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Lecture 11: Unsteady ConductionError Analysis
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Last Time…
Looked at unsteady 2D conduction problems
Discretization schemes» Explicit» Implicit
Considered the properties of this scheme
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This Time…
Complete unsteady scheme discussion» Properties of Crank-Nicholson scheme
Truncation error analysis
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Unsteady Diffusion
Governing equation:
Integrate over control volume and time step
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Discrete Equation Set
Dropping superscript (1) for
compactness
Notice old values in discrete
equation
To make sense of this
equation, we will look at
particular values of time
interpolation factor f (f = 0,
1, 0.5)
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Crank-Nicholson Scheme
Crank-Nicholson scheme uses f=0.5
tt1t0
0
1Linear profile assumption across time step
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Crank-Nicholson: Discrete Equations
Linear equation
set at current
time level
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Properties of Crank-Nicholson Scheme
Linear algebraic set at current time level – need linear solver
When steady state is reached, P= 0P. In this limit,
steady discrete equations are recovered.» Steady state does not depend on history of time
stepping» Will get the same answer in steady state by time
marching as solving the original steady state equations directly
Will show later that truncation error is O (t2)
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Properties (cont’d)
What if
Crank-Nicholson scheme can be shown to be
unconditionally stable. But if time step is too large, we
can obtain oscillatory solutions
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Truncation Error: Spatial Approximations
Face mean value of Je represented by face centroid
value Source term represented by centroid value:
Gradient at face represented by linear variation between cell centroid:
What is the truncation error in these approximations?
C P PS V S S V
E P
e ex x
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Mean Value Approximation
Consider 1-D approximation and uniform grid.
What is the error in representing the mean value over
a face (or a volume) by its centroid value?
Expand in Taylor series about P
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Mean Value Approximation (cont’d)
Integrate over control volume:
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Mean Value Approximation (cont’d)
Complete integration to find:
Rearranging:
Thus is a second-order accurate
representationP
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Gradient Approximation
To find expand about ‘e’ face
Subtract to find:
Linear profile assumption => second-order approximation
ex
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Temporal Truncation Error: Implicit Scheme
Cell centroid value () = ()P at both time levels
» Same as the mean value approximation» Second-order approximation
Value of P1 prevails over time step
Source term at (1) prevails over time step
What is the truncation error of these two
approximations?
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Truncation Error in Implicit Scheme (cont’d)
Consider a variable S(t) which we want to integrate
over the time step:
Expand in Taylor series about new time level (1):
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Truncation Error in Implicit Scheme (cont’d)
Integrate over time step:
Thus, representing the mean value over the time step
by is a first-order accurate approximation1S S
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Closure
In this lecture, we:
Completed the examination of unsteady schemes:» Crank-Nicholson
Looked at truncation error in various spatial and
temporal approximations