I. Similarly, a function is continuous at with respect to the norm topology if in I;

that is, given such that

implies

. If it is continuous, it is continuous on. Similarly for the functions, , and

.

Hence we can define the function spaces

and

as well as, and. If

, then we may assume. For, the range is restricted to the set B whereas, for, it is allowed to be in the larger set C. Since . However, has a norm (and hence a metric and a topology), but does not. (We could establish a topology for, but this is not necessary if the system states are all in.) We will use for functions that are componentwise continuous with codomain and write

,

, and

. Also, if

, we write if; that is, we use the same symbol for the restriction of a function to a smaller domain.

We give necessary and sufficient conditions for to be in .

Theorem 2.4. Proof. We show that. That is, if, then is componentwise continuous. As is a vector space, by our previous comments follows. Let and. Then

in. That is, given

such that implies

. Since

, given

such that implies

. Hence, in R so that. Hence

. ■

Theorem 2.5. If, then.

If, then. If, then

.

Proof. If (or any normed linear space)then the triangle inequality implies so the norm function

satisfies a Lipschitz condition on and hence is continuous on i.e., is in. We say it is Lipschitz continuous on. Now let

. Since is the composition of the norm function with, implies

. For, let

. Since

,

exists (converges absolutely). If

, then so that M_{0}(A) is Lipschitz continuous on so that.

Since the composition of continuous functions (to and from) is continuous,

Example 2.1. Let and for let

(15)

Then as each n_{i}(t) is continuous and,. However, , but for,. Hence either does not exist or is greater then or equal to 1. Hence in not contiuous at as does not exist. Hence does not exist in. Hence. Hence the relations

are proper.

Example 2.2. Let and for any t let

and otherwise.

Then, we have and. Obviously so even though as,.

Although not sufficient individually for

to be in, we need its range to be in,

, and. However, all of these do force to be in.

Theorem 2.6.

Proof. Let

, , and

.

Since, and. Also, , ,

and are all in. Let. Then

.

Now let. Since, we can choose N sufficiently large so that

. Since

, such that implies so that

. Since

, choose δ_{i} so that implies. Hence

.

Now choose. Hence implies

.

Hence. ■

Rather than check directly that, it may be easier to check that for each, ,

since and map from I to R. SimilarlyCorollary 2.7.

and

.

Following the standard proof for products, we also have Theorem 2.8. If and, then.

Proof. Let and. Choose δ_{1} such that

implies

and δ_{2} such that implies

. Let.

Then implies

■

SimilarlyCorollary 2.9. If and, then. If and, then. If and, then

.

We say is differentiable

(with respect to the norm topology) at, if

exists in. If exists and is in, then. We define integration componentwise. Following Theorem 2.6, we have Theorem 2.10. If, then. Also,

.

Proof. That follows from considering the limit for components. A proof of

can be obtained following the proof for scalar valued functions in calculus books (e.g., Stewart [19, p. 88]). The description of follows from Theorem 2.6. ■

If at, n(t) has an infinite number of derivatives and equals it’s Taylor series,

in a neighborhood of t_{1}, it is analytic at t_{1}. If it is analytic

, then.

Theorem 2.11.

,

,

,

,

and

.

Proof. The first containment follows from Theorem 2.10. The remaining proofs are straight forward and often similar to the proof of Theorem 2.4. ■

Theorem 2.12 (Fundamental Theorem of Calculus)

If, then

, Part 1. (16)

If, then

. Part 2. (17)

Note that the indefinite integral requires an arbitrary constant vector.

2.3. Kernels, State Spaces and Σ Spaces In the analytic context with an analytic kernel,

, Moseley [14] used the following procedure to solve DAP. He first established local uniqueness in by considering the Taylor series coefficients obtained from the initial conditions and the differential equation. However, he chose a smaller Σ space containing only distributions where if is in the Σ space, then

. He then obtained the explicit formula (6) for the (analytic) solution when A(t) is analytic. He did not rigorously isolate the unknown so he established global existence by showing that the solution given by the formula (6) was in the Σ space, checking the initial conditions (2), and then substituting the formula into (1). Since global existence holds, local uniqueness implies global uniqueness.

The problem of interest is to extend Moseley’s results for the analytic context to the continuous context. The solution given by (6) remains the same except that we now only require. Global existence may be obtained as before. However, local uniqueness is not as easy as it was in the analytic context. McLaughlin, Lamb, and McBride [13] provided local existence and uniqueness for a Continuum Agglomeration Model of linear fragmentation with coagulation as a perturbation using semigroup theory. Spouge [11] provided a local existence theorem in the physical case, but not uniqueness. The standard procedure in Brauer and Noel [20] for a finite dimensional system requires a Lipschitz condition on the right hand side to obtain local existence and local uniqueness. In this paper, we provide preliminaries for using a Lipschitz condition to prove uniqueness in the continuous context by giving equivalent problems in scalar and vector form for DAP with K(t) in a larger collection than.

Let. If,

converges, then

maps to. We say that (the restriction of) (to) is in

if, (the restriction of)

(to) is in and write. Furthermore, when

, we write

if

.

Theorem 2.13. If, and, then.

Proof. Compositions of continuous functions (in)

are continuous so that if and

, then. (See Corollary 2.2.) ■

In the continuous context we wish conditions on so that. Then for

in (any subspace of) we have

. Then the convergence and continuity condition on need not be explicitly stated for the Σ space or as a condition for solution (except as required for interpreting (1)). We begin with three classes of kernels:,

and

.

Since and , if we can prove that for, we have, then for all kernels in these three classes, if, we have . However, for clarity, we proceed class by class.

If and

, then for all we have

so that

where is the zeroth moment of the sequence. In the physical context, so that,

is the total number of particles and is the total mass of the particles (which should not change) where is the first moment of the solution and ρ is the mass density. Treat [12] suggested (as have others) on a physical basis, that these and other moments, possibly all moments, should exist (converge and be continuous). We will take our Σ space as a subspace of. Againwe view a solution as a time-varying infinite-dimensional “state vector”.

Theorem 2.14. Let so that,. If , then exists (converges absolutely) and. If, then

exists (converges absolutely) and

. If, then and.

Proof. Let so that,. By Theorem 2.5, if, then exists (converges absolutely) and. If, then and so that

exists (converges absolutely). By Corollary 2.9,. Now let. By Theorem 2.6 so that exists (converges absolutely). Since and

, and

exist (converge absolutely). As compositions and products of continuous functions

(in) are continuous, , and

are in. ■

Theorem 2.15. Let. Then

exists (converges absolutely)

and is in. If, then

.

Proof. Let. (Note that is a constant function of t for this kernel.) Then such that. Let

and. Then

so that exists (converges absolutely). Now let and. Then

Hence is Lipschitz continuous and hence in

. Since is a constant function of time,. Hence. If then and

. ■

Theorem 2.16. Let. Then exists (converges absolutely) and. If, then.

Proof. Let

and. Then

where and

(by Theorem 2.15). By Corollary 2.9

. If, then. ■

2.4. Weierstrass M-Test and Local Uniform Boundedness Let and. For, let

. We briefly review the Weierstrass M-test and succeeding theorems on absolute and uniform convergence (Kaplan [21, pp. 436-444]). This should be familiar to engineers and scientists. We then consider a fourth class of kernels. Let

Note (let

and). We say that K(t) is locally uniformly bounded in time and size.

Theorem 2.17 (Weierstrass M-Test Extended). Let

, and

. Suppose

such that, and

, then

and exist

(converge absolutely and are uniformly convergent on J so that theyare in. Since t_{1} was arbitrary, so and and are in so that. If and such that, and

, then and exist (converge absolutely)

and are uniformly convergent on J so that they are in C(J,R). Since t_{1} was arbitrary, so and and are in so that. Also,

.

For, to insure

, we will require

to satisfy a stronger local uniform boundedness condition in time.

Definition 2.2. Let.

Then is locally uniformly bounded at t_{1} if

, such that, and.

We say is locally uniformly bounded on I if it is locally uniformly bounded.

Now let

,

and

.

Moseley (2007) used (which he denoted by) as the Σ space in the analytic context. We have

.

Example 2.3. Let with

and n_{i}(t) increasing. Now for, let and

. Then

. Hence

.

It can be shown (similar to Moseley [14] in the analytic context) that in the continuous (physical) context,

where is given by (1.6) is in where

.

Theorem 2.18. Let

. Then and are in C(I,R) and. If then, and

.

Proof. Let and. Then such that

, and

. Hence,

so that. By Theorem 2.17, and are in C(J,R). Since t_{1} was arbitrary, and

are in C(I,R). Hence by Theorem 2.6,. If, then, again by Theorem 2.17, and

. Hence by Theorem 2.6, and so

. ■

Since the range of functions in is contained in, we have. Similarly,.

Corollary 2.19.

.

Also,

.

Proof. By Theorem 2.18,.

Everything else is straight forward or follows in a manner similar to the proof of Theorem 2.4. ■

We show that if and

, then. We use the local uniform boundedness of and. Let

and

Then and are vector spaces and

Theorem 2.20. Let

. For, and, exists (converges absolutely) and. If , then.

Proof. Let. Then, and such that for all and Let and where. Then

(18)

so that exists (converges absolutely for

). Since t_{1} was arbitrary exists for. Also, since

and

, by Theorem 2.17 we have that and fixed,. Hence

. However, we do not have. We may (or may not) be able to prove this with a further extension of the Weirstrauss M-test. Instead we let

. Then for and

not only do we have such that for all and but also such that

and

. Then

and

Hence by Theorem 2.17,. Hence. ■

Thus if and we choose a subspace of as our Σ space, we obviate the need to explicitly require as a condition for to be a solution (except to interpret (1.1)) or as a specific condition for the Σ space.

2.5. Equivalent Vector Problems Recall that if converges the functions, , and all map and that

maps. Now let,

, and

. These three functions map

. As with, the only explicit dependence on t is through K(t). If and, then,

, and are all in (see Theorem 2.1). Now let Then by Theorem 2.16,. If we can show that implies, , and are in, then these functions can be thought of as functions from to instead of from. We indeed show that if we restrict to, then the restrictions of, , and to all map to so these functions are all in

. Let

Then is a vector space. We show that if, then, , and are all in. We have

Theorem 2.21. Let. Then, for

the images, , and are all in. Also,

,

, and

. If

, the

, and,

, and are all in.

Proof. Let and. Then such that

. Hence for where, from (2.12) we have

so that

and

Since t_{1} was arbitrary, , is in. Furthermore, since

and

, by Theorem 2.17, for fixed,

.

Hence.

By Theorem 2.1,. Also, since

we have

where we have used (7). Hence

. Since

and

we have for fixed,

. Hence

. Since is a vector space (note

)we have that.

Now let. Then by Theorem 2.16,. By using Theorem 2.1 and above, , and are in

Unfortunately, we have not proved that

. However, assuming

we consider the Vector Problem (VP):

Vector ODE,

(19)

IVP IC

(20)

where the derivative and equality are in. That is, we now require the derivative to be defined with respect to the norm topology,

in

, and equality as equality in. For VP, we take our

Σ space as.

For we now show that VP is equivalent to

(21)

where for (the Σ space) to be a solution of (21), we require, that (12) holds; that is, integration is componentwise. Equality is in. We refer to this problem as the Integral Vector Problem (IVP)

Theorem 2.22. The distribution is a solution of VP in if and only if it is a solution of IVP in.

Proof. For both problems we have chosen the Σ space to be. Now assume that is a solution of VP so that (19) and (20) are satisfied, and the right hand side of (19) is in. We may integrate from t_{0}to t using (17) to obtain the vector equation

(19)

Applying the initial condition we obtain (21). Now assume that is a solution of (21). Then substitute to obtain (20). Since

, and

, differentiating

(componentwise) we have that

and that (19) holds. ■

Theorem 2.23. If, then

and

. If, then

and

. On the other hand, if

, then

and

. If, in addition,

, then and.

Proof. If, then by Theorem 2.16,. By Theorem 2.21,

. If, then

and

. Now let

. Then by Theorem 2.21,

and

. If, in addition,

, then and.

To define VDAP in the continuous context, we would like and

. Then for, we would have and

. When, we do have, but have only shown that so that for

, we have and. We refer to this problem with Σ space as VDAP1. When

, we settle for

and

so that if, then and

. We refer to this problem with Σ space as VDAP2. As

, if we take

as our Σ space for SDAPISDAP, and VDAP1or VDAP2, then they are all equivalent if they have the same problem parameters

.

3. Summary and Future Work For the time-varying kernel () in the analytic context, the problem parameters are

. For this problem, Moseley [14] used the following problem solving procedure. He first established local uniqueness in

. However, he chose the smaller Σ space containing only distributions where (for a time-varying kernel) if is in, then the depletion coefficients are in, He then obtained the explicit formula (6) for the (analytic) solution. He did not rigorously isolate the unknown so he established global existence by showing that the solution given by the formula (6) was in the Σ space, checking the initial conditions, and then substituting the formula into (1). Since global existence holds, local uniqueness in the analytic context implies global uniqueness.

If we choose as our Σ space, then SDAP, ISDAP, VDAP1, and IVDAP2 are all equivalent in the continuous context if they have the same problem parameters. If

and, we have, so that we need not specify this condition separately. For the time varying kernel, the solution given by (6) is inwhere in the continuous context. However, we have not shown (local) uniqueness in the continuous context. To do this we have (at least) four choices:

1) Provide a rigorous derivation of (6) that provides (existence and) uniqueness.

2) Develop and use a Lipschitz condition for: in the scalar problems SDAP and ISDAP.

3) Extend the (existence and) uniqueness results for FAP in the continuous context to obtain a unique sequential solution to DAP.

4) Develop and use a Lipschitz condition for in the vector problems VDAP1 and VDAP2.

We have provided preliminaries for the development of a Lipschitz condition for VDAP1 and VDAP2. However, all four alternatives appear to be worthwhile.

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