FluxWorks is commercializing a series of technologies developed at the Advanced Electric Machines and Power Electronics Lab (EMPE) at Texas A&M University. These efforts culminate more than ten years of efforts of four generations of PhD students and millions of dollars of R&D yielding over 10 prototypes.
Magnetically Geared Machines
Magnetic gear integrated machines are lighter, smaller, and less costly than the equivalent low-rpm direct drive motor.
Magnetically Geared Machine (MGM): 225 kN∙m Wave Energy Example
|Direct*||Inner Stator Radial MGM†||Compact Axial MGM‡|
Coaxial Magnetic Gears
Praslicka and Toliyat have several patents and patent application together related to the most technologically mature form of magnetic gears: the coaxial topology. Praslicka designed a coaxial magnetic gear being used by the industrial fan industry to increase reliability and reduce maintenance of the former planetary gear drive. He has built two for advanced air mobility and one for drone rotorcraft propulsion.
Cycloidal Magnetic Gears
This topology lends benefits to certain niche markets. Praslicka is the leading world expert in the cycloidal topology and has written more papers and built more prototypes than any researcher – university or industry. This topology can achieve higher specific torque than the equivalent mechanical cycloidal mechanical gear, and can achieve incredibly high gear ratios in a single stage.
Praslicka’s Pending Patents on the Cycloidal Magnetic Gear
- U.S. Patent Application No.: 63/141,130 “Cycloidal Radial Flux Magnetic Gear with Balanced Magnetic Moments” | January 25, 2021
- U.S. Patent Application No.: 63/185,090 “Cycloidal Radial Flux Magnetic Gear,” | May 6, 2021
- U.S. Patent Application No.: 63/188,009 “Cycloidal Radial Flux Magnetic Gear with Spaced Permanent Magnets,” | May 13, 2021
Design and Multiphysics Optimization Software
FluxWorks’ greatest strength is in it’s unrivaled in-house design and multiphysics optimization software. First developed at TAMU using expensive MATLAB, ANSYS, and HPRC resources, TAMU. Outside of the field of electric machines, HPC has invited our lab to present our novel computational optimization method leveraging machine-learning (genetic algorithm) based heuristics at supercomputing conferences in 2019 and 2020.
Friends with the creator of the GOSET tool first for MATLAB, Dr. Toliyat and Praslicka got first access to the Python version of the GOSET tool. With FluxWorks, Praslicka and Toliyat have moved to a Python-FEMM based-optimization software.