Method of measuring physical properties
A method of measuring physical properties includes a number of operations. Simulated mass flow rate data of training physical property parameter sets are generated by a computing unit based on the training physical property parameter sets and vessel shape information. Deep learning model is trained by a processor based on a training data set with an input feature vector of the simulated mass flow rate data and an output feature vector of the training physical property parameter sets. Measured fluid is received by a loader through an opening of a vessel of the vessel shape information. Weight accumulation data of weight of the loader is measured by a scale during a time period to obtain measured mass flow rate data. The measured mass flow rate data is inputted to the deep learning model to obtain measured physical property parameter set of the measured fluid.
1 . A method of measuring physical properties, comprising:
generating a plurality of simulated mass flow rate data corresponding to a plurality of training physical property parameter sets by a computing unit based on the training physical property parameter sets and a vessel shape information;
training a deep learning model by a processor based on a training data set, wherein an input feature vector of the training data set comprises the simulated mass flow rate data and an output feature vector of the training data set comprises the training physical property parameter sets;
containing a measured metal in a vessel corresponding to the vessel shape information, wherein the vessel is opaque and accommodated in a furnace;
heating the measured metal to a determined temperature by the furnace, so that the measured metal is melted into a molten metal fluid as a measured fluid;
receiving the measured fluid by a loader through an opening of the vessel;
measuring a weight accumulation data of a weight of the loader by a scale during a time period;
obtaining a measured mass flow rate data of the measured fluid based on the weight accumulation data; and
inputting the measured mass flow rate data to the deep learning model to obtain a measured physical property parameter set of the measured fluid.
2 . The method of claim 1 , wherein the training physical property parameter sets comprises a plurality of densities, a plurality of viscosities and a plurality of surface tensions, the measured physical property parameter set comprises a measured density, a measured viscosity and a measured surface tension.
3 . The method of claim 1 , wherein the vessel shape information comprises a cross-section area of the vessel, a cross-section area of the opening of the vessel and a length of the opening of the vessel.
4 . The method of claim 1 , wherein the computing unit is a computational fluid dynamics unit, generating the simulated mass flow rate data corresponding to the training physical property parameter sets comprises:
performing a simulation of a computational fluid dynamics algorithm by the computing unit to obtain the simulated mass flow rate data.
5 . The method of claim 1 , wherein the simulated mass flow rate data comprises a plurality of simulated mass flow rates with respect to a plurality of time points and/or with respect to a plurality of height heads, the height heads are a plurality of fluid height of the measured fluid in the vessel from a simulation performed by the computing unit.
6 . The method of claim 1 , wherein a guide angle is provided at a top of the opening of the vessel.
7 . The method of claim 1 , wherein the vessel is spatially separated from the loader.
8 . A method of measuring physical properties, comprising:
providing a vessel and recording a vessel shape information of the vessel;
training a deep learning model by a processor, comprising:
measuring a weight accumulation data of a weight of a fluid flowing from the vessel to a loader during a time period by a scale;
generating a mass flow rate data of the fluid based on the weight accumulation data;
converting the weight accumulation data into a first length criterion occupied by the fluid in the vessel through a predicted property parameter set and the vessel shape information;
simulating a flow of the fluid based on the predicted property parameter set and the vessel shape information to obtain a second length criterion occupied by the fluid in the vessel;
calculating a difference function based on a difference between the first length criterion and the second length criterion;
adjusting the predicted property parameter set to minimize the difference function, wherein the predicted property parameter set is selected as a physical property parameter set of the fluid when the difference function is minimized; and
training the deep learning model through a training data set, wherein an input feature vector of the training data set comprises the mass flow rate data and an output feature vector of the training data set comprises the physical property parameter set;
containing a measured metal in the vessel, wherein the vessel is opaque and accommodated in a furnace;
heating the measured metal to a determined temperature by the furnace, so that the measured metal is melted into a molten metal fluid as a measured fluid;
receiving the measured fluid flowing out from an opening of the vessel by the loader;
measuring a measured weight accumulation data of a weight of the loader receiving the measured fluid relative to a plurality of time points by the scale;
obtaining a measured mass flow rate data of the measured fluid based on the measured weight accumulation data; and
inputting the measured mass flow rate data to the deep learning model to obtain a measured physical property parameter set of the measured fluid.
9 . The method of claim 8 , wherein simulating the flow of the fluid based on the predicted property parameter set and the vessel shape information to obtain the second length criterion occupied by the fluid in the vessel comprises:
performing a simulation of a computational fluid dynamics algorithm to obtain the second length criterion.
10 . The method of claim 8 , wherein the first length criterion is a first height head obtained by converting the weight accumulation data into a first fluid height of the fluid in the vessel, and the second length criterion is a second height head obtained by simulating a second fluid height of the fluid in the vessel.
11 . The method of claim 10 , wherein the first height head and the second height head are functions relative to the time points.
12 . The method of claim 8 , wherein the predicted property parameter set comprises a density, a viscosity and a surface tension, and the measured physical property parameter set comprises a measured density, a measured viscosity and a measured surface tension of the measured fluid.
13 . The method of claim 8 , wherein a guide angle is provided at a top of the opening of the vessel, and the vessel shape information comprises a cross-section area of the vessel, a cross-section area of the opening of the vessel, a length of the opening of the vessel and the guide angle.
14 . The method of claim 8 , wherein the vessel is spatially separated from the loader.