Boiling regime prediction by deep learning and acoustic spectrogram-based model

In most systems using boiling and two-phase heat transfer methods, including nuclear power plants, figuring out the state of internal flow and boiling phenomena is important for accident prevention and effective operation, which is related to predict heat transfer coefficient and critical heat flux. The measurement of temperature, pressure, and flow rate used for system monitoring have difficulties in identifying real physical phenomena inside the system, only calculation of DNBR correlation is possible. Such that, when there is a high probability that boiling or two-phase phenomena will occur, there is no monitoring tools such as instantaneous thermal power increase of a nuclear reactor, ECCS injection during LOCA, and ex-vessel cooling using boiling heat transfer. This paper is a basic study on real-time diagnosis of internal flow and boiling phenomena with critical heat flux in systems utilizing boiling or twophase heat transfer where it is difficult to directly identify internal phenomena. To this end, a convergence study of acoustic emission signal measurement technology, which is excellent for detecting minute changes, and deep learning technology, which has excellent predictive diagnostic performance, was conducted. The acoustic emission signal is an elastic wave generated by propagating deformation energy according to the physical change of materials or a change in system pressure along the medium. The change in the displacement of the interface according to the generated acoustic wave is converted into an electrical signal and the signal is measured. Not only plastic deformation or solid crack, but also phenomena in which pressure changes such as chemical reactions, phase transformations, and phase changes occur are the source of acoustic emission (AE) signals. The AE signal related to boiling and two-phase is a sound generated when the nucleate boiling is grown, or the liquid-gas interface is broken on the free surface due to the rise of bubbles in the liquid. Another example is the highfrequency sound, which is initially loud when the electric kettle is turned on, is caused by the formation of unsaturated nucleate boiling bubbles and rapid condensation. As such, this study starts from the simple idea that a change in boiling state generates an acoustic emission signal. We postulated that the characteristics of the acoustic emission signal will be different for each boiling heat transfer regimes that have different mechanisms. Deep learning is composed of various linear algebraic functions and a differential-based optimal function and has the advantage of easily figuring out non-linear relationships that are difficult to grasp with human cognitive ability. This paper is for predicting and diagnosing the boiling regimes inside the system using acoustic signals that measured outside the boiling heat transfer system and deep learning technology. Section 2 of this paper describes the acoustic signal data measured for each boiling heat transfer regimes, Section 3 describes the deep learning-based real-time boiling heat transfer regimes prediction diagnostic model, and Section 4 describes the model performance evaluation.