Operation Oriented Digital Twin of Hydro Test Rig

Sammanfattning

It has become increasingly important to introduce the Digital Twin in additive manufacturing as it is perceived as a promising step forward in its development and a vital component of Industry 4.0. Digital Twin is an up-to-date representation of a real asset in operation. The aim of this thesis is to develop a Digital Twin of a hydro test rig. Digital Twins are created by developing and simulating mathematical models, which should be integrated and validated. A downscale turbine test rig in which its runner and drafttube are replicates of the Porjus U9 turbine. This test rig is located in the John-Fieldlaboratory of the Division of Fluid and Experimental Mechanics at Luleå University of Technology (LTU). A mathematical model of the test rig has been made in the MATLAB environment Simulink. The test rig itself has components such as a Kaplan turbine, hydraulic pump, magnetic braking system, rotor, and a flow meter in a closed loop system. It is known that some test rig parameters are unknown, and so two methods have been used to optimize these parameters during the validation of the mathematical model. Optimization means finding either the maximum or the minimum of the target function with a particular set of parameters. An optimization of seven total parameters was made for the mathematical model in Simulink. The parameters were optimized using two different methods: Fmincon in MATLAB and Bayesian Optimization, a machine learning tool. Due to the fact that Fmincon could only find local minima and get stuck in that area, it could not reach the global minima. In contrast, Bayesian Optimization produced better results for minimizing the cost function and finding the global minima.