Study on thermal-hydraulic performance of printed circuit heat exchangers with supercritical methane based on machine learning methods

[1]  Jiangfeng Guo,et al.  Theoretical analysis of a novel PCHE with enhanced rib structures for high-power supercritical CO2 Brayton cycle system based on solar energy , 2023, Energy.

[2]  M. H. Safari,et al.  Phase Equilibria Simulation of Biomaterial-Hydrogen Binary Systems Using a Simple Empirical Correlation , 2023, Processes.

[3]  W. Cai,et al.  Review on bubble dynamic of subcooled flow boiling-part b: Behavior and models , 2023, International Journal of Thermal Sciences.

[4]  W. Cai,et al.  Review on bubble dynamic of subcooled flow boiling-part a: Research methodologies , 2022, International Journal of Thermal Sciences.

[5]  Yuchao Yan,et al.  Comparison of GRNN and RF algorithms for predicting heat transfer coefficient in heat exchange channels with bulges , 2022, Applied Thermal Engineering.

[6]  Liangqing Hu,et al.  Optimization of zigzag parameters in printed circuit heat exchanger for supercritical CO2 Brayton cycle based on multi-objective genetic algorithm , 2022, Energy Conversion and Management.

[7]  Qiuwan Wang,et al.  Multi-objective optimization of printed circuit heat exchanger used for hydrogen cooler by exergoeconomic method , 2022, Energy.

[8]  Chirag R. Kharangate,et al.  A machine learning approach for predicting heat transfer characteristics in micro-pin fin heat sinks , 2022, International Journal of Heat and Mass Transfer.

[9]  L. Grbčić,et al.  Machine learning based surrogate models for microchannel heat sink optimization , 2022, Applied Thermal Engineering.

[10]  Ibragim Abu Dagga,et al.  Performance enhancement of a C-shaped printed circuit heat exchanger in supercritical CO2 Brayton cycle: A machine learning-based optimization study , 2022, Case Studies in Thermal Engineering.

[11]  L. Luo,et al.  Topology optimization of heat exchangers: A review , 2022, Energy.

[12]  J. Sarkar,et al.  Machine learning model of regenerative evaporative cooler for performance prediction based on experimental investigation , 2022, International Journal of Refrigeration.

[13]  Y. Li,et al.  Optimization of a Zigzag-channel printed circuit heat exchanger for supercritical methane flow , 2021, Cryogenics.

[14]  Sung-Min Kim,et al.  Machine learning algorithms to predict flow boiling pressure drop in mini/micro-channels based on universal consolidated data , 2021 .

[15]  Abdallah S. Berrouk,et al.  Machine learning-based efficient multi-layered precooler design approach for supercritical CO2 cycle , 2021 .

[16]  Haitao Hu,et al.  Measurement and correlation for two-phase frictional pressure drop characteristics of flow boiling in printed circuit heat exchangers , 2021 .

[17]  Sung Bin Hong,et al.  Deep learning-based prediction method on performance change of air source heat pump system under frosting conditions , 2021 .

[18]  S. Bhattacharyya,et al.  Turbulent Flow Heat Transfer through a Circular Tube with Novel Hybrid Grooved Tape Inserts: Thermohydraulic Analysis and Prediction by Applying Machine Learning Model , 2021, Sustainability.

[19]  Qiuwan Wang,et al.  Thermal-hydraulic characteristics of printed circuit heat exchanger used for floating natural gas liquefaction , 2021 .

[20]  Ammar H. Elsheikh,et al.  Machine learning algorithms for improving the prediction of air injection effect on the thermohydraulic performance of shell and tube heat exchanger , 2021 .

[21]  M. Shafii,et al.  A comparative study of various machine learning methods for performance prediction of an evaporative condenser , 2021, International Journal of Refrigeration.

[22]  Guangya Zhu,et al.  Machine learning based approach for the prediction of flow boiling/condensation heat transfer performance in mini channels with serrated fins , 2021 .

[23]  X. Duan,et al.  Investigation of the flow and heat transfer characteristics of helium gas in printed circuit heat exchangers with asymmetrical airfoil fins , 2020 .

[24]  Mehdi Bahiraei,et al.  Predicting heat transfer rate of a ribbed triple-tube heat exchanger working with nanofluid using neural network enhanced by advanced optimization algorithms , 2020 .

[25]  G. Longo,et al.  Machine learning approach for predicting refrigerant two-phase pressure drop inside Brazed Plate Heat Exchangers (BPHE) , 2020, International Journal of Heat and Mass Transfer.

[26]  Chirag R. Kharangate,et al.  Machine learning algorithms to predict flow condensation heat transfer coefficient in mini/micro-channel utilizing universal data , 2020, International Journal of Heat and Mass Transfer.

[27]  Yan-ping Huang,et al.  A review on the thermal-hydraulic performance and optimization of printed circuit heat exchangers for supercritical CO2 in advanced nuclear power systems , 2020, Renewable and Sustainable Energy Reviews.

[28]  Michael Schäfer,et al.  A Modified Normalized Weighting Factor method for improving the efficiency of the blended high-resolution advection schemes in the context of multiphase flows , 2020, Experimental and Computational Multiphase Flow.

[29]  Jaime Fern'andez del R'io,et al.  Array programming with NumPy , 2020, Nature.

[30]  D. Bui,et al.  Feature validity during machine learning paradigms for predicting biodiesel purity , 2020 .

[31]  Meijing Li,et al.  Application of support vector regression cooperated with modified artificial fish swarm algorithm for wind tunnel performance prediction of automotive radiators , 2020 .

[32]  Natalia Gimelshein,et al.  PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.

[33]  Takuya Akiba,et al.  Optuna: A Next-generation Hyperparameter Optimization Framework , 2019, KDD.

[34]  Jinliang Xu,et al.  Thermodynamic analysis and performance prediction on dynamic response characteristic of PCHE in 1000 MW S-CO2 coal fired power plant , 2019, Energy.

[35]  Yue Wang,et al.  Review on the characteristics of flow and heat transfer in printed circuit heat exchangers , 2019, Applied Thermal Engineering.

[36]  Xiaodong Sun,et al.  Thermal-hydraulic performance of printed circuit heat exchangers with zigzag flow channels , 2019, International Journal of Heat and Mass Transfer.

[37]  P. Kalaichelvi,et al.  In pursuit of the best artificial neural network configuration for the prediction of output parameters of corrugated plate heat exchanger , 2019, Fuel.

[38]  Alfred O. Hero,et al.  Scalable Mutual Information Estimation Using Dependence Graphs , 2018, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[39]  Tie-Yan Liu,et al.  LightGBM: A Highly Efficient Gradient Boosting Decision Tree , 2017, NIPS.

[40]  Mohammad Hassan Shojaeefard,et al.  Evaluating different types of artificial neural network structures for performance prediction of compact heat exchanger , 2017, Neural Computing and Applications.

[41]  Chen Yuping,et al.  Numerical investigation on heat transfer and flow characteristics of supercritical nitrogen in a straight channel of printed circuit heat exchanger , 2017 .

[42]  V. Utgikar,et al.  Experimental and numerical study of a printed circuit heat exchanger , 2016 .

[43]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[44]  M. Mohanraj,et al.  Applications of artificial neural networks for thermal analysis of heat exchangers – A review , 2015 .

[45]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[46]  Milos Manic,et al.  Optimal artificial neural network architecture selection for performance prediction of compact heat exchanger with the EBaLM-OTR technique , 2011 .

[47]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[48]  Milos Manic,et al.  EBaLM-THP : A neural network thermohydraulic prediction model of advanced nuclear system components , 2009 .

[49]  L. Breiman Random Forests , 2001, Encyclopedia of Machine Learning and Data Mining.

[50]  D. Altman,et al.  STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT , 1986, The Lancet.

[51]  Jinliang Xu,et al.  Critical supercritical-boiling-number to determine the onset of heat transfer deterioration for supercritical fluids , 2020 .