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

Contents

• Problem Statement

• Possible Modelling Approaches

• Description of the different models

• Conclusions

• Future Work

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

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

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.

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

Brazing: Significance of dewpoint

Source: Roberts, “Introduction to Brazing Technology, 2016

Reducing Atmosphere

Heat pattern in brazing furnace

Source: Roberts, “Introduction to Brazing Technology, 2016

Modelling Approach • Numerical simulation

• Zonal Modeling

• Brazing Process modelling

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

Numerical Simulation

Pieces moving direction

Muffle región (simulated)

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)

Numerical Simulation 3D Mesh (< 500.000 cells, restricted academic Ansys license)

Numerical Simulation Isothermal model, steady, RANS turbulent model for Reynolds stress tensor (high Reynolds turbulent model approach)

𝜇𝑇 = ρCμ

𝑘2

𝜖

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

Numerical Modelling Isothermal model, velocity field distribution

Numerical Modelling Isothermal model results

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

Numerical Modelling

𝜇𝑇

= ρ𝑔Cμ

𝑘2

𝜖

𝑘𝑇 =𝐶𝑝𝜇𝑇

𝑃𝑟𝑇

+𝜌𝑔𝑔

𝑃 = 𝜌𝑔𝑅𝑔𝑇𝑔

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

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

Numerical Modelling Results for the thermal model (temperature)

Numerical Modelling Results for the thermal model (velocity field)

Numerical Modelling Results for the thermal model, bouyancy effects are retained

Numerical Modelling Results for the thermal model: complex flow patterns

Numerical Modelling Next steps in the numerical model • Closure of the thermal model with the unsteady heating of the

pieces

Numerical Modelling Next steps in the numerical model

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

Zonal Modelling • 6 Zone model

– Inlet

– Preheating

– Reduction zone in muffle

– Brazing zone in muffle

– Cooling

– Exit

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]

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.

Zonal Modelling

• Drawbacks of the global model :

– Very coarse model unable to provide detail into brazing atmosphere. (both in terms of composition and temperature)

Zonal Modelling

• A more accurate model (more control volumes)

- CFD can be used now to compute mass fluxes!

Zonal Modelling

N control volumes : i = 1,2,…,N S solid surfaces : j = 1,2,…,S K working pieces : k = 1,2,…,K

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

Zonal Modelling: Equations

Energy balance at each control volume:

Zonal Modelling: Equations Energy balance on each working piece :

Zonal Modelling: Equations Energy balance on wall surfaces:

Zonal Modelling: Equations [𝐻2] balance on control volumes:

Zonal Modelling: Equations [𝐻2𝑂] balance in control volumes:

K equations to be added to model the water vapour release from working pieces :

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)

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)

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

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)

Future Work As indicated, detailed info should be compiled to create an accurate CFD + Zonal model

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)

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

Future Work

Future Work

Future Work

Future Work

Future Work

THANK YOU! Questions?