transported probability and mass density function (pdf… · transported probability and mass...
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Transported Probability and Mass Density Function (PDF/MDF)
Methods for Uncertainty Assessment and Multi-Scale Problems
Institute of Fluid Dynamics
Patrick Jenny!!
Institute of Fluid Dynamics!Swiss Federal Institute of Technology; ETH Zürich
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Institute of Fluid DynamicsPatrick Jenny
Course OutlineMotivation: For the simulation of fluid flows, probability density function (PDF) methods have advantageous properties compared to moment-based approaches or purely deterministic methods and are applicable in different fields. For example, PDF methods are used to model turbulent reactive flows, to quantify uncertainty of transport in the subsurface, and for simulations of multi-phase flows or rarefied fluids. !Goals: In this short course, the theory behind PDF methods is outlined and solution algorithms, which typically are Lagrangian particle Monte Carlo methods, are described. It will become clear, for which problems this approach is attractive, how it can be implemented and employed, and how the associated mathematical theory can help to develop and analyze models. Finally, as an illustrative example, a PDF method for rarefied gas flow simulations is discussed in more details. !Recommended Books: Statistical Mechanics of Turbulent Flows. Stefan Heinz. Springer, 2003.
http://www.springer.de !Turbulent Flows. Stephen B. Pope. Cambridge University Press, 2000.
http://eccentric.mae.cornell.edu/~pope/TurbulentFlows.html
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Institute of Fluid DynamicsPatrick Jenny
Course Outline
Part I: The first part provides an introduction, in which illustrative examples from various fields are presented, including uncertainty assessment of contaminant transport, turbulent combustion and rarefied gas flow. This is helpful to become familiar with the basic ideas of PDF modeling. !Part II: In the second part, the necessary mathematical background is introduced, whereas the main focus lies on the relation between Fokker-Planck (FP) equations for PDF transport, and continuous stochastic processes for individual realizations. This rigorous mathematical theory allows to derive deterministic PDF evolution equations from stochastic models, which is extremely powerful, even if one does not intend to employ a PDF method; for example to derive stochastic moment equations.
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Institute of Fluid DynamicsPatrick Jenny
Course OutlinePart III: In part three, the concepts and advantages of PDF methods are demonstrated in more depth with the help of an illustrative example. Starting with a simple stochastic model for molecular motion in a monatomic gas, the mathematical theory provided in the second part of this course is employed to first derive the corresponding PDF equation for molecule locations and velocities and then the consistent set of conservation laws for mass, momentum and energy. While the Navier-Stokes-Fourier system only holds in the continuum limit, that is, if the mean free path length is extremely small compared to the geometrical length scale, the PDF equation can also describe rarefied gas flow. In fact, it proved to be a very good approximation of the Boltzmann equation, but provides crucial computational advantages for solution algorithms. !Part IV: Part four is devoted to related numerical solution methods. In most cases, the probability space is high dimensional and therefore particle Monte Carlo methods are favored. Sometimes it is possible to find good closures based on moment equations, especially if the PDF shape can be presumed, but such approaches are not further discussed. Instead, various components of particle Monte Carlo methods are described with focus on accurate particle path integration.
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Institute of Fluid DynamicsPatrick Jenny
Part I: Basic Ideas of PDF Modeling
• introduction and motivation
• illustrative examples from various fields: - mixing - turbulent reactive flows - uncertainty assessment of contaminant transport - rarefied gas flow
• general structure of PDF/MDF transport equation
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Institute of Fluid DynamicsPatrick Jenny
• in many problems a statistical description is preferable example: uncertainty assessment of contaminant transport in the sub-surface !
• complex joint PDF shapeexample: turbulent mixing !
• non-linearities example: turbulent reactive flows !
• complex temporal or spatial correlation behavior example: subsurface flow !
• wide range of length and timescales example: dynamics of rarefied gases !!
• long tradition in turbulent combustion (pioneered by Stephen B. Pope)
Part I - Introduction and Motivation
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Institute of Fluid DynamicsPatrick Jenny
Turbulent MixingPart I - Illustrative Examples
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Institute of Fluid DynamicsPatrick Jenny
Turbulent MixingPart I - Illustrative Examples
(D. W. Meyer, P. Jenny)
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Institute of Fluid DynamicsPatrick Jenny
Turbulent MixingPart I - Illustrative Examples
(D. W. Meyer, P. Jenny)
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Institute of Fluid DynamicsPatrick Jenny
Turbulent Reactive FlowsPart I - Illustrative Examples
The chemical reaction source term in a turbulent reactive flow, Q�(Y, T ), is
typically a highly non-linear function of the chemical composition vector Y =
(Y1, Y2, . . . , Yn)
Tand the temperature T . Accordingly, it is generally not suit-
able to estimate hQ�(Y, T )i appearing in the mean conservation equation of Y�
by Q�(hYi, hT i). Alternatively, a solution method is required that provides the
full joint composition–temperature PDF f(Y, T ) since
hQ�(Y, T )i =
Z
Rn
Z 1
0Q�(Y, T )f(Y, T ) dT dY.
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Institute of Fluid DynamicsPatrick Jenny
Temperature Mixture fraction
(M. Hegetschweiler, P. Jenny)Turbulent Reactive FlowsPart I - Illustrative Examples
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Institute of Fluid DynamicsPatrick Jenny
(B. Zoller, P. Jenny)Turbulent Reactive FlowsPart I - Illustrative Examples
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Institute of Fluid DynamicsPatrick Jenny
Uncertainty Assessment of Sub-Surface FlowPart I - Illustrative Examples
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Institute of Fluid DynamicsPatrick Jenny
simulator
„Oil
Cut
“
time
Monte Carlo:
Uncertainty Assessment of Sub-Surface FlowPart I - Illustrative Examples
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Institute of Fluid DynamicsPatrick Jenny
Uncertainty Assessment of Contaminant TransportPart I - Illustrative Examples
Monte Carlo:
transport of tracer particles
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Institute of Fluid DynamicsPatrick Jenny
transport of tracer particles
Monte Carlo vs. PDF method:
Uncertainty Assessment of Contaminant TransportPart I - Illustrative Examples
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Institute of Fluid DynamicsPatrick Jenny
transport of tracer particles
Monte Carlo vs. PDF method:
Uncertainty Assessment of Contaminant TransportPart I - Illustrative Examples
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Institute of Fluid DynamicsPatrick Jenny
transport of tracer particles
Uncertainty Assessment of Contaminant TransportPart I - Illustrative Examples
Monte Carlo vs. PDF method:
(D. W. Meyer, P. Jenny)
publications
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Institute of Fluid DynamicsPatrick Jenny
Dynamics of Rarefied GasesPart I - Illustrative Examples
(H. Gorji and P. Jenny, Journal of Computational Physics, 2014)
•Kn: 0.05 •speed of lid: 100 m/s •wall temperature: 300 K •all the walls are diffusive
lid-driven micro cavity
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Institute of Fluid DynamicsPatrick Jenny
Dynamics of Rarefied GasesPart I - Illustrative Examples
(H. Gorji, M. Torrilhon and P. Jenny, J. Fluid Mech., 680, 2011)
micro channel; Knudsen paradox
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Institute of Fluid DynamicsPatrick Jenny
(P. Jenny, M. Torrilhon and S. Heinz, Journal of Computational Physics, 229, 2010)
PDF at left wall
PDF at center
Kn = 0.044
Kn = 5.3
Dynamics of Rarefied GasesPart I - Illustrative Examples
micro channel: velocity slip
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Institute of Fluid DynamicsPatrick Jenny
Dynamics of Rarefied GasesPart I - Illustrative Examples
micro channel; Kn = 0.2
(H. Gorji, M. Torrilhon and P. Jenny, J. Fluid Mech., 680, 2011)
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Institute of Fluid DynamicsPatrick Jenny
Dynamics of Rarefied GasesPart I - Illustrative Examples
temperature contours for hypersonic nitrogen flow over a wedge
(H. Gorji and P. Jenny, Phys. Fluids, 25, 2013)
vibrational T translational T
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Institute of Fluid DynamicsPatrick Jenny
General Structure of PDF/MDF Transport Equation Part I - Illustrative Examples
⇥�
⇥t+
⇥
⇥xi(Ui�) = S(�,x, t)
�fX(x; t)�t
+�
�xi
✓⌧DXi
Dt
���x; t�
fX(x; t)◆
= S(fX(x; t),x, t)
vector of random variables: X = (X1, X1, ...,Xn)
T
PDF of X in n-dimensional x-space: fX(x; t)
PDF transport equation:
compare with classical mass balance equation:
Homework: demonstrate for n=3 that
�fX(x; t)�t
=?
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PDF/MDF methods
deterministic quantitative predictions are not always feasible
Institute of Fluid DynamicsPatrick Jenny
Part I - Summary
deterministic quantitative predictions are not always desired
either due to huge scale differences, “chaos” or uncertainty in the input data
one may rather be interested in joint PDFs
determine probability space, evolution equation and closure thereof
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Institute of Fluid DynamicsPatrick Jenny
Part II: Necessary Mathematical Background
• illustration with Brownian motion !
• general PDF evolution equation - Kramers Moyal expansion - theorem of Pawula - Fokker-Planck equation
!• relation between PDF equation and stochastic processes for individual realizations !
• derivation of stochastic moment equations from a PDF equation
!!
• extremely powerful theory, even if one does not intend to employ a PDF method
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Institute of Fluid DynamicsPatrick Jenny
Part II - Illustration with Brownian Motion
• Brownian motion was discovered in 1827 by the botanist Robert Brown
• first phenomenon modeled with stochastic processes; Einstein (1905), Langevin (1908)
• probabilistic quantification of the distance traveled by a pollen grain over time
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Institute of Fluid DynamicsPatrick Jenny
Part II - Illustration with Brownian Motion
Particle evolves according to Brownian motion:
dXi = (2�)
1/2dWi,
where Wi(t) is a Wiener process with dWi = Wi(t+dt)�Wi(t) being independent
normal distributed random variables with
hdWii ⌘ 0 and hdWidWji = dt�ij .
A statistically exact integration of the position is achieved with
�Xi = (2��t)1/2⇠i,
where ⇠i are independent normal distributed random variables and �t is the
time step size.
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Institute of Fluid DynamicsPatrick Jenny
Sample trajectory of the stochastic model !!!for Brownian motion:
⇥Xi =p
2�⇥t �i,
How does the PDF fX(x; t) of the particle position X evolve?
Part II - Illustration with Brownian Motion
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Institute of Fluid DynamicsPatrick Jenny
Part II - Illustration with Brownian Motion
Answer:
@fX
@t
=
@
2
@xi@xi(�fX) ,
where xi is the sample space coordinate of the stochastic variable Xi.
If a huge number M of particles is considered, then the local particle number
density C represents MfX , i.e. for constant M on obtains
@C
@t
=
@
2
@xi@xi(�C) .
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Institute of Fluid DynamicsPatrick Jenny
Part II - Kramers Moyal ExpansionNext: we derive the general form of an evolution equation for fX(x; t), where
x 2 R:
fX(x; t) = h�(X(t)� x)ifX(x; t + �t) = h�(X(t)� x + �X)i
= fX(x; t) +
1X
k=1
1
k!
*✓� @
@x
◆k
�(X(t)� x) �X
k
+
= fX(x; t) +
1X
k=1
✓� @
@x
◆k ⇢h�X
k|x; tik!
fX(x; t)
�
from which follows the Kramers Moyal equation:
@fX(x; t)
@t
=
1X
k=1
✓� @
@x
◆k
8>><
>>:lim
�t!0
h�X
k|x; tik!�t| {z }
D(k)
fX(x; t)
9>>=
>>;.
Problem: 1 many terms!
Next: show that only two terms (k = 1, 2) are required, if lim�t!0 �x/�t
is bounded.
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Institute of Fluid DynamicsPatrick Jenny
Part II - Theorem of Pawula
Theorem 1 If 9m > 1 : D
(2m) = 0, then 8k > 2 : D
(k) = 0.
Proof 1 Consider the two random variables ↵ = �X
aand � = �X
bwith
a, b 2 N ^ a, b � 1.Schwarz inequality )
h↵�|x; ti2 h↵2|x; tih�2|x; tih�X
a+b|x; ti2 h�X
2a|x; tih�X
2b|x; ti
�t ! 0 ^ b = a + k )
⇣(2a + k)!D(2a+k)
⌘2 (2a)!(2a + 2k)!D(2a)
D
(2a+2k)
If D
(2a) = 0 ) D
(2a+k) = 0 8k � 1If D
(2a+2k) = 0 ) D
(2a+k) = 0 8k � 1
) if 9m > 1 : D
(2m) = 0, then 8k > 2 : D
(k) = 0.⇤
lim�t!0
h�X
k|x; tik!�t| {z }
D(k)
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Institute of Fluid DynamicsPatrick Jenny
Part II - Fokker-Planck Equation
Kramers Moyal equation:
@fX(x; t)
@t
=
1X
k=1
✓� @
@x
◆k
8>><
>>:lim
�t!0
h�X
k|x; tik!�t| {z }
D(k)
fX(x; t)
9>>=
>>;
There exist two possibilities:
1. only D
(1)and D
(2)are unequal zero, or
2. D
(2k) 6= 0 for all k � 1.
Gradiner showed that option one is true, if lim�t!0 �X/�t is bounded.
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Institute of Fluid DynamicsPatrick Jenny
Part II - Fokker-Planck EquationRelation Between PDF Equation and Stochastic Processes
From the theorem of Pawula it follows that the Fokker-Planck equation
@fX(x; t)
@t
= �@D
(1)fX(x; t)
@x
+
@
2D
(2)fX(x; t)
@x
2
with
D
(1)= lim
�t!0
h�X|x; ti�t
D
(2)= lim
�t!0
h�X
2|x; ti2�t
describes the evolution of PDF’s based on continuous processes. Note that the
PDF equation allows to ”link” stochastic processes (or rules) with a determin-
istic description.
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Institute of Fluid DynamicsPatrick Jenny
Part II - Fokker-Planck EquationRelation Between PDF Equation and Stochastic Processes
More general for high dimensional probability (sample) spaces with X(t) being
a realization in the x-space at time t:
@fX(x; t)
@t
= �@D
(1)i fX(x; t)
@xi+
@
2D
(2)ij fX(x; t)
@xi@xj
with with
D
(1)i = lim
�t!0
h�Xi|x; ti�t
D
(2)ij = lim
�t!0
h�Xi�Xj |x; ti2�t
.
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Institute of Fluid DynamicsPatrick Jenny
Part II - Fokker-Planck EquationRelation Between PDF Equation and Stochastic Processes
Remember Brownian motion example with
�Xi = (2��t)
1/2⇠i
from which follows that
D
(1)i = lim�t!0
1�t (2��t)
1/2h⇠ii = 0
and D
(2)ij = lim�t!0
12�t2��th⇠i⇠ji = ��ij .
and therefore
@f
@t
=
@
2�f
@xi@xi
as presented earlier.
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Institute of Fluid DynamicsPatrick Jenny
Part II - Fokker-Planck EquationRelation Between PDF Equation and Stochastic Processes
Remember Brownian motion example with
�Xi = (2��t)
1/2⇠i
from which follows that
D
(1)i = lim�t!0
1�t (2��t)
1/2h⇠ii = 0
and D
(2)ij = lim�t!0
12�t2��th⇠i⇠ji = ��ij .
and therefore
@f
@t
=
@
2�f
@xi@xi
as presented earlier.
Verification of Pawula’s theorem; e.g.:
D(4)ijkl = lim
�t!0
14!⇥t
(2�⇥t)2h⇥i⇥j⇥k⇥li = �ijkl�2
6lim
�t!0⇥t = 0
2prop.
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Institute of Fluid DynamicsPatrick Jenny
Part II - Summary
stochastic model for individual realizations (e.g. Lagrangian fluid elements)
structure of joint PDF/MDF transport equation: Fokker-Planck equation
coefficients in Fokker-Planck equation from stochastic model
moment equations from PDF equation
rigorous mathem
atical theory
typically involves additional closure assumptions, but useful for comparison, analysis and model development
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Institute of Fluid DynamicsPatrick Jenny
Part III: Explanation of Concepts with Concrete Example
• concepts and advantages discussed in more depth !
• illustrative example: gas dynamics stochastic model for molecular motion in a monatomic gas => corresponding MDF equation for molecule locations and velocities => consistent set of conservation laws for mass, momentum and energy => discussion and results !
• goal: understand how different descriptions (stochastic model, MDF equation, moment equations) are linked and develop some intuition
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dXi = Midt
dMi = �1�
(Mi � Ui) dt +✓
4es
3�
◆1/2
dWi + Fidt
Institute of Fluid DynamicsPatrick Jenny
considered: monatomic gas flow
position of a molecule: X(t)velocity of a molecule: M(t)
physical space: x
velovity space: V
gas velocity: U(x, t) = �M(t)|x⇥
model: continuous stochastic process
Part III - Stochastic Model for Molecular Dynamics
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Institute of Fluid DynamicsPatrick Jenny
Part III - Stochastic Model for Molecular Dynamics
model: continuous stochastic process
dXi = Midt
dMi = �1�
(Mi � Ui) dt +✓
4es
3�
◆1/2
dWi + Fidt
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Institute of Fluid DynamicsPatrick Jenny
Part III - PDF/MDF Equation
model: continuous stochastic process
PDF: fX,M (x,V ; t)
total mass: M(t)
MDF: F(x,V , t) =M(t)fX,M (x,V ; t)
gas density: �(x, t) =
RR3 F(x,V , t)dV
gas velocity: U(x, t) =
1�(x,t)
RR3 V F(x,V , t)dV
sensible energy: es(x, t) =
12�(x,t)
RR3(Vi � Ui(x, t))2F(x,V , t)dV
dXi = Midt
dMi = �1�
(Mi � Ui) dt +✓
4es
3�
◆1/2
dWi + Fidt
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Institute of Fluid DynamicsPatrick Jenny
Part III - PDF/MDF Equation
model: continuous stochastic process
�F�t
= ��Dx
i
F�x
i
� �DV
i
F�V
i
+�2Dxx
ij
F�x
i
�xj
+�2DV V
ij
F�V
i
�Vj
+�2DxV
ij
F�x
i
�Vj
specifically:
MDF equation:
⇥F⇥t
+ Vi⇥F⇥xi
+⇥FiF⇥Vi
=⇥
⇥Vi
✓1�
(Vi � Ui)F◆
+⇥2
⇥Vi⇥Vi
✓2es
3�F
◆
| {z }S(F)
dXi = Midt
dMi = �1�
(Mi � Ui) dt +✓
4es
3�
◆1/2
dWi + Fidt
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Institute of Fluid DynamicsPatrick Jenny
with the weights
conserved quantities:
MDF equation (Fokker-Planck):
Part III - Constraints
⇥F⇥t
+ Vi⇥F⇥xi
+⇥FiF⇥Vi
=⇥
⇥Vi
✓1�
(Vi � Ui)F◆
+⇥2
⇥Vi⇥Vi
✓2es
3�F
◆
| {z }S(F)
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Institute of Fluid DynamicsPatrick Jenny
Part III - Moment Equations
independent of S
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Institute of Fluid DynamicsPatrick Jenny
Part III - Moment Equations
independent of S
dependent on S
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Institute of Fluid DynamicsPatrick Jenny
Chapman-Enskog Expansion => Navier-Stokes
Part III - Consistency with Continuum Equations
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Institute of Fluid DynamicsPatrick Jenny
(H. Gorji and P. Jenny, Journal of Computational Physics, 2014)
•Kn: 0.05 •speed of lid: 100 m/s •wall temperature: 300 K •all the walls are diffusive
Part III - Results: Micro Cavity
temperature contours together with heat flux vectorsDSMC FP
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Institute of Fluid DynamicsPatrick Jenny
(H. Gorji, M. Torrilhon and P. Jenny, J. Fluid Mech., 680, 2011)
Knudsen paradox
Part III - Results: Micro Channel
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Institute of Fluid DynamicsPatrick Jenny
(P. Jenny, M. Torrilhon and S. Heinz, Journal of Computational Physics, 229, 2010)
velocity slip PDF at left wall
PDF at center
Kn = 0.044
Kn = 5.3
Part III - Results: Micro Channel
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Institute of Fluid DynamicsPatrick Jenny
profiles for Kn = 0.2
(H. Gorji, M. Torrilhon and P. Jenny, J. Fluid Mech., 680, 2011)
Part III - Results: Micro Channel
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Institute of Fluid DynamicsPatrick Jenny
Part III - Results: Shock in N2
FP (H. Gorji and P. Jenny, Phys. Fluids, 25, 2013)
DSMC (I. D. Boyd, Phys. Fluids, 3, 1991)
vibrational T translational T
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Institute of Fluid DynamicsPatrick Jenny
Part III - Summary
intuitive stochastic model for molecules; honor constraints!
conservation of mass, momentum, energy; independent of S
limit => Navier-Stokes
good results despite approximations; much more general than NS
improvements possible by adding more DOFs
continuous process to model discontinuous behavior
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Institute of Fluid DynamicsPatrick Jenny
Part IV: Numerical Solution
• numerical difficulties and simplifications - why particle methods
• particle in cell method - estimation of moments and interpolation - particle time stepping
• hybrid FP/DSMC algorithm and further topics
• examples
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Part IV - Dimensionality => Particle Method
⇥F⇥t
+ Vi⇥F⇥xi
+⇥FiF⇥Vi
=⇥
⇥Vi
✓1�
(Vi � Ui)F◆
+⇥2
⇥Vi⇥Vi
✓2es
3�F
◆
| {z }S(F)
Institute of Fluid DynamicsPatrick Jenny
model: dXi = Midt
dMi = �1�
(Mi � Ui) dt +✓
4es
3�
◆1/2
dWi + Fidt
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Institute of Fluid DynamicsPatrick Jenny
model: dXi = Midt
dMi = �1�
(Mi � Ui) dt +✓
4es
3�
◆1/2
dWi + Fidt
Part IV - Dimensionality => Particle Method
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Institute of Fluid DynamicsPatrick Jenny
x
Estimation and Interpolation of Moments in Physical Space
in many applications it is not required to explicitly “extract” the MDF one is rather interested to “extract” expectations of functions of xs at locations xp => grid and kernel function need to be 1D, 2D or 3D (and not nD)
�̂
Part IV - Particle in Cell Method
⇥̄(xp, t) ⇡ 1�̂
npX
k=1
⇣m(k)�̂
⇣X
p(k)(t)� x
p⌘⌘
eQ(xp, t) ⇡ 1�̂ ⇥̄(xp, t)
npX
k=1
⇣Q
⇣X
(k)(t), t⌘
m(k)�̂⇣X
p(k)(t)� x
p⌘⌘
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⇥̄�+1(xp) ⇡ µ⇥̄�(xp) +1� µ
�̂
npX
k=1
⇣m(k)�̂
⇣X
p(k)(t)� x
p⌘⌘
(⇥̄ eQ)�+1(xp) ⇡ µ(⇥̄ eQ)�(xp) +1� µ
�̂
npX
k=1
⇣Q
⇣X
(k)(t), t⌘
m(k)�̂⇣X
p(k)(t)� x
p⌘⌘
eQ�+1(xp) ⇡ (⇥̄ eQ)�+1(xp)⇥̄�+1(xp)
Institute of Fluid DynamicsPatrick Jenny
Statistical and Bias Error Reduction
reduction of statistical and bias errors can be achieved by employing exponentially weighted moving time averaging, i.e. use
instead of:
Part IV - Particle in Cell Method
⇥̄(xp, t) ⇡ 1�̂
npX
k=1
⇣m(k)�̂
⇣X
p(k)(t)� x
p⌘⌘
eQ(xp, t) ⇡ 1�̂ ⇥̄(xp, t)
npX
k=1
⇣Q
⇣X
(k)(t), t⌘
m(k)�̂⇣X
p(k)(t)� x
p⌘⌘
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Institute of Fluid DynamicsPatrick Jenny
Particle Time SteppingPart IV - Particle in Cell Method
model for rarefied gas dynamics:
dXi = Midt
dMi = �1�
(Mi � Ui) dt +✓
4es
3�
◆1/2
dWi + Fidt
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Institute of Fluid DynamicsPatrick Jenny
Particle Time Stepping
dXi = Midt
dMi = �1�
(Mi � Ui) dt +✓
4es
3�
◆1/2
dWi + Fidt
Part IV - Particle in Cell Method
model for rarefied gas dynamics:
problem: unphysical cooling! (P. Jenny, M. Torrilhon, S. Heinz)
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Institute of Fluid DynamicsPatrick Jenny
Particle Time SteppingPart IV - Particle in Cell Method
dXi = Midt
dMi = �1�
(Mi � Ui) dt +✓
4es
3�
◆1/2
dWi + Fidt
homogeneous adiabatic test case:
2nd order discretization:(P. Jenny, M. Torrilhon, S. Heinz)
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Institute of Fluid DynamicsPatrick Jenny
Part IV - Particle in Cell Method
goal: statistically exact scheme for constant U and es for any time step size !without loss of generality: derivation for Ui=Fi=0, i.e. consider solution of
dXi = Midt
dMi = �1�
Midt +✓
4es
3�
◆1/2
dWi
first for the velocity only:
with
Particle Time Stepping
=>
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Institute of Fluid DynamicsPatrick Jenny
Part IV - Particle in Cell MethodParticle Time Stepping
for velocity:
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Institute of Fluid DynamicsPatrick Jenny
internal energy:
Part IV - Particle in Cell MethodParticle Time Stepping
(P. Jenny, M. Torrilhon, S. Heinz)
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Institute of Fluid DynamicsPatrick Jenny
Part IV - Particle in Cell Method
(P. Jenny, M. Torrilhon, S. Heinz)
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Institute of Fluid DynamicsPatrick Jenny
Part IV - Particle in Cell Method
for position:
Particle Time Stepping
=>
=>
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Institute of Fluid DynamicsPatrick Jenny
Part IV - Particle in Cell Method
for position:
Particle Time Stepping
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Institute of Fluid DynamicsPatrick Jenny
Part IV - Particle in Cell Method
(P. Jenny, M. Torrilhon, S. Heinz)
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Institute of Fluid DynamicsPatrick Jenny
Part IV - Particle in Cell Method
correlation:
=>
Particle Time Stepping
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Institute of Fluid DynamicsPatrick Jenny
Part IV - Particle in Cell MethodParticle Time Stepping
correlation:
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Institute of Fluid DynamicsPatrick Jenny
Part IV - Particle in Cell MethodParticle Time Stepping
(P. Jenny, M. Torrilhon, S. Heinz)
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Institute of Fluid DynamicsPatrick Jenny
Part IV - Particle in Cell MethodParticle Time Stepping
(P. Jenny, M. Torrilhon, S. Heinz)
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Institute of Fluid DynamicsPatrick Jenny
Part IV - Particle in Cell Method
(P. Jenny, M. Torrilhon, S. Heinz)
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performance:
Institute of Fluid DynamicsPatrick Jenny
Part IV - Particle in Cell Method
flow around cylinder (Kn = 0.1):Knudsen paradox (Kn=5.1157):
(P. Jenny, M. Torrilhon, S. Heinz)
Particle Time Stepping
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Institute of Fluid DynamicsPatrick Jenny
Part IV - Particle in Cell MethodHybrid FP/DSMC Algorithms - Further Topics
Chapter 2. Theory
2. Theory
2.1. The Range of Applicability of Mathematical Models for Rarefied Gas Flows
Gas flows can be described mathematically either in terms of continuous fields of macroscopicvariables—i.e., stream velocity, temperature, density, etc.—or on the molecular level. The con-ventional mathematical model of gas flows based on continuous fields is provided by the Navier-Stokes (NS) equations. However, the Navier-Stokes equations become invalid as soon as gradientsbecome so steep that their characteristic lengths are of the order of the mean free path, � [4,§1.2]. The range of applicability of the NS equations is thus best described in terms of thelocal Knudsen number Kn, defined as Kn = �/L, where L is a reference length implied by themagnitude of local gradients, e.g. L = ⇥ [d⇥/dx]�1. Figure 2.1 illustrates the range of applica-bility of di✏erent mathematical models for gas flows. Any simulation method applicable to thewhole range of the Knudsen number must therefore be based on the molecular description. Themathematical model based on this description is the Boltzmann equation, which describes thestatistical evolution of the fraction of molecules in a given element of the position-velocity phasespace. Consequently, the new hybrid algorithm is based on the Boltzmann equation. Here andhenceforth we only treat monatomic, simple gas flows.
Figure 2.1.: The Knudsen number limits on the mathematical models [4, Fig. 1.1].
2
accuracy:
efficiency:
FPDSMC [G. A. Bird, 1963, 1994 (NTC)]
FP originally suggested by [C. Cercignani, 1988]
DSMC
(H. Gorji, S. Küchlin, P. Jenny)
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Institute of Fluid DynamicsPatrick Jenny
Part IV - Particle in Cell MethodHybrid FP/DSMC Algorithms - Further Topics
(H. Gorji, P. Jenny)
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Institute of Fluid DynamicsPatrick Jenny
Part IV - Particle in Cell MethodHybrid FP/DSMC Algorithms - Further Topics
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Institute of Fluid DynamicsPatrick Jenny
Part IV - Summary
particle MC scale linearly with dimensions
challenges: noise, bias error, time stepping, number control
hybrid algorithms cover whole Kn-range
errors: spatial discr., extract., interpol., part. time step., statistical and bias, number control
statistically steady state is much easier
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PDF/MDF methods
deterministic quantitative predictions are not always feasible
Institute of Fluid DynamicsPatrick Jenny
Final Summary
deterministic quantitative predictions are not always desired
either due to huge scale differences, “chaos” or uncertainty in the input data
one may rather be interested in joint PDFs
determine probability space, evolution equation and closure thereof
General Motivation
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Institute of Fluid DynamicsPatrick Jenny
stochastic model for individual realizations (e.g. Lagrangian fluid elements)
structure of joint PDF/MDF transport equation: Fokker-Planck equation
coefficients in Fokker-Planck equation from stochastic model
moment equations from PDF equation
rigorous mathem
atical theory
typically involves additional closure assumptions, but useful for comparison, analysis and model development
Final SummaryMathematical Theory
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Institute of Fluid DynamicsPatrick Jenny
intuitive stochastic model for molecules honor constraints!
conservation of mass, momentum, energy independent of S
limit =>Navier-Stokes
good results despite approximations much more general than NS
improvements possible by adding more DOFs
continuous process to model discontinuous behavior
Final SummaryIllustration: Rarefied Gas Dynamics