Error Estimation of Measured Exhaust Gas Temperature in Afterburner Mode in an Aero Gas Turbine Engine

In a turbofan engine, thrust is a key parameter which is measured or estimated from various parameters acquired during engine testing in an engine testbed. Exhaust Gas Temperature (EGT) is the most critical parameter used for thrust calculation. This work presents a novel way to measure and correct the errors in EGT measurement. A temperature probe is designed to measure EGT in the engine jet pipe using thermocouples. The temperature probe is designed to withstand the mechanical and temperature loads during the operation. Structural analysis at the design stage provided a strength margin of 90% and eigenfrequency margin of more than 20%. Thermal analysis is carried out to evaluate maximum metal temperature. Errors are quite high in high-temperature measurements which are corrected using the available methodologies. The velocity error, conduction error, and radiation error are estimated for the measured temperature. The difference of 97 K between the measured gas temperature and calculated gas temperature from measured thrust is explained. The estimated velocity error is 1 K, conduction error is 3 K, and radiation error is 69 K. Based on the error estimation, the measurement error is brought down to 24 K. After applying the above corrections, the further difference of 24 K between measured and estimated value can be attributed to thermocouple error of +/-0.4% of the reading for class 1 accuracy thermocouple, other parameter measurement errors, and analysis uncertainties. The present work enables the designer to calculate the errors in high-temperature measurement in a turbofan engine.

Design Considerations of Low Bypass Ratio Mixed Flow Turbofan Engines with Large Power Extraction

The possibility of extracting large amounts of electrical power from turbofan engines is becoming increasingly desirable from an aircraft perspective. The power consumption of a future fighter aircraft is expected to be much higher than today’s fighter aircraft. Previous work in this area has concentrated on the study of power extraction for high bypass ratio engines. This motivates a thorough investigation of the potential and limitations with regards to performance of a low bypass ratio mixed flow turbofan engine. A low bypass ratio mixed flow turbofan engine was modeled, and key parts of a fighter mission were simulated. The investigation shows how power extraction from the high-pressure turbine affects performance of a military engine in different parts of a mission within the flight envelope. An important conclusion from the analysis is that large amounts of power can be extracted from the turbofan engine at high power settings without causing too much penalty on thrust and specific fuel consumption, if specific operating conditions are fulfilled. If the engine is operating (i) at, or near its maximum overall pressure ratio but (ii) further away from its maximum turbine inlet temperature limit, the detrimental effect of power extraction on engine thrust and thrust specific fuel consumption will be limited. On the other hand, if the engine is already operating at its maximum turbine inlet temperature, power extraction from the high-pressure shaft will result in a considerable thrust reduction. The results presented will support the analysis and interpretation of fighter mission optimization and cycle design for future fighter engines aimed for large power extraction. The results are also important with regards to aircraft design, or more specifically, in deciding on the best energy source for power consumers of the aircraft.

Anomaly Detection in Asset Degradation Process Using Variational Autoencoder and Explanations

Development of predictive maintenance (PdM) solutions is one of the key aspects of Industry 4.0. In recent years, more attention has been paid to data-driven techniques, which use machine learning to monitor the health of an industrial asset. The major issue in the implementation of PdM models is a lack of good quality labelled data. In the paper we present how unsupervised learning using a variational autoencoder may be used to monitor the wear of rolls in a hot strip mill, a part of a steel-making site. As an additional benchmark we use a simulated turbofan engine data set provided by NASA. We also use explainability methods in order to understand the model’s predictions. The results show that the variational autoencoder slightly outperforms the base autoencoder architecture in anomaly detection tasks. However, its performance on the real use-case does not make it a production-ready solution for industry and should be a matter of further research. Furthermore, the information obtained from the explainability model can increase the reliability of the proposed artificial intelligence-based solution.

A new solution to the coupled problems of aero-propulsion system deceleration under supersonic state

Under supersonic state, the aero-propulsion system exhibits different coupled characters in deceleration from that in acceleration. However, the deceleration control has not been fully studied. In order to solve the coupled problems, an integrated component-level model including inlet and turbofan engine was established. Based on the integrated model, the particularity of inlet adjustment during deceleration was analyzed. And the analyzed results showed that the inlet regulation is not necessary to keep the inlet and engine working in well-matched at any time under supersonic state. Due to the coupled relationship between inlet and turbofan engine, a new optimal integrated control scheme is proposed in this paper. The inlet ramp angle is taken as an optimal control variable as the same as main fuel mass flow and nozzle throat area. The simulation results indicate that inlet ramp angle regulation showed a more effective control quality in the rapid drop of aero-propulsion–installed thrust. Furthermore, the deceleration could be completed in a shorter control time.

An improved prognostics model with its application to the remaining useful life of turbofan engine

Machinery prognostics play a crucial role in upgrading machinery service and optimizing machinery operation and maintenance schedule by forecasting the remaining useful life (RUL) of the monitored equipment, which has become more and more popular in recent years. The safety of aviation is one of the issues that people are most concerned about in the field of transportation, since it might cause disastrous loss of life and property once accident happened. The turbofan engine is an important part of the aircraft that provides thrust for plane. With aging, the turbofan engine becomes prone to failures. As a result, it would be worth studying prognostics in turbofan engine to improve the reliability of machinery and reduce unnecessary maintenance cost. Recently, a data-driven prognostics modeling strategy called the classification of predictions strategy (CPS) was proposed, in which the continuous signal and the discrete modes of an actual system come together to achieve RUL estimation. However, machine health states measured from classification rarely have just one potential situation, and this strategy cannot determine whether the fault occurs or not by a certain probability which comes closer to reality. Moreover, since there is no information and prior knowledge of prognostics application, it is hard to obtain the probability of various situations from raw measured data. Hence, based on previous work, this paper proposes an improved prognostics modeling method named the classification of predictions strategy with decision probability (CPS-DP), whose key innovations mainly include three parts: (1) decision probability process (DPP) where each step of multi-step prediction obeys geometric distribution and can judge whether the failure state occurs using the decision probability; (2) decision probability calculation (DPC) algorithm, which is first proposed by this paper and can calculate the values of decision probability without prior knowledge of prognostics application; and (3) withdrawal mechanism optimizer (WMO), which is specially designed to compensate for the shortcomings of DPP and further enhance the performance of the prognostics model. In brief, first, CPS is used to build a basic prognostics model to acquire RUL estimation results, in which the information applied to find the probability has been contained. Later, the mean of RUL estimation errors is figured from the results, which is further employed to calculate the probability using DPC algorithm. Then, CPS-DP can be achieved by means of integrating two parts: DPP and CPS. Furthermore, to further improve the performance, WMO is utilized to optimize CPS-DP with rolling back predictions. Ultimately, an enhanced prognostic model based on CPS-DP is set up through uniting CPS, DPP, and WMO. To validate the proposed method, experimental results on the turbofan engine in 2008 prognostics and health management competition are investigated.