This document discusses the history and development of computer technology. It describes early mechanical calculating devices and how digital computers evolved from electromechanical to electronic designs using vacuum tubes and then transistors. A key development was the concept of the stored-program computer proposed by Alan Turing, which allowed computers to be programmable and led to the modern general-purpose computer.

1. HEAT TRANSFER COMPUTER WALL DESIGN
ABSTRACT
A computer is a device that can be instructed to carry out an arbitrary set
of arithmetic or logical operations automatically. In this project we identified one problem on
side wall of the computer and the problem is Due to some specialized storage requirements a
very unique two-dimensional wall must be designed.
In this project we designed 3 computer walls by using cad tool (creo-2) in x,y
directions and analysed with real time boundary conditions by using CAE tool (Ansys
workbench). In this process we elected 3 different materials and applying it all physical and
thermal properties of it. In Ansys we solving results like total temperature distribution and
total heat flux for each wall with each materials.
And in this process first we took one wall (x=30cm, y=30cm) and then changing y-
direction dimensions from 30cm to 20cm and 30cm to 1cm and applying same boundary
conditions and analysing all results.
Tools were used:
Cad tool: creo-2
Cae tool: Ansys workbench

2. 1. INTRODUCTION
A computer is a device that can be instructed to carry out an arbitrary set
of arithmetic or logical operations automatically. The ability of computers to follow a sequence
of operations, called a program, make computers very flexible and useful. Such computers are
used as control systems for a very wide variety of industrial and consumer devices. This includes
simple special purpose devices like microwave ovens and remote controls, factory devices such
as industrial robots and computer assisted design, but also in general purpose devices
like personal computers and mobile devices such as smart phones. The Internet is run on
computers and it connects millions of other computers. Since ancient times, simple manual
devices like the abacus aided people in doing calculations. Early in the Industrial Revolution,
some mechanical devices were built to automate long tedious tasks, such as guiding patterns
for looms. More sophisticated electrical machines did specialized analogy calculations in the
early 20th century. The first digital electronic calculating machines were developed during World

3. War II. The speed, power, and versatility of computers increased continuously and dramatically
since then, to the point that artificial intelligence may become possible in the future.
Conventionally, a modern computer consists of at least one processing element, typically
a central processing unit (CPU), and some form of memory. The processing element carries out
arithmetic and logical operations, and sequencing and control unit can change the order of
operations in response to stored information.
History
Pre-twentieth century
Devices have been used to aid computation for thousands of years, mostly using one-to-one
correspondence with fingers. The earliest counting device was probably a form of tally stick.
Later record keeping aids throughout the Fertile Crescent included calculi (clay spheres, cones,
etc.) which represented counts of items, probably livestock or grains, sealed in hollow unbaked
clay containers. The use of counting rods is one example. The abacus was initially used for
arithmetic tasks. The Roman abacus was developed from devices used in Babylonians early as
2400 BC. Since then, many other forms of reckoning boards or tables have been invented. In a
medieval European counting house, a chickened cloth would be placed on a table, and markers
moved around on it according to certain rules, as an aid to calculating sums
The sector, a calculating instrument used for solving problems in proportion, trigonometry,
multiplication and division, and for various functions, such as squares and cube roots, was
developed in the late 16th century and found application in gunnery, surveying and navigation.
The plan meter was a manual instrument to calculate the area of a closed figure by tracing over it
with a mechanical linkage. The slide rule was invented around 1620–1630, shortly after the
publication of the concept of the logarithm. It is a hand-operated analogy computer for doing
multiplication and division. As slide rule development progressed, added scales provided
reciprocals, squares and square roots, cubes and cube roots, as well as transcendental
functions such as logarithms and exponentials, circular and hyperbolic trigonometry and
other functions. Aviation is one of the few fields where slide rules are still in widespread use,
particularly for solving time–distance problems in light aircraft. To save space and for ease of
reading, these are typically circular devices rather than the classic linear slide rule shape. A
popular example is the E6B. In the 1770s Pierre Jaquet-Droz, a Swiss watchmaker, built a

4. mechanical doll (automata) that could write holding a quill pen. By switching the number and
order of its internal wheels different letters, and hence different messages, could be produced. In
effect, it could be mechanically "programmed" to read instructions. The tide-predicting
machine invented by Sir William Thomson in 1872 was of great utility to navigation in shallow
waters. It used a system of pulleys and wires to automatically calculate predicted tide levels for a
set period at a particular location. The differential analyser, a mechanical analogy computer
designed to solve differential equations by integration, used wheel-and-disc mechanisms to
perform the integration. In 1876 Lord Kelvin had already discussed the possible construction of
such calculators, but he had been stymied by the limited output torque of the ball-and-disk
integrators. In a differential analyzer, the output of one integrator drove the input of the next
integrator, or a graphing output. The torque amplifier was the advance that allowed these
machines to work. Starting in the 1920s, Vannevar Bush and others developed mechanical
differential analyzers.
First computing device
Charles Babbage, an English mechanical engineer and polymath, originated the concept of a
programmable computer. Considered the "father of the computer",he conceptualized and
invented the first mechanical computer in the early 19th century. After working on his
revolutionary difference engine, designed to aid in navigational calculations, in 1833 he realized
that a much more general design, an Analytical Engine, was possible. The input of programs and
data was to be provided to the machine via punched cards, a method being used at the time to
direct mechanical looms such as the Jacquard loom. For output, the machine would have a
printer, a curve plotter and a bell. The machine would also be able to punch numbers onto cards
to be read in later. The Engine incorporated an arithmetic logic unit, control flow in the form
of conditional branching and loops, and integrated memory, making it the first design for a
general-purpose computer that could be described in modern terms as Turing-complete. The
machine was about a century ahead of its time. All the parts for his machine had to be made by
hand — this was a major problem for a device with thousands of parts. Eventually, the project
was dissolved with the decision of the British Government to cease funding. Babbage's failure to
complete the analytical engine can be chiefly attributed to difficulties not only of politics and
financing, but also to his desire to develop an increasingly sophisticated computer and to move

5. ahead faster than anyone else could follow. Nevertheless, his son, Henry Babbage, completed a
simplified version of the analytical engine's computing unit (the mill) in 1888. He gave a
successful demonstration of its use in computing tables in 1906.
ANALOG COMPUTERS
During the first half of the 20th century, many scientific computing needs were met by
increasingly sophisticated analog computers, which used a direct mechanical or electrical model
of the problem as a basis for computation. However, these were not programmable and generally
lacked the versatility and accuracy of modern digital computers. The first modern analog
computer was a tide-predicting machine, invented by Sir William Thomson in 1872.
The differential analyser, a mechanical analog computer designed to solve differential equations
by integration using wheel-and-disc mechanisms, was conceptualized in 1876 by James
Thomson, the brother of the more famous Lord Kelvin. The art of mechanical analog computing
reached its zenith with the differential analyzer, built by H. L. Hazen and Vannevar Bush at MIT
starting in 1927. This built on the mechanical integrators of James Thomson and the torque
amplifiers invented by H. W. Nieman. A dozen of these devices were built before their
obsolescence became obvious. By the 1950s the success of digital electronic computers had
spelled the end for most analog computing machines, but analog computers remained in use
during the 1950s in some specialized applications such as education (control systems) and
aircraft (slide rule).
Digital computers
Electromechanical
By 1938 the United States Navy had developed an electromechanical analog computer small
enough to use aboard a submarine. This was the Torpedo Data Computer, which used
trigonometry to solve the problem of firing a torpedo at a moving target. During World War
II similar devices were developed in other countries as well. Early digital computers were
electromechanical; electric switches drove mechanical relays to perform the calculation. These
devices had a low operating speed and were eventually superseded by much faster all-electric
computers, originally using vacuum tubes. The Z2, created by German engineer Konrad Zuse in
1939, was one of the earliest examples of an electromechanical relay computer.

6. In 1941, Zuse followed his earlier machine up with the Z3, the world's first
working electromechanical programmable, fully automatic digital computer. The Z3 was built
with 2000 relays, implementing a 22 bit word length that operated at a clock frequency of about
5–10 Hz. Program code was supplied on punched film while data could be stored in 64 words of
memory or supplied from the keyboard. It was quite similar to modern machines in some
respects, pioneering numerous advances such as floating point numbers. Rather than the harder-
to-implement decimal system (used in Charles Babbage's earlier design), using a binary system
meant that Zuse's machines were easier to build and potentially more reliable, given the
technologies available at that time. The Z3 was Turing complete
Vacuum tubes and digital electronic circuits
Purely electronic circuit elements soon replaced their mechanical and electromechanical
equivalents, at the same time that digital calculation replaced analog. The engineer Tommy
Flowers, working at the Post Office Research Station in London in the 1930s, began to explore
the possible use of electronics for the telephone exchange. Experimental equipment that he built
in 1934 went into operation 5 years later, converting a portion of the telephone exchange network
into an electronic data processing system, using thousands of vacuum tubes. In the US, John
Vincent Atanas-off and Clifford E. Berry of Iowa State University developed and tested
the Atanas-off–Berry Computer (ABC) in 1942, the first "automatic electronic digital
computer". This design was also all-electronic and used about 300 vacuum tubes, with capacitors
fixed in a mechanically rotating drum for memory.
During World War II, the British at Bletchley Park achieved a number of successes at breaking
encrypted German military communications. The German encryption machine, Enigma, was first
attacked with the help of the electro-mechanicalbombes. To crack the more sophisticated
German Lorenz SZ 40/42 machine, used for high-level Army communications, Max
Newman and his colleagues commissioned Flowers to build the Colossus. He spent eleven
months from early February 1943 designing and building the first Colossus. After a functional
test in December 1943, Colossus was shipped to Bletchley Park, where it was delivered on 18
January 1944 and attacked its first message on 5 February.
Colossus was the world's first electronic digital programmable computer. It used a large number
of valves (vacuum tubes). It had paper-tape input and was capable of being configured to perform

7. a variety of boolean logical operations on its data, but it was not Turing-complete. Nine Mk II
Colossi were built (The Mk I was converted to a Mk II making ten machines in total). Colossus
Mark I contained 1500 thermionic valves (tubes), but Mark II with 2400 valves, was both 5 times
faster and simpler to operate than Mark 1, greatly speeding the decoding process.
The US-built ENIAC[33] (Electronic Numerical Integrator and Computer) was the first
electronic programmable computer built in the US. Although the ENIAC was similar to the
Colossus it was much faster and more flexible. Like the Colossus, a "program" on the ENIAC
was defined by the states of its patch cables and switches, a far cry from the stored
program electronic machines that came later. Once a program was written, it had to be
mechanically set into the machine with manual resetting of plugs and switches.
It combined the high speed of electronics with the ability to be programmed for many complex
problems. It could add or subtract 5000 times a second, a thousand times faster than any other
machine. It also had modules to multiply, divide, and square root. High speed memory was
limited to 20 words (about 80 bytes). Built under the direction of John Mauchly and J. Presper
Eckert at the University of Pennsylvania, ENIAC's development and construction lasted from
1943 to full operation at the end of 1945. The machine was huge, weighing 30 tons, using 200
kilowatts of electric power and contained over 18,000 vacuum tubes, 1,500 relays, and hundreds
of thousands of resistors, capacitors, and inductors.
Modern computers
Concept of modern computer
The principle of the modern computer was proposed by Alan Turing in his seminal 1936
paper, On Computable Numbers. Turing proposed a simple device that he called "Universal
Computing machine" and that is now known as a universal Turing machine. He proved that such
a machine is capable of computing anything that is computable by executing instructions
(program) stored on tape, allowing the machine to be programmable. The fundamental concept of
Turing's design is the stored program, where all the instructions for computing are stored in
memory. Von Neumann acknowledged that the central concept of the modern computer was due
to this paper. Turing machines are to this day a central object of study in theory of computation.
Except for the limitations imposed by their finite memory stores, modern computers are said to

8. be Turing-complete, which is to say, they have algorithm execution capability equivalent to a
universal Turing machine.
Stored programs
Early computing machines had fixed programs. Changing its function required the re-wiring and
re-structuring of the machine. With the proposal of the stored-program computer this changed. A
stored-program computer includes by design an instruction set and can store in memory a set of
instructions (a program) that details the computation. The theoretical basis for the stored-program
computer was laid by Alan Turing in his 1936 paper. In 1945 Turing joined the National Physical
Laboratory and began work on developing an electronic stored-program digital computer. His
1945 report "Proposed Electronic Calculator" was the first specification for such a device. John
von Neumann at the University of Pennsylvania also circulated his First Draft of a Report on the
EDVAC in 1945
The Manchester Small-Scale Experimental Machine, nicknamed Baby, was the world's
first stored-program computer. It was built at the Victoria University of Manchester by Frederic
C. Williams, Tom Kilburn and Geoff Tootill, and ran its first program on 21 June 1948. It was
designed as a testbedfor the Williams tube, the first random-access digital storage
device. Although the computer was considered "small and primitive" by the standards of its time,
it was the first working machine to contain all of the elements essential to a modern electronic
computer. As soon as the SSEM had demonstrated the feasibility of its design, a project was
initiated at the university to develop it into a more usable computer, the Manchester Mark 1.
The Mark 1 in turn quickly became the prototype for the Ferranti Mark 1, the world's first
commercially available general-purpose computer. Built by Ferranti, it was delivered to
the University of Manchester in February 1951. At least seven of these later machines were
delivered between 1953 and 1957, one of them to Shell labs in Amsterdam. In October 1947, the
directors of British catering company J. Lyons & Company decided to take an active role in
promoting the commercial development of computers. The LEO I computer became operational
in April 1951 and ran the world's first regular routine office computer job.
Transistors

9. The bipolar transistor was invented in 1947. From 1955 onwards transistors replaced vacuum
tubes in computer designs, giving rise to the "second generation" of computers. Compared to
vacuum tubes, transistors have many advantages: they are smaller, and require less power than
vacuum tubes, so give off less heat. Silicon junction transistors were much more reliable than
vacuum tubes and had longer, indefinite, service life. Transistorized computers could contain tens
of thousands of binary logic circuits in a relatively compact space.
At the University of Manchester, a team under the leadership of Tom Kilburn designed and built
a machine using the newly developed transistors instead of valves. Their first transistorised
computer and the first in the world, was operational by 1953, and a second version was
completed there in April 1955. However, the machine did make use of valves to generate its
125 kHz clock waveforms and in the circuitry to read and write on its magnetic drum memory, so
it was not the first completely transistorized computer. That distinction goes to the Harwell
CADET of 1955, built by the electronics division of the Atomic Energy Research
Establishment at Harwell
Integrated circuits
The next great advance in computing power came with the advent of the integrated circuit. The
idea of the integrated circuit was first conceived by a radar scientist working for the Royal Radar
Establishment of the Ministry of Defence, Geoffrey W.A. Dummer. Dummer presented the first
public description of an integrated circuit at the Symposium on Progress in Quality Electronic
Components in Washington, D.C. on 7 May 1952.
The first practical ICs were invented by Jack Kilby at Texas Instruments and Robert
Noyce at Fairchild Semiconductor. Kilby recorded his initial ideas concerning the integrated
circuit in July 1958, successfully demonstrating the first working integrated example on 12
September 1958. In his patent application of 6 February 1959, Kilby described his new device as
"a body of semiconductor material ... wherein all the components of the electronic circuit are
completely integrated". Noyce also came up with his own idea of an integrated circuit half a year
later than Kilby. His chip solved many practical problems that Kilby's had not. Produced at
Fairchild Semiconductor, it was made of silicon, whereas Kilby's chip was made of germanium.
This new development heralded an explosion in the commercial and personal use of computers
and led to the invention of the microprocessor. While the subject of exactly which device was the

10. first microprocessor is contentious, partly due to lack of agreement on the exact definition of the
term "microprocessor", it is largely undisputed that the first single-chip microprocessor was the
Intel 4004, designed and realized by Ted Hoff, Federico Faggin, and Stanley Mazor at Intel.
Mobile computers become dominant
With the continued miniaturization of computing resources, and advancements in portable battery
life, portable computers grew in popularity in the 2000s. The same developments that spurred the
growth of laptop computers and other portable computers allowed manufacturers to integrate
computing resources into cellular phones. These so-called smartphones and tablets run on a
variety of operating systems and have become the dominant computing device on the market,
with manufacturers reporting having shipped an estimated 237 million devices in 2Q 2013
Programs
The defining feature of modern computers which distinguishes them from all other machines is
that they can be programmed. That is to say that some type of instructions (the program) can be
given to the computer, and it will process them. Modern computers based on the von Neumann
architecture often have machine code in the form of an imperative programming language. In
practical terms, a computer program may be just a few instructions or extend to many millions of
instructions, as do the programs for word processors and web browsers for example. A typical
modern computer can execute billions of instructions per second (gigaflops) and rarely makes a
mistake over many years of operation. Large computer programs consisting of several million
instructions may take teams of programmers years to write, and due to the complexity of the task
almost certainly contain errors.
Stored program architecture
In most cases, computer instructions are simple: add one number to another, move some data
from one location to another, send a message to some external device, etc. These instructions are
read from the computer's memory and are generally carried out (executed) in the order they were
given. However, there are usually specialized instructions to tell the computer to jump ahead or
backwards to some other place in the program and to carry on executing from there. These are
called "jump" instructions (or branches). Furthermore, jump instructions may be made to
happen conditionally so that different sequences of instructions may be used depending on the

11. result of some previous calculation or some external event. Many computers directly
support subroutines by providing a type of jump that "remembers" the location it jumped from
and another instruction to return to the instruction following that jump instruction.
Program execution might be likened to reading a book. While a person will normally read each
word and line in sequence, they may at times jump back to an earlier place in the text or skip
sections that are not of interest. Similarly, a computer may sometimes go back and repeat the
instructions in some section of the program over and over again until some internal condition is
met. This is called the flow of control within the program and it is what allows the computer to
perform tasks repeatedly without human intervention.
Comparatively, a person using a pocket calculator can perform a basic arithmetic operation such
as adding two numbers with just a few button presses. But to add together all of the numbers
from 1 to 1,000 would take thousands of button presses and a lot of time, with a near certainty of
making a mistake. On the other hand, a computer may be programmed to do this with just a few
simple instructions. The following example is written in the MIPS assembly language:
Machine code
In most computers, individual instructions are stored as machine code with each instruction being
given a unique number (its operation code or opcode for short). The command to add two
numbers together would have one opcode; the command to multiply them would have a different
opcode, and so on. The simplest computers are able to perform any of a handful of different
instructions; the more complex computers have several hundred to choose from, each with a
unique numerical code. Since the computer's memory is able to store numbers, it can also store
the instruction codes. This leads to the important fact that entire programs (which are just lists of
these instructions) can be represented as lists of numbers and can themselves be manipulated
inside the computer in the same way as numeric data. The fundamental concept of storing
programs in the computer's memory alongside the data they operate on is the crux of the von
Neumann, or stored program[citation needed], architecture. In some cases, a computer might
store some or all of its program in memory that is kept separate from the data it operates on. This
is called the Harvard architecture after the Harvard Mark I computer. Modern von Neumann
computers display some traits of the Harvard architecture in their designs, such as in CPU caches.

12. While it is possible to write computer programs as long lists of numbers (machine language) and
while this technique was used with many early computers, it is extremely tedious and potentially
error-prone to do so in practice, especially for complicated programs. Instead, each basic
instruction can be given a short name that is indicative of its function and easy to remember –
a mnemonic such as ADD, SUB, MULT or JUMP. These mnemonics are collectively known as a
computer's assembly language. Converting programs written in assembly language into
something the computer can actually understand (machine language) is usually done by a
computer program called an assembler.
Programming language
Programming languages provide various ways of specifying programs for computers to run.
Unlike natural languages, programming languages are designed to permit no ambiguity and to be
concise. They are purely written languages and are often difficult to read aloud. They are
generally either translated into machine code by a compiler or an assembler before being run, or
translated directly at run time by an interpreter. Sometimes programs are executed by a hybrid
method of the two techniques.
Low-level languages
Machine languages and the assembly languages that represent them (collectively termed low-
level programming languages) tend to be unique to a particular type of computer. For instance,
an ARM architecture computer (such as may be found in a smartphone or a hand-held
videogame) cannot understand the machine language of an x86 CPU that might be in a PC
High-level languages/third generation language
Though considerably easier than in machine language, writing long programs in assembly
language is often difficult and is also error prone. Therefore, most practical programs are written
in more abstract high-level programming languages that are able to express the needs of
the programmer more conveniently (and thereby help reduce programmer error). High level
languages are usually "compiled" into machine language (or sometimes into assembly language
and then into machine language) using another computer program called a compiler. High level
languages are less related to the workings of the target computer than assembly language, and
more related to the language and structure of the problem(s) to be solved by the final program. It

13. is therefore often possible to use different compilers to translate the same high level language
program into the machine language of many different types of computer. This is part of the
means by which software like video games may be made available for different computer
architectures such as personal computers and various video game consoles.
Fourth generation languages
Program design of small programs is relatively simple and involves the analysis of the problem,
collection of inputs, using the programming constructs within languages, devising or using
established procedures and algorithms, providing data for output devices and solutions to the
problem as applicable. As problems become larger and more complex, features such as
subprograms, modules, formal documentation, and new paradigms such as object-oriented
programming are encountered. Large programs involving thousands of line of code and more
require formal software methodologies. The task of developing large software systems presents a
significant intellectual challenge. Producing software with an acceptably high reliability within a
predictable schedule and budget has historically been difficult; the academic and professional
discipline of software engineeringconcentrates specifically on this challenge.
Bugs
Errors in computer programs are called "bugs". They may be benign and not affect the usefulness
of the program, or have only subtle effects. But in some cases, they may cause the program or the
entire system to "hang", becoming unresponsive to input such as mouseclicks or keystrokes, to
completely fail, or to crash. Otherwise benign bugs may sometimes be harnessed for malicious
intent by an unscrupulous user writing an exploit, code designed to take advantage of a bug and
disrupt a computer's proper execution. Bugs are usually not the fault of the computer. Since
computers merely execute the instructions they are given, bugs are nearly always the result of
programmer error or an oversight made in the program's design. Admiral Grace Hopper, an
American computer scientist and developer of the first compiler, is credited for having first used
the term "bugs" in computing after a dead moth was found shorting a relay in the Harvard Mark
II computer in September 1947.
Components

14. A general purpose computer has four main components: the arithmetic logic unit (ALU),
the control unit, the memory, and the input and output devices (collectively termed I/O). These
parts are interconnected by buses, often made of groups of wires. Inside each of these parts are
thousands to trillions of small electrical circuits which can be turned off or on by means of
an electronic switch. Each circuit represents a bit (binary digit) of information so that when the
circuit is on it represents a "1", and when off it represents a "0" (in positive logic representation).
The circuits are arranged in logic gates so that one or more of the circuits may control the state of
one or more of the other circuits.
Control unit
The control unit (often called a control system or central controller) manages the computer's
various components; it reads and interprets (decodes) the program instructions, transforming
them into control signals that activate other parts of the computer. Control systems in
advanced computers may change the order of execution of some instructions to improve
performance.
A key component common to all CPUs is the program counter, a special memory cell
(a register) that keeps track of which location in memory the next instruction is to be read
from.
The control system's function is as follows—note that this is a simplified description, and
some of these steps may be performed concurrently or in a different order depending on the
type of CPU:
1. Read the code for the next instruction from the cell indicated by the program counter.
2. Decode the numerical code for the instruction into a set of commands or signals for
each of the other systems.
3. Increment the program counter so it points to the next instruction.
4. Read whatever data the instruction requires from cells in memory (or perhaps from an
input device). The location of this required data is typically stored within the
instruction code.
5. Provide the necessary data to an ALU or register.

15. 6. If the instruction requires an ALU or specialized hardware to complete, instruct the
hardware to perform the requested operation.
7. Write the result from the ALU back to a memory location or to a register or perhaps an
output device.
8. Jump back to step.
Since the program counter is (conceptually) just another set of memory cells, it can be
changed by calculations done in the ALU. Adding 100 to the program counter would cause the
next instruction to be read from a place 100 locations further down the program. Instructions
that modify the program counter are often known as "jumps" and allow for loops (instructions
that are repeated by the computer) and often conditional instruction execution (both examples
of control flow).
The sequence of operations that the control unit goes through to process an instruction is in
itself like a short computer program, and indeed, in some more complex CPU designs, there is
another yet smaller computer called a microsequencer, which runs a microcode program that
causes all of these events to happen.
Central processing unit (CPU)
The control unit, ALU, and registers are collectively known as a central processing
unit (CPU). Early CPUs were composed of many separate components but since the mid-
1970s CPUs have typically been constructed on a single integrated circuit called
a microprocessor.
Arithmetic logic unit (ALU)
The ALU is capable of performing two classes of operations: arithmetic and logic.[63] The set
of arithmetic operations that a particular ALU supports may be limited to addition and
subtraction, or might include multiplication, division, trigonometry functions such as sine,
cosine, etc., and square roots. Some can only operate on whole numbers (integers) whilst
others use floating point to represent real numbers, albeit with limited precision. However, any
computer that is capable of performing just the simplest operations can be programmed to
break down the more complex operations into simple steps that it can perform. Therefore, any
computer can be programmed to perform any arithmetic operation—although it will take more

16. time to do so if its ALU does not directly support the operation. Superscalar computers may
contain multiple ALUs, allowing them to process several instructions
simultaneously. Graphics processors and computers with SIMD and MIMDfeatures often
contain ALUs that can perform arithmetic on vectors and matrices.
Memory
A computer's memory can be viewed as a list of cells into which numbers can be placed or
read. Each cell has a numbered "address" and can store a single number. The computer can be
instructed to "put the number 123 into the cell numbered 1357" or to "add the number that is
in cell 1357 to the number that is in cell 2468 and put the answer into cell 1595." The
information stored in memory may represent practically anything. Letters, numbers, even
computer instructions can be placed into memory with equal ease. Since the CPU does not
differentiate between different types of information, it is the software's responsibility to give
significance to what the memory sees as nothing but a series of numbers.
In almost all modern computers, each memory cell is set up to store binary numbers in groups
of eight bits (called a byte). Each byte is able to represent 256 different numbers (28 = 256);
either from 0 to 255 or −128 to +127. To store larger numbers, several consecutive bytes may
be used (typically, two, four or eight). When negative numbers are required, they are usually
stored in two's complementnotation. Other arrangements are possible, but are usually not seen
outside of specialized applications or historical contexts. A computer can store any kind of
information in memory if it can be represented numerically. Modern computers have billions
or even trillions of bytes of memory.
The CPU contains a special set of memory cells called registers that can be read and written to
much more rapidly than the main memory area. There are typically between two and one
hundred registers depending on the type of CPU. Registers are used for the most frequently
needed data items to avoid having to access main memory every time data is needed. As data
is constantly being worked on, reducing the need to access main memory (which is often slow
compared to the ALU and control units) greatly increases the computer's speed.
Computer main memory comes in two principal varieties:
 random-access memory or RAM

17.  read-only memory or ROM
RAM can be read and written to anytime the CPU commands it, but ROM is preloaded with
data and software that never changes, therefore the CPU can only read from it. ROM is
typically used to store the computer's initial start-up instructions. In general, the contents of
RAM are erased when the power to the computer is turned off, but ROM retains its data
indefinitely. In a PC, the ROM contains a specialized program called the BIOS that
orchestrates loading the computer's operating system from the hard disk drive into RAM
whenever the computer is turned on or reset. In embedded computers, which frequently do not
have disk drives, all of the required software may be stored in ROM. Software stored in ROM
is often called firmware, because it is notionally more like hardware than software. Flash
memory blurs the distinction between ROM and RAM, as it retains its data when turned off
but is also rewritable. It is typically much slower than conventional ROM and RAM however,
so its use is restricted to applications where high speed is unnecessary.
In more sophisticated computers there may be one or more RAM cache memories, which are
slower than registers but faster than main memory. Generally computers with this sort of
cache are designed to move frequently needed data into the cache automatically, often without
the need for any intervention on the programmer's part.
Input/output (I/O)
I/O is the means by which a computer exchanges information with the outside world. Devices
that provide input or output to the computer are called peripherals. On a typical personal
computer, peripherals include input devices like the keyboard and mouse, and output devices
such as the display and printer. Hard disk drives, floppy disk drives and optical disc
drives serve as both input and output devices. Computer networking is another form of I/O.
I/O devices are often complex computers in their own right, with their own CPU and memory.
A graphics processing unit might contain fifty or more tiny computers that perform the
calculations necessary to display 3D graphics.[citation needed] Modern desktop
computers contain many smaller computers that assist the main CPU in performing I/O. A
2016-era flat screen display contains its own computer circuitry.
Multitasking

18. While a computer may be viewed as running one gigantic program stored in its main memory,
in some systems it is necessary to give the appearance of running several programs
simultaneously. This is achieved by multitasking i.e. having the computer switch rapidly
between running each program in turn. One means by which this is done is with a special
signal called an interrupt, which can periodically cause the computer to stop executing
instructions where it was and do something else instead. By remembering where it was
executing prior to the interrupt, the computer can return to that task later. If several programs
are running "at the same time". then the interrupt generator might be causing several hundred
interrupts per second, causing a program switch each time. Since modern computers typically
execute instructions several orders of magnitude faster than human perception, it may appear
that many programs are running at the same time even though only one is ever executing in
any given instant. This method of multitasking is sometimes termed "time-sharing" since each
program is allocated a "slice" of time in turn.
Before the era of inexpensive computers, the principal use for multitasking was to allow many
people to share the same computer. Seemingly, multitasking would cause a computer that is
switching between several programs to run more slowly, in direct proportion to the number of
programs it is running, but most programs spend much of their time waiting for slow
input/output devices to complete their tasks. If a program is waiting for the user to click on the
mouse or press a key on the keyboard, then it will not take a "time slice" until the event it is
waiting for has occurred. This frees up time for other programs to execute so that many
programs may be run simultaneously without unacceptable speed loss.
Multiprocessing
Some computers are designed to distribute their work across several CPUs in a
multiprocessing configuration, a technique once employed only in large and powerful
machines such as supercomputers, mainframe computers and servers. Multiprocessor
and multi-core (multiple CPUs on a single integrated circuit) personal and laptop computers
are now widely available, and are being increasingly used in lower-end markets as a result.
Supercomputers in particular often have highly unique architectures that differ significantly
from the basic stored-program architecture and from general purpose computers.[70] They

19. often feature thousands of CPUs, customized high-speed interconnects, and specialized
computing hardware. Such designs tend to be useful only for specialized tasks due to the large
scale of program organization required to successfully utilize most of the available resources
at once. Supercomputers usually see usage in large-scale simulation, graphics rendering,
and cryptography applications, as well as with other so-called "embarrassingly parallel" tasks.
LITERATURE REVIEW
[1]J. Sacks, W. J. Welch, T. J. Mitchell, and H. P. Wynn, “Design and analysis of computer
experiments (with discussion),” Statistical Science, vol. 4, pp. 409–435, 1989. Treating a
function as arising from a stochastic process or Gaussian process was already known in
statistics, geostatistics (kriging), and optimization. But this paper introduced that formulation
for deterministic computer experiments and made it feasible for applications with many input
variables.[2] C. Currin, T. Mitchell, M. Morris, and D. Ylvisaker, “Bayesian prediction of
deterministic functions, with applications to the design and analysis of computer
experiments,” Journal of the American Statistical Association, vol. 86, no. 416, pp. 953–963,
1991. Toby Mitchell, Max Morris and Don Ylvisaker were pioneers in bringing computer
experiments to the attention of statisticians. Mitchell and Morris were based at Oak Ridge
National Laboratory, where simulations were run on the supercomputers of the day. T. J.
Santner, [3]B. J. Williams, and W. I. Notz, The Design and Analysis of Computer
Experiments. New York: Springer, 2003. A broad coverage of the literature on design and
analysis of computer experiments up to the time of publication. C. E. Rasmussen and C. K. I.

20. Williams, Gaussian Processes for Machine Learning. Cambridge, MA: The MIT Press, 2006.
A comprehensive account of Gaussian processes from a computer-science perspective. Its
coverage is broader than computer experiments, including classification for instance.
[4]Mechanical CAD systems began as automated drafting tools for the production of
engineering drawings [Krou84]. However, software applications have now found widespread
usage in various areas of mechanical engineering. [5]K. T. Fang, D. K. J. Lin, P. Winker, and
Y. Zhang, “Uniform design: Theory and application,” Technometrics, vol. 42, no. 3, pp. 237–
248, 2000. The authors briefly review the vast literature on uniform designs and illustrate use
of these designs with a computer code of a launching system. [6]J. L. Loeppky, J. Sacks, and
W. J. Welch, “Choosing the sample size of a computer experiment: A practical guide,”
Technometrics, vol. 51, pp. 366–376, 2009. The authors argue that the accuracy of a GP
emulator is affected by two summaries of the correlation sensitivity parameters and that n =
10d runs will often be enough for moderate accuracy or diagnose that accuracy cannot be
achieved without a much larger sample size. [7]D. Bingham, P. Ranjan, and W. J. Welch,
“Design of computer experiments for optimization, estimation of function contours, and
related objectives,” in Statistics in Action: A Canadian Outlook (J. F. Lawless, ed.), pp. 109–
124, Boca Raton, Florida: CRC Press, 2014. An overview of sequential design, largely based
on the next two papers, that appeared as a chapter in a book by the Statistical Society of
Canada to celebrate the International Year of Statistics. The chapter is available at[8]P.
Ranjan, D. Bingham, and G. Michailidis, “Sequential experiment design for contour
estimation from complex computer codes,” Technometrics, vol. 50, no. 4, pp. 527– 541, 2008.
The authors develop another EI criterion, this time for mapping out where y(x) equals some
pre-specified critical value. Many papers by other researchers have followed this work, to find
quantiles, percentiles, etc. of an output distribution. [8]D. Higdon, J. Gattiker, B. Williams,
and M. Rightley, “Computer model calibration using high-dimensional output,” Journal of the
American Statistical Association, vol. 103, no. 482, pp. 570–583, 2008. Again the objective is
calibration of unknown parameters in the presence of discrepancy between the computer-
model runs and physical data. The authors tackle multivariate data, which are reduced in
dimensionality via principal components. The principal component weights are then modelled
by GPs.[9] M. J. Bayarri, J. O. Berger, R. Paulo, J. Sacks, J. A. Cafeo, J. Cavendish, C.-H.
Lin, and J. Tu, “A framework for validation of computer models,” Technometrics, vol. 49, no.

21. 2, pp. 138–154, 2007. These authors point out that, as all computer models are wrong to some
extent, “validation” of a computer code against physical data amounts to an assessment of the
magnitude of the discrepancy. [10]W. Kleiber, S. Sain, M. J. Heaton, M. Wiltberger, C. S.
Reese, and D. Bingham, “Parameter tuning for a multi-fidelity dynamical model of the
magnetosphere,” Annals of Applied Statistics, vol. 7, no. 3, pp. 1286–1310, 2013. The paper
extends calibration (or tuning) to multivariate output from a space-time field. The authors also
allow several versions of the computer model and use sequential design to improve
calibration.
1.2. PROBLEM IDENTIFICATION AND AIM OF THE
PROJECT
DesignProblem
Due to some specialized storage requirements a very unique two-dimensional wall must be
designed. This two dimensional wall is shown in Figure 1 along with the boundary conditions.
It is required that a wall be designed such that the total heat rate at
the south wall per unit depth of the wall is
−1.0𝑊/𝑚 ≤ 𝑄̇𝑆 ≤ 1.0𝑊/𝑚 (1)
And the total heat rate out of the east wall is
𝑄̇𝐸 ≥ 150𝑊/𝑚 (2)

22. Where the signs on the heat flows are positive in the positive x or y–directions and negative in
the negative x or y-directions. The heat rates are given per unit depth because the depth
dimension is in and out of the paper and is considered to be infinite. Thus all heat rates are for
a unit depth of the wall.
Figure 1. Schematic of wall.
Aim of the project
Designconsideration:
Your job is to design this wall. This consists of picking a material and determining the overall
thickness, labelled with L in the figure. Assume that the thermal conductivity is uniform. You
can only choose solid materials from the tables in the back of your book for the wall. Take
your thermal conductivities at 300 K.

23. 2. INTRODUCTION:
CREO
2.1. CAD
Computer aided design (cad) is defined as any activity that involves the
effective use of the computer to create, modify, analyze, or document an engineering design.
CAD is most commonly associated with the use of an interactive computer graphics system,
referred to as cad system. The term CAD/CAM system is also used if it supports
manufacturing as well as design applications

24. 2.2. Introduction to CREO
CREO is a suite of programs that are used in the design, analysis, and manufacturing of a
virtually unlimited range of product.
CREO is a parametric, feature-based solid modeling system, “Feature based”
means that you can create part and assembly by defining feature like pad, rib, slots, holes, rounds,
and so on, instead of specifying low-level geometry like lines, arcs, and circle& features are
specifying by setting values and attributes of element such as reference planes or surfaces
direction of creation, pattern parameters, shape, dimensions and others.
“Parametric” means that the physical shape of the part or assembly is driven
by the values assigned to the attributes (primarily dimensions) of its features. Parametric may
define or modify a feature’s dimensions or other attributes at any time.
For example, if your design intent is such that a hole is centered on a block,
you can relate the dimensional location of the hole to the block dimensions using a numerical
formula; if the block dimensions change, the centered hole position will be recomputed
automatically.
“Solid Modeling” means that the computer model to create it able to contain all the
information that a real solid object would have. The most useful thing about the solid
modeling is that it is impossible to create a computer model that is ambiguous or physically
non-realizable.
There are six core CREO concepts. Those are:
 Solid Modeling
 Feature Based

25.  Parametric
 Parent / Child Relationships
 Associative
 Model Centric
2.3 Capabilities and Benefits:
1. Complete 3D modeling capabilities enable you to exceed quality arid time to arid time
to market goals.
2. Maximum production efficiency through automated generation of associative C tooling
design, assembly instructions, and machine code.
3. Ability to simulate and analysis virtual prototype to improve production performance
and optimized product design.
4. Ability to share digital product data seamlessly among all appropriate team members
5. Compatibility with myriad CAD tools-including associative data exchange and
industry standard data formats.
2.4 Features of CREO
CREO is a one-stop for any manufacturing industry. It offers effective feature,
incorporated for a wide variety of purpose. Some of the important features are as follows:
 Simple and powerful tool
 Parametric design
 Feature-based approach
 Parent child relationship
 Associative and model centric
2.4.1. Simple and Powerful Tool

26. CREO tools are used friendly. Although the execution of any operation using the tool can
create a highly complex model
2.4.2. Parametric Design
CREO designs are parametric. The term “parametric” means that the design operations
that are captured can be stored as they take place. They can be used effectively in the future
for modifying and editing the design. These types of modelling help in faster and easier
modifications of design.
2.4.3. Feature-Based Approach
Features are the basic building blocks required to create an object. CREO wildfire models
are based on the series of feature. Each feature builds upon the previous feature, to create the
model (only one single feature can be modified at a time). Each feature may appear simple,
individually, but collectively forms a complex part and assemblies.
The idea behind feature based modelling is that the designer construct on object, composed
of individual feature that describe the manner in which the geometry supports the object, if its
dimensions change. The first feature is called the base feature.
2.4.4. Parent Child Relationship
The parent child relationship is a powerful way to capture your design intent in a model. This
relationship naturally occurs among features, during the modeling process. When you create a
new feature, the existing feature that are referenced, become parent to the feature.
2.4.5. Associative and Model Centric
CREO drawings are model centric. This means that CREO models that are represented in
assembly or drawings are associative. If changes are made in one module, these will
automatically get updated in the referenced module.
2.5. CREO Basic Design Modes

27. When a design from conception to completion in CREO, the design information goes through three
basic design steps.
1. Creating the component parts of the design
2. Joining the parts in an assembly that records the relative position of the parts.
3. Creating mechanical drawing based on the information in the parts and the assembly.
2.6 Assembly in CREO:
Bottom-Up Design (Modeling):
The components (parts) are created first and then added to the assembly file. This technique
is particularly useful when parts already exist from previous designs and are being re-used.
Top-Down Design (Modeling):
The assembly file is created first and then the components are created in the
assembly file. The parts are build relative to other components. Useful in new designs
In practice, the combination of Top-Down and Bottom-Up approaches is used. As you often
use existing parts and create new parts in order to meet your design needs.
Degrees of Freedom:
An object in space has six degrees of freedom.
• Translation – movement along X, Y, and Z axis (three degrees of freedom)
• Rotation – rotate about X, Y, and Z axis (three degrees of freedom)
Assembly Constraints:
In order to completely define the position of one part relative to another, we must constrain all
of the degrees of freedom COINCIDENT, OFFSET
OFFSET
Two surfaces are made parallel with a specified offset distance.
.

28. COINCIDENT
Two selected surfaces become co-planar and face in the same direction. Can also be applied to
revolved surfaces. This constrains 3 degrees of freedom (two rotations and one translation). When
Align is used on revolved surfaces, they become coaxial (axes through the centers align).
CREO Modules:-
 Sketcher (2D)
 Part (3D)
 Assembly
 Drawing and Drafting
 Sheet Metal
 Surface modelling
Computer wall Model Developing by Using Creo-2:-
Open pro-e/creo

29. New enter namecomputer side wallok
Computer side wall dimensions:
X=30 mm and y=30 mm
Wall sketcher

30. The above sketch should follow 3 conditions those are the sketcher should be closed and there
should be no open end there should be no over lapping. By following these conditions we have to
create our model. After completion of sketch click ok and we will get below model.
X=30 mm and y=20 mm
Wall sketcher

31. The above sketch should follow 3 conditions those are the sketcher should be closed and there
should be no open end there should be no over lapping. By following these conditions we have to
create our model. After completion of sketch click ok and we will get below model.
X=30 mm and y=10 mm
Wall sketcher

32. The above sketch should follow 3 conditions those are the sketcher should be closed and there
should be no open end there should be no over lapping. By following these conditions we have to
create our model. After completion of sketch click ok and we will get below model.
3. INTRODUCTION TO FEA

33. Finite Element Analysis (FEA) was first developed in 1943 by R. Courant, who utilized the Ritz
method of numerical analysis and minimization of variational calculus to obtain approximate
solutions to vibration systems. Shortly thereafter, a paper published in 1956 by M. J. Turner, R.
W. Clough, H. C. Martin, and L. J. Topp established a broader definition of numerical analysis.
The paper centered on the "stiffness and deflection of complex structures".
FEA consists of a computer model of a material or design that is stressed and analyzed for
specific results. It is used in new product design, and existing product refinement. A company is
able to verify a proposed design will be able to perform to the client's specifications prior to
manufacturing or construction. Modifying an existing product or structure is utilized to qualify
the product or structure for a new service condition. In case of structural failure, FEA may be
used to help determine the design modifications to meet the new condition.
There are generally two types of analysis that are used in industry: 2-D modeling, and 3-D
modeling. While 2-D modeling conserves simplicity and allows the analysis to be run on a
relatively normal computer, it tends to yield less accurate results. 3-D modeling, however,
produces more accurate results while sacrificing the ability to run on all but the fastest computers
effectively. Within each of these modeling schemes, the programmer can insert numerous
algorithms (functions) which may make the system behave linearly or non-linearly. Linear
systems are far less complex and generally do not take into account plastic deformation. Non-
linear systems do account for plastic deformation, and many also are capable of testing a material
all the way to fracture.
FEA uses a complex system of points called nodes which make a grid called a mesh. This mesh
is programmed to contain the material and structural properties which define how the structure
will react to certain loading conditions. Nodes are assigned at a certain density throughout the
material depending on the anticipated stress levels of a particular area. Regions which will
receive large amounts of stress usually have a higher node density than those which experience
little or no stress. Points of interest may consist of: fracture point of previously tested material,
fillets, corners, complex detail, and high stress areas. The mesh acts like a spider web in that from
each node, there extends a mesh element to each of the adjacent nodes. This web of vectors is
what carries the material properties to the object, creating many elements.
A wide range of objective functions (variables within the system) are available for
minimization or maximization:

34.  Mass, volume, temperature
 Strain energy, stress strain
 Force, displacement, velocity, acceleration
 Synthetic (User defined)
There are multiple loading conditions which may be applied to a system. Some examples are
shown:
 Point, pressure, thermal, gravity, and centrifugal static loads
 Thermal loads from solution of heat transfer analysis
 Enforced displacements
 Heat flux and convection
 Point, pressure and gravity dynamic loads
Each FEA program may come with an element library, or one is constructed over time. Some
sample elements are:
 Rod elements
 Beam elements
 Plate/Shell/Composite elements
 Shear panel
 Solid elements
 Spring elements
 Mass elements
 Rigid elements
 Viscous damping elements
Many FEA programs also are equipped with the capability to use multiple materials within the
structure such as:
 Isotropic, identical throughout
 Orthotropic, identical at 90 degrees

35.  General anisotropic, different throughout
3.1 TYPES OF ENGINEERING ANALYSIS
Structural analysis consists of linear and non-linear models. Linear models use simple
parameters and assume that the material is not plastically deformed. Non-linear models consist of
stressing the material past its elastic capabilities. The stresses in the material then vary with the
amount of deformation as in.
Vibrational analysis is used to test a material against random vibrations, shock, and impact.
Each of these incidences may act on the natural vibrational frequency of the material which, in
turn, may cause resonance and subsequent failure.
Fatigue analysis helps designers to predict the life of a material or structure by showing the
effects of cyclic loading on the specimen. Such analysis can show the areas where crack
propagation is most likely to occur. Failure due to fatigue may also show the damage tolerance of
the material.
Heat Transfer analysis models the conductivity or thermal fluid dynamics of the material or
structure. This may consist of a steady-state or transient transfer. Steady-state transfer refers to
constant thermo properties in the material that yield linear heat diffusion.
3. 2Results of Finite Element Analysis
FEA has become a solution to the task of predicting failure due to unknown stresses by showing
problem areas in a material and allowing designers to see all of the theoretical stresses within.
This method of product design and testing is far superior to the manufacturing costs which would
accrue if each sample was actually built and tested.
In practice, a finite element analysis usually consists of three principal steps:
1. Pre-processing: The user constructs a model of the part to be analyzed in which the geometry
is divided into a number of discrete sub regions, or elements," connected at discrete points
called nodes." Certain of these nodes will have fixed displacements, and others will have
prescribed loads. These models can be extremely time consuming to prepare, and commercial
codes vie with one another to have the most user-friendly graphical “preprocessor" to assist in
this rather tedious chore. Some of these preprocessors can overlay a mesh on a preexisting

36. CAD file, so that finite element analysis can be done conveniently as part of the computerized
drafting-and-design process.
2. Analysis: The dataset prepared by the preprocessor is used as input to the finite
element
code itself, which constructs and solves a system of linear or nonlinear algebraic equations
Kijuj = fi
where u and f are the displacements and externally applied forces at the nodal points. The
formation of the K matrix is dependent on the type of problem being attacked, and this module
will outline the approach for truss and linear elastic stress analyses. Commercial codes may
have very large element libraries, with elements appropriate to a wide range of problem types.
One of FEA's principal advantages is that many problem types can be addressed with the same
code, merely by specifying the appropriate element types from the library.
3. Postprocessing: In the earlier days of finite element analysis, the user would pore
through reams of numbers generated by the code, listing displacements and stresses at
discrete positions within the model. It is easy to miss important trends and hot spots
this way, and modern codes use graphical displays to assist in visualizing the results. A
typical postprocessor display overlays colored contours representing stress levels on
the model, showing a full field picture similar to that of photo elastic or moiré
experimental results
4. INTRODUCTION TO ANSYS

37. 4.1 INTRODUCTION
ANSYS is general-purpose finite element analysis (FEA) software package. Finite Element
Analysis is a numerical method of deconstructing a complex system into very small pieces (of
user-designated size) called elements. The software implements equations that govern the
behaviour of these elements and solves them all; creating a comprehensive explanation of how
the system acts as a whole. These results then can be presented in tabulated, or graphical
forms. This type of analysis is typically used for the design and optimization of a system far
too complex to analyze by hand. Systems that may fit into this category are too complex due
to their geometry, scale, or governing equations.
ANSYS is the standard FEA teaching tool within the Mechanical Engineering Department at
many colleges. ANSYS is also used in Civil and Electrical Engineering, as well as the Physics
and Chemistry departments.
ANSYS provides a cost-effective way to explore the performance of products or processes in a
virtual environment. This type of product development is termed virtual prototyping.
With virtual prototyping techniques, users can iterate various scenarios to optimize the product
long before the manufacturing is started. This enables a reduction in the level of risk, and in
the cost of ineffective designs. The multifaceted nature of ANSYS also provides a means to
ensure that users are able to see the effect of a design on the whole behavior of the product, be
it electromagnetic, thermal, mechanical etc
4.1.1 GENERIC STEPS TO SOLVING ANY PROBLEM IN ANSYS:
Like solving any problem analytically, you need to define (1) your solution domain, (2) the
physical model, (3) boundary conditions and (4) the physical properties. You then solve the
problem and present the results. In numerical methods, the main difference is an extra step
called mesh generation. This is the step that divides the complex model into small elements
that become solvable in an otherwise too complex situation. Below describes the processes in
terminology slightly more attune to the software.
4.1.1.1 BUILD GEOMETRY

38. Construct a two or three dimensional representation of the object to be modelled and tested
using the work plane coordinates system within ANSYS.
4.1.1.2 DEFINE MATERIAL PROPERTIES
Now that the part exists, define a library of the necessary materials that compose the object (or
project) being modelled. This includes thermal and mechanical properties.
4.1.1.3 GENERATE MESH
At this point ANSYS understands the makeup of the part. Now define how the modelled
system should be broken down into finite pieces.
4.1.1.4 APPLY LOADS
Once the system is fully designed, the last task is to burden the system with constraints, such
as physical loadings or boundary conditions.
4.1.1.5 OBTAIN SOLUTION
This is actually a step, because ANSYS needs to understand within what state (steady state,
transient… etc.) the problem must be solved.
4.1.1.6 PRESENT THE RESULTS
After the solution has been obtained, there are many ways to present ANSYS’ results, choose
from many options such as tables, graphs, and contour plots.
4.2 SPECIFIC CAPABILITIES OF ANSYS:
4.2.1 STRUCTURAL
Structural analysis is probably the most common application of the finite element method as it
implies bridges and buildings, naval, aeronautical, and mechanical structures such as ship
hulls, aircraft bodies, and machine housings, as well as mechanical components such as
pistons, machine parts, and tools.
· Static Analysis - Used to determine displacements, stresses, etc. under static loading
conditions. ANSYS can compute both linear and nonlinear static analyses. Nonlinearities can
include plasticity, stress stiffening, large deflection, large strain, hyper elasticity, contact
surfaces, and creep.

39. Modal Analysis
A modal analysis is typically used to determine the vibration characteristics (natural
frequencies and mode shapes) of a structure or a machine component while it is being
designed. It can also serve as a starting point for another, more detailed, dynamic analysis,
such as a harmonic response or full transient dynamic analysis.
Modal analyses, while being one of the most basic dynamic analysis types available in
ANSYS, can also be more computationally time consuming than a typical static analysis. A
reduced solver, utilizing automatically or manually selected master degrees of freedom is used
to drastically reduce the problem size and solution time.
Harmonic Analysis - Used extensively by companies who produce rotating machinery,
ANSYS Harmonic analysis is used to predict the sustained dynamic behavior of structures to
consistent cyclic loading. Examples of rotating machines which produced or are subjected to
harmonic loading are:
 Turbines
o Gas Turbines for Aircraft and Power Generation
o Steam Turbines
o Wind Turbine
o Water Turbines
o Turbopumps
 Internal Combustion engines
 Electric motors and generators
 Gas and fluid pumps
 Disc drives
A harmonic analysis can be used to verify whether or not a machine design will successfully
overcome resonance, fatigue, and other harmful effects of forced vibrations.

40. · Transient Dynamic Analysis - Used to determine the response of a structure to
arbitrarily time-varying loads. All nonlinearities mentioned under Static Analysis above are
allowed.
· Buckling Analysis - Used to calculate the buckling loads and determine the buckling
mode shape. Both linear (eigenvalue) buckling and nonlinear buckling analyses are possible.
In addition to the above analysis types, several special-purpose features are available such as
Fracture mechanics, Composite material analysis, Fatigue, and both p-Method and Beam
analyses.
4.2.2 THERMAL
ANSYS is capable of both steady state and transient analysis of any solid with thermal
boundary conditions.
Steady-state thermal analyses calculate the effects of steady thermal loads on a system or
component. Users often perform a steady-state analysis before doing a transient thermal
analysis, to help establish initial conditions. A steady-state analysis also can be the last step of
a transient thermal analysis; performed after all transient effects have diminished. ANSYS can
be used to determine temperatures, thermal gradients, heat flow rates, and heat fluxes in an
object that are caused by thermal loads that do not vary over time. Such loads include the
following:
· Convection
· Radiation
· Heat flow rates
· Heat fluxes (heat flow per unit area)
· Heat generation rates (heat flow per unit volume)
· Constant temperature boundaries
A steady-state thermal analysis may be either linear, with constant material properties; or
nonlinear, with material properties that depend on temperature. The thermal properties of most
material vary with temperature. This temperature dependency being appreciable, the analysis
becomes nonlinear. Radiation boundary conditions also make the analysis nonlinear. Transient
calculations are time dependent and ANSYS can both solve distributions as well as create
video for time incremental displays of models.

41. 4.2.3 FLUID FLOW
The ANSYS/FLOTRAN CFD (Computational Fluid Dynamics) offers comprehensive tools
for analyzing two-dimensional and three-dimensional fluid flow fields. ANSYS is capable of
modeling a vast range of analysis types such as: airfoils for pressure analysis of airplane wings
(lift and drag), flow in supersonic nozzles, and complex, three-dimensional flow patterns in a
pipe bend. In addition, ANSYS/FLOTRAN could be used to perform tasks including:
· Calculating the gas pressure and temperature distributions in an engine exhaust
manifold
· Studying the thermal stratification and breakup in piping systems
· Using flow mixing studies to evaluate potential for thermal shock
· Doing natural convection analyses to evaluate the thermal performance of chips in
electronic enclosures
· Conducting heat exchanger studies involving different fluids separated by solid regions
4.2.4 ACOUSTICS / VIBRATION
ANSYS is capable of modeling and analyzing vibrating systems in order to that vibrate in
order to analyze
Acoustics is the study of the generation, propagation, absorption, and reflection of pressure
waves in a fluid medium. Applications for acoustics include the following:
· Sonar - the acoustic counterpart of radar
· Design of concert halls, where an even distribution of sound pressure is desired
· Noise minimization in machine shops
· Noise cancellation in automobiles
· Underwater acoustics
· Design of speakers, speaker housings, acoustic filters, mufflers, and many other similar
devices.
· Geophysical exploration
Within ANSYS, an acoustic analysis usually involves modeling a fluid medium and the
surrounding structure. Characteristics in question include pressure distribution in the fluid at
different frequencies, pressure gradient, particle velocity, the sound pressure level, as well as,
scattering, diffraction, transmission, radiation, attenuation, and dispersion of acoustic

42. waves. A coupled acoustic analysis takes the fluid-structure interaction into account. An
uncoupled acoustic analysis models only the fluid and ignores any fluid-structure interaction.
The ANSYS program assumes that the fluid is compressible, but allows only relatively small
pressure changes with respect to the mean pressure. Also, the fluid is assumed to be non-
flowing and inviscid (that is, viscosity causes no dissipative effects). Uniform mean density
and mean pressure are assumed, with the pressure solution being the deviation from the mean
pressure, not the absolute pressure.
4.2.5 COUPLED FIELDS
A coupled-field analysis is an analysis that takes into account the interaction (coupling)
between two or more disciplines (fields) of engineering. A piezoelectric analysis, for example,
handles the interaction between the structural and electric fields: it solves for the voltage
distribution due to applied displacements, or vice versa. Other examples of coupled-field
analysis are thermal-stress analysis, thermal-electric analysis, and fluid-structure analysis.
Some of the applications in which coupled-field analysis may be required are pressure vessels
(thermal-stress analysis), fluid flow constrictions (fluid-structure analysis), induction heating
(magnetic-thermal analysis), ultrasonic transducers (piezoelectric analysis), magnetic forming
(magneto-structural analysis), and micro-electro mechanical systems (MEMS).

43. 5. ANSYS PROCESS
IMPORTINGTHE COMPONEENT FROM CAD (CREO) TOOL TO CAE TOOL (ANSYS):
THERMAL ANALYSIS:-
1. Click on Ansys workbench
Thermal structural
3. Engineering dataright click enter values

44. FOR
Steel
Ex: - 200*10^9 Pa
Poison ratio: 0.30
Density: 7850 Kg/m^3
Yield strength: 250 Mpa
Thermal conductivity: 60.5 w/m-k
Aluminium
Ex: 71.4*10^9 Pa
Poison ratio: 0.33
Density: 2770 kg/m^3
Yield strength: 165 Mpa
Thermal conductivity: 160.5 w/m-k
Stainless steel
Ex: - 193*10^9 Pa
Poison ratio: 0.31
Density: 7750 Kg/m^3
Yield strength: 207 Mpa
Thermal conductivity: 15.1 w/m-k
. Geometry right click import geometry import iges format model
Model imported from pro-e tool in IGES format.

45. Meshing: - Volume Mesh - Tetmesh.
After completion of material selection here we have to create meshing for each object meshing
means it is converting single part into no of parts. And this mesh will transfer applied loads for
overall object. After completion meshing only we can solve our object. Without mesh we
cannot solve our problem. And here we are using tetra meshing and the model shown in below.

46. Boundary conditions
Steady state thermal convection select left wall film coefficient20 w/m^2*c
Bulk temperature40*c
Steady state thermal convection select bottom wall film coefficient70 w/m^2*c
Bulk temperature30*c
Steady state thermal convection select right wall film coefficient10 w/m^2*c
Bulk temperature20*c
Steady state thermal convection select top wall film coefficient40 w/m^2*c
Bulk temperature100*c
Steady state thermal conduction select front back walls apply
Atmospheric temperature22*c
After completion of boundary conditions here we have check results by solving. Just click on
solve option and select results like deformation, strain, stress values for circular tool.
Solutionsolvetotal temperature
Solutionsolvetotal heat flux
Solutionsolveheat flux in x,y,z directions.

47. Results for computer wall (x=30 cm, y=30cm)
Total temperature
(Steel material)
Total heat flux

48. Results for computer wall (x=30 cm, y=30cm)
Total temperature
Stainless steel
Total heat flux

49. Results for computer wall (x=30 cm, y=30cm)
Total temperature
Al-alloy
Total heat flux

50. Tables
Total
temperature(*C)
Heat flux
(w/m^2)
Steel 22.003 1454
Stainless
steel
22.012 1453.8
Al-alloy 22.001 1454
Graphs
Total temperature
Total heat flux
21.994
21.996
21.998
22
22.002
22.004
22.006
22.008
22.01
22.012
22.014
Steel Stainless
steel
Al-alloy
Total temperature(*C)
Total
temperature(*C)

51. 1453.7
1453.75
1453.8
1453.85
1453.9
1453.95
1454
1454.05
Steel Stainless steel Al-alloy
Heat flux (w/m^2)
Heat flux (w/m^2)

52. Results for computer wall (x=30 cm, y=20cm)
Total temperature
Steel
Total heat flux

53. Total temperature
Stainless steel
Total heat flux

54. Al-alloy
Total temperature
Total heat flux
Tables
Total
temperature(*C)
Heat flux
(w/m^2)
Steel 22.004 1709.9
Stainless
steel
22.014 1769.7
Al-alloy 22.001 1710
Total temperature

55. Total heat flux
Results for computer wall (x=30 cm, y=10cm)
Total temperature
Steel
21.99
21.995
22
22.005
22.01
22.015
Steel Stainless steel Al-alloy
Total temperature(*C)
Total temperature(*C)
1680
1690
1700
1710
1720
1730
1740
1750
1760
1770
1780
1 2 3
Heat flux (w/m^2)
Heat flux (w/m^2)

56. Total heat flux
Total temperature

57. Stainless Steel
Total heat flux

58. Total temperature
Al-alloy
Total heat flux

59. Tables
Total
temperature(*C)
Heat flux
(w/m^2)
Steel 22.004 1962.8
Stainless
steel
22.016 1962.5
Al-alloy 22.002 1962.9
Graphs
Total temperature
Heat flux
21.995
22
22.005
22.01
22.015
22.02
Steel Stainless steel Al-alloy
Total temperature(*C)
Total temperature(*C)

60. 1962.3
1962.4
1962.5
1962.6
1962.7
1962.8
1962.9
1963
1 2 3
Heat flux (w/m^2)
Heat flux (w/m^2)

61. CONCLUSION
In this project we designed 3 computer walls by using cad tool (creo-2) in x,y
directions and analysed with real time boundary conditions by using CAE tool (Ansys
workbench). In this process we elected 3 different materials and applying it all physical and
thermal properties of it. In Ansys we solving results like total temperature distribution and
total heat flux for each wall with each materials.
And in this process first we took one wall (x=30cm, y=30cm) and then changing y-
direction dimensions from 30cm to 20cm and 30cm to 1cm and applying same boundary
conditions and analysing all results.
And finally hear we observe that when we changing y-direction dimension the total
heat flux has been increasing. And also heat flux in x directions and y,z directions also
increases. But there is no high difference in total temperature distribution.

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