Volume 29, Issue 1 -

The paper demonstrates the flow behavior in a high specific speed axial-flow pump at part-load condition by numerical methods. The unsteady Reynolds averaged NavierStokes equations (URANS) with the filter-based model were solved by using the computational fluid dynamics code ANSYS CFX and the hydraulic performance of axial-flow pump was estimated. At part-load condition numerical results show that parameters of hydraulic performance predicted by the filter-based model are proved to match with the experiment results better than that of the standard k - ε model. Furthermore, we analyzed the internal flow field within the axial-flow pump in fully developed stall and in deep stall, respectively, and compared the obtained typical flow features with the flow visualization experimental results in (I. Goltz, 2003). The boundary vorticity flux (BVF) was introduced to diagnose the internal flow field inside blade passage at part-load condition and to find out the root of the instability vortices. Based on the BVF diagnosis, the differences about the numerical results of flow fields in deep stall were further analyzed between the standard k - ε model and the filter-based model. It show that the filter-based model is more practical to capture the unsteady flow structures than the standard k - ε model.

Lean combustion is a method in which combustion takes place under low equivalence ratio and relatively low combustion temperatures. As such, it has the potential to lower the effect of the relatively high activation energy nitrogen-oxygen reactions which are responsible for substantial NO X formation during combustion processes. However, lowering temperature reduces the reaction rate and deteriorates combustion stability. The objective of the present study is to reduce the lower equivalence ratio limit of the stable combustion operational boundary in lean Gas Turbine (GT) combustors while still maintaining combustion stability. A lean premixed gaseous combustor was equipped with a surrounding concentric pilot flame operating under rich conditions, thus generating a hot stream of combustion products with significant amount of reactive radicals. The main combustor's fuel-air composition was varied from stoichiometric to lean mixtures. The pilot's mixture composition was also varied by changing the air flow rate, within a limited rich mixtures range. The pilot fuel flow rate was always lower than five percent of the total fuel supply at the specific stage of the experiments.

A critical nozzle is used to measure the mass flow rate of gas. It is well known that the coefficient of discharge of the flow in a critical nozzle is a single function of the Reynolds number. The purpose of the present study is to investigate the real gas effect on discharge coefficient and thermodynamics properties through a critical nozzle by using H 2 , N 2 , CH 4 and CO 2 , with the help of a CFD method and to clarify the relationship between mass flow rate of real gas flows at the nozzle throat and Reynolds number numerically. Redlich–Kwong equation of state was employed to consider the force and volume effects of inter-molecules of these gases. Furthermore, conservative equation of vibration energy was applied to investigate the effect of relaxation phenomena involving molecular vibration.

In order to drastically exploit the potential of the aero engine and improve acceleration performance in the combat state, an on-line optimized controller based on genetic algorithms is designed for an aero engine. For testing the validity of the presented control method, detailed joint simulation tests of the designed controller and the aero engine model are performed in the whole flight envelope. Simulation test results show that the presented control algorithm has characteristics of rapid convergence speed, high efficiency and can fully exploit the acceleration performance potential of the aero engine. Compared with the former controller, the designed on-line optimized controller (DOOC) can improve the security of the acceleration process and greatly enhance the aero engine thrust in the whole range of the flight envelope, the thrust increases an average of 8.1% in the randomly selected working states. The plane which adopts DOOC can acquire better fighting advantage in the combat state.

Efficiency of gas turbine condition monitoring systems depends on quality of diagnostic analysis at all its stages such as feature extraction (from raw input data), fault detection, fault identification, and prognosis. Fault identification algorithms based on the gas path analysis may be considered as an important and sophisticated component of these systems. These algorithms widely use pattern recognition techniques, mostly different artificial neural networks. In order to choose the best technique, the present paper compares two network types: a multilayer perceptron and a radial basis network. The first network is being commonly applied to recognize gas turbine faults. However, some studies note high recognition capabilities of the second network. For the purpose of the comparison, both networks were included into a special testing procedure that computes for each network the true positive rate that is the probability of a correct diagnosis. Networks were first tuned and then compared using this criterion. Same procedure input data were fed to both networks during the comparison. However, to draw firm conclusions on the networks' applicability, comparative calculations were repeated with different variations of these data. In particular, two engines that differ in an application and gas path structure were chosen as a test case. By way of summing up comparison results, the conclusion is that the radial basis network is a little more accurate than the perceptron, however the former needs much more available computer memory and computation time.