Special Issue "Mechanical Modeling and Experimental Investigation of Metallic Materials"

A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Metal Casting, Forming and Heat Treatment".

Deadline for manuscript submissions: 8 December 2021.

Special Issue Editor

Prof. Dr. Uroš Župerl
E-Mail Website
Guest Editor
Faculty of Mechanical Engineering, University of Maribor, 2000 Maribor, Slovenia
Interests: monitoring systems; cognitive systems; cyber-physical systems;machining;optimization; modeling; applied artificial intelligence

Special Issue Information

Dear Colleagues,

Because of continuous efforts to reduce the costs of metal materials production, increasing energy efficiency has become a priority task in this industry. In successful metal production plants, careful energy managing for more and more sustainable metal materials making friendly to the environment is intensively promoted. According to European standards, governments are obliged to increase energy efficiency and minimize CO2 emissions and environmental printouts. It was estimated that in the next 10 years it would be necessary to invest several billion Euros for at least a 20% reduction of CO2 emissions. The only way to realize those targets is to modernize metal production processes, equipment, and infrastructure. The most innovative approach to the modernization of plants is the introduction of cloud technologies into metal production processes. According to paradigm 4.0, digital technologies combined with artificial intelligence have the potential to transform metal production processes to a new more efficient level. Another approach to realizing these targets is to introduce advanced process optimizations regarding productivity, product quality, and cost reductions. To reduce the expensive experimental trials used to evaluate the impact of different optimization strategies, advanced process modeling is needed. Modeling and simulations serve us as an invaluable source of information for conducting process analysis and as an alternative to expensive, dangerous, and time-consuming experimental trials.

This Special Issue of Metals will cover recent advances in the modeling and optimization of different sub-processes in metal materials production from casting, rolling, heat treating, product delivery, quality assurance, and machinability assurance, while considering the most recent experimentally obtained process data.

Prof. Dr. Uroš Župerl
Guest Editor

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 papers will be 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 1800 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

  • metal materials production
  • modeling
  • simulation
  • process analysis
  • optimization
  • artificial intelligence

Published Papers (1 paper)

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Research

Article
RANS versus Scale Resolved Approach for Modeling Turbulent Flow in Continuous Casting of Steel
Metals 2021, 11(7), 1140; https://doi.org/10.3390/met11071140 - 19 Jul 2021
Viewed by 529
Abstract
Numerical modeling is the approach used most often for studying and optimizing the molten steel flow in a continuous casting mold. The selection of the physical model might very much influence such studies. Hence, it is paramount to choose a proper model. In [...] Read more.
Numerical modeling is the approach used most often for studying and optimizing the molten steel flow in a continuous casting mold. The selection of the physical model might very much influence such studies. Hence, it is paramount to choose a proper model. In this work, the numerical results of four turbulence models are compared to the experimental results of the water model of continuous casting of steel billets using a single SEN port in a downward vertical orientation. Experimental results were obtained with a 2D PIV (Particle Image Velocimetry) system with measurements taken at various cut planes. Only hydrodynamic effects without solidification are considered. The turbulence is modeled using the RANS (Realizable k-ε, SST k-ω), hybrid RANS/Scale Resolved (SAS), and Scale Resolved approach (LES). The models are numerically solved by the finite volume method, with volume of fluid treatment at the free interface. The geometry, boundary conditions, and material properties were entirely consistent with those of the water model experimental study. Thus, the study allowed a detailed comparison and validation of the turbulence models used. The numerical predictions are compared to experimental data using contours of velocity and velocity plots. The agreement is assessed by comparing the lateral dispersion of the liquid jet in a streamwise direction for the core flow and the secondary flow behavior where recirculation zones form. The comparison of the simulations shows that while all four models capture general flow features (e.g., mean velocities in the temporal and spatial domain), only the LES model predicts finer turbulent structures and captures temporal flow fluctuations to the extent observed in the experiment, while SAS bridges the gap between RANS and LES. Full article
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