Design and Development of an Advanced Bespoke Fan Selection/Design Program
C3 Fan Installation & Selection*
Due to number of challenges posed by Industry 4.0 combined with the bespoke demand of Industrial Fans owing to their wide range of applications ,there is a need for an Intelligent Fan Configurator. Currently various Industrial Fan configurators exist most of them limited to standard fan selection process with a little to no flexibility in Machine design of its components. Some of the advanced Fan selection tools such as Centrix can be used for bespoke fan selection with the capability of machine design but it lacks flexibility , cannot automate 3D Models efficiently with detail and does not have the capability for design optimisation through Inverse Design .
Fan Industries can significantly benefit from a Unified Fan configurator program that can have fan selection capabilities , flexible component design modules, detailed CAD geometry automation combined with advanced blade geometry optimization through Inverse design.
Currently such a program is being designed and developed for an Industrial Fan Manufacturing Company called Woodcock and Wilson with the help of a software platform called Drive Works. This program can be hard coded to perform various automated tasks and integrated with the widely used CAD and CAE program called Solid works.
Currently various algorithms have been devised and coded to give bespoke fan selection capabilities hand in hand with standard classical stress and noise analysis calculations. Custom Motor Selection program along with various other sales tools have also been incorporated . Base CAD models are in the process of development from where a variety of geometric needs can be automated.
Furthermore , Additional capabilities of the program will improve the performance of fan blades by giving the best base line flow path followed by the reduction in weight whilst maintaining structural integrity through inverse design.
Currently the already existing solver solving Naiver Stokes equations giving the baseline aerodynamic shape for a turbine blade will be modified to suit for fans. A new Inverse Design solver will be created to obtain best geometry for stress suitable for a given blade stress field this process will be followed by the Use Multi-Disciplinary Optimization involving machine learning and fast algorithms to come up with the final acceptable design for both Aero and Stress performance.
Combination of fast and approximate methods with slow and accurate methods hand in hand with stochastic learning from previous designs will be devised to come up with a newly optimized design selection based on only aerodynamic and Linear/Non-Linear Structural Stress Analysis.
In conclusion, this Advanced Fan configurator can open up to a variety of industrial areas for the fan companies due to its potential for executing automated CAD generation with the capability to create complex shapes that can be used hand in hand with additive manufacturing techniques to meet a specific application purpose through inverse design .