We have already seen that the idea of “connections at infinity” can be encompassed within electrical network theory through the invention of 1-nodes. A consequence is the genesis of transfinitely connected graphs, that is, graphs having pairs of nodes that are connected through transfinite paths but not through finite ones. An example of this is provided by Figure 3.4; there is no finite path connecting a node of branch a to a node of branch α, but there are 1-paths that do so. The maximal, finitely connected subnetworks of that figure are the 0-sections of the 1-graph, and the 1-nodes described in Example 3.2-5 connect those infinitely many 0-sections into a 1-graph.

There is an incipient inductive process arising here. Just as 0-nodes connect branches together to produce a 0-graph, so too do 1-nodes connect 0-sections together to produce 1-graphs. The purpose of the present chapter is to pursue this induction. While doing so, we will discover an infinite hierarchy of transfinite graphs. It turns out that most of the electrical network theory discussed so far can be transferred to such graphs to obtain an infinite hierarchy of transfinite networks. Thus, an electrical parameter in one branch can affect the voltage-current pair of another branch, even when every path connecting the two branches must pass through an “infinity of infinite extremities.”

To think about this in another way, let each 0-section of Figure 3.4 be replaced by replicates of the entire 1-graph of that figure.

The preceding chapters were focused on the mathematical foundations of infinite electrical networks, existence and uniqueness theorems being their principal result. Generality was a concomitant aim of those discussions. For the remaining chapters, we shift our attention to particular kinds of networks (namely, the infinite cascades and grids) that are more closely related to physical phenomena. Two examples of this significance were given in Section 1.7, and more will be discussed in Chapter 8. Our proofs will now be constructive, and consequently methods for finding voltage-current regimes will be encompassed. Moreover, various properties of voltage-current regimes will examined.

We must now be specific about any network we hope to analyze. In particular, its graph and element values need to be stipulated everywhere. An easy way of doing this is to impose some regularity upon the network. Most of this chapter (Sections 6.1 to 6.8) is devoted to the simplest of such regularities, the periodic two-times chainlike structures. They are 0-networks appearing in two forms. One form will be called a one-ended grounded cascade and is illustrated in Figure 6.1; it has an infinite node as one of its spines, and a one-ended 0-path as the other spine. The other form will be called a one-ended ungrounded cascade and is shown in Figure 6.2; in this case, both spines are one-ended 0-paths. The third possibility of both spines being infinite nodes is a degenerate case and will not be discussed.

The theory of electrical networks became fully launched, it seems fair to say, when Gustav Kirchhoff published his voltage and current laws in 1847 [72]. Since then, a massive literature on electrical networks has accumulated, but almost all of it is devoted to finite networks. Infinite networks received scant attention, and what they did receive was devoted primarily to ladders, grids, and other infinite networks having periodic graphs and uniform element values. Only during the past two decades has a general theory for infinite electrical networks with unrestricted graphs and variable element values been developing. The simpler case of purely resistive networks possesses the larger body of results. Nonetheless, much has also been achieved with regard to RLC networks. Enough now exists in the research literature to warrant a book that gathers the salient features of the subject into a coherent exposition.

As might well be expected, the jump in complexity from finite electrical networks to infinite ones is comparable to the jump in complexity from finite-dimensional spaces to infinite-dimensional spaces. Many of the questions we conventionally ask and answer about finite networks are unanswerable for infinite networks – at least at the present time.

As was indicated in Example 1.6-3, the total power dissipated in the resistances by a voltage-current regime, satisfying Ohm's law, Kirchhoff's current law at finite nodes, and Kirchhoff's voltage law around finite loops, need not be finite. Moreover, these laws need not by themselves determine the regime uniquely. However, if voltage-current pairs are assigned to certain branches, the infinite-power regime may become uniquely determined. The latter result requires in addition the “nonbalancing” of various subnetworks, as is explained in the next section. In which branches the voltage-current pairs can be arbitrarily chosen and how the nonbalancing criterion can be specified are the issues resolved in this chapter. The discussion is based on a graph-theoretic decomposition of the countably infinite network into a chainlike structure, which was first discovered by Halin for locally finite graphs [63]. That result has been extended to graphs having infinite nodes [166]. The chainlike structure implies a partitioning of the network into a sequence of finite subnetworks, which can be analyzed recursively to determine the voltage-current pair for every branch. We call this a limb analysis.

As was mentioned before, in most of this book we restrict our attention to resistive networks. However, a limb analysis can just as readily embrace complex-valued voltages, currents, and branch parameters. In short, a limb analysis can be used for a phasor representation of an AC regime or for the complex representation of a Laplace-transformed transient regime in a linear RLC network [166].

Perhaps the most important infinite electrical networks with respect to physical phenomena – putting aside the finite networks – are the infinite grids. This is because finite-difference approximations of various partial differential equations have realizations as electrical networks whose nodes are located at the sample points of the approximation. Those sample points are distributed in accordance with increments in each of the coordinates, hence the gridlike structure. Moreover, if the phenomenon exists throughout an infinite domain, it is natural (but, to be sure, not always necessary) to choose an infinity of sample points. In this way, one is led to infinite electrical grids as models for the so-called “exterior problems” of certain partial differential equations. Two cases of this were presented in Section 1.7, and more will be discussed in the next chapter. The grids we examine are of two general types: the grounded grids, wherein a resistor connects each node to a common ground node, and the ungrounded grids, wherein those grounding resistors are entirely absent. Grounded grids are readily analyzed, but ungrounded grids are more problematic because of a singularity in a certain function that characterizes the network.

This chapter is devoted to rectangular grids, the natural finite difference model for Cartesian coordinates, but the analysis can be extended to other coordinate systems such as cylindrical and spherical ones [180], [183], [184], [188].

The adjective “nonlinear” will be used inclusively by taking “linear” to be a special case of “nonlinear.” As promised, we present in this chapter two different theories for nonlinear infinite networks. The first one is due to Dolezal and is very general in scope – except that it is restricted to 0-networks. It is an infinite-dimensional extension of the fundamental theory for scalar, finite, linear networks [67], [115], [127]. In particular, it examines nonlinear operator networks, whose voltages and currents are members of a Hilbert space ℋ; in fact, infinite networks whose parameters can be nonlinear, multivalued mappings restricted perhaps to subsets of ℋ are encompassed. As a result, virtually all the different kinds of parameters encountered in circuit theory – resistors, inductors, capacitors, gyrators, transformers, diodes, transistors, and so forth – are allowed. However, there is a price to be paid for such generality: Its existence and uniqueness theorems are more conceptual than applicable, because their hypotheses may not be verifiable for particular infinite networks. (In the absence of coupling between branches, the theory is easy enough to apply; see Corollary 4.1-7 below.) Nonetheless, with regard to the kinds of parameters encompassed, this is the most powerful theory of infinite networks presently available. Dolezal has given a thorough exposition of it in his two books [40], [41]. However, since no book on infinite electrical networks would be complete without some coverage of Doleza's work, we shall present a simplified version of his theory.

The purposes of this initial chapter are to present some basic definitions about infinite electrical networks, to show by examples that their behaviors can be quite different from that of finite networks, and to indicate how they approximately represent various partial differential equations in infinite domains. Finally, we explain how the transient responses of linear RLC networks can be derived from the theory of purely resistive networks; this is of interest because most of the results of this book are established in the context of resistive networks.

Notations and Terminology

Let us start by reviewing some symbols and phraseology so as to dispel possible ambiguities in our subsequent discussions. We follow customary usage; hence, this section may be skipped and referred to only if the need arises. Also, an Index of Symbols is appended for the more commonly occurring notations in this book; it cites the pages on which they are defined.

Let X be a set. X is called denumerably infinite or just denumerable if its members can be placed in a one-to-one correspondence with all the natural numbers: 0, 1, 2,. … X is called countable if it is either finite or denumerable. In this book the set of branches of any network will always be countable.

The notation {x ∈ X: P(x)}, or simply {x: P(x)} if X is understood, denotes the set of all x ∈ X for which the proposition P(x) concerning x is true.

Our first view of a concurrent process is that of a machine where every detail of its behaviour is explicit. We could take as our machine model automata in the sense of classical automata theory [RS59], also known as transition systems [Kel76]. Automata are fine except that they cannot represent situations where parts of a machine work independently or concurrently. Since we are after such a representation, we use Petri nets [Pet62, Rei85] instead. This choice is motivated by the following advantages of nets:

1. Concepts. Petri nets are based on a simple extension of the concepts of state and transition known from automata. The extension is that in nets both states and transitions are distributed over several places. This allows an explicit distinction between concurrency and sequentiality.

2. Graphics. Petri nets have a graphical representation that visualises the different basic concepts about processes like sequentiality, choice, concurrency and synchronisation.

3. Size. Since Petri nets allow cycles, a large class of processes can be represented by finite nets. Also, as a consequence of (1), parallel composition will be additive in size rather than multiplicative.

An attractive alternative to Petri nets are event structures introduced in [NPW81] and further developed by Winskel [Win80, Win87]. Event structures are more abstract than nets because they do not record states, only events, i.e. the occurences of transitions. But in order to forget about states, event structures must not contain cycles. This yields infinite event structures even in cases where finite (but cyclic) nets suffice.

Many computing systems consist of a possibly large number of components that not only work independently or concurrently, but also interact or communicate with each other from time to time. Examples of such systems are operating systems, distributed systems and communication protocols, as well as systolic algorithms, computer architectures and integrated circuits.

Conceptually, it is convenient to treat these systems and their components uniformly as concurrent processes. A process is here an object that is designed for a possibly continuous interaction with its user, which can be another process. An interaction can be an input or output of a value, but we just think of it abstractly as a communication. In between two subsequent communications the process usually engages in some internal actions. These proceed autonomously at a certain speed and are not visible to the user. However, as a result of such internal actions the process behaviour may appear nondeterministic to the user. Concurrency arises because there can be more than one user and inside the process more than one active subprocess. The behaviour of a process is unsatisfactory for its user(s) if it does not communicate as desired. The reason can be that the process stops too early or that it engages in an infinite loop of internal actions. The first problem causes a deadlock with the user(s); the second one is known as divergence. Thus most processes are designed to communicate arbitrarily long without any danger of deadlock or divergence.

A crucial test for any theory of concurrent processes is case studies. These will clarify the application areas where this theory is particularly helpful but also reveal its shortcomings. Such shortcomings can be challenges for future research.

Considering all existing case studies based on Petri nets, algebraic process terms and logical formulas, it is obvious that these description methods are immensely helpful in specifying, constructing and verifying concurrent processes. We think in particular of protocol verification, e.g. [Vaa86, Bae90], the verification of VLSI algorithms, e.g. [Hen86], the design of computer architectures, e.g. [Klu87, DD89a, DD89b], and even of concurrent programming languages such as OCCAM [INM84, RH88] or POOL [Ame85, ABKR86, AR89, Vaa90]. However, these examples use one specific description method in each case.

Our overall aim is the smooth integration of description methods that cover different levels of abstraction in a top-down design of concurrent processes. This aim is similar to what Misra and Chandy have presented in their rich and beautiful book on UNITY [CM88]. However, we believe that their approach requires complementary work at the level of implementation, i.e. where UNITY programs are mapped onto architectures.

Our presentation of three different views of concurrent processes attempts to contribute to this overall aim. To obtain a coherent theory, we concentrated on a setting where simple classes of nets, terms and formulas are used. We demonstrated the applicability of this setting in a series of small but non-trivial process constructions.

The stepwise development of complex systems through various levels of abstraction is good practice in software and hardware design. However, the semantic link between these different levels is often missing. This book is intended as a detailed case study how such links can be established. It presents a theory of concurrent processes where three different semantic description methods are brought together in one uniform framework. Nets, terms and formulas are seen as expressing complementary views of processes, each one describing processes at a different level of abstraction.

• Petri nets are used to describe processes as concurrent and interacting machines which engage in internal actions and communications with their environment or user.

• Process terms are used as an abstract concurrent programming language. Due to their algebraic structure process terms emphasise compositionality, i.e. how complex terms are composed from simpler ones.

• Logical formulas of a first-order predicate logic, called trace logic, are used as a specification language for processes. Logical formulas specify safety and liveness aspects of the communication behaviour of processes as required by their users.

At the heart of this theory are two sets of transformation rules for the top-down design of concurrent processes. The first set can be used to transform logical formulas stepwise into process terms, and the second set can be used to transform process terms into Petri nets. These rules are based on novel techniques for the operational and denotational semantics of concurrent processes.