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Enregistrement W4311043378 · doi:10.1002/srin.202200718

Special Issue: Modeling and Simulation of Metallurgical Processes in Steelmaking

2022· article· en· W4311043378 sur OpenAlexaboutno aff
Menghuai Wu

Notice bibliographique

Revuesteel research international · 2022
Typearticle
Langueen
DomaineEngineering
ThématiqueIron and Steelmaking Processes
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSteelmakingPublicationEngineeringProcess (computing)Mechanical engineeringMetallurgyCastingBlast furnaceManufacturing engineeringComputer scienceMaterials sciencePolitical science

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,095
Score d'incertitude au seuil0,316

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,004
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0030,002
Bibliométrie0,0030,002
Études des sciences et des technologies0,0010,001
Communication savante0,0060,004
Science ouverte0,0020,002
Intégrité de la recherche0,0030,003
Charge utile insuffisante (le modèle a refusé de juger)0,0950,032

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,076
Tête enseignante GPT0,375
Écart entre enseignants0,299 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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