. . "210199" . . "5"^^ . "5"^^ . "2011-05-11+02:00"^^ . "RIV/00216305:26220/11:PU92512" . "P(FR-TI1/082), P(GA102/09/1875)" . "5"^^ . "Janda, Marcel" . . . . . "26220" . . . . "[182C2628B176]" . . . . "Magnet shape optimization of brushless machine by Self Organizing Migrating Algorithm." . "IEEE" . . "RIV/00216305:26220/11:PU92512!RIV12-GA0-26220___" . "III International Conference on Power Engineering, Energy and Electrical Drives" . "Ondr\u016F\u0161ek, \u010Cestm\u00EDr" . "Skalka, Miroslav" . . "Magnet shape optimization of brushless machine by Self Organizing Migrating Algorithm."@en . "The paper deals with AC motor optimization. Optimized machine is three phases 14kW Surface Mount Permanent Magnet (SMPM) machine intended to work with servo amplifier. The optimization is based on Self Organizing Migrating Algorithm SOMA with strategy All to One. Artificial intelligence algorithms are effective methods for searching global extremes of the objective functions. Target is to achieve maximum efficiency of SMPM, minimize losses and increase output power of the machine. As optimized parameters the diameters of magnet shape and length of air gap were chosen. The optimization algorithm is created in MATLAB, SPEED laboratory is used as solver, communication link is provided by ActiveX. Improved efficiency leads into reduced losses and lower temperature rise. Motor torque is calculated via a circular path integral of the Maxwell stress tensor in ANSYS program. The Maxwell stress tensor provides a convenient way of computing forces acting on bodies by evaluating a surface integral." . . "SOMA, SMPM, losses, optimization, brushless machine"@en . "Kurf\u00FCrst, Ji\u0159\u00ED" . "Malaga (Spain)" . "Torremolinos (Malaga)" . . . "The paper deals with AC motor optimization. Optimized machine is three phases 14kW Surface Mount Permanent Magnet (SMPM) machine intended to work with servo amplifier. The optimization is based on Self Organizing Migrating Algorithm SOMA with strategy All to One. Artificial intelligence algorithms are effective methods for searching global extremes of the objective functions. Target is to achieve maximum efficiency of SMPM, minimize losses and increase output power of the machine. As optimized parameters the diameters of magnet shape and length of air gap were chosen. The optimization algorithm is created in MATLAB, SPEED laboratory is used as solver, communication link is provided by ActiveX. Improved efficiency leads into reduced losses and lower temperature rise. Motor torque is calculated via a circular path integral of the Maxwell stress tensor in ANSYS program. The Maxwell stress tensor provides a convenient way of computing forces acting on bodies by evaluating a surface integral."@en . "Magnet shape optimization of brushless machine by Self Organizing Migrating Algorithm." . . . "978-1-4244-9843-7" . . "Duro\u0148, Ji\u0159\u00ED" . . "Magnet shape optimization of brushless machine by Self Organizing Migrating Algorithm."@en . . .