Wyniki wyszukiwania - BazTech

1

CFD simulation of a vortex-controlled diffuser for a jet engine burner

Chijioke Emeka Gaius, Teodorczyk Andrzej

Combustion Engines

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2023

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R. 62, nr 4

71--82

EN

A computational flow analysis of an ideal vortex-controlled diffuser (VCD) was carried out. The simulation model used is the compressible Reynolds averaged Navier-Stokes equations(RANS), with the application of the RNG based k-ε turbulence model. The effects of important parameters like static pressure recovery, bleed fraction, position of bleed slot, have been studied and comparisons were made with respect to VCD without the bleed configuration and the following features were revealed: radial profiles of velocity at inlet, mid-planes and exit planes, including diffuser effectiveness (i.e. static pressure recovery), diffuser efficiency, reattachment length and diffuser total pressure loss. Results obtained by applying the RNG turbulence model show an instantaneous improvement in the diffuser efficiency that happen at reasonably minimal suction rates. From the calculations, it has been verified and shown in the analysis that the effect of the bleed positioning offers advantages in relation to where it is located.

2

Predictive modelling of turbofan engine components condition using machine and deep learning methods

Matuszczak Michał, Żbikowski Mateusz, Teodorczyk Andrzej

Eksploatacja i Niezawodność

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2021

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Vol. 23, no. 2

359--370

EN

The article proposes an approach based on deep and machine learning models to predict a component failure as an enhancement of condition based maintenance scheme of a turbofan engine and reviews currently used prognostics approaches in the aviation industry. Component degradation scale representing its life consumption is proposed and such collected condition data are combined with engines sensors and environmental data. With use of data manipulation techniques, a framework for models training is created and models' hyperparameters obtained through Bayesian optimization. Models predict the continuous variable representing condition based on the input. Best performed model is identified by detemining its score on the holdout set. Deep learning models achieved 0.71 MSE score (ensemble meta-model of neural networks) and outperformed significantly machine learning models with their best score at 1.75. The deep learning models shown their feasibility to predict the component condition within less than 1 unit of the error in the rank scale.

3

Examination of the explosive limits of flammable liquid vapours at various temperatures

Porowski Rafał, Grabarczyk Marcin, Teodorczyk Andrzej

Materiały Wysokoenergetyczne

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2019

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T. 11(1)

103--107

EN

The article contains the results of experimental tests on the explosive limits of methanol, ethanol and 1-butanol vapours at 40, 60, 80, 100 and 120 °C. The tests were conducted in accordance with method B of PN-EN 1839 standard. Additionally, the article presents an overview of the current knowledge regarding methods for determining the explosive limits for the safe transport and storage of flammable liquids.

PL

Artykuł zawiera wyniki badań doświadczalnych granic wybuchowości par metanolu, etanolu oraz 1-butanolu w temperaturach początkowych 40, 60, 80, 100 oraz 120 °C Badania przeprowadzono według metody B opisanej w standardzie PN-EN 1839. Dodatkowo, w treści artykułu przedstawiono przegląd stanu dotychczasowej wiedzy w zakresie metod określania granic wybuchowości na potrzeby bezpieczeństwa w transporcie i w magazynowaniu ciekłych substancji palnych.