The Development of Hierarchical Surrogate Models of Low Frequency Electromagnetic Devices for Robust Design Systems

社会越来越多地使用碳基燃料产生的能源正在污染、全球变暖等方面对环境产生不利影响。减少这些影响需要减少能源需求，即使对消耗能源的设备的需求增加。转向电机被视为减少某些影响的一种方法，导致对此类设备的需求不断增长。事实上，世界上的大部分电能既是通过机电能量转换(发电机提供98%的电力供应)产生的，也是由工业和家庭电机使用的。据估计，电动马达消耗了全球约70%的电能(加拿大的这一比例为80%)。减少能源需求，要求这些设备的效率尽可能高，并在欧洲和北美制定关于最低能效水平的法规。随着低成本电力电子控制和新材料的引入，设计满足要求的机器成为可能。然而，许多传统的设计工具已经不再适用。因此，需要开发能够在设备实际构造之前准确地预测其性能的新工具和工艺。要做到这一点，一种方法是求解设备中的电磁场，并用它来预测性能。这在计算上很昂贵，并且不适合嵌入迭代设计过程中。另一种方法是开发一个表示设备行为的高级模型(代理)。虽然从现场解决方案转移到代理相对容易，但由于代理创建中的信息丢失，向相反方向移动通常是困难的。对于能够改进数量(如效率)的设计过程，这两种映射都必须存在。该方案的创新之处在于自动生成这些映射，然后直接在图形处理单元(GPU)的核心上实现最简单的模型，从而可以构建一个低成本的大规模并行系统。将建造一系列越来越详细的替代品，使设计过程在几个层面上运行，并创建一个设计系统，以产生满足要求的优化设备。这项工作将由5名硕士和2名博士生和1名博士后研究员组成的团队实施，并将通过一系列短期目标建立在麦吉尔现有研究的基础上。这样培训的HQP将对研究界和工业界都有价值。这项工作将使加拿大公司受益，如麦格纳、TM4、GE等，通过创造一系列新的强大的设计工具，能够制造更容易和更便宜的电机。它将推动电机设计的最先进水平，并使加拿大在这项技术方面处于世界领先地位。

The increasing use of energy from carbon based fuels by society is having a detrimental effect on the environment in terms of pollution, global warming, etc. Reducing these effects requires a reduction in energy needs even as the demand for devices consuming energy increases. A movement towards electrical machines is seen as one method of reducing some of the effects, resulting in a growing demand for such devices. In fact, the majority of the world’s electrical energy is both produced by electromechanical energy conversion (generators producing 98% of the supply) and used by electric motors both industrially and domestically. Electric motors are estimated to consume about 70% of the world's electrical energy (this is 80% in Canada). Reducing energy demands, requires that the efficiencies of these devices are as high as possible and regulations exist, in Europe and North America, on minimum efficiency levels. With the introduction of low cost power electronics controls and new materials, it is possible to design machines to meet the requirements. However, many of the traditional design tools are no longer applicable. Thus there is a need to develop new tools and processes capable of predicting the performance of a device accurately before it is actually constructed. One approach to doing this is to solve for the electromagnetic field in the device and use this to predict the performance. This is computationally expensive and not viable for embedding in an iterative design process. Another approach is to develop a high level model (a surrogate) representing the behavior of the device. While moving from a field solution to a surrogate is relatively easy, moving in the reverse direction is generally difficult due to information loss in surrogate creation. For a design process which is capable of improving quantities such as efficiency, both mappings must exist. The innovation in this proposal is to generate these mappings automatically and then implement the simplest model directly on a core of a Graphics Processing Unit (GPU), thus allowing a low cost massively parallel system to be built. A series of increasingly detailed surrogates will be constructed to allow the design process to operate at several levels and a design system to be created which can generate an optimized device meeting the requirements. This work will be implemented by a team of 5 Master’s and 2 Ph.D. students and a Postdoctoral researcher and will build on existing research at McGill through a sequence of short term objectives. The HQP thus trained will be of value both to the research community and industry. The work will benefit Canadian companies, e.g. Magna, TM4, GE, etc., by creating a new range of powerful design tools capable of creating electrical machines which are easier and cheaper to manufacture. It will advance the state-of-the-art in electrical machine design and move Canada into a leading position in this technology in the world.