Examples of Electromagnetic and Thermal Modeling using sT Activate. La conferenza era dedicata ai programmi per analisi di sistema e programmi multibody.

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Examples of electromagnetic and
thermal modeling using sT Activate

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Contents:
1) Co-simulations: Flux-Activate coupling
2) Flux-Activate coupling: off-line co-simulations
3) Lumped-parameter electromagnetic models
4) Lumped-parameter thermal models
5) Animated models using the ImageDisplay block
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1) Co-simulations: Flux-Activate coupling
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DTC (direct torque and flux control) of an induction motor
V I – pole flux estimator
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Bi-phase a-b currents Speed
I[A]
w[rad/s]
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Bi-phase stator flux components:
Ψ 𝑎 on the x-axis
Ψ 𝑏 on the y-axis
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Starting of an IPM generator with torque control of the ICE
Torque-speed curve data of
the ICE are written in a 1D LUT
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Voltages ICE torque and speed
Torque[Nm]
Speed[RPM]
Speed[rpm]Torque[Nm]
Voltages[V]
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2) Flux-Activate coupling: OFF-LINE co-simulations
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Off-line co-simulations:
Flux LUT 1D/2D/3D Activate
No data exchange: coupling is indirect.
Accurate simulations in a few seconds.
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MTPA control strategy of a SynchRel (synchronous reluctance) motor
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LUT 2D:
• λd (id,iq)
• λq (id,iq)
• Ld (id,iq)
• Lq (id,iq)
• T (id,iq)

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Speed [rpm] Phase current [A]Phase voltage [V]
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3) Lumped-parameter electromagnetic models
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Solenoid valve
Electric circuit
Mechanical load
Magnetic circuit
(reluctance network
influenced by geometry)
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Solenoid valve: FEM validation with Flux
Symmetry axis
List of colors (Flux model):
• Yellow = winding
• Blue = moving core
• Red = fixed core
• Green = armature
• Black = compressible air (Flux)
• White = fixed air
Type of mechanical load:
coupled load (mass and spring)
Electric circuit:
DC supply:
Vdc = 24 V
Winding:
5000 turns
R = 960 Ω
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-1,2
-1
-0,8
-0,6
-0,4
-0,2
0
0 0,5 1 1,5 2Force[N]
Time [ms]
Electromagnetic force
Flux
Activate
-0,16
-0,14
-0,12
-0,1
-0,08
-0,06
-0,04
-0,02
0
0 0,5 1 1,5 2
Position[mm]
Time [ms]
Position of the moving core
Flux
Activate
Activate model:
• Geometry is parametrized
• Short computational time
• Reliable results (error is about 10%)
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4) Lumped-parameter thermal models
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Why thermal models?
• Some materials (e.g. wire insulators, magnets) get damaged at high temperatures
• Temperatures influence electrical and magnetic properties

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INPUT DATA: POWERS
Thermal models generally require power injections or withdrawals as inputs.
Powers Temperatures
Thermal
model
Power values can be obtained in several ways:
• Directly inserted by user as input data
• Evaluated by an electric circuit in Activate
(e.g. equivalent circuit of an IM)
• Read from a text file (e.g. “filename.csv”)
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PARAMETRIZATION
In order to have a powerful tool for thermal modelling, the Activate model is fully
parametrized: user has to insert geometric and physical data as inputs.
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Axisymmetric electromagnet
The basic idea is to radially and axially divide the electromagnet, forming annular sectors each
one with its own physical properties:
List of colors employed in the image:
Blue  moving core
Red  fixed core
Yellow  winding
White  airgap
Green  plastic sprocket
Turquoise  top disc
Brown  brass bushing
Purple  armature
R axis
Z axis
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Square-wave = 1
Electromagnet is turned ON
Moving core is pulled down
Square-wave = 0
Electromagnet is turned OFF
Wide airgap between cores
Activation
blocks
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The thermal behavior of the electromagnet is influenced by ON-OFF state, as visible below.
Temperature[°C]
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Squirrel cage induction motor
The basic idea is to radially and axially divide the motor, forming annular sectors each one with
its own physical properties:
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Output transient temperatures: Motor-CAD validation
Motor-CAD radial view
Activate Motor-CAD
Temperature[°C]
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5) Animated models using the ImageDisplay block
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Open loop V/Hz scalar control of an Induction Motor
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Speed loop of a DC motor
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Conclusions
As shown above, with 1-D simulations in Activate it is possible to describe:
electromagnetic devices
(and other complex systems)
Electromagnetic behavior
Thermal behavior
Control aspects and mechanical coupling
• Models can be fully parametrized  high flexibility
• Representation of reality through Modelica blocks  fast simulations and small errors
• Great variety of blocks and solvers  high flexibility
• Co-simulations  better accuracy (at the expense of higher computational time)

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Thanks for your attention!
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