Matches in SemOpenAlex for { <https://semopenalex.org/work/W4288056445> ?p ?o ?g. }
- W4288056445 endingPage "129811" @default.
- W4288056445 startingPage "129811" @default.
- W4288056445 abstract "In the present study, using an artificial neural network (ANN), the thermal conductivity of nanofluid containing TiO2 nanoparticles was modeled and investigated. The used experimental data to train and estimate the ANN are the thermal conductivity of the nanofluid at different volume fractions and temperatures. The ANN structure is considered with two hidden layers and five neurons for each hidden layer. To choose the best training function, several training functions available in the ANN are analyzed. The results reveal that by considering the criteria of the highest regression coefficient and the lowest mean square error (MSE), trainbr training function has the best performance with R= 99.9% 2%, MSE= 1.0211e-5 and train and test performances of 3.1198e-6 and 5.2791e-5. The maximum error of 2.5% for test data approximation in the process of knfestimation shows the precision of designed ANN. The results also show that ANN can estimate the knfof TiO2/water nanofluid (NF) at different temperatures and φpwith very good precision, and theoretical models like Pak and Cho are not able to precisely estimate the knf." @default.
- W4288056445 created "2022-07-28" @default.
- W4288056445 creator A5015389811 @default.
- W4288056445 creator A5016817662 @default.
- W4288056445 creator A5047014439 @default.
- W4288056445 date "2022-11-01" @default.
- W4288056445 modified "2023-09-26" @default.
- W4288056445 title "Investigation of different training function efficiency in modeling thermal conductivity of TiO2/Water nanofluid using artificial neural network" @default.
- W4288056445 cites W1758979016 @default.
- W4288056445 cites W1967969097 @default.
- W4288056445 cites W1968324739 @default.
- W4288056445 cites W1975118023 @default.
- W4288056445 cites W1975414241 @default.
- W4288056445 cites W1982851128 @default.
- W4288056445 cites W1991884169 @default.
- W4288056445 cites W1996465292 @default.
- W4288056445 cites W2005115326 @default.
- W4288056445 cites W2011687858 @default.
- W4288056445 cites W2027212033 @default.
- W4288056445 cites W2033970979 @default.
- W4288056445 cites W2045506000 @default.
- W4288056445 cites W2051623369 @default.
- W4288056445 cites W2054396175 @default.
- W4288056445 cites W2092763208 @default.
- W4288056445 cites W2136775225 @default.
- W4288056445 cites W2507984219 @default.
- W4288056445 cites W2591772253 @default.
- W4288056445 cites W2726631196 @default.
- W4288056445 cites W2783928583 @default.
- W4288056445 cites W2789482719 @default.
- W4288056445 cites W2803738450 @default.
- W4288056445 cites W2888413580 @default.
- W4288056445 cites W2912956423 @default.
- W4288056445 cites W2928527251 @default.
- W4288056445 cites W2955216050 @default.
- W4288056445 cites W2969894068 @default.
- W4288056445 cites W3000249690 @default.
- W4288056445 cites W3000553723 @default.
- W4288056445 cites W3006723896 @default.
- W4288056445 cites W3037946026 @default.
- W4288056445 cites W3086258500 @default.
- W4288056445 cites W3133964904 @default.
- W4288056445 cites W3159793605 @default.
- W4288056445 cites W3165298588 @default.
- W4288056445 cites W316630087 @default.
- W4288056445 cites W3184177790 @default.
- W4288056445 cites W3185391099 @default.
- W4288056445 cites W3185898339 @default.
- W4288056445 cites W4200402196 @default.
- W4288056445 cites W4213105686 @default.
- W4288056445 cites W4220934592 @default.
- W4288056445 cites W998874852 @default.
- W4288056445 cites W2899714079 @default.
- W4288056445 doi "https://doi.org/10.1016/j.colsurfa.2022.129811" @default.
- W4288056445 hasPublicationYear "2022" @default.
- W4288056445 type Work @default.
- W4288056445 citedByCount "1" @default.
- W4288056445 countsByYear W42880564452023 @default.
- W4288056445 crossrefType "journal-article" @default.
- W4288056445 hasAuthorship W4288056445A5015389811 @default.
- W4288056445 hasAuthorship W4288056445A5016817662 @default.
- W4288056445 hasAuthorship W4288056445A5047014439 @default.
- W4288056445 hasConcept C105795698 @default.
- W4288056445 hasConcept C139945424 @default.
- W4288056445 hasConcept C14036430 @default.
- W4288056445 hasConcept C154945302 @default.
- W4288056445 hasConcept C155672457 @default.
- W4288056445 hasConcept C159985019 @default.
- W4288056445 hasConcept C16910744 @default.
- W4288056445 hasConcept C171250308 @default.
- W4288056445 hasConcept C186060115 @default.
- W4288056445 hasConcept C192562407 @default.
- W4288056445 hasConcept C199360897 @default.
- W4288056445 hasConcept C21946209 @default.
- W4288056445 hasConcept C33923547 @default.
- W4288056445 hasConcept C41008148 @default.
- W4288056445 hasConcept C50644808 @default.
- W4288056445 hasConcept C78458016 @default.
- W4288056445 hasConcept C86803240 @default.
- W4288056445 hasConcept C97346530 @default.
- W4288056445 hasConceptScore W4288056445C105795698 @default.
- W4288056445 hasConceptScore W4288056445C139945424 @default.
- W4288056445 hasConceptScore W4288056445C14036430 @default.
- W4288056445 hasConceptScore W4288056445C154945302 @default.
- W4288056445 hasConceptScore W4288056445C155672457 @default.
- W4288056445 hasConceptScore W4288056445C159985019 @default.
- W4288056445 hasConceptScore W4288056445C16910744 @default.
- W4288056445 hasConceptScore W4288056445C171250308 @default.
- W4288056445 hasConceptScore W4288056445C186060115 @default.
- W4288056445 hasConceptScore W4288056445C192562407 @default.
- W4288056445 hasConceptScore W4288056445C199360897 @default.
- W4288056445 hasConceptScore W4288056445C21946209 @default.
- W4288056445 hasConceptScore W4288056445C33923547 @default.
- W4288056445 hasConceptScore W4288056445C41008148 @default.
- W4288056445 hasConceptScore W4288056445C50644808 @default.
- W4288056445 hasConceptScore W4288056445C78458016 @default.
- W4288056445 hasConceptScore W4288056445C86803240 @default.
- W4288056445 hasConceptScore W4288056445C97346530 @default.