Anti-CFD monoclonal antibody, clone E21/5

Computational fluid dynamics (CFD) and medical imaging can be integrated to derive some important hemodynamic parameters such as wall shear stress (WSS). However, CFD suffers from a relatively long computational time that usually varies from dozens of minutes to hours. Machine learning is a popular tool that has been applied to many fields, and it can predict outcomes fast and even instantaneously in most applications. This study aims to use machine learning as an alternative to CFD for generating hemodynamic parameters in real-time diagnosis during medical examinations. To perform the feasibility study, we used CFD to model the blood flow in 2000 idealized coronary arteries, and the calculated WSS values in these models were used as the dataset for training and testing. The preparation of the dataset was automated by scripts programmed in Python, and OpenFOAM was used as the CFD solver. We have explored multivariate linear regression, multi-layer perceptron, and convolutional neural network architectures to generate WSS values from coronary artery geometry directly without CFD. These architectures were implemented in TensorFlow 2.0. Our results showed that these algorithms were able to generate results in less than 1 s, proving its capability in real-time applications, in terms of computational time. Based on the accuracy, convolutional neural network outperformed the other architectures with a normalized mean absolute error of 2.5%. Although this study is based on idealized models, to the best of our knowledge, it is the first attempt to predict WSS in a stenosed coronary artery using machine learning approaches.

Different surface modification techniques have been used for enhancement of heat transfer from absorber plates of solar air heaters. It is necessary to have accurate values of design parameters within the operational window while designing the heat transfer equipments. The present work is focused on the development of empirical correlations and evaluation of maximum thermohydraulic performance of a rectangular section ribs installed in solar air heaters considering all the design combinations within the given range of input parameters. It is prescribed by design of experiment algorithms particularly with response surface methodology considering four input parameters viz., Reynolds number ranging from 4000 to 20000, relative rib pitch ranging from 5 to 60, relative rib height ranging from 0.065 to 0.252 and relative rib width ranging from 0.5 to 10. The maximum THP obtained is 2.77 at Reynolds number 20000, relative rib pitch 17.22, relative rib height 0.044 and relative rib height 0.5. The optimal values of design parameters have been verified with CFD simulations and experiments. The errors are within acceptable limit proving the accuracy of empirical correlations used for the design of solar artificial air heater and proper selection of model equations.