We will continue with our discussion on the
Newton-Raphson method. The first part of
the lecture, we will look at some convergence
characteristics of the Newton-Raphson
method. Then, we will apply the Newton-Raphson
method to one variable problem in
heat transfer. And then, we will go on to
the two variable problem namely, the truck
problem. With that, our discussion on this
successive substitution Newton-Raphson
method will end. In the next class, I will
teach you Gauss-Seidel method, the system
of
simultaneous linear equations, and very much
useful in chemical industry, petrochemical
industry, and so on. And then, that will round
off our discussion on simulation.
And then, as you can see, whether we use the
fan and duct problem; whether we are
solving the fan and duct problem or the truck
problem; you are getting some
characteristics. These characteristics are
invariably obtained basically when we do some
measurements, which the manufacturer does.
So, he has got discharge versus pressure
and all that. So, this will be available in
the form of discrete point. You have to generate
curves from that. In the next 2-3 weeks, we
will look at regression curve fitting, exact
fits, approximate fits and so on, so that
you are able to generate these curves and
use it
for a simulation. And, simulation has to be
logically followed by optimization. So,
modeling, simulation, optimization; in between
somewhere regression is also there. So,
that completes the whole thing. Then, this
can be applied to any field you want –
electrical engineering, chemical, mechanical
this thing; of course, all our problems, our
focus is on thermal systems and energy systems.
So, this will give you a broad base with
which at least you can start off; I mean this
gives you a starting point; you can pick up
other additional techniques and advanced techniques
for multivariable non-linear
problems and proceed. That is the intent of
this course.
Now, what about the convergence characteristics
of the Newton-Raphson method? What
you mean by the term convergence characteristics?
Any answers? What do you
understand by the term convergence characteristics
of a numerical scheme?
**not audible**
No; that is ok. That is very qualitatively
say how does it converge. Quantitatively,
what
are the measures?
The error in the two successive…
How is the… Why the relationship between
the error in one iterate and the successive
or
the next iterate? How does it go? How does
the error in one iteration or the error of
a
particular iterate; what is its relation to
the error in the previous iteration? If it
is linear or
quadratic or cubic and so on, then whether
the error… So, the number of decimal places.
If it is accurate, one decimal places; if
it is quadratic, the next iterate will be
accurate to
two decimal places, four decimal places and
so on. So, whether Newton-Raphson
method has a linear convergence or quadratic
convergence, we will have to examine.
How do we examine that?
You take an example.
You take an example, that is… That is what
the easy way of doing it; that is a way of
doing. But, mathematics professor would not
be impressed. What he says is you take an
example; solve the problem – Newton-Raphson
method. Do not take that example – x to
power of 8 minus 1 or something; I mean take
a decent example of f of x equal to x
minus 2 sin x or whatever. Then, look at…
You can see that the number of decimal
places… Look at the previous example. It
jumps, no – the number of decimal places
accuracy? So, you know that it has a terrific
convergence rate. You can plot it and find
out whether it is linear or quadratic. If
you fit a function linear or quadratic;
approximately, you will have an idea. That
is how an engineer will solve the problem;
that is all right.
But, from a mathematical stand point, how
do you analyze this? You have to start writing
the Taylor series, Taylor series expansion;
you have to write the Taylor series. But,
now,
you have x i; you have x i plus 1. There is
a hypothetical x true or x real; the ultimate
true rule. Then, you expand the solution at
i plus 1; you expand the solution around x
of
true value. And, Taylor series expansion – you
subtract one from the another and just try
to see whether you are able to get it. That
is the whole story. Can you start? I will
have a
delayed start. You just start; I will start
after few minutes. You understand what I am
saying? One you already know; one equation
you already know; that is, f of x i plus 1
is
equal to f of x i plus f dash of x i into
x i plus 1 minus x i. This is one equation.
The lefthand side, what is the… What about
the left-hand side? It is forced to 0. I put
approximately equal.
Now, if I say that… Now, instead of x i
plus 1, I use x true; instead of… There
are
several ways of doing it. I can have the quad…
For example, I can have one more term –
the quadratic term; I can have one more term
– quadratic term. And, this is
approximately equal. Can be replaced by exactly
equal. Are you getting the point? For
example, f of x t is equal to f of x i plus
f dash of x i into x t minus x i plus f double
dash
of xi by 2 factorial into x t minus x i the
whole square. Now, this way it may lead to
some trouble; I have expanded all right; but,
I made a small mistake on the board. What
is that? From i, I am going to i plus 1. Next,
the logical steps should be…
i plus 2.
i plus 2; that i plus 2 I am considering as
true value for example. Therefore, what I
should do is… Left-hand side is all right.
Right-hand side – I think… What is the
correction you want to make? Where do you
want to put i plus 1? The point is very
simple. Is this all right? This step is all
right. This step – we are doing some expansion.
So, if I use i, i plus 1, i plus 2, I will
not get anywhere. So, instead of i plus 2,
somewhere
I am saying, declaring that, it is true value.
Therefore, I will get an error between two
successive iterates. But, here I am not getting
that, because x t minus x i – I do not know
whether it is successive iterate or not; what
should I do?
Minus 1.
Then, I will get f dash of x i plus 1, no?
You have x t minus…
Or, we leave it like this. We leave it like
this and let us see what happens.
Now, maybe when we can subtract 2 from 1 or
1 from 2 and see what happens. This
expansion is correct? Taylor series expansion
is correct?
It should be above or around the point…
All are close to each other; do not worry
about that, Vinay. Is there an error in this
or it is
ok?
It is fine.
It is fine. Now, this is the true value. Therefore,
f of x t must equal to 0. Now, there is no
confusion.
Now, all of you please do 2 minus 1. Left
side is 0.
What what?
((In the LHS the term is zero? ))
I am forcing; x i plus 1 is always 0.
(( Is that true value?))
No; in the Newton-Raphson method, f of x plus
I – I am forcing it; I am forcing it to
0
numerically. So, that is why I have put this
. This is
approximately equal. This is exactly equal,
but I want to retain the second order term
in
order to ensure that you do not object. Now,
if I do this, what happens? This is slightly
shady proof, but it is ok. f of x i gets cancelled.
So, 0 is equal to f dash of x i into.
x t minus
x t minus?
x i plus 1.
Plus?
Why minus?
Plus.
Plus f double dash x i divided by 2 factorial
into x t minus x i the whole square. This
fellow is there. Now, what is the big deal?
x t minus x i plus 1 can be replaced by the
Good. So, we are progressing. Go ahead; this
is somewhat this thing. I did some cheating
here by putting only the second order terms.
So, it is all right. Now, what he is saying
is,
this is actually the error term, true value
and this thing. So, I can call it as 
error in the true
value and i plus 1-th iteration. Correct?
There is no problem about this nomenclature,
error in the true value at the i plus 1-th
iteration; true value minus x i plus 1; no
problem.
We can call that as 4. By the same token,
x t minus x i will be error in the true value
at
the?
i-th iteration.
Good; error in the true value at the i-th
iteration.
And then, you can take one of these terms
to the left-hand side, so that f dash of x
i into
E t i plus 1 equals minus f double dash x
i divided by 2. Where is whole square? Not
yet.
Right side is E t i square. Please correct
me if I made any mistake. Vikram, any problem;
you are frowning?
I have already ensured that is quadratically
converging. That should be edited. Anyway
we are seeking… Anyway we are seeking a
solution; we are expanding f of x and all
that
very close to the solution. It is not already
we have reached. We are looking at the
convergence, when it is closed and all that.
Therefore, I will make some additional
cheating now; I will say that…
Therefore, I will say f dash of x i can be
put as f dash of x t. All within limits; and,
everything at the true value. Now… Around
the true value, these two are stationary.
How
they are stationary? Because I made them stationary.
Now, I am just going to tell you
that, E t i plus 1 is proportional to E t
i whole square; that is it. So, the error
in the i plus
1-th step is proportional to the square of
the error in the i-th step, because the other
two
are constant, because we are looking at the
values around the true value. You may say,
sir, these are not true values; one is at
xi and one is at x i; but, I am saying x i,
x t, x i
plus 1 – they are all very close. Therefore,
that is not a serious matter. I want to know
how E t of i or E t of i plus 1 behaves with
respect to E t of I; that means if this is…
Suppose you get an error of 0.1; next, it
will 0.01; then, it will be 0.01 square; point…
So, 0.01 to the power of 4. Therefore, the
Newton-Raphson method exhibits quadratic
convergence. So, this is known as quadratic
convergence. People who are interested, can
do a similar performance, similar exercise;
can perform a similar exercise for the method
of successive substitution. And, I guarantee
that you will have only a linear convergence.
We have already seen it live. We did one problem
with the third iteration, we got the
root. So, if we apply the same technique to
the method of successive substitution, it
will
result in a linear convergence. So, this is
known as quadratic convergence. Akshay
Gulati; you surface; what happened – 2 o
clock? Putting sleep or 1 o clock? But, you
are
sleeping. So, this is called the quadratic
convergence behavior. As Vikram said, the
other
possibility is; take a numerical example and
see how actually it will work out. You can
see that it is quadratically converging.
Now, we will go to a problem, which we already
worked out using the method of
successive substitution. So, problem number
11 or what? Problem number 11. Today is
the 11-th or 12-th lecture. So, we are in
11 – problem number 11. At the end of the
course, we will have done at least 40 problems.
So, class notes is very important for this
course. So, problem number 11 – consider
an… As a simple way of writing, it would
be
revisit problem number? Revisit problem number
5 or 6 or whatever. What was it?
Revisit problem number 6. But, if you had
been absent for that class, you have to write
it
down.
Again, revisit problem number 6. I will say
it all over again. Consider an electrically
heated wire of emissivity 0.6 operating under
steady state conditions. The energy input
to
the wire is 1000 watts per meter square of
surface area and the heat transfer coefficient
h
is 10 watts per meter square per Kelvin. The
ambient temperature is 300 kelvin. So, we
are looking at steady state. There is an input
energy to the wire and it is dissipating heat
by both natural convection and surface radiation.
The ambient for the radiation is same
as the ambient for convection. In fact, we
had a small discussion about this in one of
the
earlier classes. It is not always the case
that the ambient for the convection should
be
equal to the ambient for the radiation. Generally,
it is a case. What is a governing
equation for the steady state temperature
of the wire? You just copy, paste the governing
equation from problem number 6 using the Newton-Raphson
method, rather than the
method of successive substitution. Solve the
governing equation and determine the
steady state temperature of the wire. Decide
on an appropriate stopping criterion. The
appropriate stopping criterion would be t
i plus 1 minus t i whole square. So, I have
worked it out; it takes about four iterations.
So, maybe 10 to 15 minutes.
Is it T s or T w?
T w.
1000? This one is 1000. 0.6 into 5.67 is how
much?
3.?
3.4
3.4?
02
3?
3.4024
That is ok. Then, 3.4 10 to the?
Minus 8.
Minus 8. Now, we need to get the f dash. 4
is how much? 12, 13.6. Correct? It is always
negative is it? Always negative?
((yes ))
This not good; I do not like it.
**not audible**
This will work right?
That will work.
Then ok. But, how are we work. How have we
rewritten f of…
Minus ((in the right hand side ))
So, what you have done is…
This minus sign can be very daisy; it may
lead to the downfall. We should try to avoid
this minus sign.
I will just put it as T i instead of T w i.
What is the good guess?
400.
No 360
400
400; we will start with 400. What is the answer?
((366 ))
400. Now? You have to write the algorithm.
Anyway, you can get rid of the w. This is
not good man; this is not good enough. So,
f of T i. Give me some values; 400.
T i plus 1 is equal to 363.7.
No, before? Give me the split…
Put it as a formula here.
595.34
Then?
18.701
18?
368.17
So, that is about 32… 32 square is how much?
1012
No; I want the steps.
((**laughs** ))
It is also being shown to others. No, I want
to know whether you have… Is there a quick
way of doing it in the calculator? What do
you do? Recursive relationship?
No, sir.
You put it as a function?
Yes sir.
No; see… but, you would not have an intuitive
feel; how? f ultimately should become? f
of T i must become 0. That you can see when
you do this. He is very sincere. 31.26.
Then? 366.8. 1.4; this is about 2. 3… This
is 366? 7
68.?
57.
366.30.
So, 0.4 into 0.4; how much is it? 0.16?
0.16.
We can go; one more. Can you see the quadratic
convergence?
Yes, sir.
We will stop with this. Anand, tell me the
value. You are getting something different?
Sir, I started with 350
But, it is self correcting algorithm. If you
go wrong in one step, it will correct; but,
you
will take three iterations more than your
friends; but, it is all right. But, eventually,
it will
converge. It will not go astray. We will fill
this column out and then… Sureka, you got
it? Third… Second step you got.
(( yes))
Two steps? Anybody else?
f should…
Minus 0.07.
See that means it is already there. Minus?
Derivative will stay close to 10, 15, 20.
Derivative is stable. That is very important.
If that fellow starts misbehaving; if he
becomes very close to 0, then we are in trouble.
See this is very… 0.09 by 16. So, I will
put 366.3. So, this is our answer; solution.
So, we started with 400. Fourth iteration
– we
got successive substitution. How long did
it take? It took 16 iterations. So, this is
quadratic convergence; that has to be linear
convergence. So, I can argue like that also.
But, it is very powerful. If the problem is
well-posed, then there is no… You would
not
experience any trouble. So, this is a powerful
root extraction technique. Even in two
variable problem, you can put down into one
variable problem and solve it. But, I do not
expect you to do that. But, for some cases,
you want to get quick solution. If we treat
a
problem as a one variable problem; using the
Newton-Raphson method, it is very
powerful to get.
The other will be a… There are other techniques
like the bisectional algorithm; the
bisectional algorithm, where you find out
the two places, where it crosses the 0 mark.
And then, you start bisecting. And then, you
bisect, keep on bisecting and then do it.
That is one thing. Then, there is a Regular-Falsi
method; then, there is a sequent method;
so many methods, which are available. Then,
Newton-Raphson method is one of the
more important ones; and, its convergence
is quadratic. Now, how do we extend the
Newton-Raphson method for multiple unknowns?
Because it is of practical relevance for
a course like this; because invariably in
thermal system design, you have more than
one
component. How do we handle Newton-Raphson
method in multiple unknowns?
Let us consider a three variable problem;
where, the variables are x 1, x 2, x 3. No;
for
example, it will be pressure, temperature,
density; whatever. Why do I write three
variable pressure in three variable problem?
So, x 1, x 2, x 3. How many equations you
need to close the problem mathematically?
3
3. We got three equations. So, you can start
with x 1 of i, x 2 of i and x 3 of i. You
want
to proceed to x 1 of i plus 1, x 2 of i plus
1 and x 3 of i plus 1; which means you have
to
write f as the Taylor series in a Taylor series
expansion. How do you do that? Can you
write the Taylor series expansion for a function
of several variables?
Use partial differential equation and stop
with linear terms.
Using Taylor series expansion… I am expanding
around i plus 1 from i; i is close… i
plus 1 is close enough to i, so that I can
neglect higher order terms. If the delta x
is small,
then I am allowed to… Delta x 1, delta x
2, delta x 3 are small; I am allowed to do
that.
So, this can be equation 4. Should we also
get equations 5 and 6? Sure, we will get
equations 5 and 6 for f 2 and f 3 respectively.
Now, the goal is to make the left-hand side
equal to 0, because we are seeking roots to
the equations f 1, f 2, f 3. Therefore, we
force
f 1 equal to 0, f 2 equal to 0, and f 3 equal
to 0. So, if we do that…
I will call this expansion as 5; this expansion
as 6. LHS of 4, 5 and 6 is?
0
Correct. Then, how do you write this algorithm?
The left-hand side is something
equivalent to what is called a Jacobian matrix.
It is a sensitivity matrix. The sensitivity
of… The partial derivatives of the various
functions with respect to respective variables
– we frequently refer it to as sensitivity
or the Jacobian. So, what you have to do is,
unfortunately, for us, the Jacobian matrix
is not fixed, because all the elements of
the
Jacobian matrix keep changing with iteration.
There are certain derivatives, which will
get fixed.
For example, in the engine… In the truck
problem, if t is equal to 11 omega; we will
put
f 2 is equal to t minus 11 omega. Then, dou
f by dou t will be 1. That fellow will remain
1 throughout the iteration. Dou f by dou omega
will be equal to minus 11. He will remain
the same. But, if you have got 3 omega plus
4 omega square or something, it will be 3
plus 8 omega. Then, when the omega changes,
that guy will change. So, some of the
elements will be fixed; some of the elements
will change. So, you start working out like
this. Take an initial value of x 1, x 2, x
3 i. Watch very carefully. Take an initial
value of
x 1 i, x 2 i and x 3 i. Substitute in the
respective equation for f 1, f 2, f 3. It
is possible for
you to calculate the values of f 1, f 2, f
3 at the initial starting point. So, the forcing
vector… The column vector on the right hand
side is known. Then, once you known x 1
i, x 2 i and x 3 i, evaluate all the partial
derivatives at the point x 1 i, x 2 i and
x 3 i. So,
the Jacobian or the sensitivity matrix – all
the elements are known.
You also know this. Solve this system of equations
using Gauss-Seidel or invert or
whatever; and, get this value. You can write
this as delta x 1, delta x 2, delta x 3. So,
you
solve the system of equations for delta x.
Delta x is x 1 i plus 1 minus x 1 i for delta
x 1.
You can do the same thing for delta x 2 and
delta x 3. Now, you will get the new values
of x. You will get x 1 i plus 1, x 2 i plus
1, x 3 i plus 1. Substitute; on the right-hand
side,
you will get the new values of f 1, f 2, f
3. With the new values, rework the sensitivity
matrix. Now, you feel bamboozled, because
it is very tiring and all that. But, it is
eminently programmable. You can write a matlab
script; it will do in no time. But, the
moment it exceeds 10 or 12 variables, handling
the matrix sometimes, some issues are
associated with that. Matrix inversion will
not work properly for certain number of
variables. Then, you have do Gauss-Seidel
or Gauss elimination or things like that.
But,
if you have a practical problem; if you are
really talking about system simulation of
a
power plant and all that; then, these are
the kind of things, which will be involved.
Now, I have just given a general case, where
for a three variable problem, this can be
extended to any number of variables. If it
is a two variable problem, you will have only
two elements. So, in the fan and duct problem
or in our truck problem, we will have to
solve only a 2 by 2. You will have to invert
a 2 by 2 matrix each time. But, you can see
compared to successive substitution, this
will be extremely fast. And, the Jacobian
matrix, the partial derivative matrix also
gives you additional information of the
sensitivity of a solution to the sensitivity
of f 1 and f 2 to the operating variables.
Is it
time to close or you want to start working
out the problem?
Anyway, in the next class, we will use this…
We will use this… In the next class, we
will use this technique to solve the truck
problem. And, with that, we end our discussion
on Newton-Raphson method. I will teach you
the Gauss-Seidel method subsequently.
Any doubts?
Sir; in our derivation, when we force that
error in the i plus 1-th iteration in
proportion to error i square; we tend to exactly
normalize that. And, this will only be
valid… This will only make sense if the
error values are normalized, so that my error
values are less than 1.
No, error values… In fact, I told you, why
the successive substitution does not work;
g
dash of alpha must be modulus less than 1
and all.
Yes sir.
See we are already… We already very close
to the solution. Therefore, we are looking
at
the decimal points and all that. So, it would
not… When it is wildly swinging, what you
are saying is… What you are saying is; if
it is 40; next time it will be given 1600;
what is
the point? That is what you are saying?
Yes sir.
No.
As I said, we are seeing the convergence error
values are larger. So, is the derivation
is such that there is some normalization?
No, is there any normalization?
No.
f dash of zeta.
No, that is that. There is one factor called
f double dash. That is one normalizing factor.
No, like the…
No; but, you can… Suppose that confuses
you; you will say that, already you have come
to level, where it is less than 1. If it is
less than 1, 0.9 will become 0.8 and then
0.64. It
will go quadratically low. Is that thing understood?
He says if it is 40, it becomes 1600;
1600 will become… But, there is 1 minus
f double dash xi divided by f dash something,
no? That will take… That will cool things
down. That will ensure that these fellows
do
not misbehave. So, that is the normalizing
factor.
Thank you.
