METHOD FOR DETERMINATION OF THE CHARACTERISTIC CURVE OF THE THERMAL INERTIA OF AIRCRAFT GAS TEMPERATURE SENSORS - A. F. Sabitov; I. A. Safina (Приборы и методы измерений, 2017)
The effectiveness of correction of the dynamic characteristics of gas temperature sensors in automatic control systems for the operation of aircraft gas turbine engines depends on the accuracy of the time constants of the sensors used from heat exchange conditions. The aim of this work was to develop a new method for determining the characteristic curves of the thermal inertia of gas temperature sensors.The new technique does not require finding the time constants of gas temperature sensors on the experimental transient characteristics. Characteristic curves for each time constant are defined as hyperbolic dependencies on the heat transfer coefficient of the gas temperature sensors sensing element with the gas flow. Parameters of hyperbolic dependencies are proposed to be established using two-dimensional regression analysis. For this purpose, special software has been developed in the Mathcad 14 and Mathcad 15. The software allows inputting the original data from the transient characteristics to the corresponding vectors or from tables in Excel format. It is shown that the transient characteristics in three-dimensional coordinates«time – heat transfer coefficient – the value of the transition characteristic» form a surface whose parameters are parameters of the desired hyperbolic dependencies.For a specific application of the technique, the regression functions for the dynamic characteristics of gas temperature sensors corresponding to the first and second orders are given. Analysis of the characteristic dependencies suggests that the proposed method more accurately establishes the dependence of the dynamic characteristics of aircraft gas temperature sensors on heat exchange conditions.It is shown that the algorithm of two-dimensional regression analysis realizes finding more accurate values of the parameters of the characteristic dependencies. The found parameters of the characteristic dependencies in a best way reach the surface of the chosen regression function at the same time to all the experimental transient characteristics.