Augmented Simulation Models for the Initial Multi-physics Design of Electrical Machines

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Electrical energy is fundamental to the functioning of modern society. Since the development of the basic theory related to electromagnetic physics and its application to energy conversion and transmission about 200 years ago, the growth in systems for generating, transmitting and converting electrical energy has been exponential resulting in about 26.7 Terawatthours being generated and consumed in 2018. The growth rate of the demand for electrical energy is about double that of other energy sources. However, electrical energy is not a primary source - it must be created by conversion from another source e.g. fossil fuels (coal, oil, gas), hydro, wind, solar, etc. Additionally, electrical energy, in itself, is not directly usable; it requires conversion to a final form, usually mechanical or thermal, in order to be used in a system or a device such as a dishwasher, an electric vehicle or an industrial drive. The conversions to and from electrical energy, to a major extent, involves an electromagnetic system often in the form of a generator or motor. Each of the conversion and transmission processes involve losses and, with a 90-95% efficiency for a motor or generator, the total energy loss between generation and usage is over 2.5 Terawatthours - and this represents both financial costs and an impact on global warming. In a step to reduce these losses, regulations have been created which require minimum efficiencies to be achieved for many drives. In many cases, this is met by operating motors at variable speed using a power electronic drive. This change has an impact on the design process where a performance "envelope" must now be achieved rather than a single operating point. This change in operation impacts the design process for an electrical machine and makes it, potentially, more complex and expensive. The earlier in the process that performance envelopes (or maps) for quantities such as efficiency, power factor, etc., can be determined, the less time is wasted in exploring designs which will not meet the specifications. To achieve this goal, an effective design process which accounts for the complete multi-physics performance of a machine over its entire operational envelope is needed. This is achievable with current simulation tools, but the costs are excessive and can often need weeks of computational time, in itself a huge energy cost. The objective of this research is to develop fast electrical machine emulators built on machine learning based surrogate models, i.e. develop black-box models of the electrical machine. These will allow a fast exploration of the potential design space to locate possible design candidates before moving to a full simulation system, thus reducing design costs. When linked to an additive manufacturing system, this approach will enable the construction of more efficient machines while reducing the design and manufacturing costs of the devices.