Wyniki wyszukiwania - Biblioteka Nauki

Nowa wersja platformy, zawierająca wyłącznie zasoby pełnotekstowe, jest już dostępna.
Przejdź na https://bibliotekanauki.pl

Ograniczanie wyników

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Sortuj według:

Ogranicz wyniki do:

Determination of the Geometric Properties of a Jet Engine Fan Blades Based on Modal Vibration Testing

100%

Osiewicz M. , Cieślak S. , Bielecki M.

tom Nr 3 (268)

56--74

The article presents an experimental method of determining the geometric properties of jet engine rotor airfoils based on modal vibration testing. The procedure is based on adjusting the results of analytical calculations to the laboratory outcomes. Experimental tests were carried out on a set of 20 jet engine fan blades made of AL7022-T6 aluminium alloy. Each blade differed in weight and geometric dimensions within the accepted design tolerance. Numerical analysis of five airfoils that differed in thickness was performed. Modal vibration test results were summarised and compared with the results obtained by the numerical method. The comparison revealed a high similarity of the frequency and form of vibrations acquired by numerical simulation for each of the blades in relation to the executed vibration testing. Based on the verification of the theoretical model with the results obtained through experimental testing, conclusions were drawn about the object’s dynamic behaviour and its technological quality and geometric properties, whereby each of airfoil was probably thinned.

Predicting length of fatigue cracks by means of machine learning algorithms in the small-data regime

88%

Badora M. , Sepe M. , Bielecki M. , Graziano A. , Szolc T.

tom Vol. 23, no. 3

575--585

In this paper several statistical learning algorithms are used to predict the maximal length of fatigue cracks based on a sample composed of 31 observations. The small-data regime is still a problem for many professionals, especially in the areas where failures occur rarely. The analyzed object is a high-pressure Nozzle of a heavy-duty gas turbine. Operating parameters of the engines are used for the regression analysis. The following algorithms are used in this work: multiple linear and polynomial regression, random forest, kernel-based methods, AdaBoost and extreme gradient boosting and artificial neural networks. A substantial part of the paper provides advice on the effective selection of features. The paper explains how to process the dataset in order to reduce uncertainty; thus, simplifying the analysis of the results. The proposed loss and cost functions are custom and promote solutions accurately predicting the longest cracks. The obtained results confirm that some of the algorithms can accurately predict maximal lengths of the fatigue cracks, even if the sample is small.