Prediction of nanofluids viscosity using random forest (RF) approach

Gholizadeh, Majid; Jamei, Mehdi; Ahmadianfar, Iman; Pourrajab, Rashid

doi:10.1016/j.chemolab.2020.104010

Cited by 98 publications

(40 citation statements)

References 96 publications

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“…Unlike linear or simple regression where the aim is to minimize error rate, SVR is used to fit error within a certain threshold. It is developed on the elements of SVM, whereby the data points about a hyperplane are distinctly segregated with support vectors that are the closest points to the generated hyperplane in n-dimensional feature space [66]. This model is also a popular choice for curve fitting and prediction of linear/nonlinear regression types [67].…”

Section: Support Vector Regression (Svr)mentioning

confidence: 99%

“…In existing works of literature, SVR has been used in engineering science [76], biomedical [77], and social science [78]-related researches. Specifically, it has been used for Covid-19 cases prediction [66], CO 2 sequestration study [68], solar irradiance prediction [79], hybrid nanofluid conductivity prediction [32], and other regression tasks. SVR models differ based on the regularization term used for the structural complexity and the specific choice of loss function used in measuring the empirical risk [80].…”

Section: Support Vector Regression (Svr)mentioning

confidence: 99%

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Estimation of thermophysical property of hybrid nanofluids for solar Thermal applications: Implementation of novel Optimizable Gaussian Process regression (O-GPR) approach for Viscosity prediction

Adun

Wole‐Osho

Okonkwo

et al. 2022

Neural Comput & Applic

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Solar energy technologies represent a viable alternative to fossil fuels for meeting increasing global energy demands. However, to increase the production of solar technologies in the global energy mix, the cost of production should be as competitive as other sources. This study focuses on the implementation of machine learning for estimating the thermophysical properties of nanofluids for nanofluid-based solar energy technologies as this would make the synthesis of nanofluids cost-effective. The prediction of thermal conductivity has gained a lot of research attention, whereas, the viscosity of nanofluids has less concentration of studies. The accurate prediction of the viscosity of hybrid nanofluids is important in estimating the heat transfer performance of nanofluids as regards their pump power requirements and convective heat transfer coefficient in several applications. The rigor of experimentations of hybrid nanofluids has necessitated the need for developing efficient and robust machine learning models for accurately estimating the viscosity of hybrid nanofluids for solar applications. Several studies were aimed at developing a predictive model for the viscosity of nanofluids; however, these models are limited to specific types of nanofluids. This study is aimed at developing a robust machine learning algorithm for predicting the viscosity of several hybrid nanofluids from reliable experimental data (700 datasets) culled from literature. This study implements a novel optimizable Gaussian process regression (O-GPR), which have not been previously used in this area, and compares the result with other commonly used machine learning algorithms like, Boosted tree regression (BTR), Artificial neural network (ANN), support vector regression (SVR), to accurately predict the viscosity of a wide range of Newtonian-based hybrid nanofluid. The input parameters used in training the machine learning models were temperature (T), volume fraction (VF), the acentric factor of the base fluid (ACF), nanoparticle size (NS), and nanoparticle density (ND). The prediction performance of the machine learning algorithms was tested using statistical metrics and was compared with theoretical models. The O-GPR model showed superior predictive performance with an R 2 of 0.999998 and an MSE of 0.0002552. The study conclusively states that the high accuracy prediction of thermophysical properties of nanofluid using robust machine learning models makes the design of nanofluidbased solar energy technologies more cost-effective.

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Section: Support Vector Regression (Svr)mentioning

confidence: 99%

Section: Support Vector Regression (Svr)mentioning

confidence: 99%

Estimation of thermophysical property of hybrid nanofluids for solar Thermal applications: Implementation of novel Optimizable Gaussian Process regression (O-GPR) approach for Viscosity prediction

Adun

Wole‐Osho

Okonkwo

et al. 2022

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…The ease of representing a physio-chemical process with datadriven machine learning algorithms has increased in the past few years. Different algorithms were implemented in the literature to estimate the viscosity of nanofluids, such as ANN, LSSVM, RF, and ANFIS (Adaptive Neuro-Fuzzy Inference) [35,64].…”

Section: Predictions Via Machine Learning Algorithmsmentioning

confidence: 99%

“…It was reported that the ANN method was better than correlation development. Gholizadeh et al [35] used RF approach to predict the viscosity of metallic oxides-based nanofluids and reported 0.989 R 2 for the model accuracy using multi-input parameters. It is noteworthy to mention that while the results obtained by machine learning predictive methods proliferate, the inner working of these tools remains elusive when it comes to physical insights or scientific principles.…”

Section: Introductionmentioning

confidence: 99%

Rheological behavior of stabilized diamond-graphene nanoplatelets hybrid nanosuspensions in mineral oil

Ilyas

Ridha

Sardar

et al. 2021

Journal of Molecular Liquids

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Recent advancements in thermo-fluid technology assisted with highly thermal conductive nanomaterials have shown assuring outcomes. It is also proven that thermal conductivity alone cannot define the overall heat transfer characteristics, and the viscous properties are equally significant towards thermal management. Therefore, this research involves investigating the rheological behavior of hybrid nanosuspensions containing high thermally conductive diamond and graphene nanoplatelets (1:1). These nanomaterials are dispersed in mineral oil using a two-step technique. Hybrid nanofluids' stability is achieved using a non-ionic stabilizer Span85, exhibiting no sedimentation for a minimum of five months. Nanomaterial characterizations are performed to study morphology, purity, and chemical analysis. The flow behavior of hybrid nanosuspensions is investigated at varying nanomaterial mass concentrations (0-2 %), temperatures (298.15-338.15 K), and shear rates (1-2000 s -1 ). Hybrid nanofluids exhibit shear-thinning behavior, which is also correlated with the Ostwald-de-Waele model. The temperature-viscosity relationship is well predicted using the Vogel-Fulcher-Tammann model. Hybrid nanofluids show a maximum enhancement of 35% viscosity at 2% concentration. A generalized twovariable correlation is used to express viscosity as a function of temperature and nanofluid concentration with an excellent agreement. Three different machine learning methods, i.e., Artificial Neural Network (ANN), Gradient Boosting Machine (GBM), and Random Forest (RF) algorithms are also introduced to predict the viscosity of hybrid nanofluids based on the three input parameters (temperature, concentration, and shear rate). The parity plots conclude that all algorithms can predict big-data viscous behavior with high precision.

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“…From Fig. 9 (a), (b) and (c), it is evident that the number of trees (n) that is enough to stabilize the error is about 250, 250 and 500 for tensile modulus, tensile strength, elongation at break, respectively [71,72]. Since there was a large variation of values of elongation, the extra step of scaling was carried out.…”

Section: Machine Learning Algorithms: I Random Forest Regressionmentioning

confidence: 99%

Accelerated Discovery of the Polymer Matrix for Cartilage Repair Through Machine Learning Algorithms

Mairpady

Mourad

Mozumder

2021

Preprint

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Cartilage repair is one of the most challenging tasks for the orthopedic surgeons and researchers. The primary challenge lies on the fact that the development of the extracellular matrixes requires specialized cells known as chondrocytes which are sparse in numbers. Chondrocytes’ minimal self-renewal capacity makes it further troublesome and expensive to repair the cartilages. In designing successful substitutes for the cartilages, the selection of materials used for the scaffold fabrication plays the central role among several other important factors in order to ensure the success of the survival and proliferation of any biomaterial substitutes. Since last few decades, polymer and polymers' combination have been extensively used to fabricate such scaffolds and have shown promising results in terms of mechanical integrity and biocompatibility. In an empirical approach, the selection of the most appropriate polymer(s) for cartilage repair is an expensive and time-consuming affair, as traditionally, it requires numerous trials. Moreover, it is humanly impossible to go through the huge library of literature available on the potential polymer(s) and to correlate their physical, mechanical and biological properties that might be suitable for cartilage tissue engineering. With the advancement of machine learning, material design may experience a significant reduction in experimental time and cost. The objective of this study is to implement an inverse design approach to select the best polymer(s) or composites for cartilage repair by using the machine learning algorithms, such as random forest regression (i.e., regression trees) and the multinomial logistic regression. In these algorithms, the mechanical properties of the polymers, which are similar to the cartilages, are considered as the input and the polymer(s)/composites are the predicted output. According to the random forest regression and multinomial logistic regression, the polymer(s)/composites (i.e., the output) having the closest characteristics of the articular cartilages were found to be a composite of polycaprolactone and poly(bisphenol A carbonate) and a blend of polyethylene/polyethylene-graft-poly(maleic anhydride), respectively. These composites exhibit similar biomechanical properties of the natural cartilages and initiate only minimal immune responses in the body environment.

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Prediction of nanofluids viscosity using random forest (RF) approach

Cited by 98 publications

References 96 publications

Estimation of thermophysical property of hybrid nanofluids for solar Thermal applications: Implementation of novel Optimizable Gaussian Process regression (O-GPR) approach for Viscosity prediction

Estimation of thermophysical property of hybrid nanofluids for solar Thermal applications: Implementation of novel Optimizable Gaussian Process regression (O-GPR) approach for Viscosity prediction

Rheological behavior of stabilized diamond-graphene nanoplatelets hybrid nanosuspensions in mineral oil

Accelerated Discovery of the Polymer Matrix for Cartilage Repair Through Machine Learning Algorithms

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