Advanced Simulation Technologies of Metallurgical Processing
A special issue of Metals (ISSN 2075-4701).
Deadline for manuscript submissions: closed (30 September 2019) | Viewed by 57849
Special Issue Editor
Special Issue Information
Dear Colleagues,
Traditional models describing metallurgical processes such as sintering, roasting, smelting, leaching, precipitation, electrolysis, refining, solidification, etc., range from turbulent flow to multi-phase flow models, including heat transfer. However, at the heart of these processes, very complex multi-phase and multi-physics processes, including complex chemistry, often spanning multiple time and length scales, take place. Under these circumstances, empirical data is difficult to obtain and modelling is a complementary and promising path to follow. In conjunction with experimental data, an analysis of predicted results provides a deeper insight into the physics of these processes. Furthermore, modelling and simulation is a useful tool employed to analyse metallurgical processes in depth, such as those of a blast furnace, which are usually characterized by high costs and energy consumption. In fact, process simulations derived from versatile mathematical, physical, or data-driven models have the potential to provide effective analysis tools to improve metallurgical processes, resulting in enhanced quality at lower costs and often contributing to better sustainability. Therefore, this Special Issue is intended to collect and present the latest developments in advanced simulation technologies for metallurgical processes, as well as to identify any research gaps.
The present Special Issue aims to bring together engineers and researchers from the applicable industries and scientific community, as well as to provide a lively exchange of ideas regarding state-of-the-art and future needs for simulation technologies. Therefore, topics covered in this Special Issue will include:
- mathematical formulation;
- numerical methods and implementation issues;
- solution strategies;
- multi-scale techniques;
- multi-physics approaches;
- artificial intelligence, e.g., machine learning;
- validation, verification, and uncertainty quantification (VVUQ);
- metallurgical models;
- large-scale simulation, and industrial applications and their requirements.
Prof. Dr.-Ing. Bernhard Peters
Guest Editor
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Keywords
- mathematical modelling
- numerical approaches
- machine learning
- continuous and discrete methods
- coupling
- uncertainty quantification
- multi-physics
- high-performance computing
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