Tip leakage flow and heat transfer on turbine blade tip and casing, Part 1: Effect of tip clearance height and rotation speed

Steady simulations were performed to investigate tip leakage flow and heat transfer characteristics on the rotor blade tip and casing in a single-stage gas turbine engine. A typical high-pressure gas turbine stage was modeled with a pressure ratio of 3.2. The predicted isentropic Mach number and adiabatic wall temperature on the casing showed good agreement with available experimental data under similar operating condition. The present numerical study focuses extensively on the effects of tip clearance heights and rotor rotational speeds on the blade tip and casing heat transfer characteristics. It was observed that the tip leakage flow structure is highly dependent on the height of the tip gap and the speed of the rotor. In all cases, the tip leakage flow was seen to separate and recirculate just around the corner of the pressure side of the blade tip. This region of re-circulating flow enlarges with increasing clearance heights. The separated leakage flow reattaches afterwards on the tip surface. Leakage flow reattachment was shown to enhance surface heat transfer at the tip. The interaction between tip leakage flow and secondary flows that is induced by the relative casing motion is found to significantly influence the blade tip and casing heat transfer distribution. A region of critical heat transfer exists on the casing near the blade tip leading edge and along the pressure-side edge for all the clearance heights that were investigated. At high rotation speed, the region of critical heat transfer tends to move towards the trailing edge due to the change in inflow angle.

Unsteady tip leakage characteristics and heat transfer on turbine blade tip and casing

An unsteady numerical investigation was performed to examine time dependent behaviors of the tip leakage flow structures and heat transfer on the rotor blade tip and casing in a single stage gas turbine engine. A transonic, high-pressure
turbine stage was modeled and simulated using a stage pressure ratio of 3.2. The rotor’s tip clearance was 1.2 mm in height (3% of the rotor span) and its speed was set at 9500 rpm. Periodic flow is observed for each vane passing period. Tip leakage flow as well as heat transfer data showed highly time dependent behaviors. A stator trailing edge shock appears as the turbine stage is operating at transonic conditions. The shock alters the flow condition in the rotor section, namely, the tip leakage flow structures and heat transfer rate distributions. The instantaneous Nusselt number distributions are compared to the time averaged and steady-state results. The same patterns in tip leakage flow
structures and heat transfer rate distributions were observed in both unsteady and steady simulations. However, the unsteady simulation captured the locally time-dependent high heat transfer phenomena caused by the unsteady interaction with the upstream vane trailing-edge shock and the passing wake.

Gas turbine diagnostics using a soft computing approach

A fuzzy system is developed using a linearized performance model of the gas turbine engine for performing gas turbine fault isolation from noisy measurements. By using a priori information about measurement uncertainties and through design variable linking, the design of the fuzzy system is posed as an optimization problem with low number of design variables which can be solved using the genetic algorithm in considerably low amount of computer time. The faults modeled are module faults in five modules: fan, low pressure compressor, high pressure compressor, high pressure turbine and low pressure turbine. The measurements used are deviations in exhaust gas temperature, low rotor speed, high rotor speed and fuel flow from a base line 'good engine'. The genetic fuzzy system (GFS) allows rapid development of the rule base if the fault signatures and measurement uncertainties change which happens for different engines and airlines. In addition, the genetic fuzzy system reduces the human effort needed in the trial and error process used to design the fuzzy system and makes the development of such a system easier and faster. A radial basis function neural network (RBFNN) is also used to preprocess the measurements before fault isolation. The RBFNN shows significant noise reduction and when combined with the GFS leads to a diagnostic system that is highly robust to the presence of noise in data. Showing the advantage of using a soft computing approach for gas turbine diagnostics.

Three- and Seven-Point Optimally Weighted Recursive Median Filters for Gas Turbine Diagnostics

Measured health signals incorporate significant details about any malfunction in a gas turbine. The attenuation of noise and removal of outliers from these health signals while preserving important features is an important problem in gas turbine diagnostics. The measured health signals are a time series of sensor measurements such as the low rotor speed, high rotor speed, fuel flow, and exhaust gas temperature in a gas turbine. In this article, a comparative study is done by varying the window length of acausal and unsymmetrical weighted recursive median filters and numerical results for error minimization are obtained. It is found that optimal filters exist, which can be used for engines where data are available slowly (three-point filter) and rapidly (seven-point filter). These smoothing filters are proposed as preprocessors of measurement delta signals before subjecting them to fault detection and isolation algorithms.

Fuzzy logic intelligent system for gas turbine module and system fault isolation

A fuzzy logic intelligent system is developed for gas-turbine fault isolation. The gas path measurements used for fault isolation are exhaust gas temperature, low and high rotor speed, and fuel flow. These four measurements are also called the cockpit parameters and are typically found in almost all older and newer jet engines. The fuzzy logic system uses rules developed from a model of performance influence coefficients to isolate engine faults while accounting for uncertainty in gas path measurements. It automates the reasoning process of an experienced powerplant engineer. Tests with simulated data show that the fuzzy system isolates faults with an accuracy of 89% with only the four cockpit measurements. However, if additional pressure and temperature probes between the compressors and before the burner, which are often found in newer jet engines, are considered, the fault isolation accuracy rises to as high as 98%. In addition, the additional sensors are useful in keeping the fault isolation system robust as quality of the measured data deteriorates.