Fatigue Life Prediction of Screw Blade in Screw Sand Washing Machine under Random Load with Gauss Distribution

Abstract

The purpose of this study is to present a new method for estimating the fatigue life of the screw blade in the screw sand washing machine. To ensure the accuracy of numerical simulation, the loading area and the value of load are determined by means of the theoretical analysis. To ascertain the location of the stress peak and stress range, the static analysis of the screw blade is executed via the finite element method. To reduce the research cost and ensure the feasibility of the research method, Markov chain Monte Carlo (MCMC) is employed to simulate the random load with the Gauss distribution on the screw blade. In addition, the Nondominated Sorting Genetic Algorithm (NSGA-II) is utilized to find out an optimum variation coefficient of the stress, aiming at guaranteeing the precision of the random load. The rainflow cycle extrapolation is adopted to generate the fatigue load spectrum closer to the real condition, taking account of the possibility of the extreme loads caused by overload occurrence. Subsequently, the rainflow matrix after extrapolation, the estimated P-S-N curve, Goodman stress correction method and Miner's rules are made use of assessing the service life of the screw blade. In particular, the effects of the surface roughness, residual stresses and fatigue notch factors on the fatigue life are taken into consideration. Ultimately, the non-linear surface fitting technique is used to obtain the equation concerning the fatigue life of the screw blade versus residual stresses and fatigue notch factors. The numerical results show that the stress peak is in the root of the screw blade and the service life of the screw blade declines exponentially with growing residual stresses and fatigue notch factors.

Fatigue Crack Growth Life Prediction of Screw Blade in Screw Sand Washing Machine based on Austen Growth Law

“…And also, the practice shows that the fatigue load, in most cases, has some randomness [6]. In fact, Ref.…”

“…In fact, Ref. [6] has given the method for generating the fatigue load spectrum on the screw blade. In this study, the random load with the Gauss distribution generated in Ref.…”

“…In this study, the random load with the Gauss distribution generated in Ref. [6] is used to estimate the fatigue crack growth life of the screw blade. The flow diagram of generating fatigue load spectrum is shown in Fig.…”

“…As the service life of the screw blade is closely related to the working life of the screw sand washing machine, the service life of the screw blade is an essential index used for evaluating the performance of the screw sand washing machine. Although and Fu et al (2017) predicted the fatigue crack initiation life of the screw blade under the random load with the Gauss distribution and extreme load, the investigations concerning the fatigue crack growth life prediction of the screw blade are rarely studied [6,7].…”

“…), it is widely used in the sea sand desalination field [5]. Particularly, the screw blade is an actuator of washing sea sand in screw sand washing machines, with the result that it will suffer from the sophisticated alternating load [6]. As the service life of the screw blade is closely related to the working life of the screw sand washing machine, the service life of the screw blade is an essential index used for evaluating the performance of the screw sand washing machine.…”