three-dimensional mathematical model of time dependent...
TRANSCRIPT
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Rafael O. Castro, John Duffy
SOLAR 2010:
THREE DIMENSIONAL MATHEMATICAL MODEL OF TIME –DEPENDENT CONVECTION OF FLUIDS IN
ENERGY SYSTEMS THAT USE SOLAR RADIATION
U Mass Lowell
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OUTLINE
1. Introduction2. Modeling of Convection of fluids 3. Numerical Method4. Application Example5. Design Optimization6. Concluding Remarks
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1. Introduction
World Energy Demand
Source: Official Energy Statistics from the U.S. Government, 2008
World CO2 Emissions
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1. Introduction
Objective:
To present an overview of the main aspects for the mathematical modeling of convection of fluids that are present in a wide number of energy systems.
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The mathematical modeling process:
Laws of physics
Model set-up
Solution and verification
Validation
2. Modeling of Convection
source: nasa
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What are the governing equations of fluid flows?
2. Modeling of Convection
source: http://spaceflightsystems.grc.nasa.gov
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Governing equations:
Conservation of mass
Conservation of momentum
Conservation of energy
Diffusive fluxes
2. Modeling of Convection
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The K-epsilon Model
Turbulent flows are always dissipative. Viscous shear stresses perform deformation work which increases the internal energy of the fluid at the expense of kinetic energy of turbulence.
K-epsilon:Equation for the turbulent kinetic energy κ:
Equation for the turbulent kinetic energy dissipation rate , ε:
where:
2. Modeling of Convection
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Three dimensional representation of energy systems that use solar radiation
2. Modeling of Convection
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2. Modeling of Convection
Solar radiation:
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Thermodynamic and Transport Properties
States of matter
solid
liquid
gas
plasma
COLD
HOT
Specific heat:
Coefficient of thermal expansion:
Compressibility:
2. Modeling of Convection
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Solver Layout
Loop: Time stepping
Loop: Solution Non-linear System
Loop: Solution Linear System
endend
end
-Second order implicit predictor- multicorrector (Jansen,2000)
-Preconditioned Generalized Minimal residual (GMRS) ( Scaling, Pre-preconditioning, Preconditioning)
-Globalized Newton-Krylov method(Kelly,2003)
3. Numerical Method
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4. Application example
Solar Updraft Tower
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4. Application example
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4. Application example
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Comparison of Numerical Simulation and Experimental* Results
------ Pel, measured------ Updraft velocity, measured--- -- Gh, measured
Updraft velocity, simulated (without turbine)
*Jorg Schlaich, Rudolf Bergermann, Wolfgang Schiel, Gerhard Weinreber; Design of commercial Solar Updraft Tower Systems – Utilization of Solar Induced convective Flows for Power Generation
4. Application example
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for Geometric parameter 1 = di:step1:d1ffor Geometric parameter 2 = d2i:step2:d2f
..
.for Geometric parameter n = dni: stepn: dnf
- Solve governing equations of the analyzed system- Evaluate optimization variable(s). - Save optimum variables values(s) and corresponding geometric parameters.
end .
. .
endend
Optimization Procedure Layout
_____ ________________ 5. Design Optimization
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6. Concluding Remarks
- Main considerations for modeling a wide number of energy systems have been presented. Although the 3D, time dependent, model has a high computational cost, it has the main advantage of giving more realistic solutions.
- The numerical approach presented can also be used to model highly conducting fluids in presence of a electromagnetic (EM) field, in which case the inclusion of the governing equations of EM fields have to be included.
- Analytical solutions of the governing equations of fluid flows for a system with a generalized 3D geometry, as presented, have not been obtained yet, and only solutions by numerical methods are feasible now.
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6. Concluding Remarks
- Ways to lower the computational time will be explored on future applications of the model presented for energy systems.