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Nanofluids, characterized by the dispersion of nanoparticles in a base liquid, have attracted significant attention in recent years due to their exceptional thermal properties. Specifically, the specific heat capacity of nanofluids plays a crucial role in the design and optimization of heat transfer systems. Traditional experimental methods for determining the specific heat capacity of nanofluids are often limited in terms of cost, time, and operating condition ranges. To address these limitations, this research focuses on the development of a novel predictive model for estimating the specific heat capacity of nanofluids. This study aims to develop a machine learning regression model called Gradient Boost Regression (GBR) with Grid Search optimization (GSO) for accurately predicting the specific heat capacity of aluminum nitride (Al2N3) nanoparticles that are suspended in both water and an ethylene glycol (EG) solution. The GBR-GSO model capitalizes on the strengths of GBR, which can effectively capture complex relationships, and GSO, a metaheuristic optimization technique inspired by the law of gravity. By integrating these two approaches, we aim to create a robust and accurate predictive model for specific heat capacity in nanofluids. To develop and validate the GBR-GSO model, a diverse dataset based on experimental-specific heat capacity collected from the literature has been designed. The performance of the model has been evaluated by comparing its predictions with experimental data. The GBR-GSO model achieved 99.99% accuracy with the experimental data of specific heat capacity. This research contributes to the advancement of nanofluid-based heat transfer systems by providing an effective tool for predicting the specific heat capacity of nanofluids. The developed model can facilitate the design and optimization of various engineering applications, leading to the development of energy-efficient and sustainable technologies. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to introduce a newly proposed operating principle for the heat transfer use sector, which originates in an ECHA restriction proposal on partially hydrogenated terphenyls.1 Heat transfer fluids are substances that enable processes to maintain desired temperature ranges accurately and are used in nearly every manufacturing sector. Heat transfer fluids achieve this through their thermal stability properties, and when operated in a closed system, can provide manufacturing processes with years of function with little maintenance. A new operating principle focused on this theme of closed system operation is called Strictly Controlled Closed Systems (SCCS). The SCCS will serve to standardise safe operation of heat transfer systems in the European Union by limiting exposures to workers and releases to the environment. The SCCS is a combination of several safety standards, principles, and guidelines from inside and outside the heat transfer use sector. This combination of resources has resulted in a novel inspection checklist that should aid in industrial compliance and review of enforcement agencies. In addition, SCCS should provide a regulatory path for hazardous heat transfer fluids to follow as a class. SCCS will allow even hazardous heat transfer fluids to have cradle-to-the-grave standardised management aimed at minimisation of environmental and human exposures. It is our expectation that adoption of this standard will be a role model to ensure the continued use of important chemistries in applications and manufacturing that drive the EU economy. [ABSTRACT FROM AUTHOR]

A hybrid nanofluid (HYNF) is an excellent working medium for improved heat transfer performance. To investigate the convective heat transfer performance of an HYNF of various particle mixing ratios (MR), at first samples of MgO-SiO2/water HYNF are prepared by varying the MR of MgO to SiO2 as 0:100, 40:60, 50:50, 60:40, and 100:0 at 0.05 mass % of concentration; then convective heat transfer characteristics are determined experimentally by exposing different mass flows of HYNF from 0.03–0.27 kg/s to a constant heat flux of 5630 W/m2. The results describe that the variation in MR strongly influences the Nusselt number (Nu) of HYNF. The maximum Nu, which is 1.94 times (~ 94.50%) higher than the same with base fluid, is obtained at MR of 60:40 and the mass flow rate of 0.13 kg/s. The variation of MR does not affect the friction factor of HYNF and it is nearly comparable to that of the base fluid. Further, a new concept has been proposed to determine the thermo-economic feasibility of HYNF in heat transfer applications. It was recommended to maintain the MR at 60:40 and 0.13 kg/s mass flow rate for optimum thermo-economic performance. [ABSTRACT FROM AUTHOR]

Nanofluid has emerged as a promising heat transfer fluid (HTF) due to their significant thermophysical, and optical characteristics enhancement over base fluids. Hybrid nanofluids with multiple nanomaterials have the advantage of synergistic properties in comparison to monocomponent nanofluids. The present study proposes an energy-efficient and cleaner synthesis method for developing carbon quantum dot (C-dot), MXene, and a hybrid MXene/C-dot hybrid nanofluids, for heat transfer application. In-situ microwave pyrolysis technique and two-step method were adopted for nanomaterial and nanofluid synthesis. The morphological, phase structural, chemical, and elemental compositional analysis of the nanomaterials was performed. The material characterization confirms the hybridization of C-dot on MXene nanosheets. The thermal conductivity and volumetric heat capacity of the nanofluids were measured using the transient plane source (TPS) method. Thermal conductivity was observed to increase with nanofluid concentration and temperature. Results indicate that MXene has the highest thermal conductivity enhancement (50 %) over water, followed by hybrid (42.2 %) and C-dot nanofluid (33.2 %). The volumetric heat capacity of nanofluids decreased with concentration and temperature. A semi-empirical correlation, as a function of nanofluid concentration and temperature, was coined for predicting thermal conductivity and volumetric heat capacity. Optical property characterization study shows that C-dot nanofluid exhibited considerable absorption along the UV range, while MXene nanofluid showed absorption in the visible and near-infrared (NIR) region. Hybrid nanofluids demonstrated complementary absorption properties of C-dot and MXene nanofluids. [Display omitted] • Novel MXene/C-dot hybrid nanofluid was synthesized for heat transfer applications. • Study reveals the material and thermal-optical properties of C-dot, MXene and hybrid nanofluids. • Semi-empirical correlation were coined to predict thermal properties of nanofluids. • Highest thermal conductivity enhancement was exhibited by 0.2 wt% MXene nanofluid. • Hybrid nanofluid showed complementary absorption property of component nanofluids. [ABSTRACT FROM AUTHOR]