Effect of particle size and concentration on thermal performance of cylindrical shaped heat pipe using silver-DI water nanofluid

Chavda, N. K.; Bumataria, Rakesh

doi:10.1080/01430750.2022.2127887

Cited by 8 publications

(1 citation statement)

References 32 publications

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“…The experimental condition (Q: 100W, I: 45 o , ṁ: 0.0050 kg/s, and R: 0.8) produced the highest outlet temperature of 320.726 K, whereas the operating state (Q: 40W, I: 0 o , ṁ: 0.0033 kg/s, and R: 0.6) resulted in the lowest outlet temperature (303.6 K). The maximum outlet temperature is produced when the working fluid has the highest silver content (80%), consistent with the reports of Chavda and Bumataria [34]. Increases in the heat input, inclination, flow rate, and silver content resulted in a higher outlet temperature compatible with the studies carried out by Ali et al [35].…”

Section: Resultssupporting

confidence: 90%

Performance evaluation of hybrid nanofluid-filled cylindrical heat pipe by machine learning algorithms

Kumararaja,

Sıvaraman,

Saravanan

2024

Journal of Thermal Engineering

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The current study attempts to predict the outlet temperature of a hybrid nanofluid heat pipe using three machine learning models, namely Extra Tree Regression (ETR), CatBoost Re-gression (CBR), and Light Gradient Boosting Machine Regression (LGBMR), in the Python environment. Based on 7000 experimental data (various heat input, inclination angle, flow rate, and fluid ratio), different training (95%–5%) and testing (5%–95%) split sizes, a closer prediction was attained at 85:15. The three attempted machine learning models are capable of predicting the outlet temperature, as evidenced by the less than 5% deviation from the experi-mental results. Of the three attempted machine learning models, the ETR model outperforms the other two with a higher accuracy (98%). Further, the sensitivity analysis indicates the ab-sence of data overfitting in the attempted models.

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Section: Resultssupporting

confidence: 90%