Geometric, Linear, and Dynamic Programming 597 and for the others 1–1 1–2 1–3 2–1 2–2 2–3 3–1 3–2 3–3 12 15 19 15 13 17 18 18 16 10.20. Solve the dynamic programming problem shown in Figure P10.20, with the time (in minutes) between the various locations given as A–1 A–2 A–3 A–4 1–D 2–D 3–D 4–D 10 14 12 15 16 12 10 8 and 1–1 1–2 1–3 1–4 2–1 2–2 2–3 2–4 16 20 14 17 12 14 15 6 3–1 3–2 3–3 3–4 4–1 4–2 4–3 4–4 10 11 13 18 8 11 14 22 Determine the optimum path that leads to the minimum amount of time in going from A to D. A D CB 44 33 22 11 FIGURE P10.20 599 11 Knowledge-Based Design and Additional Considerations The basic approach to the design and optimization of thermal systems has been presented in the preceding chapters. Different steps in the design process, starting with the formulation and the concept, have been presented to obtain acceptable designs, followed by optimization. Many additional aspects that must be included in practice for a successful design have also been outlined. These include eco- nomic, safety environmental, regulatory, legal, and other issues that may be tech- nical or nontechnical in nature. These considerations are of critical importance, since a design that is technically feasible may not be acceptable because of the excessive cost or because it violates regulations regarding safety or the environ- mental impact. In this chapter, we will consider knowledge-based design, which is a non-tra- ditional design methodology based on experience, informal approaches or heu- ristics, information on existing systems, and current practice. The main elements of this method and the overall scheme are outlined, followed by examples of a few thermal systems to demonstrate the power and usefulness of this approach. Also considered are some additional important issues with respect to the design of thermal systems, such as professional ethics and other constraints. Also dis- cussed in this chapter are the sources of information that may be employed to provide inputs for design. Some of the important sources for information on material property data, characteristics of components, economic variables, opti- mization techniques, computer software, etc., are given. An overview of the design and optimization of thermal systems is also presented. This overview serves to put the entire design and optimization process in perspective. Several design projects are included as problems at the end of the chapter to cover the entire process for typical thermal systems. Groups of students may use these as projects in design courses that involve design and optimization undertaken over the period of a semester. 11.1 KNOWLEDGE-BASED SYSTEMS With the extensive growth in computer-based design, considerable effort has been directed at streamlining the design process, improving the design methodology, automating the use of existing information, and developing strategies for rapid con- vergence to the nal design. Many of these techniques are discussed in the literature 600 Design and Optimization of Thermal Systems (Suh, 1990). A particularly important approach that is nding increasing use as a component in the design process is that of knowledge-based design. The develop- ment and use of this tool is based on the premise that the more the machine or computer knows or learns as it proceeds, the more effective and efcient this process will be. Therefore, an attempt is made to include relevant information on the system, process, and current practice, adding to this information with time and employing the information base to guide the design. The experience gained by a designer over time and various so-called “rules-of-thumb” or heuristics are also included. Recent advancements in computer science in areas such as information storage and retrieval, articial intelligence, and symbolic languages are used in developing knowledge- based design aids, which are then used to provide inputs to the design process. 11.1.1 INTRODUCTION Storing the knowledge and experience of an expert in a particular area and using these to make logical decisions for selection, diagnostics, and design is the basic concept behind knowledge-based systems. Therefore, knowledge-based systems are also known as expert systems and involve articial intelligence features such as 1. Stored expert knowledge and experience 2. Reasoning 3. Decision making and logic 4. Learning Articial intelligence is used in many areas such as natural languages, database systems, expert consulting systems, theorem proving, manufacturing, scheduling, pattern recognition, image processing, model development, and design. Examples of software currently in use include MYSIN, which diagnoses diseases; PROS- PECTOR, which evaluates potential ore deposits; MACSYMA, which solves problems in calculus by using symbolic manipulation; and DENDRAL, which nds structures of complex organic compounds. Several expert systems have been developed for the design of different types of systems, including thermal systems, and are discussed later in this chapter. The knowledge-based methodology requires efcient storage of expert knowl- edge so that repetition is avoided, minimum space is taken, and rapid retrieval of information is possible. A common arrangement used for the storage of informa- tion is a tree structure, in which objects are organized in a hierarchical scheme with certain objects taken as subclasses of other objects. These subclasses inherit the common features from the objects above it. Therefore, a relationship similar to that of a parent and child is established with respect to inheritance of charac- teristics and properties. Figure 11.1 shows a tree structure for storing information on animals, with only two choices at each step. So if we consider a cat, its hierar- chy indicates that it is a nonvegetarian, nonying land animal. Only information specic to cats needs to be placed at the particular location, with more general features being derived from its hierarchy. Knowledge-Based Design and Additional Considerations 601 Similar tree structures can be developed for thermal processes, as given in Figure 11.2 for cooling systems for electronic equipment. Different types of cool- ing arrangements, uids, and transport mechanisms are included. Figure 2.7 gave a similar tree structure for forced convection cooling, considering different types Animals Land Fly Don’t fly Also land Water Only water Not fish Small Cats Not cats Large Fish Vegetarian Nonvegetarian Nonamphibian Amphibian SmallLarge FIGURE 11.1 Tree structure for storing data on animals. Forced Air Natural Boiling Convection Water Blower FanOtherHorizontalVertical Horizontal Vertical Other Plate Cylinder Mixed Pure Liquid N 2 Forced Natural Liquid Cooling of electronic equipment FIGURE 11.2 An example of a tree structure for storing data on cooling of electronic equipment. 602 Design and Optimization of Thermal Systems of systems. Similarly, Figure 2.32 gives a tree structure that can be used to store information on different types of materials. Again, the use of subclasses helps in information storage and retrieval. The types of information that may be stored are knowledge and experience available with an expert, material characteristics, design rules, empirical data, and other inputs that may be used for design. In many practical cases, intuitive ideas, heuristics, and general features are used to guide the design. These may also be built into the system to obtain an acceptable or optimal design. 11.1.2 BASIC COMPONENTS The main components of a knowledge-based design system, shown in Figure 11.3, are 1. Front end 2. Computational modules 3. Material databases 4. Graphics output 5. Knowledge base The user interacts with the front end, which interfaces with the other com- ponents of the system. Numerical, symbolic, or graphical inputs are provided by the user to the front end. The geometry, conguration, dimensions, materials, and operating conditions are entered. The front end then obtains material property data and supplies these to the computational modules to obtain the simulation results needed for design. Empirical data, correlations, component characteris- tics, etc., may be included in the computational modules to complete the modeling and the simulation. These are linked with the knowledge base. The given design rules are then used to obtain the nal design, which is then communicated as graphical or tabulated results. Material database Computational modules Graphics output Front end Knowledge base User FIGURE 11.3 Components of a knowledge-based system for design. Knowledge-Based Design and Additional Considerations 603 Front End and Knowledge Base The front end contains the design rules, constraints, requirements, design vari- ables, and other aspects pertaining to the given system. Some of these, partic- ularly constraints due to material limitations, are obtained from the databases associated with the system. The knowledge base contains the expert knowledge, which includes 1. Information from previous designs 2. Rules of thumb 3. Heuristics based on informal methods 4. Safety and environmental regulations 5. Information on existing and similar systems 6. Current engineering practice 7. Other information that constitutes the experience of a designer All this knowledge may be used in the development of a realistic and success- ful design. Therefore, the knowledge base is a very important component of this design methodology. It helps a designer avoid mistakes made in the past and use earlier design efforts for accelerating the iterative design process. It is worth not- ing that many of these aspects are typically employed in the design process even if the systematic approach given here is not followed. Computational Modules The computational modules house algorithms for numerical simulation of the system. This component is particularly important for the design and optimization of thermal systems, since computer simulation results usually form the basis for design. Even if items like pumps, heat exchangers, and compressors are only to be selected for the thermal system, the computational effort is needed for analyz- ing the system to ensure that the given requirements and constraints are satised. Various computational techniques are stored in the form of subroutines, which can be called from the front end to provide computational results. Examples of methods that may be included for thermal systems are 1. Gauss-Jordan method for matrix inversion 2. Least squares method for best t 3. Numerical differentiation and integration 4. Successive over relaxation (SOR) method for linear algebraic equations 5. Runge-Kutta method for ordinary differential equations 6. Finite difference and nite element methods for partial differential equations Separate modules may be developed for a given problem, such as a glass fur- nace, air-conditioning system, diesel engine, etc. Information on the discretiza- tion methodology, convergence criteria, data storage for graphics, etc., is provided 604 Design and Optimization of Thermal Systems to enable accurate results to be obtained and linked with the other parts of the sys- tem. Programming languages such as Fortran and C or software like MATLAB and Mathcad are used for carrying out rapid computations. Parallel computing, with a large number of processors, may also be employed for faster response from these modules. Empirical data, usually in the form of correlations, are also included here. Material Databases The material databases contain information on various materials that are of inter- est for the types of systems under consideration. Important items that may be included are 1. Thermal properties 2. Allowable ranges of temperature and temperature gradient 3. Strength data, hardness, malleability, and other physical characteristics 4. Cost per unit mass or volume 5. Availability, including import considerations 6. Manufacturability or ease of fabrication Thermal properties, such as thermal conductivity, diffusivity, specic heat, density, and latent heat, are stored for thermal systems, usually at different temperatures or as functions of temperature. In order to avoid damaging them, constraints on temperature and temperature gradient are given for the various materials. Damage may occur, for instance, due to the melting or charring of the material, thermal stresses, deformation at high temperatures, etc. Cost, avail- ability, manufacturability, strength, and other relevant properties are important in material selection and should also be included. The information stored is usually strongly dependent on the application. Again, the information is stored in terms of classes and subclasses of materials, as shown in Figure 2.32, to facilitate inclu- sion of additional property data and information retrieval. Graphical Input/Output The graphical output is important for convenience and proper use of the system for design. Impressive advancements have been made in graphics software, and it is quite easy to obtain the outputs in different forms suitable for a wide variety of applications. Some of the important features available in current systems are 1. Line graphs and contour plots 2. Menu-driven software 3. Real-time output 4. Three-dimensional plots 5. Choice of scales 6. Different viewing angles 7. Color graphics Knowledge-Based Design and Additional Considerations 605 Therefore, the outputs can be ne-tuned to a given application. For example, if a plastic screw extruder is being designed, the pressure and temperature rise in the extruder as the material ows from the hopper to the die may be displayed. Color graphics or contour plots may be used to indicate hot and cold regions in the ow. As an example, Figure 11.4 shows the temperature distribution in the channel of a plastic extruder in terms of isotherms. The temperature distributions across the channel at four down-channel locations are also shown. This gure shows how the plastic heats up as it moves from the hopper at z *  0 to the die at the other end of the channel. In iterative design, the results may be displayed after each iteration, allowing the user to observe the convergence to the nal design and to intervene if the iterative process appears to be diverging or if the design emerging from the design process is not satisfactory. Graphical inputs to the front end are also important in many applications since the geometry, boundary conditions, and dimensions are most conveniently entered on a graphical display. An example of casting is seen in Figure 11.5, where the mold, cast cavity, runner, and thermal conditions at the outer surfaces are shown. Such a schematic may be displayed and the user may interactively enter the appropriate quantities and parameters such as dimensions, heat trans- fer coefcients, and materials. Many available programming languages, such as Visual Basic, are particularly suited for such graphical inputs. Languages The programming language employed in the knowledge-based design system forms another important consideration. Symbolic languages, such as LISP, PROLOG, and SMALLTALK, which allow the use of symbols rather than just numbers for manipulation and control of the software, are particularly useful for the front end and the knowledge base. For instance, descriptions of a surface as “smooth,” viscosity as “high,” and disturbances as “small” are all symbolic in form and digital values may or may not be associated with these. This is similar to the concept of fuzzy logic discussed in Chapter 7. In the storage and use of knowledge, we need symbolic representations for 1. Symbolic manipulation of objects 2. Rules of thumb and heuristic arguments in symbolic form 3. Inputs/outputs given in symbolic form 4. Use of symbolic notation for storage of data 5. Symbolic representation of design rules A symbolic environment allows the versatility and exibility needed for specifying design rules, constraints, expert knowledge, and other pertinent infor- mation. LISP is a commonly used language and variations of LISP are often used to develop expert system shells in which the rules and expert knowledge for a given application can be easily entered (Winston and Horn, 1989). PROLOG and its various versions, such as Sigma PROLOG, have a variety of other features that 606 Design and Optimization of Thermal Systems FIGURE 11.4 Isotherms and temperature distributions in the channel of a plastic screw extruder. Here S, y * , and z * are dimensionless temperature, cross-channel coordinate distance, and down-channel distance, respectively. H is the channel height. (Adapted from Karwe and Jaluria, 1990.) 1.0 0.8 0.6 0.4 0.2 0.0 1.0 Barrel ( = 1.0) Isotherms 0.8 0.8 0.6 0.6 1.0 0.6 0.4 0.2 0.2 0.0 0.0 0.0 20.0 40.0 Screw (adiabatic) 60.0 80.0 100.0 120.0 140.0 160.0 180.0 200.0 0.5 1.0   = 0  = 0.8 * = /H * = /H * 1.5 2.0 1.0 0.8 0.6 0.4 0.2 0.0 0.0 0.5 1.0  * 1.5 2.0 1.0 0.8 0.6 0.4 0.2 0.0 0.0 0.5 1.0  * 1.5 2.0 1.0 0.8 0.6 0.4 0.2 0.0 0.0 0.5 1.0  * 1.5 2.0 [...]... to be extruded and the range of mass flow rates are also given The design variables are the geometry or shape of the flow channel, entrance and land lengths, material and thickness of the walls, and temperature or heat transfer conditions at the outer surface of the wall Constraints B B BB FIGURE 11.10 A circular cross section of an extrusion die 61 6 Design and Optimization of Thermal Systems arise... stages of the design process, starting with the selection of an initial design and varying dimensions, geometry, materials, and other design variables to obtain the final design The model used 61 0 Design and Optimization of Thermal Systems for simulation is also varied, as needed, for accurate results The most important contribution of this methodology is that it includes the experience and expertise of. .. examples illustrate the use of knowledge-based design methodology for the design and optimization of thermal systems Example 11.1: Casting Let us consider the casting of a material in an enclosed region, as sketched in Figure 1 .3 This is an important manufacturing process and is used extensively for metals and alloys The need for design and optimization of the system arises because of the desire to reduce... 19 86; Sriram and Fenves, 1988; Rychener, 1988; Luger and Stubblefield, 1989; Tong and Sriram, 2007) The application of knowledge-based methodology to the design of thermal systems has received even less attention because of the complexity of these systems and the need to couple numerical simulation with design rules for typical systems Nevertheless, there is growing interest in knowledge-based design. .. Strategies for tackling failure FIGURE 11.12 Schematic for the redesign module (Adapted from Jamalabad et al., 1994.) 61 8 Design and Optimization of Thermal Systems 1.8 Exhaustive redesign rules Selective redesign rules 1 .6 Numeric heuristic value 1.4 1.2 1.0 0.8 0 .6 0.4 0.2 0.0 0 5 10 15 Redesign iteration number 20 FIGURE 11. 13 Comparison of redesign strategies for a 1 cm circular die for extruding low-density... functioning of the 62 0 Design and Optimization of Thermal Systems system The expert knowledge on these systems may be used to expand the items that may be varied for a feasible design For instance, we could consider 1 Different modes of cooling: Air cooling, liquid immersion, boiling 2 Different types of flow systems: Fans, blowers, natural convection 3 Different geometries: Arrangement of boards, sources,... Simulation Mathematical and other models, graphical and numerical input/outputs, numerical methods, design variables, off -design conditions, etc 4 Design Use of computed results with design rules and knowledge base to obtain acceptable designs; evaluation of designs 5 Optimization Design variables are adjusted to optimize chosen objective function, knowledge base is used to guide the process and select the... Trial des02 des 03 des 06 des07 des010 0.072 0. 0 36 0.000 0.0 140.0 280.0 420.0 Time (s) 560 .0 700.0 FIGURE 11.9 Results for design of an ingot casting system, showing solid-liquid interface movement with time for many design trials Knowledge-Based Design and Additional Considerations 61 5 given system Each successful design may be stored for help in future designs This is the process of improving the... design of similar systems to choose between different alternatives at various stages of the design process The inclusion of this expert knowledge is often crucial to the development of a successful design For instance, the designer may be aware of the types of materials that have been used in previous designs and may be able to narrow the search rapidly by this expert knowledge Similarly, the choice of. .. 11.1.5 APPLICATION TO THERMAL SYSTEMS A lot of work has been done on the use of knowledge-based design aids in areas such as electronic and mechanical systems, particularly in the selection of components like resistors, capacitors, gears, bearings, cams, and dampers As discussed in Chapter 1, selection is a much simpler process than design, though it may form part of the overall design process Information . locations given as A–1 A–2 A 3 A–4 1–D 2–D 3 D 4–D 10 14 12 15 16 12 10 8 and 1–1 1–2 1 3 1–4 2–1 2–2 2 3 2–4 16 20 14 17 12 14 15 6 3 1 3 2 3 3 3–4 4–1 4–2 4 3 4–4 10 11 13 18 8 11 14 22 Determine. N 2 Forced Natural Liquid Cooling of electronic equipment FIGURE 11.2 An example of a tree structure for storing data on cooling of electronic equipment. 60 2 Design and Optimization of Thermal Systems of systems. Similarly,. provided 60 4 Design and Optimization of Thermal Systems to enable accurate results to be obtained and linked with the other parts of the sys- tem. Programming languages such as Fortran and C or software