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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) - 14 - 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. - 15 -