scholarly journals Thermal Conductivity of Nanofluids: A Review on Prediction Models, Controversies and Challenges

2021 ◽  
Vol 11 (6) ◽  
pp. 2525
Author(s):  
Inês Gonçalves ◽  
Reinaldo Souza ◽  
Gonçalo Coutinho ◽  
João Miranda ◽  
Ana Moita ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Machine Learning ◽  
Thermal Conductivity ◽  
Prediction Models ◽  
Statistical Machine Learning ◽  
Fundamental Parameters ◽  
Long Term Stability ◽  
Heat Transfer Fluids ◽  
Classical Models

In recent years, the nanofluids (NFs) have become the main candidates for improving or even replacing traditional heat transfer fluids. The possibility of NFs to be used in various technological applications, from renewable energies to nanomedicine, has made NFs and their thermal conductivity one of the most studied topics nowadays. Hence, this review presents an overview of the most important advances and controversial results related to the NFs thermal conductivity. The different techniques used to measure the thermal conductivity of NFs are discussed. Moreover, the fundamental parameters that affect the NFs thermal conductivity are analyzed, and possible improvements are addressed, such as the increase of long-term stability of the nanoparticles (NPs).The most representative prediction classical models based on fluid mechanics, thermodynamics, and experimental fittings are presented. Also, the recent statistical machine learning-based prediction models are comprehensively addressed, and the comparison with the classical empirical ones is made, whenever possible.

A New Type of Nanoengineered Heat Transfer Fluids: Nanoemulsion Fluids

2006 ◽  
Author(s):  
Bao Yang ◽  
Zenghu Han
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Solid Particles ◽  
Long Term Stability ◽  
Heat Transfer Fluids ◽  
Thermal Fluids ◽  
New Type ◽  
Improved Performance ◽  
Novel Concept

Thermal management in the next decade of microelectronics and optoelectronics will require heat transfer fluids with improved performance over those currently available. The strategy of adding SOLID particles to fluids for improving thermal conductivity has been pursued for nearly a century. In this work, a novel concept of using LIQUID nanodroplets for enhancing thermal conductivity has been developed and was experimentally-demonstrated in water-in-FC72 suspensions, called "nanoemulsion-fluids". The thermal conductivity of FC72 is found to be increased by up to 52% for a nanoemulsion-fluid containing 12vol% water nanodroplets of radius 9.8nm. Such types of nanoemulsion-fluids possess long-term stability and can be mass produced because of no needs for SOLID nanoparticles. The development of nanoemulsion-fluids would open a new direction for thermal fluids studies.

Study of thermal conductivity of nanofluids for the application of heat transfer fluids

2007 ◽  
Vol 455 (1-2) ◽  
pp. 66-69 ◽  
Author(s):  
Dae-Hwang Yoo ◽  
K.S. Hong ◽  
Ho-Soon Yang
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Heat Transfer Fluids

Heat Transfer Properties of Engine Oils

2005 ◽  
Author(s):  
Scott Wrenick ◽  
Paul Sutor ◽  
Harold Pangilinan ◽  
Ernest E. Schwarz
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Diesel Engine ◽  
Specific Heat ◽  
Mineral Oil ◽  
Engine Oil ◽  
Group V ◽  
Engine Oils ◽  
Heat Transfer Fluids ◽  
Transfer Properties

The thermal properties of engine oil are important traits affecting the ability of the oil to transfer heat from the engine. The larger the thermal conductivity and specific heat, the more efficiently the oil will transfer heat. In this work, we measured the thermal conductivity and specific heat of a conventional mineral oil-based diesel engine lubricant and a Group V-based LHR diesel engine lubricant as a function of temperature. We also measured the specific heat of ethylene glycol. The measured values are compared with manufacturers’ data for typical heat transfer fluids. The Group V-based engine oil had a higher thermal conductivity and slightly lower specific heat than the mineral oil-based engine oil. Both engine oils had values comparable to high-temperature heat transfer fluids.

Analysis of nanofluids as a means of thermal conductivity enhancement in heavy machineries

2014 ◽  
Vol 66 (2) ◽  
pp. 238-243 ◽  
Author(s):  
Ayush Jain ◽  
Imbesat Hassan Rizvi ◽  
Subrata Kumar Ghosh ◽  
P.S. Mukherjee
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Ethylene Glycol ◽  
Base Fluid ◽  
High Concentration ◽  
Content Type ◽  
Conductivity Enhancement ◽  
Heavy Machinery ◽  
Heat Transfer Fluids ◽  
Experimental Findings

Purpose – Nanofluids exhibit enhanced heat transfer characteristics and are expected to be the future heat transfer fluids particularly the lubricants and transmission fluids used in heavy machinery. For studying the heat transfer behaviour of the nanofluids, precise values of their thermal conductivity are required. For predicting the correct value of thermal conductivity of a nanofluid, mathematical models are necessary. In this paper, the effective thermal conductivity of various nanofluids has been reported by using both experimental and mathematical modelling. The paper aims to discuss these issues. Design/methodology/approach – Hamilton and Crosser equation was used for predicting the thermal conductivities of nanofluids, and the obtained values were compared with the experimental findings. Nanofluid studied in this paper are Al2O3 in base fluid water, Al2O3 in base fluid ethylene glycol, CuO in base fluid water, CuO in base fluid ethylene glycol, TiO2 in base fluid ethylene glycol. In addition, studies have been made on nanofluids with CuO and Al2O3 in base fluid SAE 30 particularly for heavy machinery applications. Findings – The study shows that increase in thermal conductivity of the nanofluid with particle concentration is in good agreement with that predicted by Hamilton and Crosser at typical lower concentrations. Research limitations/implications – It has been observed that deviation between experimental and theoretical results increases as the volume concentration of nanoparticles increases. Therefore, the mathematical model cannot be used for predicting thermal conductivity at high concentration values. Originality/value – Studies on nanoparticles with a standard mineral oil as base fluid have not been considered extensively as per the previous literatures available.

scholarly journals Freeze and Frost Protection with Aqueous Foam— Foam Development

1999 ◽  
Vol 9 (4) ◽  
pp. 661-669 ◽  
Author(s):  
Christopher Y. Choi ◽  
Werner Zimmt ◽  
Gene Giacomelli
Keyword(s):  
Heat Transfer ◽  
Physical Properties ◽  
Radiative Heat ◽  
Weather Conditions ◽  
Long Term Stability ◽  
Aqueous Foam ◽  
Foam Generation ◽  
Frost Protection ◽  
And Performance

Aqueous foam was developed to serve as a barrier to conductive, convective, and radiative heat transfer. Through the use of a bulking agent, the physical properties of gelatin-based foam were more stable, adhesive, biodegradable, and long lasting. The phytotoxicity, possible environmental hazard and removal of the foam were also considered. Resistance to freezing-thawing, heating-evaporation, and wind were evaluated. Studies to determine the foam's long-term stability under field weather conditions were completed. The handling and performance characteristics of the foam necessary for development of this application were determined. Factors that affect the physical properties and the utilization of the foam were quantified. These included the proportions of the foam components, the mixing temperature of the prefoam solution, the application temperature, and the rate of foam generation. The newly developed foam might be ideal for freeze and frost protection in agriculture.

scholarly journals Is it time to use machine learning survival algorithms for survival and risk factors prediction instead of Cox proportional hazard regression? A comparative population-based study

2021 ◽  
Author(s):  
Sara Morsy ◽  
Truong Hong Hieu ◽  
Abdelrahman M Makram ◽  
Osama Gamal Hassan ◽  
Nguyen Tran Minh Duc ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Survival Duration ◽  
Brier Score ◽  
Survival Prediction ◽  
Proportional Hazard ◽  
Cancer Data ◽  
Cox Proportional Hazard ◽  
Short And Long Term

Purpose Applying machine learning in medical statistics offers more accurate prediction models. In this paper, we aimed to compare the performance of the Cox Proportional Hazard model (CPH), Classification and Regression Trees (CART), and Random Survival Forest (RSF) in short-, and long-term prediction in glioblastoma patients. Methods We extracted glioblastoma cancer data from the Surveillance, Epidemiology, and End Results database (SEER). We used the CPH, CART, and RSF for the prediction of 1- to 10-year survival probabilities. The Brier Score for each duration was calculated, and the model with the least score was considered the most accurate. Results The cohort included 26473 glioblastoma patients divided into two groups: training (n = 18538) and validation set (n = 7935). The average survival duration was seven months. For the short- and long-term predictions, RSF was the best algorithm followed by CPH and CART. Conclusion For big data, RSF was found to have the highest accuracy and best performance. Using an accurate statistical model for survival prediction and prognostic factors determination will help the care of cancer patients. However, more developments of the R packages are needed to allow more illustrations of the effect of each covariate on the survival probability.

scholarly journals Variations of thermophysical properties and heat transfer performance of nanoparticle-enhanced ionic liquids

2019 ◽  
Vol 6 (4) ◽  
pp. 182040 ◽  
Author(s):  
Fang-Fang Zhang ◽  
Fei-Fei Zheng ◽  
Xue-Hong Wu ◽  
Ya-Ling Yin ◽  
Geng Chen
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Mass Fraction ◽  
Local Heat Transfer ◽  
Local Heat ◽  
Measurement Range ◽  
Maximum Enhancement ◽  
Wide Range ◽  
Mass Fractions ◽  
Classical Models

The ionic liquid (IL) 1-ethyl-3-methylimidazolium acetate ([EMIm]Ac) was investigated as a promising absorbent for absorption refrigeration. To improve the thermal conductivity of pure [EMIm]Ac, IL-based nanofluids (ionanofluids, INFs) were prepared by adding graphene nanoplatelets (GNPs). The thermal stability of the IL and INFs was analysed. The variations of the thermal conductivity, viscosity and specific heat capacity resulting from the addition of the GNPs were then measured over a wide range of temperatures and mass fractions. The measured data were fitted with appropriate equations and compared with the corresponding classical models. The results revealed that the IL and INFs were thermally stable over the measurement range. The thermal conductivity greatly increased with increasing mass fraction, while only slightly changed with increasing temperature. A maximum enhancement in thermal conductivity of 43.2% was observed at a temperature of 373.15 K for the INF with a mass fraction of 5%. The numerical results revealed that the dispersion of the GNPs in the pure IL effectively improved the local heat transfer coefficient by up to 28.6%.

scholarly journals Prediction of aqueous intrinsic solubility of druglike molecules using Random Forest regression trained with Wiki-pS0 database

ADMET & DMPK ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 29-77 ◽  
Author(s):  
Alex Avdeef
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Prediction Models ◽  
Chemical Space ◽  
Solubility Data ◽  
Statistical Machine Learning ◽  
Random Forest Regression ◽  
Factors Affecting ◽  
Intrinsic Solubility

The accurate prediction of solubility of drugs is still problematic. It was thought for a long time that shortfalls had been due the lack of high-quality solubility data from the chemical space of drugs. This study considers the quality of solubility data, particularly of ionizable drugs. A database is described, comprising 6355 entries of intrinsic solubility for 3014 different molecules, drawing on 1325 citations. In an earlier publication, many factors affecting the quality of the measurement had been discussed, and suggestions were offered to improve ways of extracting more reliable information from legacy data. Many of the suggestions have been implemented in this study. By correcting solubility for ionization (i.e., deriving intrinsic solubility, S0) and by normalizing temperature (by transforming measurements performed in the range 10-50 °C to 25 °C), it can now be estimated that the average interlaboratory reproducibility is 0.17 log unit. Empirical methods to predict solubility at best have hovered around the root mean square error (RMSE) of 0.6 log unit. Three prediction methods are compared here: (a) Yalkowsky’s general solubility equation (GSE), (b) Abraham solvation equation (ABSOLV), and (c) Random Forest regression (RFR) statistical machine learning. The latter two methods were trained using the new database. The RFR method outperforms the other two models, as anticipated. However, the ability to predict the solubility of drugs to the level of the quality of data is still out of reach. The data quality is not the limiting factor in prediction. The statistical machine learning methodologies are probably up to the task. Possibly what’s missing are solubility data from a few sparsely-covered chemical space of drugs (particularly of research compounds). Also, new descriptors which can better differentiate the factors affecting solubility between molecules could be critical for narrowing the gap between the accuracy of the prediction models and that of the experimental data.

Chemical Robotics Enabled Exploration of Stability and Photoluminescent Behavior in Multicomponent Hybrid Perovskites via Machine Learning

2020 ◽  
Author(s):  
Kate Higgins ◽  
Sai Mani Valleti ◽  
Maxim Ziatdinov ◽  
Sergei Kalinin ◽  
Mahshid Ahmadi
Keyword(s):  
Machine Learning ◽  
Optimization Methods ◽  
Combinatorial Synthesis ◽  
Ambient Conditions ◽  
Term Stability ◽  
Long Term Stability ◽  
Composition Space ◽  
Materials Research ◽  
The Stability

<p>Hybrid organic-inorganic perovskites have attracted immense interest as a promising material for the next-generation solar cells; however, issues regarding long-term stability still require further study. Here, we develop automated experimental workflow based on combinatorial synthesis and rapid throughput characterization to explore long-term stability of these materials in ambient conditions, and apply it to four model perovskite systems: MA<sub>x</sub>FA<sub>y</sub>Cs<sub>1-x-y</sub>PbBr<sub>3</sub>, MA<sub>x</sub>FA<sub>y</sub>Cs<sub>1-x-y</sub>PbI<sub>3</sub>, (Cs<sub>x</sub>FA<sub>y</sub>MA<sub>1-x-y</sub>Pb(Br<sub>x+y</sub>I<sub>1-x-y</sub>)<sub>3</sub>) and (Cs<sub>x</sub>MA<sub>y</sub>FA<sub>1-x-y</sub>Pb(I<sub>x+y</sub>Br<sub>1-x-y</sub>)<sub>3</sub>). We also develop a machine learning-based workflow to quantify the evolution of each system as a function of composition based on overall changes in photoluminescence spectra, as well as specific peak positions and intensities. We find the stability dependence on composition to be extremely non-uniform within the composition space, suggesting the presence of potential preferential compositional regions. This proposed workflow is universal and can be applied to other perovskite systems and solution-processable materials. Furthermore, incorporation of experimental optimization methods, e.g., those based on Gaussian Processes, will enable the transition from combinatorial synthesis to guide materials research and optimization.</p>

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