Final Presentation
A. Davey, N. Moniz, T. Spilhaus
UMass Dartmouth
August 16, 2012
A. Davey, N. Moniz, T. Spilhaus (UMass Dartmouth) Final Presentation August 16, 2012 1 / 14
Presentation Overview
I Project overview and parameters.
I Motivation for using a particular method to solve the problem at hand.
I Methodology and description of mathematical procedures.
I Numerical results.
I Future goals and possible applications.
A. Davey, N. Moniz, T. Spilhaus (UMass Dartmouth) Final Presentation August 16, 2012 2 / 14
Problem Overview and Description
I Using finite element method we will attempt to model the steady-state heatprofile across a flat plate with different boundary conditions and right handside functions.
I We will model Poisson’s equation −∇ · (κ(x , y)∇u(x , y)) = f (x , y) on a unitsquare [−1, 1]2.
I Boundary conditions will begin with homogeneous Dirichlet set equal to zeroand then vary as the project progesses.
I κ(x , y) = 16 + ε1x + ε2y
I In the beginning, ε1 and ε2 will be drawn at random from a normal Gaussiandistribution with σ = 1
2 .
A. Davey, N. Moniz, T. Spilhaus (UMass Dartmouth) Final Presentation August 16, 2012 3 / 14
Advantages and Disadvantages of FEM
I Used in a wide variety of areas tosolve and model military,commercial, and industrialproblems.
I Elements can be individuallycustomized to model objects withcomplex geometries in multipledimensions.
I Computationally intensive if highgrid refinement is used and sourcecode can become quite large.
Figure 1: Finite element simulation of jetturbine engine.
A. Davey, N. Moniz, T. Spilhaus (UMass Dartmouth) Final Presentation August 16, 2012 4 / 14
Methodology: Outline
I Derive the weak formulation of our partial differential equation.
I Discretize over space (Ω) creating a two-dimensional mesh of arbitraryrefinement.
I Select shape and weight functions.
I Assemble the linear system.
I Apply boundary conditions (either homogeneous Dirichlet or mixed Dirichletand Neumann) and solve system.
A. Davey, N. Moniz, T. Spilhaus (UMass Dartmouth) Final Presentation August 16, 2012 5 / 14
Methodology: Deriving the Weak Formulation
−∇ · (κ(x , y)∇u(x , y)) = f (x , y)
We then multiply both sides by a test function v .
−v∇ · (κ(x , y)∇u(x , y)) = vf (x , y)
We now integrate over the domain Ω.
−∫
Ωv∇ · (κ(x , y)∇u(x , y)) =
∫Ωvf (x , y)
Then integrating by parts will yield the following.∫Ω∇v · κ∇u(x , y)−
∫Γvn · ∇u =
∫Ωvf (x , y)
Lastly, we apply homogeneous Dirichlet boundary conditions, u(x , y) = 0, along theboundary Γ.∫
Ω∇v · κ∇u(x , y) =
∫Ωvf (x , y)
A. Davey, N. Moniz, T. Spilhaus (UMass Dartmouth) Final Presentation August 16, 2012 6 / 14
Methodology: Basis Functions and Generating the Mesh
I we find an approximation functionof u to satisfy our equation for allfunctions of v.
uh =∑
j cjφj
I we can now define our test functionas shown below.
v = ciφi , i = 0, · · · ,N
I we also need a mesh onto which wewill map our shape functions.
Figure 2: Example of a generated mesh
A. Davey, N. Moniz, T. Spilhaus (UMass Dartmouth) Final Presentation August 16, 2012 7 / 14
Methodology: Basis Functions and Generating the Meshcontinued
I then we define the weak form of the discrete problem and substitute in our uh∫Ω∇v · κ∇
∑j cjφj =
∫Ωvf (x , y)
I we then write this in terms of a linear system
Ac = F
I A and F are then replaced with weighted sums over a set of points on eachcell in there domain and we set these as the approximations:
Akij =
∑q∇φi (xkq ) · ∇φj(xkq )wk
q
F ki =
∑q φi (x
kq )f (xkq )wk
q
A. Davey, N. Moniz, T. Spilhaus (UMass Dartmouth) Final Presentation August 16, 2012 8 / 14
Numerical Results
I several different conditions applied
I average solutions
I average kappa compared to average solution
A. Davey, N. Moniz, T. Spilhaus (UMass Dartmouth) Final Presentation August 16, 2012 9 / 14
Solutions of each Applied Condition
I
I
I
I
Figure 3: Top-Left: homogeneous Dirichlet conditions, Bottom-Left: Mixed Boundaries,Top-Right: added Gaussian on RHS, Bottom-Right: Sigma changed from 1/2 to 5
A. Davey, N. Moniz, T. Spilhaus (UMass Dartmouth) Final Presentation August 16, 2012 10 / 14
Average Solutions of each Applied Condition
I
I
I
I
Figure 4: Top-Left: homogeneous Dirichlet conditions, Bottom-Left: Mixed Boundaries,Top-Right: added Gaussian on RHS, Bottom-Right: Sigma changed from 1/2 to 5
A. Davey, N. Moniz, T. Spilhaus (UMass Dartmouth) Final Presentation August 16, 2012 11 / 14
Comparison overlay of average solutions with sigma 5 andsigma 1/2
Figure 5: Overlay of average of 100 solutions with σ = 12
in green and σ = 5 in red andblue.
A. Davey, N. Moniz, T. Spilhaus (UMass Dartmouth) Final Presentation August 16, 2012 12 / 14
Convergence of average solution to the average kappasolution
A. Davey, N. Moniz, T. Spilhaus (UMass Dartmouth) Final Presentation August 16, 2012 13 / 14
Possible Future Goals
I Modeling more realistic physical systems, such as changing our Kappa to bethe actual heat conductivity of a known material.
I Adding in Time Dependance to the problem being modeled and seeing how itevolves.
I Using FEM to approximate higher dimensional systems
I Modeling Higher Order Methods for higher accuracy results
A. Davey, N. Moniz, T. Spilhaus (UMass Dartmouth) Final Presentation August 16, 2012 14 / 14
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