Adaptive modelling (AM) based Gas Path Analysis (GPA) is a powerful diagnostic and prognostic technique for turbofan engine maintenance. This involves the assessment of turbofan component condition using thermodynamic models that can iteratively adapt to measurements values in the gas path by changing component condition parameters. The problem with this approach is that newer turbofan engines such as the General Electric GEnx-1B have fewer gas path sensors installed causing the AM equation systems to become underdetermined. To overcome this problem, a novel approach has been developed that combines the AM model with an Evolutionary Algorithm (EA) optimization scheme and applies it to multiple operating points. Additionally, these newer turbofan engines provide performance data continuously during flight. Information on variable geometry and bleed valve position, active clearance control state and power off-take is included and can be accounted for to further enhance AM model accuracy. A procedure is proposed where the selection of operating points is based on steady-state stability requirements, cycle model operating point uncertainty and parameter outlier filtering. The Gas turbine Simulation Program (GSP) is used as the non-linear GPA modelling environment. A Multiple Operating Point Analysis (MOPA) is chosen to overcome the problem of underdetermination by utilizing multiple data sets at different operating points. The EA finds the best fit of health parameter deviations by minimizing the multi-point objective function using the GSP AM model. A sub-form of the EA class named Differential Evolution (DE) has been chosen as the optimizer. Like all EAs, DE is a parallel direct search method in which a population of parameter vectors evolves following genetic operations towards an optimum output candidate. The resulting hybrid GPA tool has been verified by solving for different simulated deterioration cases of a GSP model. The tool can identify the direction and magnitude of condition deviation of 10 health parameters using 6 gas path sensors. It has subsequently been validated using historical in-flight data of the GEnx-1B engine. It has demonstrated successful tracking of engine component condition for all 10 health parameters and identification of events such as turbine blade failure and water washes. The authors conclude that the tool has proven significant potential to enhance turbofan engine condition monitoring accuracy for minimizing maintenance costs and increasing safety and reliability.

The maximum attainable performance of small gas turbines represents a strong limitation to the operating altitude and endurance of high-altitude unmanned aerial vehicles (UAVs). Significant improvement of the cycle thermal efficiency can be achieved through the introduction of heat exchangers, with the consequent increase of the overall engine weight. Since semi-closed cycle engines can achieve a superior degree of compactness compared to their open cycle counterparts, their use can offset the additional weight of the heat exchangers. This paper applies semi-closed cycles to a high-altitude UAV propulsion system, with the objective of assessing the benefits introduced on the engine performance and weight. A detailed model has been created to account for component performance and size variation as function of thermodynamic parameters. The sizing has been coupled with a multi-objective optimisation algorithm for minimum specific fuel consumption and weight. Results of two different semi-closed cycle configurations are compared with equivalent state-of-the-art open cycles, represented by a recuperated and an intercooled-recuperated engine. The results show that, for a fixed design power output, engine weight is approximately halved compared to state-of-the-art open cycles, with slightly improved specific fuel consumption performance. Optimum semi-closed cycles furthermore operate at higher overall pressure ratios than open cycles and make use of recuperators with higher effectiveness as the mass penalty of the recuperator is smaller due to the lower engine mass flow rates.

The demand for more environmentally friendly and economic power production has led to an increasing interest to utilize alternative fuels. In the past, several investigations focusing on the effect of low-calorific fuels on the combustion process and steady-state performance have been published. However, it is also important to consider the transient behavior of the gas turbine when operating on nonconventional fuels. The alternative fuels contain very often a large amount of dilutants resulting in a low energy density. Therefore, a higher fuel flow rate is required, which can impact the dynamic behavior of the gas turbine. This paper will present an investigation of the transient behavior of the all-radial OP16 gas turbine. The OP16 is an industrial gas turbine rated at 1.9 MW, which has the capability to burn a wide range of fuels including ultra-low-calorific gaseous fuels. The transient behavior is simulated using the commercial software GSP including the recently added thermal network modeling functionality. The steady-state and transient performance model is thoroughly validated using real engine test data. The developed model is used to simulate and analyze the physical behavior of the gas turbine when performing load sheds. From the simulations, it is found that the energy density of the fuel has a noticeable effect on the rotor over-speed and must be considered when designing the fuel control.