The effect of variable temperature and location on relative thermal conductivity (RTC) on the heat pipe in the presence of AL2O3 nanoparticles: Numerical and optimization approaches

Mohammadiun, Mohammad; Mohammadiun, Hamid; Alizadeh, Rasool; Mesgarpour, Mehrdad; Younesian, Amir; Jowkar, Saeed

doi:10.1016/j.jtice.2021.04.050

Cited by 13 publications

(4 citation statements)

References 52 publications

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“…This study found smaller nanoparticles (30 nm) resulted in a minor temperature difference and better thermal performance, unlike larger nanoparticles (50 nm and 80 nm) with a lower heat transfer rate. These findings suggest that nanoparticle diameter is essential when designing and optimizing heat transfer systems [28].…”

Section: Resultsmentioning

confidence: 91%

Nanoparticle Size and Heat Pipe Angle Impact on the Thermal Effectiveness of a Cylindrical Screen Mesh Heat Pipe

Alphonse,

Muthukumarasamy,

Dhairiyasamy

2023

Applied Mechanics

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This study examines the effects of particle size and heat pipe angle on the thermal effectiveness of a cylindrical screen mesh heat pipe using silver nanoparticles (Ag) as the test substance. The experiment investigates three different particle sizes (30 nm, 50 nm, and 80 nm) and four different heat pipe angles (0°, 45°, 60°, and 90°) on the heat transmission characteristics of the heat pipe. The results show that the thermal conductivity of the heat pipe increased with an increase in heat pipe angle for all particle sizes, with the highest thermal conductivity attained at a 90° heat pipe angle. Furthermore, the thermal resistance of the heat pipe decreased as the particle size decreased for all heat pipe angles. The thermal conductivity measurements of the particle sizes—30, 50, and 80 nm—were 250 W/mK, 200 W/mK, and 150 W/mK, respectively. The heat transfer coefficient values for particle sizes 30 nm, 50 nm, and 80 nm were 5500 W/m2K, 4500 W/m2K, and 3500 W/m2K, respectively. The heat transfer coefficient increased with increased heat pipe angle for all particle sizes, with the highest heat transfer coefficient obtained at a 90° heat pipe angle. The addition of Ag nanoparticles at a volume concentration of 1% reduced the thermal resistance of the heat pipe, resulting in improved heat transfer performance. At a heat load of 150 W, the thermal resistance decreased from 0.016 °C/W without nanoparticles to 0.012 °C/W with 30 nm nanoparticles, 0.013 °C/W with 50 nm nanoparticles, and 0.014 °C/W with 80 nm nanoparticles. This study also found that the heat transfer coefficient increased with increased heat pipe angle for all particle sizes, with the highest heat transfer coefficient obtained at a 90° heat pipe angle.

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Section: Resultsmentioning

confidence: 91%

Nanoparticle Size and Heat Pipe Angle Impact on the Thermal Effectiveness of a Cylindrical Screen Mesh Heat Pipe

Alphonse,

Muthukumarasamy,

Dhairiyasamy

2023

Applied Mechanics

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“…3 shows the variations of droplet mass and drag force with time and Reynold number, respectively. Expectedly, the droplet mass decreases over time [58] while drag force is calculated [38,59,60]. In Fig.…”

Section: Dropletsmentioning

confidence: 92%

“…Turbulence zone LES-RANS Dissipation rate-Re 15, 000 < Re < 87, 320 [34] Transient zone GEKO Re-turbulence intensity 5, 300 < Re < 15, 000 [35] Laminar model Laminar Zone -Re < 5, 300 [36] Evaporation method Arbitrary-Lagrangian-Eulerian Droplet diameter Upperrangeddroplet < 250μmLowerrangeddroplet > 1μm [37,38] Droplet temperature Non-uniform profile Temperature gradient of droplet and ambient…”

Section: Viscous Modelsmentioning

confidence: 99%

Predicting the effects of environmental parameters on the spatio-temporal distribution of the droplets carrying coronavirus in public transport – A machine learning approach

Mesgarpour

Abad

Alizadeh

et al. 2022

Chemical Engineering Journal

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“…The calculations are carried out on the top-performing solutions (µ) of the current generation, typically using 25% of the best solutions. The process consists of five steps (1 to 5) that are executed until an acceptable solution is found or a predetermined number of iterations is reached [25,26].…”

Section: Presented Equation Using Evolutionary Strategy Algorithmmentioning

confidence: 99%

Urea-Self Powered Biosensors: A Predictive Evolutionary Model for Human Energy Harvesting

Mohebbi Najm Abad,

Farahbakhsh,

Mir

et al. 2023

Sensors

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The objective of this study is to create a reliable predictive model for the electrochemical performance of self-powered biosensors that rely on urea-based biological energy sources. Specifically, this model focuses on the development of a human energy harvesting model based on the utilization of urea found in sweat, which will enable the development of self-powered biosensors. In the process, the potential of urea hydrolysis in the presence of a urease enzyme is employed as a bioreaction for self-powered biosensors. The enzymatic reaction yields a positive potential difference that can be harnessed to power biofuel cells (BFCs) and act as an energy source for biosensors. This process provides the energy required for self-powered biosensors as biofuel cells (BFCs). To this end, initially, the platinum electrodes are modified by multi-walled carbon nanotubes to increase their conductivity. After stabilizing the urease enzyme on the surface of the platinum electrode, the amount of electrical current produced in the process is measured. The optimal design of the experiments is performed based on the Taguchi method to investigate the effect of urea concentration, buffer concentration, and pH on the generated electrical current. A general equation is employed as a prediction model and its coefficients calculated using an evolutionary strategy. Also, the evaluation of effective parameters is performed based on error rates. The obtained results show that the established model predicts the electrical current in terms of urea concentration, buffer concentration, and pH with high accuracy.

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The effect of variable temperature and location on relative thermal conductivity (RTC) on the heat pipe in the presence of AL2O3 nanoparticles: Numerical and optimization approaches

Cited by 13 publications

References 52 publications

Nanoparticle Size and Heat Pipe Angle Impact on the Thermal Effectiveness of a Cylindrical Screen Mesh Heat Pipe

Nanoparticle Size and Heat Pipe Angle Impact on the Thermal Effectiveness of a Cylindrical Screen Mesh Heat Pipe

Predicting the effects of environmental parameters on the spatio-temporal distribution of the droplets carrying coronavirus in public transport – A machine learning approach

Urea-Self Powered Biosensors: A Predictive Evolutionary Model for Human Energy Harvesting

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