No abstract

No abstract

No abstract

This work developed the modeling and supervisory control for gas turbine. A CTPN (continuous timed Petri Net) model of a gas turbine, using a first linear order approximation for every state of the Brayton cycle is obtained. The Brayton cycle rules the functioning of a gas turbine, and it is composed by four states: compression, combustion, expansion and cooling. The principle of the gas turbine is developed by the Brayton cycle, a thermodynamic process which intervenes in the gas turbine components. The steady-state behavior of the gas turbine has been widely investigated in engineering area. Moreover, the dynamic behavior has been studied using non-linear models of its components, leading to complicated mathematical representations. The methodology of the current work begins with a simplification of the dynamical relations in every state (excepting the cooling phase) of the Brayton cycle. Temperature and pressure are modeled as first order linear systems, therefore, every system is translated into a CTPN. Furthermore, to guarantee a safety operation, an SC (supervisory controller) is designed to ensure the combustion chamber temperature is lower than 1,000 °C. Although the model presented is extremely simplified, it will be used as a starting point to develop more complex models.

The present work develops an estimator for thermal conductivity using a simple experiment implemented in a simulated solid metallic bar. The bar is sectioned in a finite number of segments, lately called nodes, and a discretization of the Fourier heat equation is applied in each node to generate a timed-spaced model of the temperature behavior along the bar. Considering only one-dimensional heat flow, an algorithm based on the temperature measured in each node generates the calculus of the estimated thermal conductivity for every segment of the bar. The calculations of thermal conductivity depends on previous values, such as temperature measurements and adjacent segments thermal conductivity, leading to an error propagation. The analysis of uncertainty related to this values is used to establish a range of values for thermal conductivity estimation. Using the proposed technique allows to calculate thermal conductivity in real time and add to the results a uncertainty estimation for thermal conductivity, providing a more complete information about the measurement procedure. Knowing the uncertainty allows to indicate, in statistical terms, the dispersion of the actual values for thermal conductivity, since the values calculated may vary from real, a higher uncertainty implies a lest reliable calculation according to statistics.