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ABM is diversifying its research domains to encompass fluid dynamics by integrating cutting-edge flow visualization technologies and leveraging data-driven machine learning methodologies. Additionally, our research scope extends to biochip development and advanced flow modeling investigations. This expansion reflects our commitment to exploring innovative approaches and staying at the forefront of scientific advancements in these multidisciplinary fields.
Development of flow model using Machine learning
Developing a methodology to predict high-dimensional, three-dimensional blood flow fields through machine learning–based nonlinear mapping from low-dimensional vascular information and establishing a generalized computational framework that preserves physical consistency—thereby complementing and extending conventional high-dimensional, computation-driven paradigms for hemodynamic analysis.
CFD-based biofluid flow analysis
Computational Fluid Dynamics (CFD) is a numerical method used to analyze fluid flow by solving governing equations. In this study, CFD is applied to investigate biofluid flow in blood vessels or biomedical devices, evaluating key parameters such as velocity, pressure, and shear stress.
Microfluidic applications
The development of application devices based on microparticle flow focuses on analyzing and controlling particle transport in fluid systems. Flow characteristics and particle behavior are used to evaluate and improve system performance.
Experimental fluid flow using MRV
Magnetic Resonance Velocimetry (MRV) is a non-invasive measurement technique that uses MRI to obtain velocity fields within a fluid. It enables three-dimensional visualization of flow without disturbing the system.
MRV is used to measure blood flow within vascular models, allowing the analysis of velocity distribution and overall flow characteristics. This approach provides experimental data for understanding biofluid behavior in vascular environments.
Parameters such as pressure are measured to improve measurement accuracy under MR conditions. By accounting for the MR environment, the reliability of the flow measurement results can be enhanced.
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