Real-time Ab Initio Modeling of Electric Machines

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Nearly all electricity is produced through electric generators, and 65% of industrial electricity is consumed by electric motors. By 2030, the annual energy consumption of electric motors is expected to exceed 13 quadrillion watt-hours. The ability to undertake optimal design and accurate analysis of electric machines is important to the success of any sustainable energy policy. Weight-critical applications, such as electrified transportation fleets, require radical structural designs with a high power density, high efficiency, and wide operating ranges. To facilitate such aggressive industry-wide transformation, physics-based machine models and high-performance computer simulation tools are indispensable. The fundamental compromise between modeling fidelity and simulation speed for design and analysis of electric machines is investigated in this project. The project is expected to enable a quantum leap in desktop-scale analysis that might not otherwise be achievable until several decades of computational advances lead to the computational capacity needed for real-time analysis of electric machines. First-principle models will be constructed systematically to mimic the characteristics of hardware prototypes, and free the designer from needing expert knowledge and approximations. This is a superior alternative to the existing approaches, namely behavioral approaches that use many simplifying assumptions that compromise model performance, and physical models, which are prohibitively slow. Hybrid order-reduction algorithms and massively parallel hardware-centric tools will accelerate the simulation by up to a million times faster, providing a suitable environment for performance analysis, design optimization, and fault prediction and diagnostics of electric machines. Planned knowledge dissemination of the research results will contribute to multi-disciplinary curricula that help prepare the next generation of power engineers, and to materials that help update the existing workforce to be able to address challenges of the emerging energy conversion devices and systems. The team will engage in STEM-related outreach activities to high school students as well as under-represented groups at UT Arlington, which is a Hispanic-serving institution. The project will investigate an analytical paradigm for electric machines using first-principle physics, and achieve real-time analysis of the resulting ab initio models. The vision is to shift the modeling paradigm away from outdated rule-of-thumb approaches and move toward physics-based yet intuitive models that account for emerging construction materials, computational capabilities, and power electronics-enabled control. The research objectives are to i) construct universal and physical models of electric machines without making heuristic or empirical assumptions common in existing behavioral models, ii) investigate model order reduction algorithms tailored to the resulting ab initio models, iii) map the reduced-order models to massively-parallel hardware-centric simulation platforms to speed up the model execution by as much as six orders of magnitude, and iv) experimentally validate all research findings using prototypes of various machine types commonly used in electric vehicles, electric aircraft, and wind energy conversion systems. This project highlights the fundamental cross-cutting challenges across the domains of power electronics, control systems theory, and scientific computing relevant to electromechanical energy conversion systems. Investigation of such models and tools will enable significantly reduced design cycle time for more efficient electric machines, realistic representation of machine-drive systems for smarter control of energy flow, and co-simulation of temporally-diverse dynamic systems involving both the field equations and grid dynamics for electric machine-to-power grid integration (e.g., in wind farms).