Prediction of Two-Phase Heat Transfer Coefficients in a Horizontal Pipe for Different Inclined Positions With Artificial Neural Networks

Sobhanifar, Najmeh; Ahmadloo, Ebrahim; Azizi, Sadreddin

doi:10.1115/1.4029865

Cited by 12 publications

(5 citation statements)

References 33 publications

(34 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Typically, RMSRE (Root Mean Squared Relative Error), RMSE (Root Mean Square Error), RRMSE (Relative Root Mean Square Error), MSE (Mean Square Error), MAE (Mean Absolute Error) or MRE (Mean Relative Error) are used as indicators for measuring accuracy of results [55][56][57][58][59]. In the present work, it was important to use an indicator that does not depend on units of physical quantities.…”

Section: Validation Of the Machine Learning Modelmentioning

confidence: 99%

Neural Network as a Tool for Design of Amorphous Metal Alloys with Desired Elastoplastic Properties

Galimzyanov

Doronina

Мокшин

2023

Metals

View full text Add to dashboard Cite

The development and implementation of the methods for designing amorphous metal alloys with desired mechanical properties is one of the most promising areas of modern materials science. Here, the machine learning methods appear to be a suitable complement to empirical methods related to the synthesis and testing of amorphous alloys of various compositions. In the present work, it is proposed a method to determine amorphous metal alloys with mechanical properties closest to those required. More than 50,000 amorphous alloys of different compositions have been considered, and the Young’s modulus E and the yield strength σy have been evaluated for them by the machine learning model trained on the fundamental physical properties of the chemical elements. Statistical treatment of the obtained results reveals that the fundamental physical properties of the chemical element with the largest mass fraction are the most significant factors, whose values correlate with the values of the mechanical properties of the alloys, in which this element is involved. It is shown that the values of the Young’s modulus E and the yield strength σy are higher for amorphous alloys based on Cr, Fe, Co, Ni, Nb, Mo and W formed by the addition of semimetals (e.g., Be, B, Al, Sn), nonmetals (e.g., Si and P) and lanthanides (e.g., La and Gd) than for alloys of other compositions. Increasing the number of components in alloy from 2 to 7 and changing the mass fraction of chemical elements has no significantly impact on the strength characteristics E and σy. Amorphous metal alloys with the most improved mechanical properties have been identified. In particular, such extremely high-strength alloys include Cr80B20 (among binary), Mo60B20W20 (among ternary) and Cr40B20Nb10Pd10Ta10Si10 (among multicomponent).

show abstract

Section: Validation Of the Machine Learning Modelmentioning

confidence: 99%

Neural Network as a Tool for Design of Amorphous Metal Alloys with Desired Elastoplastic Properties

Galimzyanov

Doronina

Мокшин

2023

Metals

View full text Add to dashboard Cite

show abstract

“…Note that our main attention in this section is paid to modeling the interphase heat and mass transfer closures which can be used in CFD solvers or engineering design. Most of these works have been focused on direct modeling of heat/mass transfer coefficients − or modeling the indirect variables that determine the transfer coefficient such as thermal conductivity, Nusselt number, Sherwood number, and Prandtl number . Bansal et al mined the data from the literature over 22 000 experimental conditions and then used ANN and SVM methods to train the data.…”

Section: Current Status and Challengesmentioning

confidence: 99%

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

Zhu

Chen

Ouyang

et al. 2022

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

Artificial intelligence (AI), machine learning (ML), and data science are leading to a promising transformative paradigm. ML, especially deep learning and physics-informed ML, is a valuable toolkit that complements incomplete domain-specific knowledge in conventional experimental and computational methods. ML can provide flexible techniques to facilitate the conceptual development of new robust predictive models for multiphase flows and reactors by finding hidden pattern/information/mechanism in a data set. Due to such emergence, we thereby comprehensively survey, explore, analyze, and discuss key advancements of recent ML applications to hydrodynamics, heat and mass transfer, and reactions in single-phase and multiphase flow systems from different aspects: (1) development of multiphase closure models of drag force, turbulence stresses and heat/mass transfer to improve the accuracy and efficiency of typical CFD simulations; (2) image reconstruction, regime identification, key parameter predictions, and optimization of multiphase flow and transport fields; (3) reaction kinetics modeling (e.g., predictions of reaction networks, kinetic parameters, and species production) and reaction condition optimization. These sections also discuss and analyze the key advantages and weakness of ML for solving the problems in the domain of multiphase flows and reactors. Finally, we summarize the under-solving challenges and opportunities in order to identify future directions that would be useful for the research community. Future development and study of multiphase flows and reactors are envisaged to be accelerated by ML and data science.

show abstract

“…In recent years, the ANN technique has been applied for solving complex engineering problems in different application areas. Sobhanifar et al (2015) used the ANN technique for the computing convective HTCs. Hojjat et al (2011) and Longo et al (2012) used ANN models for the prediction of the thermal conductivity of oxide nanoparticles suspended in water.…”

Section: Artificial Neural Network Modelmentioning

confidence: 99%

A Review on Heat Transfer Enhancement With Nanofluids

Guo

2020

J Enh Heat Transf

View full text Add to dashboard Cite

Advances in technology miniaturization with increasing power density call for new technologies for enhancing heat transfer. Enhancement of heat transfer with the use of nanofluids has been a hectic topic of research and development since the term "nanofluid" was first used in 1995, mainly because the thermophysical properties of nanofluids in most reports in the literature showed supremacy or improvement over their base fluids, which may not allow fulfillment of the present cutting-edge technology needs. Significant progress in this field has been made in the past two decades. This review summarizes a variety of the experimentally measured thermal properties of common nanofluids, the enhancement mechanisms discovered or hypothesised, the models used for properties and heat transfer characteristics, and the applications of nanofluids for enhancing heat transfer. The model of an artificial neutral network is particularly emphasized. Applications to cooling technology, renewable energy and energy systems, and building technology are detailed. Challenges and areas for future research are identified.

show abstract

Prediction of Two-Phase Heat Transfer Coefficients in a Horizontal Pipe for Different Inclined Positions With Artificial Neural Networks

Cited by 12 publications

References 33 publications

Neural Network as a Tool for Design of Amorphous Metal Alloys with Desired Elastoplastic Properties

Neural Network as a Tool for Design of Amorphous Metal Alloys with Desired Elastoplastic Properties

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

A Review on Heat Transfer Enhancement With Nanofluids

Contact Info

Product

Resources

About