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Model-based reasoning (MBR) uses computational models to support automated inference about mechanisms. It is known to out-perform older, associative methods in differentiating sensor failure from mechanism failure. Heretofore, MBR has used constraint-set models. Recently, there has been interest in taking the simulation models that are built as part of the design process and re-using them in diagnosis and sensor placement. This paper examines the requirements for such a re-use of these models, particularly those built for modeling process-flow systems. The switch from constraint-set to simulation models will force new requirements on the inference theory (and its implementations,) on the specification languages for simulations, and on the simulation code itself. The paper identifies areas in which the theory of MBR is not yet completely adequate for using the information that simulations can yield, and reviews recent work in these areas.