论文标题
裂缝方法中裂纹成核的“平行宇宙”方案
A "parallel universe" scheme for crack nucleation in the phase field approach to fracture
论文作者
论文摘要
在许多工业应用中,裂纹成核至关重要。裂缝的相位场方法将裂纹成核问题转化为弹性势能和裂纹表面能量之和的最小化问题。由于制剂的多凸度,从无裂缝的固体开始,标准的牛顿迭代可能会导致没有裂纹的溶液,即使破裂的溶液的总能量较低。因此,开裂的临界负载被高估了。在这里,我们提出了一种称为“平行宇宙”算法以捕获全局最小值的算法。该算法具有两种关键成分:(a)仅根据当前的无裂纹解决方案进行破裂的必要条件,并且(b)从满足这种情况时,具有两个初始猜测的牛顿迭代,一个裂纹,一个裂纹和一个破裂的局面,都将执行,并且将融合的候选解决方案在负载步骤中被接受为较低的能量。一旦接受破裂的候选解决方案,无裂缝就被丢弃,即仅保留一个宇宙。对于所有负载步骤,通过解决一系列类似的最小化问题,仅通过逐渐降低临界裂纹能量释放速率来获得一次破裂的初始猜测。各向同性和各向异性临界裂纹能量释放速率的数值示例表明,所提出的算法比标准的牛顿迭代和众所周知的回溯算法更可靠(由于不需要追溯)和更有效。
Crack nucleation is crucial in many industrial applications. The phase field method for fracture transforms the crack nucleation problem into a minimization problem of the sum of the elastic potential energy and the crack surface energy. Due to the polyconvexity of the formulation, starting from a crackless solid, a standard Newton iteration may lead to a solution with no crack, even though a cracked solution has a lower total energy. As such, the critical load for cracking is highly overestimated. Here, we propose an algorithm termed "parallel universe" algorithm to capture the global minimum. This algorithm has two key ingredients: (a) a necessary condition for cracking solely based on the current crackless solution, and (b) beginning from when this condition is met, Newton iteration with two initial guesses, a crackles one and a cracked one, will both be performed and the converged candidate solution with lower energy is accepted as the solution at that load step. Once the cracked candidate solution is accepted, the crackless one is discarded, i.e., only one universe is retained. This cracked initial guess is obtained only once for all load steps by solving a series of similar minimization problems with a progressively reduced critical crack energy release rate. Numerical examples with isotropic and anisotropic critical crack energy release rates indicate that the proposed algorithm is more reliable (as there is no need to retrace) and more efficient than the standard Newton iteration and a well-known backtracking algorithm.