Download Customized tools for efficient virtual coupling CN65

CEDRAT News - N° 65 - December 2013
Customized tools for efficient virtual prototyping:
How to perform faster and more accurate simulations: examples.
L. Frigoli & A. Tassi - SPIN Applicazioni Magnetiche Srl.
I
n recent years the requirement for accurate virtual design has
grown constantly. In order to secure competitive run times, the
need for better accuracy in simulation predictions, generating
increasingly complex analytical representations, has to be
compensated for by more computationally efficient models: the
talk here is of virtual prototyping efficiency.
Greater computational efficiency means less time to get results
while performing rapid, detailed analyses on a device. The overall
goal is to have a streamlined, automated and customized model
creation and simulation process.
This is even more important with FEM (Finite Element Method)
software, where simulation run times are usually very high and the
post-processing analysis sometimes complex. A flexible tool, easily
linked to other software is crucial to satisfy this kind of request.
This paper presents some case studies on different electromagnetic
devices and explains the wide range of advantages that efficient,
well-structured virtual prototyping can deliver.
Pre-processing automation
The second example involves the load computation of an
embedded permanent magnet electric motor with 48 slots and
8 poles (Figure 3). A customized post-processing macro was
built, for the calculation of absorbed current, mechanical torque
and efficiency. These are the typical quantities
involved in the permanent magnet brushless
electric motor design, so this automation
can substantially reduce performance
analysis time in the design process.
Torque
Mechanical set vs magnetic torque rotating
Pre-processing automation
The first example is about the creation of a pre-processing
interface for a three phase transformer 3-D model. The device
was represented in an electromagnetic finite element simulation
environment (Flux® 3D by CEDRAT), and the short circuit test was
studied. The software scripting capabilities, in Python language,
were used to create a macro for implementing a full model
parameterization, without directly interacting with the software
building commands. A very user-friendly interface made it
possible to change geometrical and physical parameters, such
as dimensions, winding, material and circuit, simply by entering
the relevant values in a table (Figure 1), and running the macro.
Once the simulation was complete, the typical results in the
transformer short circuit test could be checked: leakage reactance,
reactive power and short circuit voltage. With this approach, the
FE 3-D model was built automatically at each iteration and the
simulation carried out with a simple click on a button, without
time-consuming programming or other steps. Figure 2 shows the
results for a three-phase transformer with a power rating equal to
150 MVA and a voltage rating equal to 132/14.1 kV.
Leakage reactance @ 50 Hz
Figure 3: PM motor geometry description
and post-processed results.
Automation thanks to external tools
or table
The third case involves the rating of an electric motor, but focusing
on a specific issue: the possible partial demagnetization of the
permanent magnets in the machine.
When subject to external magnetic fields and/or temperature
variations, the magnetic properties of permanent magnets may
change, initially leading to a reversible demagnetization, which
can, at a later stage, become a permanent demagnetization.
Because of its impact on the electrical machine’s performance, it
is very important to take this phenomenon into account during
the design phase, even more so during a cost reduction process,
when considering, for example a reduction in permanent magnet
volume or magnet grade. In particular, this problem can affect
high torque electric motors as high current is generated.
Figure 4 shows the three-phase brushless electric motor with
24 slots and 4 poles modeled in this case. The magnet material,
Samarium Cobalt (Sm2Co17), was defined in the software tool
with its proprieties in the second quadrant of the hysteresis curve
(also referred to as the demagnetization curve) with a remanence
value of 1.08 T @20°C and intrinsic coercivity equal to 560 kA/m
(Figure 5).
13.9 Ohm/phase
(High voltage winding)
0.16 Ohm/phase
(Low voltage winding)
Total reactive power @ 50 Hz
18 MVA
Short circuit voltage
9106 V/phase
Figures 1 & 2: Transformer description and
results obtained.
Figures 4 & 5: Brushless electric motor
and magnet demagnetization curve.
(continued on page 15)
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CEDRAT News - N° 65 - December 2013
Magnet length = 7 mm
Magnet length = 3.5 mm
T= 20°C, 500 rpm
18.6 Nm
(no demagnetization)
15.43 Nm
(no demagnetization)
T= 20°C, 1500 rpm
9.95 Nm
(no demagnetization)
9.32 Nm
(no demagnetization)
T= 200°C, 1500 rpm
7.83 Nm
(7.84 Nm) *
6.65 Nm
(7.41 Nm) *
* not considering the demagnetization tool
BCS® is a really intuitive and easy to use software, featuring a userfriendly interface, where parameters and constraints are set, and a
numerical optimization algorithm, which assess the configuration
being analyzed at each step in order to define improvement areas,
through to identification of the optimum solution. This means that
no macro creation or any other form of programming is necessary,
as BCS® is based on a “black box” idea (Figure 9).
Figure 6: Torque obtained depending on the speed and
temperature range, and the magnets lengths.
Figure 6 presents the results of different case studies, where
both the speed range and the temperature range were modified
according to two magnet lengths. Examination of this figure
indicates that the most critical condition for the thinner magnet
is defined by a temperature of 200°C and a speed of 1500 rev/min,
and the resulting torque reduction is around 10%.
Optimization using GOT-It
The next case deals with the optimization of an electromagnetic
actuator, carried out with a classical optimization tools (GOT-It)
coupled to a finite element software (Flux® 3D). GOT-It is a powerful
and reliable optimization tool, based on modern mathematical
optimization methods, with a user-friendly interface, including
interactive command; the user has access to the most advanced
optimization functions and the required input is reduced to the
parameters, constraint and values to optimize. From this data,
GOT-It will define the best configuration(s).
Initial configuration
Final configuration
Losses
8.533 W
7.465 W
Force *
171.8 N
198.9 N
* target: 200 N
Initial configuration Final configuration
780 W
751 W
77.6 %
83.1 %
Winding temperature
57 °C
62 °C
Power
Efficiency
Figures 9 & 10: Initial and final configuration results comparison.
The efficiency increase achieved at the end of the optimization
process was around 4%, as shown in Figure 10. It is important
to note that this result complies with the European commission
regulation - (EC) 640/2009 - on Eco-design requirements for
electric motors in industrial applications.
Conclusion
Figures 7 & 8: Actuator magnetic
circuit and results comparison of the
initial / optimized case.
Simulation software now allows easy creation of real virtual
prototypes, but only with efficient and tailored tools has it become
possible to take full advantage of the potential opened up by
modern computer programs.
Figure 7 shows the electromagnetic actuator magnetic circuit
analyzed in this example, consisting of two coils wrapped around
a U-shaped magnetic core. The goal of this study was to optimize
the actuator in respect of losses (10% reduction target), for a given
generated force, while respecting the size and other physical
constraints (saturation, maximum current density).
Optimization was performed using the SSO algorithm, a
deterministic algorithm based on the response surface and a
surrogate model. The results are summarized in Figure 8. The
major conclusion drawn from this case is that the sequential
surrogate methods are efficient, providing quick solutions to a
given problem. Moreover, the optimization process involved only
35 iterations, with an overall calculation time of approximately 30
minutes. In other words, it offers an economical way to finding
solutions with a good degree of accuracy.
Figure 11 summarizes the software’s performance with respect to
calculation time and accuracy for all the examples shown in this
paper. This figure clearly show that the time involved in a new
design assessment can be reduced to the magnitude of minutes/
hours, whilst if a new device had to physically built and tested, it
would more likely take days/weeks.
Optimization using BCS®
Another optimization process is presented as a final example,
but, in this case, the simulations were performed using electric
machine design software based on lumped-circuit analysis. In
particular, SPEED by CD-Adapco was chosen for electromagnetic
design and Motor-CAD by Motor Design was chosen for thermal
analysis. The study focused on the design optimization of a three
phase induction motor, with 4 poles, 24 slots and 19 bars, with
the objective of achieving maximum possible efficiency within
the project constraints.
A specific optimization tool developed in SPIN, named BCS® (Best
Configuration Searcher), was utilized to drive the overall process.
TIME
CONSUMPTION
ACCURATE
SHORT CIRCUIT TEST for a three
phase transformer
++
=
LOAD COMPUTATION
for an embedded permanent magnet
electric motor
++
=
DEMAGNETIZATION TEST for a
permanent magnet
++
++
OPTIMIZATION TOOLS
+++
++
Figure 11:
Simulation
efficiency
depending on
the method
used in the
examples.
Modern and customized virtual prototyping software tools are
invaluable, because they are fast and intuitive, have the capability
to easily automate long sequences of time-consuming tasks,
and make it possible to analyse a large number of different
configurations in a very short time, identifying the best possible
design option with the most appropriate optimization algorithms.
In other words, these tools offer a wide range of options to support
the design process and, thanks to tailored customization, their
full potential can be exploited in the assessment of radical design
changes, as they allow real “what-if” scenarios to be evaluated
quickly and inexpensively.
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