| dc.description.abstract | Abstract
This study aims to develop a novel pressure vessel detection system through an acoustic method. This monitoring system is designed to detect temperature changes within the vessel to enable preventative
actions and avoid potential equipment failure. The method is based on the principle that the speed of sound is temperature-dependent. The proposed method uses transducers to transmit ultrasonic waves
through the vessel. By measuring the time-of-flight of the signal and predetermining the material and geometry of the vessel, it is possible to calculate the temperature of the encapsulated gas. A time-offlight range is established based on the operating temperature of the vessel. If the time-of-flight of a transmitted signal is detected outside of the established range, it indicates a temperature change. This study used a non-invasive ultrasonic method to address the challenge of detecting temperature
changes inside a pressure vessel. Through a series of simulations, the computational model demonstrated its capability to measure the time-of-flight of ultrasonic waves accurately. Key findings from the test cases provided the following insights:
• Test Case 1 confirmed the model's validity compared to previous work.
• Test Case 2 demonstrated improvements in the geometric model, reducing the time-of-flight
and showing stronger pressure signals at the receiver.
• Test Case 3 revealed that material changes to steel did not significantly affect the time-offlight due to the minimal thickness of the plates, but significantly weakened the signal strength.
• Test Case 4 highlighted the model's sensitivity to extreme temperatures, where the increased temperature substantially shortened the time-of-flight and weakened the signal strength.
The conclusion of this study states that a computational model has established the method's feasibility; however, work is required to develop a functional prototype. The proposed further work involves conducting physical experiments to translate these computational findings into practical applications and validate the model under real-world conditions. | |
