How Computational Modeling can Help to Predict Gas Transfer in Artificial Lungs Early in the Design Process

Area of Science:

• Biomedical Engineering
• Cardiovascular Engineering
• Medical Device Design

Background:

• Wearable extracorporeal membrane oxygenation (ECMO) offers potential for ambulatory patients but faces challenges.
• Device-induced complications like blood trauma and oxygenator thrombosis require improved reliability.
• Efficient membrane use is crucial for reducing device size, priming volume, and weight for portability.

Purpose of the Study:

• To apply a micro-scale computational fluid dynamics (CFD) model for predicting gas transfer in a small oxygenator.
• To evaluate the accuracy of CFD predictions against in-vitro experimental data.
• To assess the potential of CFD for optimizing oxygenator design and improving performance.

Main Methods:

• A micro-scale CFD model was applied to the full fiber bundle of a small oxygenator.
• Simulations were performed using Ansys CFX for three randomized geometries and staggered/in-line configurations.
• Three laboratory oxygenator prototypes were built and tested in-vitro with porcine blood (ISO 7199).

Main Results:

• The CFD model showed low error in predicting averaged oxygen saturations (2.4%–4.6%) compared to in-vitro data across various flow rates.
• The model successfully predicted gas transfer for small oxygenators with unidirectional fibers.
• High computational costs and complex flow distribution present challenges for clinical-scale oxygenators.

Conclusions:

• Micro-scale CFD is a viable tool for predicting gas transfer in small oxygenator designs.
• Further development, including extended mass transfer models, is needed for a priori prediction in clinically relevant oxygenators.
• CFD modeling can guide the optimization of wearable ECMO devices to enhance reliability and portability.