{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T10:27:52Z","timestamp":1763202472796,"version":"3.37.3"},"reference-count":68,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2021,1,31]],"date-time":"2021-01-31T00:00:00Z","timestamp":1612051200000},"content-version":"vor","delay-in-days":1,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,4,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The traditional model-driven methods are not much efficient to predict the viscosity of nanofluids accurately. This study presents a novel approach of using physics-guided deep learning technique for predicting viscosity of water-based nanofluids from large dataset containing both experimental and simulated data of spherical oxide nanoparticles $\\rm{Al2O3}$, $\\rm{CuO}$, $\\rm{SiO2}$, and $\\rm{TiO2}$. Further, this study introduces a novel methodology of combining deep learning methods and physics-based models to leverage their complementary strengths. To the best of the author\u2019s knowledge, theory-guided deep learning prediction model was never used to predict viscosity before. The theory-guided deep neural networks (TGDNN) model is trained by minimizing the mean square error (MSE) and regularization terms using Adam optimization technique. The investigations reveal that the values of R2, RMSE, and AARD% are, respectively, 0.999868, 0.001143, and 2.198887 on experimental testing dataset. The TGDNN model learns non-linear relationship among the input variables from the training data. Additionally, the results show that the proposed method performed better than the other well-known existing theoretical and computer-aided models to predict the viscosity in wide range with high level of accuracy.<\/jats:p>","DOI":"10.1093\/jcde\/qwab001","type":"journal-article","created":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T12:15:52Z","timestamp":1609762552000},"page":"600-614","source":"Crossref","is-referenced-by-count":11,"title":["Physics-based smart model for prediction of viscosity of nanofluids containing nanoparticles using deep learning"],"prefix":"10.1093","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7704-8315","authenticated-orcid":false,"given":"Satyasaran","family":"Changdar","sequence":"first","affiliation":[{"name":"Department of Information Technology, Institute of Engineering & Management, Saltlake, Kolkata 700091, West Bengal, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bivas","family":"Bhaumik","sequence":"additional","affiliation":[{"name":"Department of Applied Mathematics, University of Calcutta, 92, Acharya Prafulla Chandra Road, Kolkata 700009, West Bengal, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8988-3679","authenticated-orcid":false,"given":"Soumen","family":"De","sequence":"additional","affiliation":[{"name":"Department of Applied Mathematics, University of Calcutta, 92, Acharya Prafulla Chandra Road, Kolkata 700009, West Bengal, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2021,1,30]]},"reference":[{"issue":"11","key":"2021042911565216500_bib1","doi-asserted-by":"crossref","first-page":"962","DOI":"10.1016\/j.ijengsci.2010.08.012","article-title":"On the effective viscosity of suspensions","volume":"48","author":"Abedian","year":"2010","journal-title":"International Journal of Engineering Science"},{"issue":"4","key":"2021042911565216500_bib2","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1016\/j.ijheatfluidflow.2009.02.003","article-title":"Effects of variable viscosity and thermal conductivity of al2o3\u2013water nanofluid on heat transfer enhancement in natural convection","volume":"30","author":"Abu-Nada","year":"2009","journal-title":"International Journal of Heat and Fluid Flow"},{"key":"2021042911565216500_bib3","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.jtice.2018.06.003","article-title":"Determination of thermal conductivity ratio of cuo\/ethylene glycol nanofluid by connectionist approach","volume":"91","author":"Ahmadi","year":"2018","journal-title":"Journal of the Taiwan Institute of Chemical Engineers"},{"key":"2021042911565216500_bib4","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.colsurfa.2018.01.030","article-title":"Thermal conductivity ratio prediction of al2o3\/water nanofluid by applying connectionist methods","volume":"541","author":"Ahmadi","year":"2018","journal-title":"Colloids and Surfaces A: Physicochemical and Engineering Aspects"},{"issue":"6","key":"2021042911565216500_bib5","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1080\/10407782.2018.1505092","article-title":"Thermal conductivity and dynamic viscosity modeling of fe2o3\/water nanofluid by applying various connectionist approaches","volume":"74","author":"Ahmadi","year":"2018","journal-title":"Numerical Heat Transfer, Part A: Applications"},{"issue":"1","key":"2021042911565216500_bib6","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1007\/s10973-018-7035-z","article-title":"A proposed model to predict thermal conductivity ratio of al 2 o 3\/eg nanofluid by applying least squares support vector machine (lssvm) and genetic algorithm as a connectionist approach","volume":"135","author":"Ahmadi","year":"2019","journal-title":"Journal of Thermal Analysis and Calorimetry"},{"issue":"1","key":"2021042911565216500_bib7","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1080\/19942060.2019.1571442","article-title":"Applicability of connectionist methods to predict dynamic viscosity of silver\/water nanofluid by using ann-mlp, mars and mpr algorithms","volume":"13","author":"Ahmadi","year":"2019","journal-title":"Engineering Applications of Computational Fluid Mechanics"},{"issue":"3","key":"2021042911565216500_bib8","doi-asserted-by":"crossref","first-page":"034909","DOI":"10.1063\/1.3182807","article-title":"Rheological and flow characteristics of nanofluids: Influence of electroviscous effects and particle agglomeration","volume":"106","author":"Anoop","year":"2009","journal-title":"Journal of Applied Physics"},{"issue":"1","key":"2021042911565216500_bib9","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1080\/19942060.2018.1542345","article-title":"Developing an anfis-based swarm concept model for estimating the relative viscosity of nanofluids","volume":"13","author":"Baghban","year":"2019","journal-title":"Engineering Applications of Computational Fluid Mechanics"},{"key":"2021042911565216500_bib10","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1016\/j.ijheatmasstransfer.2018.09.041","article-title":"Sensitivity analysis and application of machine learning methods to predict the heat transfer performance of cnt\/water nanofluid flows through coils","volume":"128","author":"Baghban","year":"2019","journal-title":"International Journal of Heat and Mass Transfer"},{"issue":"1","key":"2021042911565216500_bib11","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1017\/S0022112077001062","article-title":"The effect of brownian motion on the bulk stress in a suspension of spherical particles","volume":"83","author":"Batchelor","year":"1977","journal-title":"Journal of Fluid Mechanics"},{"issue":"4","key":"2021042911565216500_bib12","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1063\/1.1700493","article-title":"The viscosity of concentrated suspensions and solutions","volume":"20","author":"Brinkman","year":"1952","journal-title":"The Journal of Chemical Physics"},{"issue":"2","key":"2021042911565216500_bib13","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.expthermflusci.2009.10.022","article-title":"Experimental investigations and theoretical determination of thermal conductivity and viscosity of al2o3\/water nanofluid","volume":"34","author":"Chandrasekar","year":"2010","journal-title":"Experimental Thermal and Fluid Science"},{"issue":"4\u20136","key":"2021042911565216500_bib14","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.cplett.2007.07.046","article-title":"Rheological behaviour of ethylene glycol based titania nanofluids","volume":"444","author":"Chen","year":"2007","journal-title":"Chemical Physics Letters"},{"issue":"1","key":"2021042911565216500_bib15","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1016\/j.enconman.2010.06.072","article-title":"Empirical correlating equations for predicting the effective thermal conductivity and dynamic viscosity of nanofluids","volume":"52","author":"Corcione","year":"2011","journal-title":"Energy Conversion and Management"},{"article-title":"Approximation with artificial neural networks (MSc thesis)","year":"2001","author":"Cs\u00e1ji","key":"2021042911565216500_bib16"},{"issue":"2","key":"2021042911565216500_bib17","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.jcde.2017.09.001","article-title":"Thermal stratification effects on mhd radiative flow of nanofluid over nonlinear stretching sheet with variable thickness","volume":"5","author":"Daniel","year":"2018","journal-title":"Journal of Computational Design and Engineering"},{"key":"2021042911565216500_bib18","first-page":"178","article-title":"Learning while searching in constraint-satisfaction problems","volume-title":"AAAI'86: Proceedings of the Fifth AAAI National Conference on Artificial Intelligence","author":"Dechter","year":"1986"},{"key":"2021042911565216500_bib19","first-page":"1","article-title":"Deep extreme learning machine and its application in eeg classification","volume":"2015","author":"Ding","year":"2015","journal-title":"Mathematical Problems in Engineering"},{"issue":"4","key":"2021042911565216500_bib20","doi-asserted-by":"crossref","first-page":"706","DOI":"10.1016\/j.expthermflusci.2009.01.005","article-title":"Measurement of temperature-dependent thermal conductivity and viscosity of tio2-water nanofluids","volume":"33","author":"Duangthongsuk","year":"2009","journal-title":"Experimental Thermal and Fluid Science"},{"issue":"1\u20133","key":"2021042911565216500_bib21","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.ijheatmasstransfer.2009.09.024","article-title":"An experimental study on the heat transfer performance and pressure drop of tio2-water nanofluids flowing under a turbulent flow regime","volume":"53","author":"Duangthongsuk","year":"2010","journal-title":"International Journal of Heat and Mass Transfer"},{"issue":"7","key":"2021042911565216500_bib22","first-page":"2121","article-title":"Adaptive subgradient methods for online learning and stochastic optimization","volume":"12","author":"Duchi","year":"2011","journal-title":"Journal of Machine Learning Research"},{"issue":"2","key":"2021042911565216500_bib23","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1002\/andp.19063240204","article-title":"Eine neue bestimmung der molek\u00fcldimensionen","volume":"324","author":"Einstein","year":"1906","journal-title":"Annals of Physics"},{"key":"2021042911565216500_bib24","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.icheatmasstransfer.2016.05.015","article-title":"Effects of temperature and concentration on rheological behavior of mwcnts\/sio2 (20\u201380)-sae40 hybrid nano-lubricant","volume":"76","author":"Esfe","year":"2016","journal-title":"International Communications in Heat and Mass Transfer"},{"issue":"5","key":"2021042911565216500_bib25","doi-asserted-by":"crossref","first-page":"1359","DOI":"10.1016\/j.ijrefrig.2012.03.012","article-title":"Viscosity and thermal conductivity measurements of water-based nanofluids containing titanium oxide nanoparticles","volume":"35","author":"Fedele","year":"2012","journal-title":"International Journal of Refrigeration"},{"key":"2021042911565216500_bib26","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.ymssp.2015.11.014","article-title":"Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings","volume":"72","author":"Gan","year":"2016","journal-title":"Mechanical Systems and Signal Processing"},{"issue":"2","key":"2021042911565216500_bib27","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/s10916-019-1519-7","article-title":"Prevalence and diagnosis of neurological disorders using different deep learning techniques: A meta-analysis","volume":"44","author":"Gautam","year":"2020","journal-title":"Journal of Medical Systems"},{"key":"2021042911565216500_bib28","doi-asserted-by":"crossref","first-page":"002858","DOI":"10.1109\/SMC.2016.7844673","article-title":"Deep learning for solar power forecasting an approach using autoencoder and lstm neural networks","volume-title":"2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","author":"Gensler","year":"2016"},{"key":"2021042911565216500_bib29","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1016\/j.powtec.2017.10.038","article-title":"Prediction of viscosity of several alumina-based nanofluids using various artificial intelligence paradigms-comparison with experimental data and empirical correlations","volume":"323","author":"Gholami","year":"2018","journal-title":"Powder Technology"},{"key":"2021042911565216500_bib30","doi-asserted-by":"crossref","first-page":"104010","DOI":"10.1016\/j.chemolab.2020.104010","article-title":"Prediction of nanofluids viscosity using random forest (rf) approach","volume":"201","author":"Gholizadeh","year":"2020","journal-title":"Chemometrics and Intelligent Laboratory Systems"},{"issue":"4","key":"2021042911565216500_bib31","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1080\/08916150903564796","article-title":"Experimental investigation on the thermal conductivity and viscosity of silver-deionized water nanofluid","volume":"23","author":"Godson","year":"2010","journal-title":"Experimental Heat Transfer"},{"key":"2021042911565216500_bib32","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.chemolab.2016.03.031","article-title":"Accurate prediction of nanofluid viscosity using a multilayer perceptron artificial neural network (mlp-ann)","volume":"155","author":"Heidari","year":"2016","journal-title":"Chemometrics and Intelligent Laboratory Systems"},{"key":"2021042911565216500_bib33","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.rser.2017.07.049","article-title":"On the evaluation of the viscosity of nanofluid systems: Modeling and data assessment","volume":"81","author":"Hemmati-Sarapardeh","year":"2018","journal-title":"Renewable and Sustainable Energy Reviews"},{"key":"2021042911565216500_bib34","doi-asserted-by":"crossref","first-page":"7596","DOI":"10.1109\/ICASSP.2013.6639140","article-title":"Audio-visual deep learning for noise robust speech recognition","volume-title":"2013 IEEE International Conference on Acoustics, Speech and Signal Processing","author":"Huang","year":"2013"},{"issue":"3)","key":"2021042911565216500_bib35","doi-asserted-by":"crossref","first-page":"201","DOI":"10.5829\/idosi.ije.2012.25.03b.07","article-title":"Experimental investigation on the viscosity of nanofluids","volume":"25","author":"Jamshidi","year":"2012","journal-title":"International Journal of Engineering"},{"key":"2021042911565216500_bib36","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1137\/1.9781611975673.63","article-title":"Physics guided rnns for modeling dynamical systems: A case study in simulating lake temperature profiles","volume-title":"Proceedings of the 2019 SIAM International Conference on Data Mining","author":"Jia","year":"2019"},{"article-title":"Physics-guided neural networks (pgnn): An application in lake temperature modeling","year":"2018","author":"Karpatne","key":"2021042911565216500_bib37"},{"key":"2021042911565216500_bib38","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.ymssp.2017.11.024","article-title":"A review on the application of deep learning in system health management","volume":"107","author":"Khan","year":"2018","journal-title":"Mechanical Systems and Signal Processing"},{"issue":"19\u201320","key":"2021042911565216500_bib39","doi-asserted-by":"crossref","first-page":"4410","DOI":"10.1016\/j.ijheatmasstransfer.2011.04.048","article-title":"A critical synthesis of thermophysical characteristics of nanofluids","volume":"54","author":"Khanafer","year":"2011","journal-title":"International Journal of Heat and Mass Transfer"},{"article-title":"Adam: A method for stochastic optimization","year":"2017","author":"Kingma","key":"2021042911565216500_bib40"},{"issue":"6","key":"2021042911565216500_bib41","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Communications of the ACM"},{"issue":"12","key":"2021042911565216500_bib42","doi-asserted-by":"crossref","first-page":"5690","DOI":"10.1021\/je1006407","article-title":"Effects of temperature and particle size on the thermal property measurements of al2o3-water nanofluids","volume":"55","author":"Kwek","year":"2010","journal-title":"Journal of Chemical & Engineering Data"},{"issue":"7553","key":"2021042911565216500_bib43","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"issue":"11\u201312","key":"2021042911565216500_bib44","doi-asserted-by":"crossref","first-page":"2651","DOI":"10.1016\/j.ijheatmasstransfer.2007.10.026","article-title":"Effective viscosities and thermal conductivities of aqueous nanofluids containing low volume concentrations of al2o3 nanoparticles","volume":"51","author":"Lee","year":"2008","journal-title":"International Journal of Heat and Mass Transfer"},{"issue":"2","key":"2021042911565216500_bib45","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1017\/S002211207200120X","article-title":"Slow flow through stationary random beds and suspensions of spheres","volume":"51","author":"Lundgren","year":"1972","journal-title":"Journal of Fluid Mechanics"},{"issue":"4","key":"2021042911565216500_bib46","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1016\/j.jcde.2019.04.005","article-title":"Effectiveness of hall current and exponential heat source on unsteady heat transport of dusty tio2-eo nanoliquid with nonlinear radiative heat","volume":"6","author":"Mahanthesh","year":"2019","journal-title":"Journal of Computational Design and Engineering"},{"issue":"4","key":"2021042911565216500_bib47","doi-asserted-by":"crossref","first-page":"874","DOI":"10.1016\/j.ijheatmasstransfer.2011.10.021","article-title":"Latest developments on the viscosity of nanofluids","volume":"55","author":"Mahbubul","year":"2012","journal-title":"International Journal of Heat and Mass Transfer"},{"issue":"3\u20136","key":"2021042911565216500_bib48","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1016\/j.spmi.2003.09.012","article-title":"Heat transfer behaviours of nanofluids in a uniformly heated tube","volume":"35","author":"Maiga","year":"2004","journal-title":"Superlattices and Microstructures"},{"issue":"3","key":"2021042911565216500_bib49","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1007\/s10973-010-0721-0","article-title":"A new dimensionless group model for determining the viscosity of nanofluids","volume":"100","author":"Masoud\u00a0Hosseini","year":"2010","journal-title":"Journal of Thermal Analysis and Calorimetry"},{"issue":"5","key":"2021042911565216500_bib50","doi-asserted-by":"crossref","first-page":"055501","DOI":"10.1088\/0022-3727\/42\/5\/055501","article-title":"A new model for calculating the effective viscosity of nanofluids","volume":"42","author":"Masoumi","year":"2009","journal-title":"Journal of Physics D: Applied Physics"},{"key":"2021042911565216500_bib51","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.jtice.2015.05.032","article-title":"A novel correlation approach for viscosity prediction of water based nanofluids of al2o3, tio2, sio2 and cuo","volume":"58","author":"Meybodi","year":"2016","journal-title":"Journal of the Taiwan Institute of Chemical Engineers"},{"key":"2021042911565216500_bib52","first-page":"807","article-title":"Rectified linear units improve restricted boltzmann machines","volume-title":"Proceedings of the 27th International Conference on Machine Learning (ICML-10)","author":"Nair","year":"2010"},{"issue":"6","key":"2021042911565216500_bib53","doi-asserted-by":"crossref","first-page":"1492","DOI":"10.1016\/j.ijheatfluidflow.2007.02.004","article-title":"Temperature and particle-size dependent viscosity data for water-based nanofluids\u2013hysteresis phenomenon","volume":"28","author":"Nguyen","year":"2007","journal-title":"International Journal of Heat and Fluid Flow"},{"key":"2021042911565216500_bib54","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.ijthermalsci.2014.07.017","article-title":"Review on combined heat and mass transfer characteristics in nanofluids","volume":"87","author":"Pang","year":"2015","journal-title":"International Journal of Thermal Sciences"},{"issue":"6","key":"2021042911565216500_bib55","doi-asserted-by":"crossref","first-page":"064301","DOI":"10.1063\/1.3187732","article-title":"A study on stability and thermophysical properties (density and viscosity) of al 2 o 3 in water nanofluid","volume":"106","author":"Pastoriza-Gallego","year":"2009","journal-title":"Journal of Applied Physics"},{"issue":"1\u20132","key":"2021042911565216500_bib56","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.fluid.2010.10.015","article-title":"Cuo in water nanofluid: influence of particle size and polydispersity on volumetric behaviour and viscosity","volume":"300","author":"Pastoriza-Gallego","year":"2011","journal-title":"Fluid Phase Equilibria"},{"issue":"4","key":"2021042911565216500_bib57","doi-asserted-by":"crossref","first-page":"352","DOI":"10.2174\/1574893612666170707095707","article-title":"The advances and challenges of deep learning application in biological big data processing","volume":"13","author":"Peng","year":"2018","journal-title":"Current Bioinformatics"},{"issue":"13","key":"2021042911565216500_bib58","doi-asserted-by":"crossref","first-page":"133108","DOI":"10.1063\/1.2356113","article-title":"Measurements of nanofluid viscosity and its implications for thermal applications","volume":"89","author":"Prasher","year":"2006","journal-title":"Applied Physics Letters"},{"issue":"1","key":"2021042911565216500_bib59","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1007\/s10973-018-7916-1","article-title":"Rigorous smart model for predicting dynamic viscosity of al 2 o 3\/water nanofluid","volume":"137","author":"Ramezanizadeh","year":"2019","journal-title":"Journal of Thermal Analysis and Calorimetry"},{"key":"2021042911565216500_bib60","doi-asserted-by":"crossref","first-page":"109345","DOI":"10.1016\/j.rser.2019.109345","article-title":"A review on the utilized machine learning approaches for modeling the dynamic viscosity of nanofluids","volume":"114","author":"Ramezanizadeh","year":"2019","journal-title":"Renewable and Sustainable Energy Reviews"},{"issue":"7\u20138","key":"2021042911565216500_bib61","doi-asserted-by":"crossref","first-page":"2042","DOI":"10.1016\/j.ijheatmasstransfer.2008.10.025","article-title":"Laminar convective heat transfer and viscous pressure loss of alumina\u2013water and zirconia\u2013water nanofluids","volume":"52","author":"Rea","year":"2009","journal-title":"International Journal of Heat and Mass Transfer"},{"issue":"4","key":"2021042911565216500_bib62","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1109\/TASLP.2014.2303296","article-title":"Application of deep belief networks for natural language understanding","volume":"22","author":"Sarikaya","year":"2014","journal-title":"IEEE\/ACM Transactions on Audio, Speech, and Language Processing"},{"issue":"2)","key":"2021042911565216500_bib63","first-page":"99","article-title":"Experimental investigation of viscosity and thermal conductivity of suspensions containing nanosized ceramic particles","volume":"34","author":"Tavman","year":"2008","journal-title":"Archives of Materials Science"},{"key":"2021042911565216500_bib64","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.asoc.2016.05.052","article-title":"Hydrocarbon reservoir model detection from pressure transient data using coupled artificial neural network\u2013wavelet transform approach","volume":"47","author":"Vaferi","year":"2016","journal-title":"Applied Soft Computing"},{"issue":"4","key":"2021042911565216500_bib65","doi-asserted-by":"crossref","first-page":"474","DOI":"10.2514\/2.6486","article-title":"Thermal conductivity of nanoparticle-fluid mixture","volume":"13","author":"Wang","year":"1999","journal-title":"Journal of Thermophysics and Heat Transfer"},{"issue":"1","key":"2021042911565216500_bib66","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1115\/1.1532008","article-title":"Investigation on convective heat transfer and flow features of nanofluids","volume":"125","author":"Xuan","year":"2003","journal-title":"Journal of Heat Transfer"},{"issue":"4","key":"2021042911565216500_bib67","doi-asserted-by":"crossref","first-page":"409","DOI":"10.3390\/app7040409","article-title":"Viscosity prediction of different ethylene glycol\/water based nanofluids using a rbf neural network","volume":"7","author":"Zhao","year":"2017","journal-title":"Applied Sciences"},{"key":"2021042911565216500_bib68","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.powtec.2015.04.058","article-title":"Modeling and prediction of viscosity of water-based nanofluids by radial basis function neural networks","volume":"281","author":"Zhao","year":"2015","journal-title":"Powder Technology"}],"container-title":["Journal of Computational Design and Engineering"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/jcde\/article-pdf\/8\/2\/600\/37562237\/qwab001.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/academic.oup.com\/jcde\/article-pdf\/8\/2\/600\/37562237\/qwab001.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,29]],"date-time":"2021-04-29T12:02:27Z","timestamp":1619697747000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jcde\/article\/8\/2\/600\/6124680"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,30]]},"references-count":68,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,4,28]]}},"URL":"https:\/\/doi.org\/10.1093\/jcde\/qwab001","relation":{},"ISSN":["2288-5048"],"issn-type":[{"type":"electronic","value":"2288-5048"}],"subject":[],"published-other":{"date-parts":[[2021,4]]},"published":{"date-parts":[[2021,1,30]]}}}