An explicit cavitation multigrid algorithm for accelerating simulation of surface-textured bearings

Abstract

The cavitation has a potential effect on the performance of surface-textured bearings. The traditional mass-conserving cavitation algorithms based on the Gauss-Seidel iteration method become slow as the grid increases. The computation time is unacceptable once the grid consists of a large array of textures. This study developed an explicit cavitation multigrid algorithm to accelerate the simulation of surface-textured bearings. The algorithm was based on the Ausas cavitation algorithm with the advantage of a matrix-free implementation. Novel numerical technologies were developed to overcome the difficulty of the floating free boundaries caused by the cavitation characteristics. An example of surface-dimpled thrust bearings was studied to analyze the algorithm efficiency. The algorithm is shown to be approximate optimal under reasonable solution parameters. It is an alternative to other cavitation algorithms.

Transient tribo-dynamic performance of journal bearings considering wear behavior during start-up

“…[8][9][10][11][12][13] As a result, numerical models for simulating the dynamic behavior of journal bearing start-up processes have gained popularity in recent years. [14][15][16][17] In recent years, the numerical prediction of bearings during start-up has become increasingly sophisticated. Cui et al incorporated the dynamic equations of the rotor into a mixed lubrication model and numerically investigated the dynamic contact behavior of a journal bearing during start-up, discussing the effects of relative clearance and acceleration time.…”

“…8–13 As a result, numerical models for simulating the dynamic behavior of journal bearing start-up processes have gained popularity in recent years. 14–17…”

A decoupled approach to enhance the Elrod algorithm

“…18 Therefore, many improvements to the Elrod algorithm have been proposed. [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33] Vijayaraghavan and Keith 19 developed a combined algorithm instead of the ADI method. The algorithm, based on Newton iteration and approximate factorization (AF) techniques, showed superiority in efficiency and robustness.…”

“…Many improvements have been made to the LCP. [24][25][26][28][29][30][31][32][33] Fesanghary and Khonsari 18 proposed a modification of the switch function in which the step characteristic of the switch function was mitigated by a proposed gFactor. The initial crash problem of the Elrod algorithm was overcome.…”