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Record W4311043378 · doi:10.1002/srin.202200718

Special Issue: Modeling and Simulation of Metallurgical Processes in Steelmaking

2022· article· en· W4311043378 on OpenAlex
Menghuai Wu

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venuesteel research international · 2022
Typearticle
Languageen
FieldEngineering
TopicIron and Steelmaking Processes
Canadian institutionsnot available
Fundersnot available
KeywordsSteelmakingPublicationEngineeringProcess (computing)Mechanical engineeringMetallurgyCastingBlast furnaceManufacturing engineeringComputer scienceMaterials sciencePolitical science

Abstract

fetched live from OpenAlex

This special issue of steel research international was planned to publish selected articles for the 9th International Conference on Modeling and Simulation of Metallurgical Processes in Steelmaking (STEELSIM 2021), which was held in Leoben, Austria, on October 5–7, 2021. The conference was made online because the world was still suffering from the COVID-19 pandemic. As the digital computer was introduced to the field of metallurgy in the 1960s, an international conference, with the participation of 290 metallurgists and mathematicians, was held to discuss the topic of numerical modeling of metallurgical processes [J. M. van Langen, et al., Proceedings of the conference on Mathematical models in metallurgical process development, Feb. 12–13. 1969, London, ISBN 0900497114]. Although the capacity of computer hardware was minimal (CPU ≈ 100 kHz, memory ≈ 100 kb), the presented papers covered the model for rapid-heating furnaces; solidification mechanisms of steel; mathematical study of the continuous casting; temperature distribution during hot rolling; model of the blast-furnace process, etc. To the best knowledge of the guest editor, this was the first conference of this kind with as-published proceedings. Fifty years have passed, the capacity of computers has increased by a factor of 104–5, the tremendous progress has been made in this field. In order to facilitate the scientific exchanges between the model developers (academic researchers and the front model-users in metallurgical industries), a specific conference series on the Modeling and Simulation of Metallurgical Processes in Steelmaking (STEELSIM) was initiated in 2005, held regularly every two years. It started in Brno (Czech Republic) in 2005, continued in Graz (Austria) in 2007, Leoben (Austria) in 2009, Düsseldorf (Germany) in 2011, Ostrava (Czech Republic) in 2013, Bardolino (Italy) in 2015, Qingdao (China) in 2017, Toronto (Canada) in 2019, Leoben (Austria) in 2021. This conference covered a broad spectrum of topics related to the modeling and simulation of ironmaking and steelmaking. There were 23 sessions, dealing with 11 main topics: raw materials and ironmaking; blast furnace; slag, refractory and their interactions with steel; ladle metallurgy and steel refining; flow control and solidification; continuous casting and quality control; metal forming, rolling and thermo-mechanics; microstructure and mechanical properties of steels; advanced iron/steel processing, environmental impact; integration of AI, modeling, data mining; the processing of special steels (ESR, VAR, VIM, etc.). Figure 1 shows the distributions of contributed presentations in each of the above topics. Following the traditions of this conference series, the largest area is still in “continuous casting and quality control” with 28% of contributions. For the first time, the topic “integration of AI, modeling, data mining” was also included in this conference, and it has attracted great attention with 12% of contributions. This special issue includes 16 articles. They were carefully selected from the STEELSIM 2021 by considering the following criteria: 1) to cover all the above 11 main topics as dealt by the conference; 2) to represent the state-of-the-art of relevant areas; 3) to balance the academic research and industry applications. With the continuously improved understanding of different metallurgical phenomena and the increased computer capacity (hardware/software), the numerical models will become more and more complex, precise, and closer to reality. The selected articles in this special issue can serve as milestones of the relevant research/development activities achieved in the early 2020s. Prof. Dr. Menghuai Wu did his Master degree at Northwestern Polytechnical University in China, a Ph.D. degree in 2000 at the Foundry Institute, RWTH Aachen in Germany, and Habilitation (professorial certificate) in 2008 at Montanuniversitaet Leoben in Austria. His main research interests are the modeling and simulation of solidification and related phenomena. The volume average-based multiphase solidification models, as developed by him and co-workers, have been applied to different industry processes: steel ingot, continuous and semi-continuous castings of steel, ESR and VAR, freeze lining in pyro-metallurgical furnaces, unidirectional solidification of turbine blades for superalloy, etc.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.076
GPT teacher head0.375
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it