Simulating turbulence in fluids is a fascinating part of physics which requires a high amount of computational power. Since for transitional Reynolds numbers each simulation run can be performed on a single contemporary CPU, turbulence studies are ideally suited for distributed computing where each node performs a simulation for a single initial condition. The approach presented in this paper makes use of unused computational power by integrating a dynamically changing set of possibly unreliable desktop PCs into a grid infrastructure of attentively administered dedicated cluster resources. The basic idea is to use peer-to-peer (P2P) technology for managing the set of computers and develop a "bridge" to interface the P2P network with a grid meta-scheduler which in turn interfaces with the grid middleware. This eliminates the need for central administration and continuous resource availability. It provides distributed scheduling, replicated storage and system monitoring capabilities. Experimental results obtained from an evaluation of our implementation show that our approach is both scalable and resilient in the presence of node failures and network churn.