**1. Introduction**

There was a lot of consideration in the production of working fluids with superior thermal characteristics to enhance heat transfer efficiency in heated pipes [1,2]. The latest research on nanofluids indicates that suspending extremely thermally conductive nanomaterials into the base fluid (e.g., water (DW) or Ethylene glycol (EG)) increases thermal conductivity, an increase in the base fluid's convective heat transfer rate [3,4]. Reducing thermal boundary layer thickness generated by the existence of nanomaterials and their random movement within the base fluid might have a significant impact on such convective heat transfer coefficient augmentation [5,6]. An increase in the nanoparticle mass/volume concentration frequently improves the heat transfer rate of the base fluid. Adding more nanomaterials to the base fluid enhances the Brownian motion-driven variations in the fluid, which leads to a fast heat transfer from the wall to the nanofluid [7,8].

In the heat transfer and hydrodynamic applications, thermal conductivity shows a crucial role in evaluating nanofluid thermal efficiency, which has been calculated based on different factors, involving inlet temperature, nanomaterial types and mass percentage, the nanostructure of particles, base fluid properties, pH values, and types of surfactants/additives [9–11]. Additionally, dynamic viscosity influences the determination of heat and momentum transfer and the device's pumping amount [12]. Meanwhile, less effort was dedicated to density, thermal expansion, and specific heat capacity [13]. The values of density and specific capacity of different nanofluids have been estimated through empirical correlations and equations based on the volume fraction of nanoparticles [14,15].

Numerous experimental and numerical investigation analyses evaluated forced convection heat transfer using different metal and metallic oxides such as Al, Cu, CuO, SiO2, Al2O3, TiO2, and MWCNTs during various flow regimes. Several mechanical or thermal equipment used constant wall heat flux (WHF) for heat transfer applications. Numerical and experimental efforts studied the effects of thermal and momentum diffusivity on the heat conductivity of various nano-powders under turbulent forced convective heat transfer [11]. The study examined various nanofluid samples (Al2O3-H2O, SiO2-H2O, and Cu-H2O) and various volume percentages (1–3 vol.%) at 30 ◦C. Improved thermal conductivity had no impact on heat transfer efficiency; but, the Prandtl number (Pr) of nanofluids significantly influenced the Nusselt number value at constant Re. Numerical and experimental analyses observed forced convective heat transfer by flowing graphene nanoplatelet nanofluids through a fully turbulent system inside a horizontally smooth heated pipe [16]. GNPs@H2O nanofluids increased from 7.96% to 25% in thermal efficiency. Furthermore, Nuavg at 0.1 wt.% revealed enhancements of 75%, 79%, and 83% at 8231, 10,351, and 12,320 W/m<sup>2</sup> , respectively. Zubir and his group [17] produced reduced graphene oxide (R-GO) and its influence on a heat exchanger's turbulent convective heat transfer performance. Furthermore, the study noticed substantial improvement in the Nusselt number up to 144% and 63% at the upstream and downstream of the test section, respectively. Laboratory work of PGGNPs with SSA-750 m2/g was carried out to assess nanofluid flow and heat transfer enhancement [18]. The test section was heated with two rates such as 23,870 and 18,565 W/m<sup>2</sup> . Meanwhile, the Re-number during the investigation ranged from 3900 to 11,700. From the results, the heat transfer coefficient improved significantly (around 119% & 84%) at the two heat rates. The performance index of all samples was larger than one, indicating that the synthesized PGGNP@DW nanofluids were effective for convective heat transfer. Yarmand et al. [19] reported the effect of pressure loss, thermophysical characteristics, and convective heat transfer on the stable-doped GNPs nanofluids. Their results showed positive improvements in both Nu-number and heat transfer coefficients by about 26.5% and 19.68%, respectively, at 0.1 wt.%. Lastly, Sadri

et al. [20] prepared graphene nanoplatelets using green synthetization. They prepared three samples of C-GNPs nanofluids in 0.025, 0.075, and 0.1 wt.%. The results showed optimum improvements in the Nu-number (18.69%) and convective heat transfer coefficient (37.54%) at Re = 15,927 and 0.1 wt.%. It was clear that the performance index for all CGNP-DW nanofluids was larger than one. This demonstrated the advantage of employing environmentally friendly nanofluids in heat transfer systems. The overall thermal performance of using TiO2-DW nanofluids reached up to 1.519 as the best value, then reduced by increasing the nanofluid flow [21]. The thermal efficiency of SiO2-DW in a triangular tube with various turbulators was consistently greater than one. The index increased first with the increase in Re number and then decreased with it. This index reached its maximum at the Reynolds number Re = 6000 [22]. Additionally, in corrugated tubes, several working fluids (DW, GNP-SDBS@DW, Al2O3@DW, and SiO2@DW) and tube shapes (rectangular, triangular, trapezoidal, and curved ribs) were investigated [23]. The overall performance can be enhanced by up to 37% by combining the approaches (GNP-SDBS@DW nanofluids and curved pipe).

A closer look at the literature reveals several gaps and shortcomings of overall thermal performance using carbon and metal-oxide nanofluids within heated pipes. The main purpose was to compare the performance of functionalized carbon nanostructured nanofluids and commercial metallic oxides-based nanofluids. The prepared nanomaterials were characterized via different examinations to show successful chemical reactions. Meanwhile, the nanofluids' thermo-physical properties of PEG@GNPs, PEG@TGr, Al2O3, and SiO<sup>2</sup> were measured in the range of temperatures (20–60 ◦C). The heat transfer and nanofluids flow were evaluated based on several parameters such as the average Nu-number, relative pumping power, and different performance indicators under fully developed turbulent forced convective flow.
