Probabilistic Analysis of Gas Turbine Field Performance

A gas turbine thermodynamic cycle was computationally simulated and probabilistically evaluated in view of the several uncertainties in the performance parameters, which are indices of gas turbine health. Cumulative distribution functions and sensitivity factors were computed for the overall thermal efficiency and net specific power output due to the thermodynamic random variables. These results can be used to quickly identify the most critical design variables in order to optimize the design, enhance performance, increase system availability and make it cost effective. The analysis leads to the selection of the appropriate measurements to be used in the gas turbine health determination and to the identification of both the most critical measurements and parameters. Probabilistic analysis aims at unifying and improving the control and health monitoring of gas turbine aero-engines by increasing the quality and quantity of information available about the engine’s health and performance. Conventional engineering design methods are deterministic. The components of a machine are considered as ideal systems and parameter optimizations provide single point estimates of the system response. In reality, many engineering systems are stochastic where a probability assessment of the results is required. Probabilistic engineering design analysis assumes probability distributions of design parameters, instead of mean values only. This enables the designer to design for a specific reliability and hence maximize safety, quality and cost. The approaches for incorporating probabilistic effects in design include the use of factors of safety, the use of the worst case design and the use of probabilistic design. Utilizing the uncertainties in the estimations, deterministic engineering design uses factors of safety to assure that the nominal operational condition does not come too close to the point where the system will fail. The approximation of minimum properties and maximum loads known as the absolute worst case gives information about this critical point. This approach limits the optimization capability of a system and fails to provide important information about the system lifetime. The design procedures of the advanced aerospace vehicles must account for uncertainties calculating the risk or reliability. These calculations will involve probabilistic analysis. When compared with traditional factor of safety methods, probabilistic methods require additional inputs but provide higher quality outputs. The uncertain or random variables are assumed to have a probability density function. The output will be a probability density function for the response quantities. A robust design is one that has been created with a system of design tools that reduce product or process variability while guiding the performance toward an optimal setting. Robustness means achieving excellent performance under a wide range of operating conditions. All engineering systems function reasonably well under ideal conditions, but robust designs continue to function well when the conditions are non-ideal. Analytical robust design attempts to determine the values of design parameters which maximize the reliability of the product without tightening the material or environmental tolerances. Probabilistic design and robust design go hand in hand. In order to determine the domains of stability, the system has to be analyzed probabilistically.