Life usage algorithms constitute one of the principal components of gas turbine engines monitoring systems. These algorithms aim to determine the remaining useful life of gas turbines based on temperature and stress estimation in critical hot part elements. Knowing temperatures around these elements is therefore very important. This paper deals with blades and disks of a high pressure turbine (HPT). In order to monitor their thermal state, it is necessary to set thermal boundary conditions. The main parameter to determine is the total gas temperature in relative motion at the inlet of HPT blades Tw*. We propose to calculate this unmeasured temperature as a function of measured gas path variables using gas path thermodynamics. Five models with different thermodynamic relations to calculate the temperature Tw* are proposed and compared. All temperature models include some unmeasured parameters that are presented as polynomial functions of a measured power setting variable. A nonlinear thermodynamic model is used to calculate the unknown coefficients included in the polynomials and to validate the models considering the influence of engine deterioration and operating conditions. In the validation stage, the polynomial’s inadequacy and the errors caused by the measurement inaccuracy are analyzed. Finally, the gas temperature models are compared using the criterion of total accuracy and the best model is selected.
One of the main functions of gas turbine monitoring is to estimate important unmeasured variables, for instance, thrust and power. Existing methods are too complex for an online monitoring system. Moreover, they do not extract diagnostic features from the estimated variables, making them unusable for diagnostics. Two of our previous studies began to address the problem of “light” algorithms for online estimation of unmeasured variables. The first study deals with models for unmeasured thermal boundary conditions of a turbine blade. These models allow an enhanced prediction of blade lifetime and are sufficiently simple to be used online. The second study introduces unmeasured variable deviations and proves their applicability. However, the algorithms developed were dependent on a specific engine and a specific variable. The present paper proposes a universal algorithm to estimate and monitor any unmeasured gas turbine variables. This algorithm is based on simple data-driven models and can be used in online monitoring systems. It is evaluated on real data of two different engines affected by compressor fouling. The results prove that the estimates of unmeasured variables are sufficiently accurate, and the deviations of these variables are good diagnostic features. Thus, the algorithm is ready for practical implementation.
Algorithms for predicting the remaining lifetime of an engine play an important role in gas turbine monitoring systems. This paper addresses the improvement of models to determine the thermal boundary conditions that are necessary to calculate engine lifetime in critical hot components. Two methods for model development are compared. The first method uses physics-based models. The second method formulates the models based on a similarity concept. The object of analysis is a cooled blade of a high-pressure turbine. Two unmeasured thermal boundary conditions are considered: the heating temperature and the heat transfer coefficient. Instrumental and truncation errors are estimated for each model and 10 faulty conditions are considered to take into account the existing engine-to-engine differences and performance deterioration. The blade temperature and the thermal stress at the critical points are calculated using the results obtained by the developed models as boundary conditions. The results of the comparison show that the physics-based models are more robust to power plant faults. The best models for the heating temperature and the heat transfer coefficient were chosen. It is shown that the accuracy of the heating temperature model is more important for reliable lifetime prediction.
The known studies in the area of gas turbine lifetime prediction do not result in the algorithms for on-line engine monitoring. This article introduces and investigates a new method for developing ''light'' mathematical models to estimate static thermal boundary conditions for gas turbine hot elements. In contrast to the previous developments, these models allow on-line lifetime monitoring of such elements. The blade of the high-pressure turbine of a two-spool free turbine power plant was chosen as a test case. The models of blade boundary conditions were developed based on well-known thermodynamic relations and a steady-state nonlinear physics-based model of this engine. Many candidate models are analyzed in the article, and the best models are selected by their accuracy and robustness to engine faults using instrumental and truncation errors as criteria. The instrumentation errors are induced by measurement inaccuracy of gas path variables used. For the analysis of the model robustness, the truncation errors are computed. They appear when performance of an engine deviates from a baseline due to normal degradation of the engine and because of its faults. The gas path parameters under healthy and faulty engine health conditions are simulated by the thermodynamic model. These simulated quantities are used as the input data to perform the comparison of the candidate models. The final accuracy analysis shows that the proposed method improves the estimates of the thermal boundary conditions. As a result, prediction of an engine lifetime becomes significantly more accurate. The article also determines the positive effect of the compressor discharge temperature sensor. When it is installed in addition to a standard gas path measurement system, the accuracy of the measurement-based lifetime prediction grows drastically.
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