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DR MAGNETO.AI is a sophisticated Artificial Intelligence based autonomous tool to design Electrical Transformers. The tool outperforms even the most experienced Engineers in terms of generating optimized designs for all modern Electrical Transformers with applications ranging from mobile phones to electric vehicles to power transmission stations. Once fully developed this application is expected to be a game changer in Electrical Transformer design process.
The application uses complex mathematical models and advanced Artificial Intelligence based algorithms to generate the most optimum solution from multiple objective functions such as optimizing for Cost, Power Loss, Weight, Efficiency, Physical Dimensions, Temperature Rise, Voltage Regulation, etc.
To generate the optimum designs DR MAGNETO.AI uses Mixed Integer Non-Linear Equations with user defined Constraints and Industry Regulations. Over 40 design parameters such as attributes related to Core Materials, Conductor Materials, Core Configurations, Conductor Types, Physical Dimensions, Cooling Techniques are analyzed in arriving at the most optimum solution w ithin the application constraints.
DR MAGNETO.AI is capable of designing Electrical Transformers for a wide power rating ranging from milli Watts to mega Watts, frequencies ranging from Hz to MHz and working voltages ranging from Volts to kilo Volts.
As opposed to the conventional design approaches DR MAGNETO.AI uses the parametric design approach that produces multiple solutions for a given application and illustrates that on an easy-to-understand multi-dimensional graphical representation.
Further, DR MAGNETO.AI adopts an innovative application of deep neural network algorithms that can approximate solutions to partial differential equations for thermal simulation in place of traditional numerical approaches.
When designing magnetic components, a vast number of parameters, totaling over forty, can affect the final design outcome. These parameters can each have over two hundred possible values, resulting in a nearly infinite number of possible design permutations for a given set of input parameters, which can make it challenging to determine an optimal design solution. A human designer with significant expertise and experience can reduce the number of potential design permutations, but different experts may have varying opinions on the significance of each parameter, leading to subjective design outputs. Exhaustively evaluating all parameters using traditional computational approaches is impossible due to the vast number of possible permutations exceeding the number of atoms in the world. However, an advanced autonomous approach that employs artificial intelligence algorithms has been developed to surpass even the capabilities of a superhuman designer.
The analysis of magnetic components design involves considering more than forty design parameters, each with over two hundred possible values. While some of these parameters have constraints based on the application requirements, designers may have limited freedom in selecting certain parameter values. However, making slight changes to these values can result in advantages for the magnetic design and the overall system. For instance, increasing inductance slightly can filter higher order harmonics in the current waveforms, making it possible to use standard magnetic materials for inductors instead of exotic ones. Unfortunately, this aspect is often overlooked due to time constraints and the complexity of conducting a quantifiable analysis.
Additionally, in most cases, the values of all the parameters are not known during the initial design stage. Even the suitable range of values for some of the governing parameters may not be known at the start of the design. However, in certain situations, ideal values for certain parameters are known and available to the magnetic designer. This availability depends on the degree of circuit simulations and analysis performed by the application engineers.
