Cardiovascular engineering and technology · 2025
Pediatric Cardiovascular Multiscale Modeling using a Functional Mock-up Interface.
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Garven Ellen E, Kung Ethan, Stevens Randy M, Throckmorton Amy L
Аннотация
PURPOSE: Computational models of the cardiovascular system continue to increase in complexity. As more elements of the physiology are captured in multiscale models, there is a need to efficiently integrate subsystems. The objective of this study is to demonstrate the effectiveness of a coupling methodology, called functional mock-up interface (FMI), as applied to multiscale cardiovascular modeling.
METHODS: The multiscale model is composed of two subsystems: a computational fluid dynamics (CFD) model coupled to a lumped parameter model (LPM). The LPM is packaged using the FMI standard and imported into the CFD subsystem using an FMI co-simulation architecture. The functionality of an FMI coupling was demonstrated in a univentricular parallel circulation by means of compatible tools, including ANSYS CFX and Python. Predicted pressures and flows were evaluated in comparison with clinical data and a previously developed computational model.
RESULTS: The two models exchanged pressure and flow data between their boundaries at each timestep, demonstrating sufficient inter-subsystem communication. The models recreated pressures and flows from clinical measurements and a patient-specific model previously published.
CONCLUSION: FMI integrated with ANSYS CFX is an effective approach for interfacing cardiovascular multiscale models as demonstrated by the presented univentricular circulatory model. FMI offers a modular approach towards tool integration and is an advantageous strategy for modeling complex systems.
METHODS: The multiscale model is composed of two subsystems: a computational fluid dynamics (CFD) model coupled to a lumped parameter model (LPM). The LPM is packaged using the FMI standard and imported into the CFD subsystem using an FMI co-simulation architecture. The functionality of an FMI coupling was demonstrated in a univentricular parallel circulation by means of compatible tools, including ANSYS CFX and Python. Predicted pressures and flows were evaluated in comparison with clinical data and a previously developed computational model.
RESULTS: The two models exchanged pressure and flow data between their boundaries at each timestep, demonstrating sufficient inter-subsystem communication. The models recreated pressures and flows from clinical measurements and a patient-specific model previously published.
CONCLUSION: FMI integrated with ANSYS CFX is an effective approach for interfacing cardiovascular multiscale models as demonstrated by the presented univentricular circulatory model. FMI offers a modular approach towards tool integration and is an advantageous strategy for modeling complex systems.