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Fatigue Damage

2017· book· es· W4212811643 on OpenAlexfundno aff
Filippo Berto

Bibliographic record

Venuenot available
Typebook
Languagees
FieldEngineering
TopicMechanical Failure Analysis and Simulation
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNational Research Foundation of KoreaNational Science and Technology Major ProjectSouthwest UniversityCentral South UniversityFundamental Research Funds for the Central UniversitiesHrvatska Zaklada za ZnanostKorea Atomic Energy Research InstituteNational Natural Science Foundation of ChinaSouthwest Jiaotong UniversityMinistry of Science, ICT and Future PlanningBeihang UniversityNational Research Foundation
KeywordsPsychology

Abstract

fetched live from OpenAlex

fatigue design [2].The special issue treats this topic in a comprehensive way, providing an overview of the state of the art of recent developments useful for the readers of Metals. Fatigue and Creep InteractionThe interest in fatigue assessment of steels and different alloys at high temperature has increased continuously in recent years.The applications in which the fatigue phenomenon is affected by high temperature are of considerable interest, and involve various industrial sectors, such as transportation, energy, and metal-manufacturing (e.g., jet engine components, nuclear power plant, pressure vessel, hot rolling of metal).To provide as optimal a performance as possible in these highly demanding conditions, it is necessary to be aware of the application and of the proper tools for performing the fatigue assessment, including at high temperatures.Interaction between creep and fatigue is a crucial point for the design, and it is well treated in some contributions to the present Special Issue (see, for example Ref.[3]). Fatigue in Corrosive MediaAn aggressive environment can be extremely critical for the fatigue life of a structure working in an aggressive environment, and protection against corrosion is necessary to maintain adequate fatigue properties and warrant the safety of the component.Designers must consider corrosion in service for a proper design against fatigue loadings.Corrosion is also undesirable for reasons related to a safe and economic use of a structure during its service life.Some recent advances in this area are well presented in [4], providing a useful and up-to-date overview of the problem in connection with fatigue damage of structural materials. Conclusions and OutlookA variety of connected topics have been compiled in the present Special Issue of Metals, providing a wide overview of recent developments on different aspects of fatigue damage.Hopefully, this special issue will be a starting point for future discussions and scientific debate on challenging topics related to fatigue damage and fatigue design.The topic, in fact, remains current, with a high and relevant impact for many applications.The selected papers touch on a variety of important topics related to fatigue and fracture in structural materials.Scale effect and multiscaling approaches are a fundamental part of these topics, and allow a better understanding of the fatigue damage at different scale levels.As guest editor of this special issue, I am very happy with the final result, and hope that the present papers will be useful to researchers and designers, working towards the demanding objective of failure prevention in presence cyclic loadings.I would like to warmly thank all the authors for their contributions, and all of the reviewers for their efforts in ensuring a high-quality publication.At the same time, I would like to thank the many anonymous reviewers who assisted me in the reviewing process.Sincere thanks also to Editors of Metals for their continuous help, and to the Metals Editorial Assistants for the valuable and inexhaustible engagement and support during the preparation of this volume.In particular, my sincere thanks to Natalie Sun for her help and support.3.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.081
Threshold uncertainty score0.272

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0810.046

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 head0.038
GPT teacher head0.278
Teacher spread0.241 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2017
Admission routes1
Has abstractyes

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