  The use of Digital Twins is set to transform the manufacturing sector by
aiding monitoring and real-time decision making. For several applications in
this sector, the system to be modeled consists of a mix of discrete-event and
continuous processes interacting with each other. Building simulation-based
Digital Twins of such systems necessitates an open, flexible simulation
framework which can support easy modeling and fast simulation of both
continuous and discrete-event components, and their interactions. In this
paper, we present an outline and key design aspects of a Python-based framework
for performing mixed discrete-continuous simulations. The continuous processes
in the system are assumed to be loosely coupled to other components via
pre-defined events. For example, a continuous state variable crossing a
threshold may trigger an external event. Similarly, external events may lead to
a sudden change in the trajectory, state value or boundary conditions in a
continuous process. We first present a systematic events-based interface using
which such interactions can be modeled and simulated. We then discuss
implementation details of the framework along with a detailed example. In our
implementation, the advancement of time is controlled and performed using the
event-stepped engine of SimPy (a popular discrete-event simulation library in
Python). The continuous processes are modelled using existing frameworks with a
Python wrapper providing the events interface. We discuss possible improvements
to the time advancement scheme, a roadmap and use cases for the framework.
