Wyniki wyszukiwania - BazTech

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JMATRIX - package for relativistic J-MATRIX calculations in elastic scattering of electrons from model potentials

Syty P., Sienkiewicz J. E.

TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk

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2017

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Vol. 21, No 1

17--42

EN

We present a software package JMATRIX, consisting of two computer codes written in FORTRAN 95 and parallelized with OpenMP, implementing the so-called J-matrix method, applied to elastic scattering of electrons on the radial potential, vanishing faster than the Coulomb potential. In the J-matrix method, the physical scattering problem is replaced by using a well-defined model which is solved analytically. The presented software implements both non-relativistic and relativistic versions of the method, and allows calculations of scattering phase shifts as well as cross sections, in cases when the scattering potential is given through an analytical formula. We performed test calculations for the scattering potential modeled as a truncated Coulomb potential. We show that the numerical phase shifts to converge as we increase the size of the basis used to truncate the scattering potential, and that the method is suitable for calculating the total differential momentum transfer and spin polarization cross sections, using the partial-wave analysis.

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Przesłanki i przykład parametrycznej metody oceny stanu technicznego łopatki maszyny wirnikowej z eliminacją jej niemierzalnego otoczenia

Lindstedt P., Grądzki R.

Journal of KONBiN

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2011

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No. 1 (17)

179--194

PL

W artykule przedstawiono wyniki badań metod monitorowania stanu technicznego łopatek maszyn wirnikowych bazujących na modelach A2T12,T01 oraz φT12,T01 podczas ich użytkowania. Pierwsza metoda wykorzystująca model diagnostyczny A2T12,T01 jest w postaci ilorazu wzmocnienia amplitudowego sygnału diagnostycznego y(t) wynikającego z działania łopatki i sygnału x(t) jej otoczenia podczas zbliżania się wierzchołka łopatki do czujnika i wzmocnienia amplitudowego tych sygnałów podczas oddalania się wierzchołka łopatki od czujnika. Druga metoda bazująca na modelu diagnostycznym φT12,T01 wykorzystuje różnicę przesunięcia fazowego tychże sygnałów podczas oddalania się łopatki od czujnika i zbliżania się wierzchołka łopatki do czujnika. Przyjęte modele diagnostyczne pośrednio uwzględniają aktualne otoczenie łopatki x(t) bez konieczności jego pomiaru [13,15]. Wyniki otrzymane dla modelu φT12,T01 porównano z wynikami otrzymanymi dla modelu A2T12,T01.

EN

This paper presents tests results of methods of monitoring technical condition of turbomachine blades based on diagnostic models A2T12,T01 and φT12,T01 during their use. The first method utilizes the diagnostic model A2T12,T01 which is in the form of the amplitude amplification quotient of the diagnostic signal y(t) resulting from blade operation and the signal x(t) of its environment while the blade tip approaches the sensor and amplitude amplification of those signals while the blade tip moves away from the sensor. The second method which is based on the diagnostic model φT12,T01 utilizes the difference of phase shift of those signals when the blade moves away from the sensor and when the blade tip approaches the sensor. Adopted diagnostic models indirectly take into account present blade environment x(t) without the need to measure it [13,15]. Results obtained for the model φT12,T01 were compared with the results obtained for the model A2T12,T01. The aim of this study is to present the use of diagnostic models for diagnostics of turbomachine blades during operation without the need to measure the environmental signal.

3

Stator inter-turn fault detection of an induction motor using neuro-fuzzy techniques

Dash R. N., Subudhi B.

Archives of Control Sciences

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2010

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Vol. 20, no. 3

363-376

EN

Motivated by the superior performances of neural networks and neuro-fuzzy approaches to fault detection of a single phase induction motor, this paper studies the applicability these two approaches for detection of stator inter-turn faults in a three phase induction motor. Firstly, the paper develops an adaptive neural fuzzy inference system (ANFIS) detection strategy and then compares its performance with that of using a multi layer perceptron neural network (MLP NN) applied to stator inter-turn fault detection of a three phase induction motor. The fault location process is based on the monitoring the three phase shifts between the line current and the phase voltage of the induction machine.