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arXiv 2501.08855math.NAcs.NA
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一种易于实现的用于求解定常Navier-Stokes方程的Newton法非线性预处理方法

A simple-to-implement nonlinear preconditioning of Newton's method for solving the steady Navier-Stokes equations

Muhammad Mohebujjaman, Mengying Xiao, Cheng Zhang

AI总结:

针对求解定常Navier-Stokes方程的Newton法易受初始猜测影响失效的问题,提出加入Anderson加速Picard步的AAPicard-Newton非线性预处理方法,扩大收敛域且保留二次收敛性,数值测试验证了理论结果。

摘要:

求解定常Navier-Stokes方程(NSE)的Newton法收敛速度快,但可能因初始猜测不佳而失效。本文提出一种易于实现的Newton迭代非线性预处理方法,该方法保留二次收敛性并扩大了收敛域。所提出的AAPicard-Newton方法在求解NSE的Newton法每次迭代中加入Anderson加速Picard步,已被证明在Anderson加速优化步中松弛参数$\beta_{k+1}\equiv1$时全局稳定,具有二次收敛性,且在大雷诺数下收敛速度更快、收敛率更小。多个基准数值测试结果与理论结果吻合良好。

英文摘要:

The Newton's method for solving stationary Navier-Stokes equations (NSE) is known to convergent fast, however, may fail due to a bad initial guess. This work presents a simple-to-implement nonlinear preconditioning of Newton's iteration, that remains the quadratic convergence and enlarges the domain of convergence. The proposed AAPicard-Newton method adds the Anderson accelerated Picard step at each iteration of Newton's method for solving NSE, which has been shown globally stable for the relaxation parameter $β_{k+1}\equiv1$ in the Anderson acceleration optimization step, convergent quadratically, and converges faster with a smaller convergence rate for large Reynolds number. Several benchmark numerical tests have been tested and are well-aligned with the theoretical results.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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