Cite
Estimation of thermophysical property of hybrid nanofluids for solar Thermal applications: Implementation of novel Optimizable Gaussian Process regression (O-GPR) approach for Viscosity prediction.
MLA
Adun, Humphrey, et al. “Estimation of Thermophysical Property of Hybrid Nanofluids for Solar Thermal Applications: Implementation of Novel Optimizable Gaussian Process Regression (O-GPR) Approach for Viscosity Prediction.” Neural Computing & Applications, vol. 34, no. 13, July 2022, pp. 11233–54. EBSCOhost, https://doi.org/10.1007/s00521-022-07038-2.
APA
Adun, H., Wole-Osho, I., Okonkwo, E. C., Ruwa, T., Agwa, T., Onochie, K., Ukwu, H., Bamisile, O., & Dagbasi, M. (2022). Estimation of thermophysical property of hybrid nanofluids for solar Thermal applications: Implementation of novel Optimizable Gaussian Process regression (O-GPR) approach for Viscosity prediction. Neural Computing & Applications, 34(13), 11233–11254. https://doi.org/10.1007/s00521-022-07038-2
Chicago
Adun, Humphrey, Ifeoluwa Wole-Osho, Eric C. Okonkwo, Tonderai Ruwa, Terfa Agwa, Kenechi Onochie, Henry Ukwu, Olusola Bamisile, and Mustafa Dagbasi. 2022. “Estimation of Thermophysical Property of Hybrid Nanofluids for Solar Thermal Applications: Implementation of Novel Optimizable Gaussian Process Regression (O-GPR) Approach for Viscosity Prediction.” Neural Computing & Applications 34 (13): 11233–54. doi:10.1007/s00521-022-07038-2.