Deep learning for intelligent bubble size detection in the SNS visual target  
Fayaz Rasheed, Elvis E. Dominguez-Ontiveros, Justin R. Weinmeister, Charlotte N. Barbier

*The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory (ORNL) will undergo proton power upgrade (PPU) increasing proton beam power from 1.4 MW to 2.8 MW. From the 2.8 MW, 2.0 MW will go to the current First Target Station (FTS) while the rest of the power will go to the future Second Target Station (STS). FTS uses a liquid mercury target which is contained in a 316L stainless steel vessel. The proton beam is pulsed at 60 Hz with a pulse of about 0.7ms. When the proton beam hits the target, the intense energy deposition leads to a rapid rise in temperature in the mercury. This temperature rise creates pressure waves that propagate through the mercury and cause cavitation erosion. The power upgrade will cause stronger pressure waves that will further increase damage due to cavitation. Injection of small helium bubbles in the mercury has been an efficient method of mitigating the pressure wave at 1.4 MW. However, at higher power, additional mitigation is necessary. To address this, the 2MW target vessel will be equipped with swirl bubblers and an additional gas injection port near the nose to inject more gas in the target. To develop gas injection strategy and design, flow visualization in water with a transparent prototypical target (“visual target”) were performed. Bubble sizes and their spatial distribution in the flow loop are crucial to understanding the effectiveness of the bubbles in mitigating pressure waves. Bubbles were generated in the visual target under varied conditions of input pressures with both helium and air. Images were captured using a high-speed camera at varied frame rates at different positions away from the swirl bubbler and different depths in the flow loop, under varying lighting conditions. Initially, methods such as circular Hough transforms were applied to images after series of images processing, to obtain a general distribution of bubble sizes. Bubbles with a diameter or size less than 500* *mm are preferred to effectively mitigate the effect of pressure waves, and this demands an accurate bubble detection and sizing system. Intelligent detection and identification of bubble sizes alleviate misdetection and improves accuracies. Employing neural networks, intelligent detection of bubble sizes and their distribution was developed and provides a robust alternative to traditional techniques. Human intervention is employed to label in-focus and out-of-focus bubbles in the set of training images. An object detection network using a pre-trained Convolutional Neural Network was created that extracted the features from the training images. Data augmentation was used to improve network accuracy through a random transformation of the original data.*


Deep Learning for Intelligent Bubble Size Detection in the Spallation Neutron Source Visual Target

Division / Sponsors: 
Aerospace, Bioengineering, Applied Mechanics, Design Engineering Division, Advanced Energy Systems, Manufacturing Engineering Division, Noise Control and Acoustics, Nondestructive Evaluation, Diagnosis, and Prognosis (NDPD), Nuclear Engineering Division, Process Industries Division, Fluids Engineering, Heat Transfer, Materials, Microelectromechanical Systems, Safety Engineering and Risk Analysis, Computers & Information in Engineering


Paper Proceedings: https://asmedigitalcollection.asme.org/IMECE/IMECE2020/volume/84478

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