The goal of the proposed computational model was to evaluate the dynamical properties of air gauges in order to exploit them in such industrial applications as in-process control, form deviation measurement, dynamical measurement. The model is based on Reynolds equations complemented by the k-ε turbulence model. The boundary conditions were set in different areas (axis of the chamber, side surfaces, inlet pipeline and outlet cross-section) as Dirichlet's and Neumann's ones. The TDMA method was applied and the efficiency of the calculations was increased due to the "line-by-line" procedure. The proposed model proved to be accurate and useful for non-stationary two-dimensional flow through the air gauge measuring chamber.
In the article, the authors analyze and discuss several models used to the calculation of air gauge characteristics. The model based on the actual mass flow (which is smaller than the theoretical one) was proposed, too. Calculations have been performed with a dedicated software with the second critical parameters included. The air gauge static characteristics calculated with 6 different models were compared with the experimental data. It appeared that the second critical parameters model (SCP) provided the characteristics close to the experimental ones, with an error of ca. 3% within the air gauge measuring range.
The commercially available metal-oxide TGS sensors are widely used in many applications due to the fact that they are inexpensive and considered to be reliable. However, they are partially selective and their responses are influenced by various factors, e.g. temperature or humidity level. Therefore, it is important to design a proper analysis system of the sensor responses. In this paper, the results of examinations of eight commercial TGS sensors combined in an array and measured over a period of a few months for the purpose of prediction of nitrogen dioxide concentration are presented. The measurements were performed at different relative humidity levels. PLS regression was employed as a method of quantitative analysis of the obtained sensor responses. The results of NO2 concentration prediction based on static and dynamic responses of sensors are compared. It is demonstrated that it is possible to predict the nitrogen dioxide concentration despite the influence of humidity.
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