(334q) Spatial Grand Canonical Monte Carlo Algorithms For Fluid Simulation

A new grand canonical Monte Carlo algorithm for continuum fluid models is proposed. The algorithm is characterized by the absence of strict detailed balance, and it is based on a generalization of sequential Monte Carlo algorithms for lattice gas systems. The elementary moves (particle insertions and removals) are constructed by analogy with a lattice gas system. The updating is implemented by selecting points in space (spatial updating) and the type of move (insertion or removal) is deduced based on the local environment of a point in space. Results on two-dimensional square-well fluids indicate that the proposed algorithm converges faster than all currently available grand canonical algorithms that satisfy strict detailed balance. Due to the nature of the updating, additional reduction of simulation time may be achieved by parallel implementation through domain decomposition. With strict detailed balance, parallel Monte Carlo simulation through domain decomposition cannot be validated with conventional Markov chain theory, which describes an intrinsically serial stochastic process. We show that spatial updating is the key to improve efficiency in parallel simulations through domain decomposition. A parallel scheme is proposed to reduce inter-processor communication or synchronization which slows down parallel simulation with increasing number of processors. Our parallel simulation results show substantial reduction of simulation time for systems of moderate and large size.