Automatic measurement and acoustic analysis of Leidenfrost phenomenon
Date Issued
2010
Date
2010
Author(s)
Tseng, Ku-Wei
Abstract
The purpose of this study is to use the signal processing method of time domain and frequency domain analyzing the acoustic of droplet touching high temperature surface, and use the characteristic of the acoustic signal of time domain and frequency domain to determine if reach Leidenfrost temperature or not. In the past, Leidenfrost experiment usually costs very long time and intensive human power. When a person is fatigue, there will be more errors with experiment data. Therefore, the first step is to make the experiment automatic. We can separate the system equipment into 4 parts, real-time image identification, micro-pump control, surface temperature control and acoustic information recording. By using LabVIEW software, we wrote a program to link these 4 sections to achieve automation. As for the stability of the system, we compared human eye and image identification, the maximum difference is 3.8 seconds to read the same Leidenfrost temperature. We used stainless steel panel as heating surface, and the fluid we used are water, water/ethylene glycol(mass percentage 0%, 5%, 50%, 70%), water/IPA(mole fraction 0, 0.01, 0.02), ethanol /n-heptane (mole fraction 0, 0.01, 0.02). We obtained the acoustic information of the experiment via the automation equipment, and indentified Leidenfrost temperature trough four signal processing methods (the largest volume method, the total volume method, the first collision time method, as well as wavelet transform method). If the result is in between ±6oC of accurate Leidenfrost temperature, we considered it’s a successful identification. The source of the sound in this experiment is from the bubble explosive, therefore, the time domain methods fail to successfully indentify the Leidenfrost temperature. The most successful method of time domain is the first collision time method; it successfully indentified 19 out of 50 sets of experiments. The methods of frequency domain can identify Leidenfrost temperature more effectively, wavelet transform method successfully indentified 36 out of 50 sets of experiments.
Subjects
Leidenfrost phenomenon, automation, signal processing, spectrum analsis
Type
thesis
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