Research on short term prediction method of thermal hydraulic transient operation parameters based on automated deep learning

Abstract

Automated deep learning is a new development direction of deep learning based on automated machine learning. Its main purpose is to achieve the automated operation of deep learning algorithm design process from design, training, parameter adjustment and so on. Due to a large number of repeated operations such as parameter adjustment and optimization model, the algorithm design process relies heavily on designers and consumes a lot of design time. Based on the automated deep learning method, this paper designs a short term prediction method of thermal hydraulic transient operation parameters, which can achieve the whole design process hosting operation. This method only needs the designer to upload the data set to the prediction model to complete the algorithm design, and does not need the designer’s intervention in the whole process, so as to simplify the operation and save the design time. At the same time, quantum genetic algorithm and model agnostic meta learning are introduced to ensure that the algorithm is still reliable and effective when the data set is expanded and the prediction problem is more complex. And the algorithm can be porting and application on quantum computer. In order to verify the algorithm, the steam mass flow rate at the outlet of main steam pipe of steam generator and the water temperature at the bottom of pressurizer are predicted respectively after 1 s, 2 s, 3 s and 5 s. The calculations show that the maximum error of the prediction models is about 4%, and the average prediction time of a group of parameters is about 0.7 ms. The prediction model based on the combination of the above algorithms can be used to predict the change trend of thermal transient operation parameters in a short time and provide effective judgment basis for the reactor accident early warning.