12/22/2009 - Materials Technologypiet/edu/dnt/pdf/rob/M020-sheets.pdf · 12/22/2009 1 Optimisation...

12/22/2009

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Optimisation for Thermo-Fluids

Engineering

Dr. R.J.M. (Rob) Bastiaans

Combustion Technology

Mechanical Engineering

4M020 Design Tools

4M020 Design Tools; Optimisation in Thermo-Fluids Engineering

Optimisation

Eggert 2000: Engineering design

� Engineering design is the set of decision-making

processes and activities used to determine the form of

an object given the functions desired by the customer.

� During the parametric design phase we determine values

for the controllable parameters, called design variables,

identified as unknown during the configuration phase.

� CAE refers to computer software and hardware systems

used in the analysis of engineering designs to validate

functional performance.


Thermo-Fluids Engineering

What is Thermo-Fluids Engineering

� Covered by

� Energy Technology

� Process Technology

� Combustion Technology

� Common factor: Fluid flow

� Often multi-scale multi-physics problems

� Much research less optimal design

� Implication on how to use computer-capacity


Fluid Flow

Many problems in many areas

� Meteorology

� Astrophysics

� Biology

� Agriculture

� Process technology

Common factor: Navier Stokes Equations


Multi-Scale flows

Examples

� Turbulence

� Atmospheric dispersion


Multi-Physics

Often flow is not the problem but interactions are

� Buoyancy induced flows

� Mixing of different fluids

� Dispersion of pollutants

� Flows with heat transfer

� Reactive flows; combustion

� Compressible flows

� Acoustics

� Shock waves

� MHD (Magneto Hydro-Dynamics)

� Flow structure interaction

� Combinations of the above

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Multi-Scale Multi-Physics flows research

Examples

� Turbulent combustion:

� Compressible flow

� Heat transfer

� Many chemical species and reactions

� Acoustics, stability

� Flame-thickness independent length scale

� Application: Gas-turbines for aeroplanes and el. power generation

� Very important for society: Emissions, Climate, Energy

� Optimisation??


Multi-Scale Multi-Physics flows research

Gas turbines:


Modelling of reactive flows

Turbulent combustion

� Not only interesting from an industrial point of

view, but also from an academic point of view

� Large range of time and length scales makes

numerical simulation of turbulent combustion

far from easy and very expensive

� Development of accurate and efficient models

for turbulent combustion is one of the most

challenging tasks facing the combustion

community today

DL

R, G

erm

an

y


Approach

From small to large scale, from fundamentals to application

� One-dimensional flame modelling with detailed description of

chemistry and transport

� Flamelet-based reduction (FGM) to simplify chemistry model

� Direct numerical simulation (DNS) of turbulent flame to unravel

chemistry-turbulence interaction

� Model for turbulence-chemistry interaction (e.g. a sub-grid scale

model for large-eddy simulations)

� Large-eddy simulation (LES) of lab-scale flames

� Reynolds-averaged Navier-Stokes (RANS) simulations of

industrial applications


DNS of turbulent flame kernels

DNS of spherically expanding premixed

turbulent flames

� Validation of FGM vs detailed chemistry

� Analyse turbulence/chemistry interaction

� Practical relevance is found in IC engines

Leeds, UK


Validation of FGM

Mass fraction of OH radical. FGM 100 times faster than detailed chemistry!

This enables paramteric studies.

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From DNS to LES of reactive flows

� Averaging DNS results enables a-priori testing of LES sub-grid scale

models.

� Application of LES-FGM in premixed turbulent Bunsen flame:


RANS of gas turbine combustor

NOx formation in gas turbine combustor

� Fired in lean premixed mode:

� Fired in diffusion mode (start-up): NOmax is ~100x larger

� Investigate the influence of hydrogen addition


Application to biomass conversion

� Application of fundamental knowledge of reactive flows to biomass

conversion

� Multi-scale, multi-physics approach:

� Small scale: single particle

kinetics, pyrolysis, heat/mass transfer

� Intermediate scale: fixed/fluidized bed

two-phase flow, heat/mass transfer

� Large scale: reactor, furnace

flow pattern, radiation, control

Info

rma

tion


Multi-scale multi-physics

� Multi-scale multiphysics problems require multi-scale multi-physics approach

� Because physics at the smallest scales can have a large impact on large

scale design constraints

� NOx formation in turbulent combustion has its origin in very thin oxidation layers

� typically 100 µm

� combustor: typically 1 m

� for accuracy you need 10 points for a relevant gradient to resolve: 10 µm grid

� 3D problems require (105)3 =1015 calculation points

� Time dependent problems require 105 timesteps

� Problem size: n x 1020 operations.


Large computer power for solving physics

Turbulent combustion in gas turbines

http://www.cerfacs.fr/cfd/

Helicopter engine, using 2000 procs:


Large computer power for solving physics

Big machines required:

Worldwide:

http://www.top500.org/list/2008/06/100

Netherlands:

http://www.sara.nl/

(Huygens)

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Optimisation of design

Calculating for design

� BOEING (conservation)

� Bernoulli

� Potential flow models

� Potential flow with viscous layers

� Euler

� Navier-Stokes

� Incompressible

� Boussinesq

� Variable density

� Compressible

Analytical

Partial differential

equations


Flow models and optimisation

General: Trade off

� Analytical/Exact but crude physics

� Accurate physics but crude approximations; numerics

� Accuracy very important in optimisation!!

� You cannot optimise in % if your calculation/prediction

accuracy is not on the same level

� Low Reynolds laminar steady flows (nano-technology,

micro compact, heat exchangers, lab-on-a-chip)

� Control problems (e.g. suppressing vortex shedding)

� Future: Optimisation for more and more complex problems

General: Trade off/limitations


Optimisation in CFD

Approach

� Good strategy requires detailed knowledge of fluid dynamics and

computational methods.

� Use of Computational Fluid Dynamics (CFD),

� Solving sets of partial differential equations (PDE’s);

� started in the 80s;

� philosophy: calculate and analyse a certain design

� Recent: B. Mohammadi & O. Pironneau, Shape Optimization in Fluid

Mechanics, Annual Rev. Fluid Mech., 2004, 36, pp 255-279.


Optimisation in CFD

Approach

� Highly educated designer determines the parameters to optimize

� Changing parameters will often lead to edge (predefined) optima

� Pushing the boundaries will often result in changing physics

� e.g. becomes unsteady, no Stokes flow anymore etc.

� CFD learns designers what parameters might be important

� Parameter study learns designers to really look at new concepts

� Automatic optimization in multi-parameter/multi-physics still far away

� Multi-parameter is domain of mathematicians, genetic algorithms etc.


Optimisation in CFD

Certainly a great future for

Opimisation in CFD!!� Relatively unexplored


Optimisation in CFD

Examples:

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Optimisation in CFD

Examples:

� Q. Li et al., Evolutionary Structural Optimization, Int.J.Heat Mass Transfer, 42, (1999)

� A. Gersborg-Hansen et al., Topology optimization of channel flow problems, Struct.

Multidisc. Optim., 30, (2005)

� D.N. Srinath, S. Mittal, A stabilized finite element method for shape optimization in

low Reynolds number flows, Int. J. Num. Meth. Fluids, 54, (2007)

� A. Gersborg-Hansen et al., Topology optimization of heat conduction problems

using the finite volume method, Struct. Multidisc. Optim., 31, (2006)

� D.E. Hertzog et al., Optimization of a microfluidic mixer for studying protein folding

kinetics, Anal. Chem., 78, (2006)

� H. Antil et al., Optimal design of stationary flow problems by path-following interior

point methods, Struct. Multidisc. Optim., submitted (2007)

� L.Debiane et al., Temperature and pollution control in flames, Proc. Summer Progr.,

Center for Turbulence Research, (2004).


Optimisation in CFD

Examples:

� Limited application

� Mathematical methods

� Limited parameter space

� Hertzog et al.:

� Navier Stokes

� Convection-Diffusion

� Comsol

� 40 % reduction in mixing time

� Debiane et al., Center for Turbulence Research 2004:

� Application in flames

� I was there but I found this article only yesterday!!


Optimisation in CFD


Optimisation in CFD


Optimisation in Comsol

Conclusion:

Let us just start ourselves with an experiment in Comsol:

Double glazing:



� Optimise double glazing design

� Optimisation parameters:

� Minimization of heat flux

� Maximization of acoustic isolation

� Maximization of mechanical strength, resitance to impact

� Minimization of costs

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� Hypothesis 1

� The thicker the air layer the more isolating

� But the air is not stagnant, so

� Hypothesis 2

� At larger distance, L, the Ra number becomes higher

– Third power:

– Flow becomes more vigorous

– Eventually instationary

– Heat transfer by convection increases

– More heat losses



� Physical problem:

� Conduction

� Natural convection

� Partial differential equations:

� Convection and Conduction (CC)

� Navier-Stokes equations (NS)

� Mutual influence

• Buoyancy force as function of T solved by CC in NS

• Velocities for convection of heat, solved from NS in CC



� Setup and boundary conditions

� All (other) walls: no slip, adiabatic

� H=0.1 m, L(initial)=0.01 m, d=0.002 m

T=320 K T=280 K


Equations in Comsol

� Equations

T=320 K T=280 K



� Parameters:

� Distance between glazing

� Thickness of the glass

� Height of the glass/how to simulate full height

– Variation of height

– Inflow/outflow

� Relevant temperature difference



� Parameters:

� Physical properties of the glass

– Conductivity

– Density

– Heat capacity

� Physical properties of the medium (argon, water)

� Pressure: What isolates better

– Low pressure (low density, capacity)

– High pressure (higher force needed for momentum)

� Instationary behaviour?

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� Setup the model in Comsol; save in Matlab

� What do we do with the pressure?

� We are going to change the geometry, what does

this mean for the gridding?

� Constants:

� Air: density 1.2, k=0.025, Cp=1006, eta=1.7 10-5

� Glass: density 2500, k=1.1, Cp=840

� Units?



Heat flux analysis:



Flow analysis:



Flow analysis:

Instationary

behaviour?


Assignment

3 possibilities:

� Double glazing: more parameter variations

– Research possible unsteady behaviour

– Influence of glass thickness

– Use argon and water (determine changes in

Ra and Pr in advance)

� New: Cilinder in a box, disturbing convection

– Box is a lid driven cavity

– Scalar flux (temperature, species) at the top

– Fixed value at the bottom

– Research influence of position and size of a

cilinder, with no slip walls.


Assignment

Cilinder in a box:

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Assignment

Cilinder in a box:

– Determine base flow

– Add the scalar problem

– Put cilinder in

– Vary, determine cost and analyse


Assignment

3rd possibility:

Your own optimisation problem

(in this case you need to know and discuss with

me today)


Further information:

Dr. R.J.M. Bastiaans (Rob)

Combustion Technology

Mechanical Engineering, WH 3.141Eindhoven University of Technology

P.O. Box 513, 5600 MB Eindhoven, The Netherlands

E: [email protected]: +31 40 2474836

F: +31 40 2433445

www.combustion.tue.nl

12/22/2009 - Materials Technologypiet/edu/dnt/pdf/rob/M020-sheets.pdf · 12/22/2009 1 Optimisation...

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