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PROBLEM 2 Modelling of a brazing furnace
Participants: Ashwin Sadanand Nayak Youssef Fathallah Hayley Wragg Carlos Coroas Fombella Mª Dolores Gómez Pedreira José Luis Ferrín González
EcoMT: Nerea Vilela Barreira Damián Bazarra Castro Coordinators: Fernando Varas Mérida Elena Martín Ortega
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Contents
• Problem Statement
• Possible Modelling Approaches
• Description of the different models
• Conclusions
• Future Work
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ECOMT works with BorgWarner, a leading industry provider of automotive parts, to improve the efficiency of the manufacturing
processes
In focus, is the brazing furnace which is used to join various metal parts.
Problem Statement
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Problem Statement
Goals : To provide a “digital twin” of the furnace which responds like the actual furnace
To optimize the speed of conveyor belt
To optimize the quantity of hydrogen used
To optimize the feeding layout of the workpieces
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Brazing
• The brazing process uses a filling metal very close to the metal base and melts this to join the metals. The filler metal must have a lower melting temperature than the materials it is joining.
• The filler metal used for brazing must be of a similar base to the metals it is joining with a lower melting temperature.
• The filler is placed at the top of the joint and fills the gap as it melts down.
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Oxides on metals at the joint can hamper the brazing quality
A mixture of Hydrogen and Nitrogen is added to the furnace to reduce these oxides.
Brazing
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Brazing: Significance of dewpoint
Source: Roberts, “Introduction to Brazing Technology, 2016
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Reducing Atmosphere
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Heat pattern in brazing furnace
Source: Roberts, “Introduction to Brazing Technology, 2016
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Modelling Approach • Numerical simulation
• Zonal Modeling
• Brazing Process modelling
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Numerical Simulation
• First 2D, then 3D
• Isothermal Simulation (to warm up)
• Thermal simulation (to get an idea about the real flow movement and the heat exchange)
• Solved with ANSYS Fluent 18.2
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Numerical Simulation
Pieces moving direction
Muffle región (simulated)
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Numerical Simulation Simplified geometry of the muffle furnace area (2D and 3D)
Incoming pieces direction (prescribed velocity)
Cooling region
Injection of H2 + N2
Mass flow inlet condition, estimated areas
Incoming flow of H2 + N2
Simplified pieces (blocks), estimated
Conveyor (solid)
3D muffle zone (wider than conveyor belt)
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Numerical Simulation 3D Mesh (< 500.000 cells, restricted academic Ansys license)
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Numerical Simulation Isothermal model, steady, RANS turbulent model for Reynolds stress tensor (high Reynolds turbulent model approach)
𝜇𝑇 = ρCμ
𝑘2
𝜖
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Numerical Modelling Isothermal model Boundary conditions: • Prescribed mass flow inlet in the injection area (given data)
• Prescribed mass flow coming from the cooling area (given
data)
• Prescribed gauge pressure at the pieces inlet section
• Furnace and pieces walls: Standard wall functions approach
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Numerical Modelling Isothermal model, velocity field distribution
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Numerical Modelling Isothermal model results
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Numerical Modelling Thermal model: • Steady
• Turbulent (high Reynolds turbulence model approach)
• Compressible
• One gas component (thermal properties mixture of H2+ N2),
does not solve species transport
• It includes conduction and radiation (Surface to surface model), grey surfaces and opaque, gas does not participate in radiation
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Numerical Modelling
𝜇𝑇
= ρ𝑔Cμ
𝑘2
𝜖
𝑘𝑇 =𝐶𝑝𝜇𝑇
𝑃𝑟𝑇
+𝜌𝑔𝑔
𝑃 = 𝜌𝑔𝑅𝑔𝑇𝑔
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Numerical Modelling Boundary conditions for the thermal model • Prescribed wall surfaces temperatures (uniform 1100ºC) • Prescribed temperature at the pieces surfaces
View/Gebhardt factors calculation done by the software
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Numerical Modelling Boundary conditions for the thermal model • Standard wall functions at the walls
• Prescribed mass flow gases at the injection area (room
temperature)
• Prescribed mass flow gases coming from the cooling area (at higher temperature)
• Prescribed pressure at the inlet of the pieces
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Numerical Modelling Results for the thermal model (temperature)
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Numerical Modelling Results for the thermal model (velocity field)
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Numerical Modelling Results for the thermal model, bouyancy effects are retained
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Numerical Modelling Results for the thermal model: complex flow patterns
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Numerical Modelling Next steps in the numerical model • Closure of the thermal model with the unsteady heating of the
pieces
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Numerical Modelling Next steps in the numerical model
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Numerical Modelling Next steps in the numerical model
• Species transport modelling (including surface reduction on
the pieces, thus, consumption of H2 on pieces surfaces) • More realistic:
- furnace geometry (cylindrical chamber) - gases inlets location - injection areas and inclination
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Zonal Modelling • 6 Zone model
– Inlet
– Preheating
– Reduction zone in muffle
– Brazing zone in muffle
– Cooling
– Exit
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Zonal Modelling: Methodology
Step 1:
Hydraulic network [Computing mass flows between zones]
Step 2:
Zone model [Gas temperature, working pieces temperature, H2 concentration, H2O concentration (and “trapped” H2O in working piece) are computed]
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Zonal Modelling : Variables
• Temperature
• Conc [H2]
• Conc [H2O]
• Working piece temperature
• “Trapped” water in working piece
For each position. K x 2 equations
for each zone i.e. 6x3 equations.
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Zonal Modelling
• Drawbacks of the global model :
– Very coarse model unable to provide detail into brazing atmosphere. (both in terms of composition and temperature)
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Zonal Modelling
• A more accurate model (more control volumes)
- CFD can be used now to compute mass fluxes!
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Zonal Modelling
N control volumes : i = 1,2,…,N S solid surfaces : j = 1,2,…,S K working pieces : k = 1,2,…,K
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Zonal Modelling: Equations
Energy balance in each control volume N equations
Energy balance at each working piece position K equations
[H2] balance in each control volume N equations
[H2O] balance in each control volume N equations
Energy balance on wall surfaces Sw equations
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Zonal Modelling: Equations
Energy balance at each control volume:
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Zonal Modelling: Equations Energy balance on each working piece :
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Zonal Modelling: Equations Energy balance on wall surfaces:
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Zonal Modelling: Equations [𝐻2] balance on control volumes:
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Zonal Modelling: Equations [𝐻2𝑂] balance in control volumes:
K equations to be added to model the water vapour release from working pieces :
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Zonal Modelling: Model parameters
• Gebhart factors can be “easily” computed (possibly using some software)
• Parameters in the hydraulic network can be estimated but calibration from plant measurements is absolutely needed.
• Heat transfer coefficients (HTC) can be obtained from correlations
• The whole model should be validated in plant tests (using for example DATAPAQ measurements for working piece temperature)
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Brazing Modelling
• More detailed model for predicting brazing performance – Reduction of metal oxides all along the gap ( [H2O] will
vary as reduction proceeds) – Thermal pattern in the working piece when filler
material melts has a strong influence on reactive melting process (melting temperature of the new alloy higher than that of the filler material)
– Surface tension driven flow (dynamic contact line with variable surface tension due to reactive wetting)
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Conclusions
• Combination of Zonal Models and CFD provide valuable information of local brazing conditions
• Modeling brazing process itself is a more challenging problem
• Optimization requires fast solution of the model
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Future Work
• CFD-Zone model development
• Validation to analyze furnace operation
- Possible experimental test • Inverse design algorithm based on reduced order
models (following Martin et al. Applied Thermal Engineering 2012 approach)
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Future Work As indicated, detailed info should be compiled to create an accurate CFD + Zonal model
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Future Work Validation tests should be performed. They provide vital information about the process (thermocouples to sensor the pieces heating temperature evolution and gases temperature zones)
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Future Work Once the furnace model is validated: • Generation of a database results for different operational
conditions: - Varying mass flow gases at injections - Varying temperatures of control zones - Varying conveyor belt velocity
• Generation of a surrogate (reduced order) model for a fast
prediction and optimization of operational parameters
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Future Work
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Future Work
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Future Work
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Future Work
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Future Work
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THANK YOU! Questions?