Reliable prediction of thermophysical properties of nanofluids for enhanced heat transfer in process industry: a perspective on bridging the gap between experiments, CFD and machine learning

Ullah, Atta; Kılıç, Mustafa; Habib, Ghulam; Sahin, Mahir; Khalid, Rehan Zubair; Sanaullah, Khairuddin

doi:10.1007/s10973-023-12083-7

Cited by 8 publications

(5 citation statements)

References 112 publications

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“…Kilic et al (2017) conducted both experimental and numerical investigations (shown in Figure 12) to examine the heat transfer phenomenon occurring from a heated flat plate positioned within a rectangular channel subjected to an impinging air jet. The effects of heat transfer were investigated by employing both air impingement and jet impingement methods in conjunction with nanofluids, taking into account the intense turbulence generated in the process (Kiliç & Özcan, 2018; Ullah et al, 2023).…”

Section: Drying Methodsmentioning

confidence: 99%

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Preservation of fruits through drying—A comprehensive review of experiments and modeling approaches

Kilic,

Sahin,

Hassan

et al. 2024

J Food Process Engineering

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A significant part of the world's population still has problems in accessing food. The growing world population will exacerbate this issue in the future. Innovative studies conducted in this field play a crucial role in addressing the issue of drying and storage of foods. Atmospheric drying methods, such as rotary, tunnel, conveyor, cabinet, tower, and kiln dryers, offer advantages in relation to high production capacities, cost‐effective initial setup, and economical operating conditions. However, concurrently, the weaknesses of these methods arise from factors such as drying duration, uneven moisture content, and space requirements. The solar dryer method is especially effective in dehydrating agricultural products, offering an energy‐saving advantage compared to other methods. However, it is important to note that this approach, which involves exposing crops to direct sunlight, comes with several drawbacks affecting both food quality and health. In cases where the quality of highly valued foodstuffs is crucial, subatmospheric drying methods like vacuum, freeze, and microwave freeze are typically preferred. However, the primary drawback of this approach lies in its high operating costs, particularly in terms of energy consumption. Artificial neural networks (ANNs) can be used for predictive modeling, helping to forecast drying behavior and optimize process parameters in food drying applications especially nonlinear connections among variables. ANNs are adept at managing nonlinearities, offering a more precise depiction of the intricate interactions within food drying systems. This review examines articles from the last 5 years in the literature, synthesizing research conducted in food drying. The findings indicate a predominant interest among researchers in methodologies with lower environmental impact, prompting increased attention to studies addressing this aspect. There is a notable emphasis on the frequent exploration of energy‐efficient systems. The ongoing research focuses on the development of methods utilizing ultrasonic, infrared radiation, and electrohydrodynamic techniques to achieve more effective, shorter‐duration, energy‐efficient drying processes with enhanced control over the final product.

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Section: Drying Methodsmentioning

confidence: 99%

“…into account the intense turbulence generated in the process (Kiliç & Özcan, 2018;Ullah et al, 2023).…”

Section: Air Impingement Dryingmentioning

confidence: 99%

Preservation of fruits through drying—A comprehensive review of experiments and modeling approaches

Kilic,

Sahin,

Hassan

et al. 2024

J Food Process Engineering

Self Cite

View full text Add to dashboard Cite

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“…ω can be defined as the ratio of the volume of the particle to the total volume of a mixture, including base fluid and dispersed particle. It can give accurate predictions at low volume fraction of solid particles for the most cases [11] .…”

Section: Existing Correlation For Predicting Densitymentioning

confidence: 98%

“…The density and specific heat theoretical correlations do give explicit dependence of temperature .The specific heat correlations also do not give dependence on the particle size. The correlations mentioned above also have applicability range limitations, so a new wide range of experimental data-based models are required [11,37] .…”

Section: Cp Specific Heat ω Volume Fractionmentioning

confidence: 99%

“…The main inspiration behind the nanofluids was the enhancement of thermal conductivity by dispersing solid particles in the base fluid, so most of the work has been done on determining the empirical correlations for the thermal conductivity, and after that, researchers have prioritized developing viscosity correlations. Very little work has been done on determining correlations for specific heat and density [6,7,11] .…”

Section: Introductionmentioning

confidence: 99%

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Development of Generalized Correlations for Predicting Density and Specific Heat of Nanofluids for Enhanced Heat Transfer

Habib,

Hussain,

Kilic

et al. 2024

J Mod Nanotechnol

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Objective: Energy-generating devices such as automotive vehicles, power reactors, computing machines etc. require effective cooling for safe and efficient operation. Additionally, at the same time, heat losses must be minimized. In industry, pure fluids such as water, air etc. are usually used for these purposes. Nanofluids have recently been explored as an alternative to traditional coolants. Nanofluids have attracted attention due to their superior thermophysical properties as compared to conventional fluids. Nanofluids are nanosized particles of metallic, non-metallic, or organic origin suspended in base fluids. Some examples of these nanoparticles include silver, copper, metal oxides, metal nitrides, carbon materials (such as carbon nanotubes, metal carbides) etc. Several options for base fluids exist, such as ethylene glycol, water, transformer oil, etc. Methods: The thermophysical properties of nanofluids of interest include thermal conductivity, viscosity, density, and specific heat. Predicting these physical properties, which depend upon particle concentration, temperatures, particle sizes, etc., is a significant challenge. Researchers have developed correlations for predicting these properties based on available experimental data. However, a thorough literature review suggests that generalized correlations do not exist, especially for the density and specific heat. Most researchers have done work on thermal conductivity and viscosity. The present work has developed generalized correlations to predict nanofluids' thermophysical properties density, and specific heat. Experimental data was collected for the various operating and geometric parameters affecting nanofluids' density and specific heat. Results: By performing regression analysis, four dimensionless correlations are proposed for each of predicting two important thermophysical properties, i.e., heat capacity and density of nanofluids. The developed correlations were compared with available experimental data. Conclusion: It was found that the correlations generated were fitted the experimental data for density and specific heat with an accuracy of ±10% band of actual value.

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Correlation and modelling the thermodynamics properties of mixtures containing zinc (II) chloride-based deep eutectic solvent and alkanols with PC-SAFT EoS, and the Nomoto’s relation

Mgxadeni,

Hernández,

Bahadur

et al. 2024

Journal of Molecular Liquids

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Reliable prediction of thermophysical properties of nanofluids for enhanced heat transfer in process industry: a perspective on bridging the gap between experiments, CFD and machine learning

Cited by 8 publications

References 112 publications

Preservation of fruits through drying—A comprehensive review of experiments and modeling approaches

Preservation of fruits through drying—A comprehensive review of experiments and modeling approaches

Development of Generalized Correlations for Predicting Density and Specific Heat of Nanofluids for Enhanced Heat Transfer

Correlation and modelling the thermodynamics properties of mixtures containing zinc (II) chloride-based deep eutectic solvent and alkanols with PC-SAFT EoS, and the Nomoto’s relation

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