An artificial neural network model to predict mini/micro-channels saturated flow boiling heat transfer coefficient based on universal consolidated data

Qiu, Yue; Garg, Deepak; Zhou, Liwei; Kharangate, Chirag R.; Kim, Sung-Min; Mudawar, Issam

doi:10.1016/j.ijheatmasstransfer.2019.119211

Cited by 104 publications

(22 citation statements)

References 75 publications

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“…MLP and random forest (RF)based models exhibited improved predictability on departure from nucleate boiling (DNB) compared to standalone artificial neural networks and correlations. Even though various machine learning models are being applied to two-phase flow analysis as a breakthrough against the elusive relationships between variables and output accompanying the reduced computational time as aforementioned, most of studies [30][31][32][33][34][35][36] have focused on the boiling regimes from nucleate boiling to CHF conditions.…”

Section: Introductionmentioning

confidence: 99%

High-resolution prediction of quenching behavior using machine learning based on optical fiber temperature measurement

Kim

Hurley

Duarte

2022

International Journal of Heat and Mass Transfer

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Quenching heat transfer is a representative complex phenomenon in thermal-hydraulic engineering. Despite the tremendous effort s to precisely predict the quenching behavior, conventional analysis methodology and correlations have exhibited limited prediction capacity on quenching heat transfer according to axial locations of heater rods. The deviations of existing models result from the uncertainty of axial heat conduction with low-resolution temperature measurement and limited regression performance. In this study, machine learning models were trained with optical fiber temperature measurement data having high spatial resolution about quenching to overcome the intrinsic limitation of conventional prediction methods. Support vector machine (SVM), random forest (RF), and multi-layer perceptron (MLP) models were trained, and the MLP model showed best prediction performance (R 2 = 0.9999 and test RMSE = 1.41 °C) due to its strong analysis of the underlying relationship between input and output with interpolation capacity. The optimal MLP model (5-30-30-30-1 architecture) showed good agreement with experimental data for bottom quenching behaviors of Inconel-600 and Monel K500, in aspects of minimum film boiling temperature, quench front propagation velocity, and transient boiling curve by providing spatially resolutive quenching behavior that cannot be obtained from conventional correlations and having high uncertainty in existing computational analysis methods. The developed MLP model has the capability to predict the quenching behavior of FeCrAl accident tolerant fuel cladding surface, and better predictability on minimum film boiling temperature (average error of 3.7%) than conventional correlation having 7.1% average error. The suggested methodology using machine learning technique will contribute to innovatively improve the predictability on quenching phenomena with reducing uncertainty.

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Section: Introductionmentioning

confidence: 99%

High-resolution prediction of quenching behavior using machine learning based on optical fiber temperature measurement

Kim

Hurley

Duarte

2022

International Journal of Heat and Mass Transfer

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“…Due to the ease in implementation and availability of vast amounts of data, ML algorithms are being used in fields such as biochemistry, finance, social media, transportation, geology, and sensors [30][31][32][33]. ML has also been recently employed to predict the thermal behavior of complex systems that are challenging to model from first principles [20,21,[34][35][36]. Machine learning models are commonly characterized by the tradeoff between prediction accuracy and model interpretability.…”

Section: Description Of Machine Learning Models Used In This Studymentioning

confidence: 99%

“…Such algorithms serve as an alternative to first-order modeling techniques to predict the influence of various parameters on the thermal performance of the package. Notably, machine learning has been used to model boiling and condensation heat transfer performance and identify various boiling regimes [18][19][20][21][22]. This enhanced understanding has important applications for high heat flux thermal management of power electronics.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Predictions of Benefits of High Thermal Conductivity Encapsulation Materials for Power Electronics Packaging

Acharya

Lokanathan

Ouroua

et al. 2021

Journal of Electronic Packaging

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Machine learning (ML)-based predictive techniques are used in conjunction with a game-theoretic approach to predict the thermal behavior of a power electronics package and study the relative influence of encapsulation material properties and thermal management in influencing hotspot temperatures. Parametric steady-state and transient thermal simulations are conducted for a commercially available 1.2 kV/444 A SiC half-bridge module. An extensive databank of 2592 (steady-state) and 1200 (transient) data points generated via numerical simulations is used to train and evaluate the performance of three ML algorithms (random forest, support vector machine and neural network) in modeling the thermal behavior. The parameter space includes the thermal conductivities of the encapsulant, baseplate, heat sink and cooling conditions deployed at the sink; the parametric space covers a variety of materials and cooling scenarios. Excellent prediction accuracies with R2 values > 99.5% are obtained for the algorithms. SHAP (Shapley Additive exPlanations) dependence plots are used to quantify the relative impact of device and heat sink parameters on junction temperatures. We observe that while heatsink cooling conditions significantly influence the steady-state junction temperature, their contribution in determining the junction temperature in dynamic mode is diminished. Using ML-SHAP models, we quantify the impact of emerging polymeric nanocomposites (with high conductivities and diffusivities) on hotspot temperature reduction, with the device operating in static and dynamic modes. Overall, this study highlights the attractiveness of ML-based approaches for thermal design, and provides a framework for setting targets for future encapsulation materials.

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“…A typical ANN structure consists of an input layer, hidden layers, and an output layer, which are connected to each other by neurons. To produce an output signal, a weighted sum of input signals to a neuron is passed through an activation function ( ) [6]. Through an iterative process, weights are updated using gradient descent algorithms with a learning rate ( ).…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network to Predict Pressure Drops in Heat Sinks

Mengesha¹,

Shaeri²,

Sarabi³

2022

International Conference on Fluid Flow, Heat and Mass Transfer

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In this study, pressure drop ( ) across air-cooled heat sinks (HSs) are predicted using an artificial neural network (ANN). A multilayer feed-forward ANN architecture with two hidden layers is developed. Backpropagation algorithm is used for training the network, and the accuracy of the network is evaluated by the root mean square error. The input data for training the neural network is prepared through three-dimensional simulation of air inside the channels of heat sinks using a computational fluid dynamics (CFD) approach. The developed ANN-based model in this study predicts with a high accuracy and within of the CFD-based data. The present study suggests that developing an ANN-based model with a high level of accuracy overcomes the limitations of physics-based correlations that their accuracy strongly depends on identifying and implementing key variables that affect the physics of a thermo-fluid phenomenon.

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An artificial neural network model to predict mini/micro-channels saturated flow boiling heat transfer coefficient based on universal consolidated data

Cited by 104 publications

References 75 publications

High-resolution prediction of quenching behavior using machine learning based on optical fiber temperature measurement

High-resolution prediction of quenching behavior using machine learning based on optical fiber temperature measurement

Machine Learning-Based Predictions of Benefits of High Thermal Conductivity Encapsulation Materials for Power Electronics Packaging

Artificial Neural Network to Predict Pressure Drops in Heat Sinks

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