Contents of Journal of Mechanical Engineering 52, 2 (2001) 


 

M.-F. ROBBE, N. VIVIEN, M. VALETTE, E. BERGLAS: Use of thermalhydraulic 
and mechanical linked computations to estimate the mechanical 
consequences of a steam explosion                                        65

B. SAMANTA, K. R. AL-BALUSHI, S. A. AL-ARAIMI: Application of wavelets 
and artificial neural network in fault diagnosis of rolling 
element bearings                                                         91

M. MUSIL: Crack localisation and quantification in a vibrating 
structure                                                               103

M. KOMPIS, K. B. NIELSEN: Inverse determination of constitutive 
parameters from deep drawing experiment                                 117

Abstracts



Use of thermalhydraulic and mechanical linked computations to estimate the mechanical consequences of a steam explosion

M.-F. ROBBE, N. VIVIEN, M. VALETTE, E. BERGLAS

The safety studies of the Pressurised Water Reactors consider severe accidents such as steam explosions. If the reactor core melts partially and falls into the water laying below, the heat transfer between the hot corium and the cold water may be energetic enough to vaporise violently the water and create a steam explosion. The precision of the evaluation of the explosion mechanical consequences depends on the premixing phase preceding the explosion and on the explosion escalation and propagation. This paper presents the method used to link the thermalhydraulic code MC3D devoted to the steam explosion premixing phase and the fast dynamics code EUROPLEXUS dealing with the explosion and the structure response. An application concerning the computation of an in-vessel steam explosion resulting from a large central core degradation is also described.

Application of wavelets and artificial neural network in fault diagnosis of rolling element bearings

B. SAMANTA, K. R. AL-BALUSHI, S. A. AL-ARAIMI

A procedure for fault diagnosis of rolling element bearings through wavelet transforms and artificial neural network (ANN) is presented. The time domain vibration signals of a rotating machine with normal and defective bearings are processed through discrete wavelet transform to decompose in terms of low-frequency and high-frequency components. The extracted features from the decomposed signals are used as inputs to an ANN based diagnostic approach. The ANN is trained using backpropagation algorithm with a subset of experimental data for known machine conditions and tested using the remaining set of data. The procedure is illustrated through experimental vibration data of a pump.

Crack localisation and quantification in a vibrating structure

M. MUSIL

The possibility of localising and quantifying a crack in a vibrating structure, based on measured vibration amplitudes of the first and second harmonic in some locations of the structure and utilizing the mathematical model of an undamaged system, is the focus of this paper. The effect of the crack is modelled by a non-linear (fractional) stiffness of the element with the crack. The excitation of the system is characterized by the simultaneous effect of static and dynamic harmonic load. The method is documented on elementary examples, in which simulated measured data are determined by the use of the numerical solution of the non-linear analytical model of a structure with a crack.

Inverse determination of constitutive parameters from deep drawing experiment

M. KOMPIS, K. B. NIELSEN

Constitutive material parameters obtained from different experimentalmethods can differ. In order to get higher accuracy of identified material parameters for the deep drawing process, an inverse method is employed. Inverse methods enable to use deep drawing experiment for inverse determination of material parameters from the load-displacement curve. Principle of the method presented is minimization of error between experimentally achieved and calculated load-displacement curves in the least square sense. The core of the inverse module used is optimization based on Levenberg-Marquardt algorithm where the constants of Hollomon material model are taken as design variables. Convergence of the inverse method connected with deep drawing experiment is examined and good results are achieved.