An optimised deep learning method for the prediction of dynamic viscosity of MXene-based nanofluid
暂无分享,去创建一个
Mohammad Reza Chalak Qazani | Z. Said | Houshyar Asadi | A. Afzal | M. Arıcı | Navid Aslfattahi | M. Schmirler | V. Kulish | H. M. D. Kabir
[1] G. Shu,et al. Deep reinforcement learning-PID based supervisor control method for indirect-contact heat transfer processes in energy systems , 2023, Eng. Appl. Artif. Intell..
[2] Z. Said,et al. Experimental analysis of novel ionic liquid-MXene hybrid nanofluid's energy storage properties: Model-prediction using modern ensemble machine learning methods , 2022, Journal of Energy Storage.
[3] E. Asmatulu,et al. Discovery and prediction capabilities in metal-based nanomaterials: An overview of the application of machine learning techniques and some recent advances , 2022, Adv. Eng. Informatics.
[4] A. Ullah,et al. Numerical investigation of effect of different parameter on heat transfer for a crossflow heat exchanger by using nanofluids , 2021, Journal of Thermal Engineering.
[5] S. M. Yahya,et al. Prediction of the Dynamic Viscosity of MXene/palm Oil Nanofluid Using Support Vector Regression , 2021, Lecture Notes in Mechanical Engineering.
[6] Meikandan Megaraj,et al. Experimental investigations of stability, density, thermal conductivity, and electrical conductivity of solar glycol-amine-functionalized graphene and MWCNT-based hybrid nanofluids , 2021, Environmental Science and Pollution Research.
[7] D. Ramasamy,et al. Heat transfer performance of a radiator with and without louvered strip by using Graphene-based nanofluids , 2021, Journal of Thermal Engineering.
[8] N. Ali,et al. Carbon-Based Nanofluids and Their Advances towards Heat Transfer Applications—A Review , 2021, Nanomaterials.
[9] Hussein M. Maghrabie,et al. Thermophysical properties of graphene-based nanofluids , 2021, International Journal of Thermofluids.
[10] M. Hemmat Esfe,et al. Optimization, modeling, and prediction of relative viscosity and relative thermal conductivity of AlN nano-powders suspended in EG , 2021 .
[11] B. Liu,et al. Prospects of measuring $$R_b$$ R b in hadronic $$\mathrm{Z}$$ , 2021 .
[12] O. Bamisile,et al. A neural network-based predictive model for the thermal conductivity of hybrid nanofluids , 2020 .
[13] S. Zaidi,et al. 2D Transition Metal Carbides (MXene) for Electrochemical Sensing: A Review , 2020, Critical reviews in analytical chemistry.
[14] S. M. Yahya,et al. Performance optimization of a hybrid PV/T solar system using Soybean oil/MXene nanofluids as A new class of heat transfer fluids , 2020 .
[15] R. Kavitha,et al. Prediction of nanofluid viscosity using multilayer perceptron and Gaussian process regression , 2020, Journal of Thermal Analysis and Calorimetry.
[16] Navid Nasajpour Esfahani,et al. Using of Artificial Neural Networks (ANNs) to predict the thermal conductivity of Zinc Oxide–Silver (50%–50%)/Water hybrid Newtonian nanofluid , 2020 .
[17] T. Al‐Ansari,et al. An updated review of nanofluids in various heat transfer devices , 2020, Journal of Thermal Analysis and Calorimetry.
[18] S. M. Yahya,et al. An artificial neural network approach for the prediction of dynamic viscosity of MXene-palm oil nanofluid using experimental data , 2020, Journal of Thermal Analysis and Calorimetry.
[19] M. Sadeghzadeh,et al. Prediction of Thermo-Physical Properties of TiO2-Al2O3/Water Nanoparticles by Using Artificial Neural Network , 2020, Nanomaterials.
[20] Hua Wang,et al. Stability, thermal performance and artificial neural network modeling of viscosity and thermal conductivity of Al2O3-ethylene glycol nanofluids , 2020 .
[21] D. Toghraie,et al. Measurement of the thermal conductivity of MWCNT-CuO/water hybrid nanofluid using artificial neural networks (ANNs) , 2020, Journal of Thermal Analysis and Calorimetry.
[22] F. Yousefi,et al. Viscosity, thermal conductivity and density of carbon quantum dots nanofluids: an experimental investigation and development of new correlation function and ANN modeling , 2019, Journal of Thermal Analysis and Calorimetry.
[23] Milad Heidari,et al. The thermophysical properties and the stability of nanofluids containing carboxyl-functionalized graphene nano-platelets and multi-walled carbon nanotubes , 2019, International Communications in Heat and Mass Transfer.
[24] Akbar Maleki,et al. Thermal conductivity prediction of nanofluids containing CuO nanoparticles by using correlation and artificial neural network , 2019, Journal of Thermal Analysis and Calorimetry.
[25] Bart de Boer,et al. Proceedings of the Genetic and Evolutionary Computation Conference , 2019, GECCO.
[26] A. Karimipour,et al. Experimental investigation toward obtaining nanoparticles' surficial interaction with basefluid components based on measuring thermal conductivity of nanofluids , 2019, International Communications in Heat and Mass Transfer.
[27] R. Moradi,et al. Application of Neural Network for estimation of heat transfer treatment of Al2O3-H2O nanofluid through a channel , 2019, Computer Methods in Applied Mechanics and Engineering.
[28] Ilyas Khan,et al. Effects of Different Shaped Nanoparticles on the Performance of Engine-Oil and Kerosene-Oil: A generalized Brinkman-Type Fluid model with Non-Singular Kernel , 2018, Scientific Reports.
[29] Mohammad Hossein Ahmadi,et al. A review of thermal conductivity of various nanofluids , 2018, Journal of Molecular Liquids.
[30] I. Pop,et al. Modeling and optimization of thermal conductivity and viscosity of MnFe2O4 nanofluid under magnetic field using an ANN , 2017, Scientific Reports.
[31] Ningbo Zhao,et al. Experiment and Artificial Neural Network Prediction of Thermal Conductivity and Viscosity for Alumina-Water Nanofluids , 2017, Materials.
[32] Mohammad Hadi Hajmohammad,et al. Optimization, modeling and accurate prediction of thermal conductivity and dynamic viscosity of stabilized ethylene glycol and water mixture Al2O3 nanofluids by NSGA-II using ANN , 2017 .
[33] Davood Toghraie,et al. Designing an artificial neural network to predict dynamic viscosity of aqueous nanofluid of TiO2 using experimental data , 2016 .
[34] G. Karimi,et al. Prediction of thermal conductivity of alumina water-based nanofluids by artificial neural networks , 2015 .
[35] G. Xia,et al. Effects of surfactant on the stability and thermal conductivity of Al2O3/de-ionized water nanofluids , 2014 .
[36] Zhengguo Zhang,et al. Surfactant-free ionic liquid-based nanofluids with remarkable thermal conductivity enhancement at very low loading of graphene , 2012, Nanoscale Research Letters.
[37] B. Raj,et al. Effect of clustering on the thermal conductivity of nanofluids , 2008 .
[38] D. Ruppert. The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .
[39] Eric R. Ziegel,et al. The Elements of Statistical Learning , 2003, Technometrics.
[40] D. Goldberg,et al. BOA: the Bayesian optimization algorithm , 1999 .
[41] S. Hochreiter,et al. Long Short-Term Memory , 1997, Neural Computation.
[42] M. Meikandan,et al. Experimental investigation on thermal performance of nano coated surfaces for air-conditioning applications , 2017 .
[43] Islamabad, Pakistan. , 1998 .
[44] Stephen U. S. Choi. Enhancing thermal conductivity of fluids with nano-particles , 1995 .