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Modular robotic systems typically assemble using deterministic processes where modules are directly placed into their target position. By contrast, stochastic modular robots take advantage of ambient environmental energy for the transportation and delivery of robot components to target locations, thus offering potential scalability. The inability to precisely predict component availability and assembly rates is a key challenge for planning in such environments. Here, we describe a computationally efficient simulator to model a modular robotic system that assembles in a stochastic fluid environment. This simulator allows us to address the challenge of planning for stochastic assembly by testing a series of potential strategies. We first calibrate the simulator using both high-fidelity computational fluid-dynamics simulations and physical experiments. We then use this simulator to study the effects of various system parameters and assembly strategies on the speed and accuracy of assembly of topologically different target structures.