Numerical multiscale methods usually rely on some coupling between a macroscopic and a microscopic model. The macroscopic model is incomplete as effective quantities, such as the homogenized material coefficients or fluxes, are missing in the model. These effective data need to be computed by running local microscale simulations followed by a local averaging of the microscopic information. Motivated by the classical homogenization theory, it is a common practice to use local elliptic cell problems for computing the missing homogenized coefficients in the macro model. Such a consideration results in a first order error $O(\varepsilon/\delta)$, where $\varepsilon$ represents the wavelength of the microscale variations and $\delta$ is the size of the microscopic simulation boxes. This error, called "resonance error", originates from the boundary conditions used in the micro-problem and typically dominates all other errors in a multiscale numerical method. Optimal decay of the resonance error remains an open problem, although several interesting approaches reducing the effect of the boundary have been proposed over the last two decades. In this paper, as an attempt to resolve this problem, we propose a computationally efficient, fully elliptic approach with exponential decay of the resonance error.
翻译:数字多尺度方法通常依赖于宏观和微观模型之间的某种混合。 宏观模型不完全, 因为模型中缺少有效数量, 如同质物质系数或通量。 这些有效数据需要通过运行本地微尺度模拟, 并辅之以微观信息的本地平均值来计算。 受经典的同质化理论的驱动, 使用本地椭圆细胞问题来计算宏模型中缺失的同质系数是一种常见做法。 这样的计算结果导致第一个顺序错误 $O (\ varepsilon/\delta) $, 其中$\varepsilon 代表微尺度变异的波长, $\delta$ 是微观模拟框的大小 。 这个错误, 叫做“ 共振错误 ”, 源于在微比例模型中使用的边界条件, 通常在多尺度的数值方法中支配所有其他错误 。 共性错误的优劣仍是一个开放式问题, 尽管有几种有趣的 el- el- varepslon, 其中的美元代表了微尺度变差的波长, $\\\\ developmental progrational pritional prolational pritional decess laction the the the developtional pritional pritional procolceal lacutional lacutional progislation the the the the procideald pald pal degrational degrational pal proglementald pal pal degrational)