Construction Of Gas Ultrasonic Spectrum And Its Application In Gas Detection

Gas detection has been used in many areas:industry, agriculture, environmental industry, national defense, aerospace industry, and daily life. Compared with traditional technologies of gas sensing, acoustic-based gas sensing has many advantages:no calibration, strong repeatability, real-time response, simultaneously detecting several kinds of gas, no need to preprocess gas, and no loss of gas. Acoustic gas detection (AGD) has become cutting-edge technology in the field of gas-information sensing and detecting techniques.AGD is primarily based on establishment of gas ultrasonic spectrums (GUS) which consist of gas acoustic absorption spectrums-gas acoustic absorption coefficient dependent on the acoustic frequency, and sound speed spectrums-sound speed dependent on the acoustic frequency. So the first thing needed for AGD is analyzing the gas acoustic relaxation process to establish GUS. After constructing the GUS, signal processing technology is used for the further research in ultrasonic-spectrum-based gas detection which is cutting-edge research of interdisciplinary including acoustics, quantum physics, and signal processing.Based on acoustic propagation-in-gas characteristics and acoustic wave equations under the condition of gas being disturbed by acoustic waves, via researching gas acoustic absorption theory, gas sound speed theory, and ultrasonic signal processing theory, a decomposition model and a multi-relaxation time model are obtained in the process of gas acoustic relaxation; GUS is also built up. Based on gas acoustic relaxation absorption spectrum (GARAS), the gas detection is realized by utilizing wavelet multi-resolution analysis (WMRA) and multi-class support vector machine (MSVM).The main research results are as follows:1A decomposition model---the decomposition relationship between effective specific heat capacity (ESHC) and relaxation time---and the general method of getting the model is obtained via researching the model of molecule energy transfer among vibration-translation (V-T) and vibration-vibration (V-V) in gas acoustic relaxation process. Compared with current acoustic relaxation models, this model has two characteristics:(1) the relationship between vibrational specific heat capacity and relaxation time in the process of V-T and V-V relaxation is obtained;(2) it is discovered that higher energy level is the determining factor of causing relaxation. Then, the model is modified by fine tuning collision diameter. The modified model suits the GARAS closer to experimental data by comparing with spectral lines from existing theories.2A reciprocal-sum model of multi-relaxation time is proposed, and using the relationship between the model and ESHC, GARAS is built up. GARAS functions to offer two physical effective relaxation frequency and effective relaxation time. Such method of establishing GARAS is correct:via comparison between collected experimental data and GARAS obtained by this method, only small error exits and the trend of spectra lines are the same. And the method is effective:through comparing data between spectrums from this method and spectrums from several existing theories, it is discovered that the changing rule of data are the same. This method is used to establish the GARAS of air and the gas mixtures including air to supply fundament in the further research.3Based on GUS theories and data collected systematically in published papers, and the data extant by the decomposition model, a database of gas acoustic spectra, which includes acoustic absorption spectrums and sound speed spectrums, has been built to record common gas mixtures from gases as diverse as N2、O2、H2O、CH4、H2、CO2and air. By statistics of GARAS key information---the primary peaks---from the database, a graph concerning GARAS key information areas is obtained. By researching the graph, two methods are obtained:(1) detecting qualitatively gas-components;(2) sensing quantitatively gas-components based on the changing range of the maximum acoustic relaxation absorption coefficient and acoustic frequency. Physically, the result proves that gas compositions detection based on gas acoustic spectra is feasible.4WMRA and MSVM, classic signal processing methods, are introduced for the first time in AGD. WMRA and MSVM are used for analyzing GARAS numerically---the key of AGD technology. WMRA is used to get features of GARAS. And then, the feature coefficients with high recognition rate and low computation cost are selected from these features and put into MSVM to train and to test it. The trained MSVM will help recognize four types of gas mixtures (air, air and CO, air and CO2, air and CH4) successfully. The simulation results demonstrate that the recognize accuracy of the approach is100%for four types of gas mixtures. In place of a traditional way is a new method of detecting one or several gases (CO, CO2, and CH4) from multi-component mixtures of gas such as air---a theoretical research of gas detection based on ultrasonic spectrums.This dissertation is funded by two projects listed in the National Natural Science Foundation of China:ultrasonic-based gas detection (Grant Nos.60971009) and the research of effective-relaxation-time-based gas sensing (Grant Nos.61001011). This dissertation introduces several research findings:(1) the relationship between ESHC and relaxation time is obtained via analyzing gas acoustic relaxation process;(2) the theory model of reciprocal-sum of multi-relaxation time is obtained by utilizing translational relaxation time and vibrational relaxation time and used to build up GUS;(3) the GUS database is established, which physically proves that ultrasonic-spectrum-based gas detection is practical;(4) WMRA and MSVM are used to acoustic gas detection, which suggests a new train of thought in the area of acoustic gas detection.