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- W4383093113 abstract "Refrigerant filling is a process of transitioning from compressed liquid to gas-liquid discrete state, which poses difficulties in monitoring the quality of refrigerant charging. This paper proposed a novel approach based on physics-informed deep learning for refrigerant filling mass flow metering. First, a shock discontinuity flow physical model is established related to the refrigerant filling mass flow rate, velocity and pressure. Additionally, a filling mass metering method based on PINN is proposed, as well as a residual points-adding optimization method for PDEs with discontinuous solution. The results demonstrate that PINN accurately predict mass flow rate, pressure, and velocity for refrigerant filling, and the error relative to numerical solution is less than 3%. Finally, a refrigerant filling experiment was conducted to verify the feasibility of the method of refrigerant mass flow metering." @default.
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- W4383093113 date "2023-10-01" @default.
- W4383093113 modified "2023-10-06" @default.
- W4383093113 title "Physics-informed deep learning method for the refrigerant filling mass flow metering" @default.
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- W4383093113 doi "https://doi.org/10.1016/j.flowmeasinst.2023.102418" @default.
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