Current Issue : October-December Volume : 2021 Issue Number : 4 Articles : 5 Articles
Compared with the three-dimensional rotor model for a central tie rod rotor, an equivalent one-dimensional model can greatly improve the computational efficiency in rotor dynamics analysis with a certain accuracy. However, little research work can be found on improving the modeling accuracy of one-dimensional models using experimental data. In this paper, a onedimensional discrete mass model considering pretightening force is proposed for central tie rod rotors to achieve the purpose of both efficient and accurate modeling. Experimental testing and three-dimensional model analysis are used as reference and verification approaches. A sensitivity-based method is adopted to update the proposed one-dimensional model via minimizing the error in the critical speed comparing with the corresponding three-dimensional finite element model which has been verified by a modal test. Prediction of damped unbalanced response is conducted to show the practicality of the updated onedimensional model. Results show that the method presented in this research work can be used to simulate a complex preloaded rotor system with high efficiency and accuracy....
This paper presents a study regarding the noise reduction of the turbojet engine, in particular the jet noise of a micro turbojet engine. The results of the measurement campaign are presented followed by a performances analysis which is based on the measured data by the test bench. Within the tests, beside the baseline nozzle other two nozzles with chevrons were tested and evaluated. First type of nozzle is foreseen with eight triangular chevrons, the length of the chevrons being L = 10 percentages from the equivalent diameter and an immersion angle of I = 0 deg. For the second nozzle the length and the immersion angle were maintained, only the chevrons number were increased at 16. The micro turbojet engine has been tested at four different regimes of speed. The engine performances were monitored by measuring the fuel flow, the temperature in front of the turbine, the intake air flow, the compression ratio, the propulsion force and the temperature before the compressor. In addition, during the testing, the vibrations were measured on axial and radial direction which indicate a normal functioning of the engine during the chevron nozzles testing. Regarding the noise, it was concluded that at low regimes the noise doesn’t presents any reduction when using the chevron nozzles, while at high regimes an overall noise reduction of 2–3 dB(A) was achieved. Regarding the engine performances, a decrease in the temperature in front of the turbine, compression ratio and the intake air and fuel flow was achieved and also a drop of few percent of the propulsion force....
In physics-based prognostics, model parameters are estimated by minimizing the error or maximizing the likelihood between model predictions and measured data. When multiple model parameters are strongly correlated, it is challenging to identify individual parameters by measuring degradation data, especially when the data have noise. This paper first presents various correlations that occur during the process of model parameter estimation and then introduces two methods of identifying the accurate values of individual parameters when they are strongly correlated. The first method can be applied when the correlation relationship evolves as damage grows, while the second method can be applied when the operating (loading) conditions change. Starting from manufactured data using the true parameters, the accuracy of identified parameters is compared with various levels of noise. It turned out that the proposed method can identify the accurate values of model parameters even with a relatively large level of noise. In terms of the marginal distribution, the standard deviation of a model parameter is reduced from 0.125 to 0.03 when different damage states are used. When the loading conditions change, the uncertainty is reduced from 0.3 to 0.05. Both are considered as a significant improvement....
Operational safety in the airport is the focus of the aviation industry. Target recognition under low visibility plays an essential role in arranging the circulation of objects in the airport field, identifying unpredictable obstacles in time, and monitoring aviation operation and ensuring its safety and efficiency. From the perspective of transfer learning, this paper will explore the identification of all targets (mainly including aircraft, humans, ground vehicles, hangars, and birds) in the airport field under low-visibility conditions (caused by bad weather such as fog, rain, and snow). First, a variety of deep transfer learning networks are used to identify well-visible airport targets. The experimental results show that GoogLeNet is more effective, with a recognition rate of more than 90.84%. However, the recognition rates of this method are greatly reduced under the condition of low visibility; some are even less than 10%. Therefore, the low-visibility image is processed with 11 different fog removals and vision enhancement algorithms, and then, the GoogLeNet deep neural network algorithm is used to identify the image. Finally, the target recognition rate can be significantly improved to more than 60%. According to the results, the dark channel algorithm has the best image defogging enhancement effect, and the GoogLeNet deep neural network has the highest target recognition rate....
Fiber reinforced polymers play a crucial role as enablers of lightweight and high performing structures to increase efficiency in aviation. However, the ever-increasing awareness for the environmental impacts has led to a growing interest in bio-based and recycled ‘eco-composites’ as substitutes for the conventional synthetic constituents. Recently, the international collaboration of Chinese and European partners in the ECO-COMPASS project provided an assessment of different eco-materials and technologies for their potential application in aircraft interior and secondary composite structures. This project summary reports the main findings of the ECO-COMPASS project and gives an outlook to the next steps necessary for introducing eco-composites as an alternative solution to fulfill the CLEAN SKY target....
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