The inspection of aero engines is a complex and time-consuming process, often requiring the disassembling of the engine or boroscopic examinations. The development of a method to locate and characterize defects and damage at an early stage, without disassembling the engine would accelerate the inspection process. For that purpose, the spatial density distribution pattern of the exhaust jet of aircraft engines may be measured with the Background Oriented Schlieren method (BOS). The hypothesis is that defects in the hot gas path have a noticeable impact on the density pattern of the exhaust jet. To establish the connection between defects and measurable patterns, in the present paper numerical simulations of an aero engine are performed including three potential defects. Non-uniformities resulting from a burner malfunction, the increase of the radial gap between blade tip and casing as well as burned trailing edges are propagated with only small degree of dispersion through the turbine and reach the engine exit. The paper shows that each considered defect results in a different exhaust density pattern.

Defects in combustion chambers of aircraft engines might have an impact on the reliability of the downstream turbine and the machine’s performance. Detecting failures in the combustion chamber of an aircraft engine during operation may improve the resource management and the availability of the system. Aim of the ongoing research project is to find an approach to evaluate the state of the jet engine by analyzing the temperature and emissions field in the exhaust jet. This investigation is part of the collaborative research center SFB 871. The SFB 871 deals with the improvement of the regeneration process of complex capital goods such as aircraft engines. Maintenance, repair, and overhaul processes would be more efficient if the internal status of the engine would be known while still on the wing before it is disassembled. The feasibility of this approach is investigated for a pilot scaled model combustor, which provides optical access and allows the selection of “defined errors” in the combustor. It consists of an atmospheric tubular combustor with an array of eight premixed swirl burners with a maximum output of 160 kW. The operating conditions of one of the eight burners concerning power and air-fuel ratio, can be controlled. A power distribution between the burners is typical fault in an aircraft combustor and will be investigated in this study. It is observed that it is possible to determine small deviations by measuring density profiles applying a tomographic background-oriented schlieren (BOS) technique behind the combustor. Additionally, particle image velocimetry is used to measure differences in the velocity field of the exhaust gases. This study shows that a minimum power deviation of one burner in an array of a total of eight burners is detectable in the exhaust plane with the above mentioned measurement techniques.

The maintenance, repair, and overhaul process of aircraft engines might be optimised to reduce long and expensive downtime. At present, only limited forecasts of the engine condition can be made prior to disassembly. Since faulty conditions inside an engine affect the internal flow and spread out minimally through the turbine, a new methodological approach is to analyse the exhaust jet in order to detect anomalies. Failures lead to streaks of temperature or concentration through the combustion chamber, the turbine and downstream to the exhaust jet and, thus, can be observed with suitable measurement techniques. In the current work the influence of possible damages inside an aircraft engine on the temperature and velocity pattern at the exit of the combustion chamber is investigated in detail applying three-dimensional computational methods (CFD) to simulate the flow and reaction processes. It can be shown that a burner with reduced load leads to a clear influence on the exit pattern; whereas damages in the outer or inner liner, i.e. a mismatch of the secondary air flow, do not significantly affect the exhaust jet. Future studies focus on quantifying also the mixing processes in the subsequent turbine passage and developing approaches for the measurement of structures in the exhaust jet.

A significant challenge in improving the regeneration process of jet engines is the reduction of engine down-time during inspection. As such, early defect detection without engine disassembly will speed up the regeneration process. Defects in the engines hot-gas path (HGP) influence the density distribution of the flow and lead to irregularities in the density distribution of the exhaust jet which can be detected with the optical Background-Oriented Schlieren (BOS) method in a tomographic set-up. The present paper proposes a combination of tomographic BOS measurements and supervised learning algorithms to develop a methodology for an automatic defect detection system. In a first step, the methodology is verified by analyzing the exhaust jet of a swirl burner array with a non-uniform fuel-supply of single burners with tomographic BOS measurements. The measurements are used to implement a Support Vector Machine (SVM) pattern recognition algorithm. It is shown that the reconstruction quality of tomographic BOS measurements is high enough to be combined with pattern recognition algorithms. The results strengthen the hypothesis, that it is possible to automatically detect defects in jet engines with tomographic BOS measurements and pattern recognition algorithms.

A significant challenge in improving the regeneration process of jet engines is the reduction of engine down-time during inspection. As such, early defect detection without engine disassembly will speed up the regeneration process. Defects in the engines hot-gas path (HGP) influence the density distribution of the flow and lead to irregularities in the density distribution of the exhaust jet which can be detected with the optical background-oriented Schlieren (BOS) method in a tomographic setup. The present paper proposes a combination of tomographic BOS measurements and supervised learning algorithms to develop a methodology for an automatic defect detection system. In the first step, the methodology is tested by analyzing the exhaust jet of a swirl burner array with a nonuniform fuel-supply of single burners with tomographic BOS measurements. The measurements are used to implement a support vector machine (SVM) pattern recognition algorithm. It is shown that the reconstruction quality of tomographic BOS measurements is high enough to be combined with pattern recognition algorithms. The results strengthen the hypothesis that it is possible to automatically detect defects in jet engines with tomographic BOS measurements and pattern recognition algorithms.