Generalized polynomial chaos a reliable framework for many problems of uncertainty quantification in computational fluid dynamics. However, it fails for long-time integration of unsteady problems with stochastic frequency. In this work, the asynchronous time integration technique, introduced in previous works to remedy this issue for systems of ODEs, is applied to the Kármán vortex street problem. For this purpose, we make use of a stochastic clock speed that provides the phase shift between the realizations and enables the simulation of an in-phase behavior. Results of the proposed method are validated against Monte Carlo simulations and show good results for statistic fields and point-wise values such as phase portraits, as well as PDFs of the limit cycle. We demonstrate that low-order expansions are sufficient to meet the demands for some statistic measures. Therefore, computational costs are still competitive with those of the standard form of intrusive generalized polynomial chaos (igPC) and its non-intrusive counterpart (NigPC).

广义多项式为在计算流体动力学中不确定性量化的许多问题提供了一个可靠的框架。但是，它无法长期整合具有随机频率的不稳定问题。在这项工作中，先前的工作中引入的异步时间积分技术为ODE系统解决了该问题，该技术已应用于Kármán涡街问题。为此，我们利用了随机时钟速度，该时钟速度提供了实现之间的相移，并能够模拟同相行为。所提出的方法的结果通过蒙特卡洛模拟进行了验证，对于统计字段和逐点值（例如相像）以及极限周期的PDF而言，都显示出良好的结果。我们证明了低阶展开足以满足某些统计量度的需求。因此，计算成本仍与标准形式的介入式广义多项式混沌（igPC）及其非介入式相对应的成本（NigPC）相比具有竞争力。