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

电能是现代社会运转的基础。自约200年前发展与电磁物理相关的基础理论并将其应用于能源转换和传输以来，发电、传输和转换电能的系统呈指数级增长，导致2018年发电和消耗约26.7太瓦时。电力需求的增长速度大约是其他能源的两倍。然而，电能不是主要来源--它必须通过从另一种来源转换而产生，例如化石燃料(煤、石油、天然气)、水能、风能、太阳能等。此外，电能本身不能直接使用；它需要转换成最终形式，通常是机械或热能，以便用于系统或设备，如洗碗机、电动汽车或工业驱动器。电能的转换和转换在很大程度上涉及一个电磁系统，通常是以发电机或电机的形式。每个转换和传输过程都涉及损耗，电机或发电机的效率为90%-95%，从发电到使用之间的总能量损失超过2.5太瓦时--这既是财务成本，也是对全球变暖的影响。为了减少这些损失，已经制定了法规，要求许多驱动器达到最低效率。在许多情况下，这是通过使用电力电子驱动器以可变速度操作电机来实现的。这一变化对设计过程产生了影响，在设计过程中，现在必须实现性能“包络”，而不是单一操作点。操作上的这种变化影响了电机的设计过程，并潜在地使其更加复杂和昂贵。越早确定效率、功率因数等数量的性能包络(或图)，在探索不符合规格的设计时浪费的时间就越少。为了实现这一目标，需要一种有效的设计过程来考虑机器在其整个运行范围内的完整的多物理性能。这是目前的模拟工具可以实现的，但成本过高，往往需要数周的计算时间，本身就是一项巨大的能源成本。本研究的目标是开发基于机器学习的快速电机仿真器，即开发电机的黑盒模型。这将允许快速探索潜在的设计空间，以在转移到完整的模拟系统之前找到可能的设计候选者，从而降低设计成本。当连接到附加制造系统时，这种方法将能够建造更高效的机器，同时降低设备的设计和制造成本。

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.