Machine learning (ML) techniques are now widely being used in almost all areas of application. Six months back, CCTech Research started investigating how we may use ML in the area of Design of Mechanical Systems. One of the key application we were particularly interested is in Control Valve industry. Control Valve Performer - a cloud app developed by our simulationHub team is already helping valve manufacturers get quick results. Given a 3D model, our app provides the flow performance parameters such as flow coefficient C_V, Coefficient of Hydrodynamic Torque C_dt in less than an hour. Although this is a drastic time saving over traditional CFD (which could take a week) and experimental techniques (a month), one still yearns for a solution that could predict the flow performance in less than 5 seconds. Once this kind of solution becomes available to the designer, he will do away with excel sheet formula based design basis calculation.

We decided that the first version of our app will have limited scope, supporting only one most used control valve i.e. Symmetric Butterfly Valve. Objective of the app was defined as : given the design parameters for the disc of the valve, our application should instinctively be able to predict flow coefficient C_V and Coefficient of Hydrodynamic Torque C_dt with a high level of accuracy (less than 5% error as that of CFD). We also wanted the app to accurately answer whether new output disc-shaft parametric design can withstand pressure forces i.e. predict Factor of Safety FoS from structural point of view. The second major feature we wanted was if user changes the flow performance curve then our app should be able to answer inverse query i.e. to predict design parameters. This inverse problem is much more difficult for design engineers in practice as all CFD tools are able to give Design C_V and but not C_V Design. One of the fundamental reasons for the inverse problem to be complex is that there can be many possible viable solutions. So, designer has no choice but to try out/experiment with several designs to achieve the desired flow performance.

Yes, we did it and with overwhelmingly fast, accurate solution!! We ourselves had few doubts about whether this exercise would work as it involves building a prediction model of the underlying physics. Design => C_V prediction is a complex problem, as in this case underlying physics is fluid dynamics and structural mechanics. On this dichotomy, I had even written my musings on the use of ML as a tool for predicting physics in my last blog.