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

Yang, Bin; Zhu, Xin; Wei, Boan; Liu, Minzhang; Li, Yifan; Lv, Zhihan; Wang, Faming

doi:10.3390/en16031500

Cited by 14 publications

(4 citation statements)

References 115 publications

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“…Another popular subject is fluid property estimation with ML algorithms, a promising alternative to prohibitively expensive experimental measurements.Most reviews have exploited historical data from experiments or simulations to predict fluid behaviour and calculate the properties of interest [87][88][89][90][91][92][93][94][95][96]. Furthermore, heat transfer applications, concerning the addition of nanoparticles in simple fluids [97][98][99][100][101][102] or the investigation of flows in microfluidic channels [103][104][105][106][107][108] play a central role.…”

Section: A Brief Overview and Methods Classificationmentioning

confidence: 99%

Can Artificial Intelligence Accelerate Fluid Mechanics Research?

Drikakis

Sofos

2023

Fluids

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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.

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Section: A Brief Overview and Methods Classificationmentioning

confidence: 99%

Can Artificial Intelligence Accelerate Fluid Mechanics Research?

Drikakis

Sofos

2023

Fluids

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“…For this reason, in response to the rapid progress of computer vision techniques, a few researchers have started to use image processing and machine learning to automatically analyze HSV images of two-phase flows [ 12 ]. The use of these tools not only enables the rapid analysis of several thousand HSV image frames in a matter of seconds while maintaining a standardized quantitative approach but also serves as a safeguard against the omission of crucial information due to human error.…”

Section: Introductionmentioning

confidence: 99%

Computer-Vision- and Deep-Learning-Based Determination of Flow Regimes, Void Fraction, and Resistance Sensor Data in Microchannel Flow Boiling

Schepperle,

Junaid,

Woias

2024

Sensors

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The aim of this article is to introduce a novel approach to identifying flow regimes and void fractions in microchannel flow boiling, which is based on binary image segmentation using digital image processing and deep learning. The proposed image processing pipeline uses adaptive thresholding, blurring, gamma correction, contour detection, and histogram comparison to separate vapor from liquid areas, while the deep learning method uses a customized version of a convolutional neural network (CNN) called U-net to extract meaningful features from video frames. Both approaches enabled the automatic detection of flow boiling conditions, such as bubbly, slug, and annular flow, as well as automatic void fraction calculation. Especially CNN demonstrated its ability to deliver fast and dependable results, presenting an appealing substitute to manual feature extraction. The U-net-based CNN was able to segment flow boiling images with a Dice score of 99.1% and classify the above flow regimes with an overall classification accuracy of 91%. In addition, the neural network was able to predict resistance sensor readings from image data and assign them to a flow state with a mean squared error (MSE) < 10−6.

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“…As the identification of heat transfer in a room or chamber is essential for designing an efficient HVAC system, some papers have already investigated this subject using computer vision: [1], a pure time delay model with fractional order pole [2], or the Takagi-Sugeno fuzzy model [3].…”

Section: Introductionmentioning

confidence: 99%

Heat Flow Process Identification Using ANFIS-GA Model

Vesović¹,

Jovanović²

2023

Proceedings of the International Scientific Conference - Sinteza 2023

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This paper provides a nonlinear technique that uses a fuzzy inference system and neural networks for the identification purposes of heat flow transfer in the chamber. Firstly, linear models are obtained by transfer functions with delay using Matlab identification tools for heat exchange. Three different transfer functions are provided (for three sensors in different positions along the chamber), and after it has been concluded that the second model has the smallest error, it is tested using different input. In this case, the linear model failed to represent the behaviour of the system precisely, making the error more than 1.5 C in the steady state. This was expected because linear models are trustworthy only around certain operating ranges. In order to make the new model, which will be unique and valid in the whole state space, another identification method using an adaptive neuro-fuzzy inference system (ANFIS) was presented. Furthermore, for the best performance, the ANFIS architecture was found using one of the most famous population-based optimizations: the genetic evolutionary algorithm. With two inputs and 70 parameters found by optimization (40 premises and 30 consequent) ANFIS greatly outperforms standard identification technique in terms of the mean square error. This nonlinear model was also tested on the different input, which was not used in the training process, and it was concluded that the nonlinear model identifies the real object with a neglectable error, which is 45 times smaller than the linear one.

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

Cited by 14 publications

References 115 publications

Can Artificial Intelligence Accelerate Fluid Mechanics Research?

Can Artificial Intelligence Accelerate Fluid Mechanics Research?

Computer-Vision- and Deep-Learning-Based Determination of Flow Regimes, Void Fraction, and Resistance Sensor Data in Microchannel Flow Boiling

Heat Flow Process Identification Using ANFIS-GA Model

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