The document discusses using the Laplace transform to solve the heat equation for transient conduction. It presents a problem of calculating the temperature over time within a semi-infinite slab where the initial temperature is suddenly changed at the boundary. By taking the Laplace transform, the PDE is converted to an ODE, making the solution easier to obtain. The solution is found in the transform domain and then inverted back to the time domain to give the temperature as a function of position and time within the slab. Transforming the time variable rather than the space variable is preferred because it avoids issues with evaluating boundary terms involving derivatives.
The presentation is about how to evaluate the probability of finding the system in any particular state at any later time when the simple Hamiltonian was added by time dependent perturbation. So now the wave function will have perturbation-induced time dependence.
The presentation is about how to evaluate the probability of finding the system in any particular state at any later time when the simple Hamiltonian was added by time dependent perturbation. So now the wave function will have perturbation-induced time dependence.
Pure substance
Phases of a pure substance
Phase change processes of pure substances
Compressed liquid, Saturated liquid, Saturated vapor, Superheated vapor Saturated temperature and Satuated pressure
Property diagrams for phase change processes
The T-v diagram, The P-v diagram, The P-T diagram, The P-v-T diagram
Property tables
Enthalpy
Saturated liquid, Saturated vapor, Saturated liquid vapor mixture, Superheated vapor, compressed liquid
Reference state and Reference values
The ideal gas equation of state
Is water vapor an ideal gas?
NUMERICAL METHODS IN STEADY STATE, 1D and 2D HEAT CONDUCTION- Part-IItmuliya
This file contains slides on NUMERICAL METHODS IN STEADY STATE 1D and 2D HEAT CONDUCTION – Part-II.
The slides were prepared while teaching Heat Transfer course to the M.Tech. students in Mechanical Engineering Dept. of St. Joseph Engineering College, Vamanjoor, Mangalore, India, during Sept. – Dec. 2010.
Contents: Methods of solving a system of simultaneous, algebraic equations - 1D steady state conduction in cylindrical and spherical systems - 2D steady state conduction in cartesian coordinates - Problems
Numerical methods in Transient-heat-conductiontmuliya
This file contains slides on Numerical methods in Transient heat conduction.
The slides were prepared while teaching Heat Transfer course to the M.Tech. students in Mechanical Engineering Dept. of St. Joseph Engineering College, Vamanjoor, Mangalore, India, during Sept. – Dec. 2010.
Contents: Finite difference eqns. by energy balance – Explicit and Implicit methods – 1-D transient conduction in a plane wall – stability criterion – Problems - 2-D transient heat conduction – Finite diff. eqns. for interior nodes – Explicit and Implicit methods - stability criterion – difference eqns for different boundary conditions – Accuracy considerations – discretization error and round–off error - Problems
Jean-Paul Gibson: Transient Heat Conduction in a Semi-Infinite Plate Compare...Jean-Paul Gibson
The final project for a Thermo/Fluids Lab I took in school. A 12" long stainless rod was heated until six thermocouples attached to it all read 400K. The rod was then allowed to cool for 10 minutes while taking the temperature reading for each thermocouple every 30 seconds. ANSYS was used to compare the variance of actual temperatures as tested to what the theoretical values should be.
This file contains slides on Transient Heat conduction: Part-I
The slides were prepared while teaching Heat Transfer course to the M.Tech. students in Mechanical Engineering Dept. of St. Joseph Engineering College, Vamanjoor, Mangalore, India, during Sept. – Dec. 2010. Contents: Lumped system analysis – criteria for lumped system analysis – Biot and Fourier Numbers – Response time of a thermocouple - One-dimensional transient conduction in large plane walls, long cylinders and spheres when Bi > 0.1 – one-term approximation - Heisler and Grober charts- Problems
This file contains slides on Transient Heat conduction: Part-II
The slides were prepared while teaching Heat Transfer course to the M.Tech. students in the year 2010.
Contents: Semi-infinite solids with different BC’s - Problems - Product solution for multi-dimension systems -
Summary of Basic relations for transient conduction
Ph2A Win 2020 Numerical Analysis Lab
Max Yuen
Mar 2020
(use g = 9.8m/s2 for all problems.)
Background
Many physics problems cannot be solved directly by hand or analytically. We resort to numerical
methods to give us approximations to the problem. In this lab you will learn the Euler method,
which allows you to solve Newton’s laws of motion. This is done by treating the velocity as a
piecewise linear function with many time intervals and during interval the acceleration is assumed
to be uniform. This allows us to use the kinematic equations we learned about in the first half
of the class to approximate the motion. If we choose to partition the motion into smaller time
intervals, the approximation becomes much better since the differences between adjacent intervals
become smaller. In this lab, this numerical analysis method will be applied to the motion of a
falling object under the influence of gravity and drag force. If you are adventurous, you can even
try to extend this to 2D and compute the realistic trajectory of a baseball. You might even try
some other problems, such as a mass attached to a spring.
Euler’s Method Foundations
This method is well suited for problems where the acceleration is a function of the velocity, as in
the case of a falling object under the influence of gravity and drag:
a = f(v) (1)
Falling object with drag force
The model for drag fits the prescription for using Euler’s method since the net force on a falling
object with drag is given by:
ma = −mg −FD (2)
ma = −mg −
1
2
ρairACDv
2 · sgn(v) (3)
a = −g
(
1 +
ρairACDv
2 · sgn(v)
2mg
)
(4)
a = f(v) ← Equation of Motion (5)
where m is the mass of the falling object, a is the acceleration of the object (which is positive when
pointed up), ρair is the density of air (about 1.29 ·10−3kg/m3), A is the cross-sectional area, CD is
the drag coefficient, v is the object’s velocity, and sgn(v) is the signum function which returns the
sign of the argument. The second signum function is there to guarantee that the direction of the
drag force is always in the opposite direction of the velocity function. Note that we see that the
acceleration is an explicit function of v, which sort of makes this a chicken or egg problem. This is
because we need a to get v, but to get a we need v, so which one do we compute first? Hold that
thought. We’ll talk more on how to program this in EXCEL or Google Sheets later.
1
Figure 1: FBD for an object falling under the pull of gravity and resistance by drag force
Terminal Velocity
In lecture, we talked about how after waiting for some time, if the object started at rest the
speed will increase and the drag force will also become larger and eventually balance out with the
gravitational force. When this happens, we have reached terminal velocity vterm = −v. This can
be solved by setting a = 0:
0 = −mg −
1
2
ρairACDv
2 · sgn(v) (6)
2mg = ρairACDv
2
term (7)
→ vterm =
√
2mg
ρairACD
(8)
Using this definition for the terminal ...
390 Guided Projects
Guided Project 31: Cooling coffee
Topics and skills: Derivatives, exponential functions
Imagine pouring a cup of hot coffee and letting it cool at room temperature. How does the temperature of the
coffee decrease in time? How long must you wait until the coffee is cool enough to drink? When should you
add an ounce of cold milk to the coffee to accelerate the cooling as much as possible?
A fairly accurate model to describe the temperature changes in a conducting object is Newton’s Law of
Cooling. Suppose that at time t ≥ 0 an object has a temperature of T(t). The Law of Cooling says that the rate at
which the temperature of the object increases or decreases is given by
( ( ) ) , (1)
dT
k T t A
dt
= − −
where A is the ambient (surrounding) temperature and k > 0 is a constant called the conductivity (which is a
property of the cooling object). Newton’s Law of Cooling assumes that the cooling body has a uniform
temperature throughout its interior. This is not strictly accurate, as a cooling body loses heat through its surface.
1. Explain in words what equation (1) means. Specifically, in terms of T and A, when is 0
dT
dt
> and when is
0
dT
dt
< ? For the case of hot coffee cooling to room temperature, which case do you expect to see?
2. Verify by substitution that the solution to equation (1) subject to the initial condition T(0) = T0 is
0( ) ( ) . (2)
ktT t A T A e−= + −
3. Before graphing the temperature function, use equation (2) to evaluate T(0) and limt→∞ T(t). Are these the
values you expect?
4. Consider the case of a cup of hot coffee cooling with an ambient room temperature of A = 60◦ F and the
initial temperature of the coffee is T0 = 200
◦ F. Use a graphing utility to plot the temperature function for
k = 0.3, 0.2, 0.1, and 0.05. Comment on how the curves change with k. Do larger values of k produce faster
or slower rates of temperature change?
5. For the values of A and T0 in Step 4, estimate the value of k that describes the case in which the coffee
cools to 100 degrees in 10 minutes.
Here is an interesting question. Suppose you want to cool your hot coffee to 100◦ F as quickly as possible.
Suppose also that you have one ounce of cold milk with a temperature of 40◦ F that you can add to the
cooling coffee at any time. When should you add the milk to cool the coffee to 100◦ F as quickly as
possible?
6. We need to make an assumption about the effect of cold milk on the temperature of the coffee. A
reasonable assumption is that when milk is added to coffee, the temperature of the coffee immediately
decreases to the average of the coffee temperature and the milk temperature, where the average is weighted
by the volumes. So if we add 1 ounce of milk with temperature Tm to 8 ounces of coffee with temperature
T, the temperature of the mixture will be
1 8 8
. (3)
1 8 9
m m
new
T T T T
T
⋅ .
390 Guided Projects
Guided Project 31: Cooling coffee
Topics and skills: Derivatives, exponential functions
Imagine pouring a cup of hot coffee and letting it cool at room temperature. How does the temperature of the
coffee decrease in time? How long must you wait until the coffee is cool enough to drink? When should you
add an ounce of cold milk to the coffee to accelerate the cooling as much as possible?
A fairly accurate model to describe the temperature changes in a conducting object is Newton’s Law of
Cooling. Suppose that at time t ≥ 0 an object has a temperature of T(t). The Law of Cooling says that the rate at
which the temperature of the object increases or decreases is given by
( ( ) ) , (1)
dT
k T t A
dt
= − −
where A is the ambient (surrounding) temperature and k > 0 is a constant called the conductivity (which is a
property of the cooling object). Newton’s Law of Cooling assumes that the cooling body has a uniform
temperature throughout its interior. This is not strictly accurate, as a cooling body loses heat through its surface.
1. Explain in words what equation (1) means. Specifically, in terms of T and A, when is 0
dT
dt
> and when is
0
dT
dt
< ? For the case of hot coffee cooling to room temperature, which case do you expect to see?
2. Verify by substitution that the solution to equation (1) subject to the initial condition T(0) = T0 is
0( ) ( ) . (2)
ktT t A T A e−= + −
3. Before graphing the temperature function, use equation (2) to evaluate T(0) and limt→∞ T(t). Are these the
values you expect?
4. Consider the case of a cup of hot coffee cooling with an ambient room temperature of A = 60◦ F and the
initial temperature of the coffee is T0 = 200
◦ F. Use a graphing utility to plot the temperature function for
k = 0.3, 0.2, 0.1, and 0.05. Comment on how the curves change with k. Do larger values of k produce faster
or slower rates of temperature change?
5. For the values of A and T0 in Step 4, estimate the value of k that describes the case in which the coffee
cools to 100 degrees in 10 minutes.
Here is an interesting question. Suppose you want to cool your hot coffee to 100◦ F as quickly as possible.
Suppose also that you have one ounce of cold milk with a temperature of 40◦ F that you can add to the
cooling coffee at any time. When should you add the milk to cool the coffee to 100◦ F as quickly as
possible?
6. We need to make an assumption about the effect of cold milk on the temperature of the coffee. A
reasonable assumption is that when milk is added to coffee, the temperature of the coffee immediately
decreases to the average of the coffee temperature and the milk temperature, where the average is weighted
by the volumes. So if we add 1 ounce of milk with temperature Tm to 8 ounces of coffee with temperature
T, the temperature of the mixture will be
1 8 8
. (3)
1 8 9
m m
new
T T T T
T
⋅ .
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1. Solving the Heat Equation for Transient Conduction using Laplace Transform March 20 2010 Desmond Adair School of Engineering University of Tasmania School of Engineering University of Tasmania
2.
3. To show how it is implemented in MathematicaSchool of Engineering University of Tasmania
4. Problem formulation Consider a semi-infinite slab where the distance variable, y goes from 0 to ∞. The temperature is initially uniform within the slab and can be considered to be 0. At t = 0, the temperature at y = 0 is suddenly increased to 1. It is required to calculate the temperature as a function of time, t, within the slab. This problem is commonly solved by a similarity variabletechnique that arises because of the absence of a physical length scale. Here the Laplace transform is used, which is an integral transform that effectively converts (linear) PDEs (if we wish to use it on say time) to a set of ODEs in frequency space. This is a set of ODEs because the transform creates an explicit parameter, s, which is effectively a frequency and the equations need to be solved for all positive s. School of Engineering University of Tasmania
5. Problem formulation equation boundary conditions the boundary conditions are School of Engineering University of Tasmania
6. Problem analysis If a transform technique is used the intention is to simplify the problem by transforming the pde to an ode (or an algebraic equation from an ode). Once the transform is done, the need is to evaluate the integral that arises at the boundaries. So the boundary conditions and the domain of the problem must be in a form conducive to this. The Laplace transform is defined from 0 to ∞. In this problem both of the domains are from 0 to ∞, however the first try is to do the transform in time. In Mathematica this command is LaplaceTransform[heateq, t, s] and the new parameter is s. School of Engineering University of Tasmania
7. Problem analysis The first two terms make an ode in the transformed θ(t, y). Let’s call this The last term, which arose from integration by parts of the term must be evaluated from the boundary conditions. The temperature for all y at t = 0 is zero, thus this term is 0. Note that the t -> ∞ boundary term is usually zero ( this is not in this calculation) because it is multiplied by Exp[-s t] (and thus Mathematicaautomatically makes it 0). This results an ode in (y) and have generated an explicit parameter, s. The LaplaceTransform [t,s] does not affect any y derivatives. We can write School of Engineering University of Tasmania
8. Problem analysis This is easily by doing Now get the solution out of the {{}}’s We see that we cannot stand an exponentially increasing part so that c2 = 0. Now we need to evaluate (y, s) at y= 0 or some place that we know it. Well, we know θ [t, y] at y = 0 (= 1). School of Engineering University of Tasmania
9. Problem analysis Thus we can transform this to get a value for θ [y = 0, s]. Now find the value C[1] after setting C[2] = 0 and evaluating the expression at y = 0. Thus our solution in transformed space is School of Engineering University of Tasmania
10. Plot solution in frequency space to see what s does The top plot is for s = 10. It is a difficult to tell for sure, but the different values s represent different modes. The term s is roughly a frequency so that higher s’s decay faster!! School of Engineering University of Tasmania
11. Examine the issue of time to “frequency” We can more of what s means by transforming tn- - > s So, we see the inverse relation of t and s – which that is a little justification for calling s the “frequency”. School of Engineering University of Tasmania
12. Put solution in frequency space to see what α does Now if we make plots for different values of α we get: The top curve is for α =10. it is seen that the value of α gives the rate of “diffusion” which we also expect once the solution is transformed back. School of Engineering University of Tasmania
13. Transform back to time Now we must transform back to get the solution in physical space. There are three ways. One is to get a table of transforms and inverse transforms. A good table is in Spiegel’s mathematics handbook (M.R. Spiegel, Mathematical Handbook, Schaum’s Outline Series, McGraw-Hill, 1968). The second way is to use Mathematica and the problem would be, do you believe the answer. The third way is to do the complex integration yourself, the problem here is, would you believe the answer? However, Mathematicaagrees with the tables. School of Engineering University of Tasmania
14. Plot the solution in physical space Note that the bottom curve is for t = .02
15. Plot the solution in physical space Again the bottom curve is for t = 0.02. By comparing the two plots for different α, we can see that the value of the diffusivity controls how fast heat transfer occurs.
16. Why transform time variable instead of space variable? Now suppose that initially the y variable had been transformed Now we need to evaluate the boundary terms. The first one sθ[t,0] = s for all t of interest. However, the second one presents a bit of a problem. We don’t really know the heat flux at the boundary so we don’t know the derivative. Consequently, transforming y to s does not help solve the problem! School of Engineering University of Tasmania