## 4 Research Perspectives
Merit criteria should be more systematically used, as was done by [PERSON] et al. for \(Al_{2}O_{3}\) water-based nanofluids in turbulent pipe flows [44] and laminar channel flows [78]. The most common criteria include:  
* the \(C_{\mu}/C_{k}\) ratio introduced by [PERSON] et al. [79], which gathers the thermal conductivity and dynamic viscosity of the nanofluid. * the Mouromtseff number \(Mo=\rho^{a}k^{b}Cp^{c}/\mu^{d}\), which gathers the four main thermophysical properties and where the exponents \(a\), \(b\), \(c\), and \(d\) take on values appropriate for the heat transfer mode of interest and the corresponding heat transfer correlation, as proposed by [PERSON] [80]. * the performance evaluation criterion \(PEC=\dot{n}\epsilon Cp_{nf}\delta T/(\dot{V}\delta P)\) introduced by [PERSON] et al. [81], which is based on the ratio of heat transferred to the requiring pumping. \(\dot{n}\), \(\delta T\), \(\dot{V}\), and \(\delta P\) are the mass flow rate, temperature difference, volumetric flow rate, and pressure drop, respectively. * the overall efficiency \(\eta=Nu_{nf}\delta P_{bf}/(Nu_{bf}\delta P_{nf})\), where \(Nu\) represents the Nusselt number. * the efficiency of nanofluids based on the first and the second laws of thermodynamics can be tackled by considering the performance parameter \(PE\) proposed by [PERSON] and [PERSON] [82]: \(PE=Nu_{nf}Ns_{bf}/(Nu_{bf}Ns_{nf})\), where \(Ns\) is the dimensionless entropy generation. Artificial neural networks (ANNs) are a valuable tool for the predictions of both the thermophysical properties of nanofluids and their thermal performances in industrial applications. Moreover, they can serve to compare experimental and/or numerical results obtained through different methods and then explain the huge variability of published results. Coupled to multiobjective algorithms like genetic ones, they could also be used to find the optimal design of thermal systems involving nanofluids, whose performances are dependent on a large number of geometrical and operational parameters. ANN models have been widely applied for water-based nanofluids, but less for ethylene- or propylene-glycol-based nanofluids. It can be explained by a lack of reliable experimental data used to train, test, and validate ANNs. For ethylene-glycol-based nanofluids, one can cite only the works of [PERSON] et al. on the thermal conductivity of suspensions with SWCNT-\(Al_{2}O_{3}\) nanoparticles [83] and on the thermal conductivity and dynamic viscosity of suspensions with \(Al_{2}O_{3}\) nanoparticles [84]. Similar studies must be generalized to other types of nanofluids to predict their thermal properties, as well as their performances in real applications. Finally, due to the lack of studies on the nanoparticle agglomeration, settling, and erosion phenomena over several heating/cooling cycles at low temperatures (\(\leq\)0 \({}^{\circ}\)C), the use of nanofluids in industrial applications still remains questionable and certainly hinders their commercialization. Serious attempts should be carried out to fill this gap and then exploit the results found to develop more efficient and compact thermal systems working with enhanced thermal properties of nano-coolants. Both authors wrote the paper and revised it. This research received no external funding. The authors would like to thank the NSERC chair on industrial energy efficiency funded by Hydro-Quebec, Natural Resources Canada (CannetEnergy-Varennes) and Rio Tinto Alcan established at Universite de Sherbrooke between 2014 and 2019. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: