Application of Machine Learning Algorithms in Predicting Rheological Behavior of BN-diamond/Thermal Oil Hybrid Nanofluids

Application of Machine Learning Algorithms in Predicting Rheological Behavior of BN-diamond/Thermal Oil Hybrid Nanofluids

The use of nanofluids in heat transfer applications has significantly increased in recent times due to their enhanced thermal properties. It is therefore important to investigate the flow behavior and, thus, the rheology of different nanosuspensions to improve heat transfer performance. In this stud...

Full description

Bibliographic Details
Main Authors:	Abulhassan Ali, Nawal Noshad, Abhishek Kumar, Suhaib Umer Ilyas, Patrick E. Phelan, Mustafa Alsaady, Rizwan Nasir, Yuying Yan
Format:	Article
Language:	English
Published:	MDPI AG 2024-01-01
Series:	Fluids
Subjects:	artificial neural network gradient boost regression Gaussian regression machine learning nanofluids random forest
Online Access:	https://www.mdpi.com/2311-5521/9/1/20

Similar Items

AddGBoost: A gradient boosting-style algorithm based on strong learners
by: Moshe Sipper, et al.
Published: (2022-03-01)

Leveraging machine learning to evaluate the effect of raw materials on the compressive strength of ultra-high-performance concrete
by: Mohamed Abdellatief, et al.
Published: (2025-03-01)

Comparing the Performance of GPR, XGBoost and CatBoost Models for Calculating Reference Crop Evapotranspiration in Jiangxi Province
by: LIU Xiaoqiang, et al.
Published: (2021-01-01)

Computational analysis of controlled drug release from porous polymeric carrier with the aid of Mass transfer and Artificial Intelligence modeling
by: Saad M Alshahrani, et al.
Published: (2024-11-01)

Perbandingan Prediksi Depresi Mahasiswa dengan Linear Regression, Random Forest, dan Gradient Boosting
by: Ainindzi Nur Meiza Pudjianto, et al.
Published: (2024-12-01)

Rheological behavior predictions of non-Newtonian nanofluids via correlations and artificial neural network for thermal applications
by: Nik Eirdhina Binti Nik Salimi, et al.
Published: (2024-09-01)

Machine Learning Models for Predicting the Occurrence of Respiratory Diseases Using Climatic and Air-Pollution Factors
by: Yunseo Ku, et al.
Published: (2022-05-01)

Developing and Preliminary Testing of a Machine Learning-Based Platform for Sales Forecasting Using a Gradient Boosting Approach
by: Antonio Panarese, et al.
Published: (2022-10-01)

Gradient Boosting Machines, A Tutorial
by: Alexey eNatekin, et al.
Published: (2013-12-01)

RegBoost: a gradient boosted multivariate regression algorithm
by: Wen Li, et al.
Published: (2020-03-01)

A System for Filling Store Displays: Pitting a Single Model against a Set of Demand Forecasting Models
by: Artur Myna, et al.
Published: (2023-02-01)

Boosting Functional Regression Models with FDboost
by: Sarah Brockhaus, et al.
Published: (2020-09-01)

Improved Confidence-Interval Estimations Using Uncertainty Measure and Weighted Feature Decisions for Cuff-Less Blood-Pressure Measurements
by: Soojeong Lee, et al.
Published: (2025-01-01)

Unveiling the potential of machine learning approaches in predicting the emergence of stroke at its onset: a predicting framework
by: Sheela Lavanya J M, et al.
Published: (2024-08-01)

Statistical Analysis of the Mathematical Model of Entropy Generation of Magnetized Nanofluid
by: Munawwar Ali Abbas, et al.
Published: (2019-06-01)

Machine Learning Approach to Simulate Soil CO<sub>2</sub> Fluxes under Cropping Systems
by: Toby A. Adjuik, et al.
Published: (2022-01-01)

Deep Learning for Multi-Output Regression Using Gradient Boosting
by: Seyedsaman Emami, et al.
Published: (2024-01-01)

Drought Forecasting: Application of Ensemble and Advanced Machine Learning Approaches
by: Geetabai S. Hukkeri, et al.
Published: (2023-01-01)

Machine learning driven prediction of lattice constants in transition metal dichalcogenides
by: Bhupendra Sharma, et al.
Published: (2023-11-01)

Machine learning driven prediction of lattice constants in transition metal dichalcogenides
by: Bhupendra Sharma, et al.
Published: (2023-11-01)

Comparative Analysis of Crop Coefficient Approaches and Machine Learning Models for Predicting Water Requirements in Three Major Crops in Coastal Saline-Alkali Land
by: Shide Dong, et al.
Published: (2025-02-01)

Modelling of Intra-Field Winter Wheat Crop Growth Variability Using in Situ Measurements, Unmanned Aerial Vehicle-Derived Vegetation Indices, Soil Properties, and Machine Learning Algorithms
by: Lwandile Nduku, et al.
Published: (2023-11-01)

Creating High-Resolution Precipitation and Extreme Precipitation Indices Datasets by Downscaling and Improving on the ERA5 Reanalysis Data over Greece
by: Ntagkounakis Giorgos, et al.
Published: (2024-08-01)

Fake News Detection Model Basing on Machine Learning Algorithms
by: Mohammed A. Taha, et al.
Published: (2024-08-01)

Use of Machine Learning with Fused Spectral Data for Prediction of Product Sensory Characteristics: The Case of Grape to Wine
by: Claire E. J. Armstrong, et al.
Published: (2023-02-01)

Using Market Indicators to Refine Estimates of Corporate Bankruptcy Probabilities
by: Daria S. Leonteva
Published: (2022-12-01)

A High-Accuracy Thermal Conductivity Model for Water-Based Graphene Nanoplatelet Nanofluids
by: Elif Begum Elcioglu
Published: (2021-08-01)

Hierarchical Facial Age Estimation Using Gaussian Process Regression
by: Manisha M. Sawant, et al.
Published: (2019-01-01)

Estimating latent heat flux of subtropical forests using machine learning algorithms
by: Harekrushna Sahu, et al.
Published: (2025-01-01)

A New Approach in Predicting the Higher Heating Value of Natural Gas from Ghana’s Oil Fields
by: Eric Broni-Bediako, et al.
Published: (2023-12-01)

Growth conditions of tree species relative to climate change and sea level rise in low-lying Mid Atlantic coastal forests
by: LeeAnn Haaf, et al.
Published: (2024-08-01)

Unveiling spatial variations of high forest live biomass carbon stocks of Gabon using advanced remote sensing techniques
by: Le Bienfaiteur Takougoum Sagang, et al.
Published: (2024-01-01)

Forecasting spring maize yield using vegetation indices and crop phenology metrics from UAV observations
by: Lun Bao, et al.
Published: (2024-01-01)

Developing an ensembled machine learning model for predicting water quality index in Johor River Basin
by: L. M. Sidek, et al.
Published: (2024-04-01)

Feature Importance in Gradient Boosting Trees with Cross-Validation Feature Selection
by: Afek Ilay Adler, et al.
Published: (2022-05-01)

Deep and machine learning for daily streamflow estimation: a focus on LSTM, RFR and XGBoost
by: Özlem Terzi, et al.
Published: (2023-10-01)

Predicting lung cancer survivability: A machine learning regression model
by: Iffat Jabin, et al.
Published: (2021-06-01)

Prediction of home energy consumption based on gradient boosting regression tree
by: Peng Nie, et al.
Published: (2021-11-01)

Application of Even Span Greenhouse Solar Dryer (ESGSD) for drying Persian shallot; Kinetic analysis, machine learning modeling and quality evaluation
by: Morteza Taki, et al.
Published: (2025-04-01)

Rheology of deionized water based titania nanofluids /
by: Afiqah Firdus, 1991-, author, et al.
Published: (2013)