# Thermal Conductivity of Nanofluids: A Review on Prediction Models, Controversies and Challenges
## 4 Prediction Models for Thermal Conductivity and Other Properties of the NFs
### Specific Heat Capacity  
In a similar way to what was previously presented for the density, the specific heat capacity can also be determined in the same way, using Equation (15). The values obtained through this method are very similar to the BF. However, further experimental tests are required to improve the theoretical approaches [3]:  
\[C_{pnf}=\frac{\varphi\rho_{p}C_{pnp}+(1-\varphi)\rho_{bf}C_{pbf}}{\rho_{nf}} \tag{15}\]  
Here \(C_{pbf}\) corresponds to BF specific heat capacity, \(C_{pnp}\) to NP specific heat capacity and \(C_{pbf}\) to NF specific heat capacity.  
\begin{table}
\begin{tabular}{c c c} \hline
**Reference** & **Work Focus** & **Machine Learning Involvement** \\ \hline [PERSON] et al., 2016 [93] & Prediction of thermal conductivity and viscosity based on ANN and applications in automotive radiators & ANN data-driven modeling \\ \hline [PERSON] et al., 2019 [94] & Characteristics of different machine learning methods including MLP-ANN, GMDH, ANFIS, RBF, and LS-SVM combined with GA, PSO, and ICA. Applications of machine learning methods to dynamic viscosity modeling of nanofluids & Machine learning for viscosity prediction \\ \hline [PERSON] et al., 2019 [95] & AI algorithms inluding ANNs, fuzzy logic optimization methods and hybrid AI algorithms used for prediction and optimization properties of nanofluids & Machine learning algorithms for prediction and optimization \\ \hline [PERSON] 2020 [96] & Overview on measured thermal properties, enhancement mechanisms, models for properties and heat transfer characteristics and applications of nanofluids to cooling, renewable energy, and energy and building technologies & ANN model for thermal conductivity prediction \\ \hline [PERSON] et al., 2020 [86] & Development of a machine learning model for density prediction of nitrides in ethylene glycol. The developed is much more accurate than the [PERSON] and Cho empirical model & SVM model for density prediction \\ \hline [PERSON] and [PERSON] 2020 [89] & Machine learning glass transition temperature of polymers prediction using a GPR (_Gauss Process Regression_) data-driven model & GPR model for glass transition temperature prediction \\ \hline [PERSON] and [PERSON] 2020 [91] & Machine learning decomposition onset temperature of lubricant additives prediction using a GPR data-driven model & GPR model for temperature prediction \\ \hline [PERSON] et al., 2020 [88] & CMIS (_Committee Machine Intelligent System_) machine learning model for nanofluid viscosity estimation & CMIS model for viscosity prediction \\ \hline [PERSON] et al., 2020 [87] & BSVR (_Bayesian Support Vector Regression_) and ANN machine learning models for nanofluid viscosity prediction & BSVR and ANN models for viscosity prediction \\ \hline [PERSON] et al., 2021 [92] & Nanofluid heat transfer machine learning research applied to renewable energy & Machine learning description and applications \\ \hline \end{tabular}
\end{table}
Table 2: Representative review papers on nanofluid heat transfer research using machine learning prediction models.