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Metals
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Computational Modeling of Alloys
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Computational Modeling of Alloys

A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Computation and Simulation on Metals".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 1767

Special Issue Editors

Dr. Seshadev Sahoo

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan (Deemed to be University), Khandagiri, Bhubaneswar 751030, India
Interests: computational materials engineering; solidification; addiitve manufcaturing; multiscale simulation
Dr. Natraj Yedla

E-Mail Website
Guest Editor
Department of Metallurgical and Materials Engineering, National Institute of Technology, Rourkela, India
Interests: computational materials engineering; solidification; addiitve manufcaturing; multiscale simulation

Special Issue Information

Dear Colleagues,

Metallic materials include elemental metals and compounds or alloys. Today, they are one of the most important engineering materials. Present developments have led to an increasing demand for diverse new metallic materials in addition to sustainable recycling, digital manufacturing, and the environmental- and climate-friendly production of devices and parts. Therefore, obtaining comprehensive knowledge regarding metallic materials on scales ranging from the atomic, micro-, meso- and macroscopic levels has gained importance as of late.

Computational materials science has become an important and necessary tool to study and develop new alloys. With the introduction of high-power computer resources and different computational techniques that use a multiscale modeling approach, it is easier for resarchers to develop new alloys via process modeling by minimizing experimental trials. 

In this Special Issue, we welcome articles that focus on the development of new computational techniques, multiscale methods, optimization methods to model and develop new alloys, and different material processing techniques. Additive manufacturing, machine learning, and artificial intelligence applied for materials processing and alloy developement coupled with computational materials science are of special interest.

Dr. Seshadev Sahoo
Dr. Natraj Yedla
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • metallic materials
  • alloys
  • computational techniques
  • modeling
  • additive manufacturing
  • materials processing
  • multiscale simulation
  • optimization

Published Papers (1 paper)

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Research

19 pages, 1777 KiB  
Article
Estimation of Component Activities and Molar Excess Gibbs Energy of 19 Binary Liquid Alloys from Partial Pair Distribution Functions in Literature
by Chunlong Wang, Xiumin Chen and Dongping Tao
Metals 2023, 13(5), 996; https://doi.org/10.3390/met13050996 - 21 May 2023
Cited by 3 | Viewed by 1437
Abstract
This work proposes a new method for estimating the molar excess Gibbs energy and activity of liquid alloy based on recent research. The local composition theory provides a connection between the structures of liquid alloys and the thermodynamic models. The partial pair distribution [...] Read more.
This work proposes a new method for estimating the molar excess Gibbs energy and activity of liquid alloy based on recent research. The local composition theory provides a connection between the structures of liquid alloys and the thermodynamic models. The partial pair distribution function (PPDF) was utilized to calculate the parameters of the MIVM, RSM, Wilson, and NRTL. The statistics of the number of molecular pairs of MIVM and RSM were rewritten, which resulted in new forms of the two models. To enhance the NRTL’s estimation performance, the coordination number was incorporated into it (M-NRTL). The aforementioned model and Quasi-chemical model (QCM) were utilized to estimate the excess Gibbs energy and activity of 19 alloys. The alloys contained multiple sets of PPDFs, which enabled the calculation of multiple sets of model parameters. The work examined the impact of expressing the model parameters as first-order linear functions of the components or as constants on the accuracy of the estimation. The parameters were treated as constants. MIVM, RSM, and M-NRTL provided an average relative deviation (ARD) of activity of less than ±20% for 15, 10, and 9 alloys by estimation. When model parameters were expressed as a function of components, QCM showed the best estimation performance, having nine alloys with an ARD of less than ±20%. The number of alloys with an ARD of less than ±20% corresponding to MIVM, RSM, Wilson, NRTL, and M-NRTL was six, five, three, five, and two, respectively. This new method offers simplicity, numerical calculation stability, and excellent reproducibility. Full article
(This article belongs to the Special Issue Computational Modeling of Alloys)
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