Big data have altered the way we manage, explore, evaluate, analyze, and leverage data across many different industries [...]

/pdf/medical-big-data-and-artificial-intelligence-for-healthcare-14q31v49.pdf

Medical Big Data and Artificial Intelligence for Healthcare

The significant growth of artificial intelligence (AI) methods in machine learning (ML) and deep learning (DL) has opened opportunities for fluid dynamics and its applications in science, engineering and medicine. Developing AI methods for fluid dynamics encompass different challenges than applications with massive data, such as the Internet of Things. For many scientific, engineering and biomedical problems, the data are not massive, which poses limitations and algorithmic challenges. This paper reviews ML and DL research for fluid dynamics, presents algorithmic challenges and discusses potential future directions.

Can Artificial Intelligence Accelerate Fluid Mechanics Research?

In the field of heat transfer, machine learning (ML) is used to analyze the large amounts of data that are collected through experiments, field observations, and simulations. It’s important to write a review paper that looks at how ML techniques are used in different heat transfer applications. We made a standard database with 900 publications for systematic reviews. So, the main goal of this review is to show a systematic state-of-the-art by analyzing how well ML works in heat transfer applications using PRISMA guidelines. Based on the results, most studies used the correlation coefficient as the most reliable and overall way to judge the ML tools in different heat transfer applications. Also, the Decision Tree (DT), the Random Forest (RF), and the Artificial Neural Network (ANN) have the most uses. On the other hand, the best performance is when people work together and use hybrid ML techniques. We’ll also publish and keep updating the latest research results so we can keep up with how quickly technology changes.

Machine Learning in Heat Transfer: Taxonomy, Review and Evaluation

Heat dissipation in high-heat flux micro-devices has become a pressing issue. One of the most effective methods for removing the high heat load of micro-devices is boiling heat transfer in microchannels. A novel approach to flow pattern and heat transfer recognition in microchannels is provided by the combination of image and machine learning techniques. The support vector machine method in texture characteristics successfully recognizes flow patterns. To determine the bubble dynamics behavior and flow pattern in the micro-device, image features are combined with machine learning algorithms and applied in the recognition of boiling flow patterns. As a result, the relationship between flow pattern evolution and boiling heat transfer is established, and the mechanism of boiling heat transfer is revealed.

/pdf/computer-vision-and-machine-learning-methods-for-heat-3s87nae9.pdf

Yifan Li

Papers

Computer Vision and Machine Learning Methods for Heat Transfer and Fluid Flow in Complex Structural Microchannels: A Review