Special Issue on the 2023 ASME-JSME-KSME Fluids Engineering Division Summer Meeting
Bibliographic record
Abstract
The ASME Journal of Fluids Engineering (JFE) and the ASME Fluids Engineering Division (FED) present a special issue of papers that were presented at the 2023 ASME-JSME-KSME Fluids Engineering Division Summer Meeting (AJKFluids 2023). The first FED joint meeting of the mechanical engineering societies between the United States, Japan, and Korea was in 2011. The 2023 FED conference marks the second year with Japan as the host country. The conference cochairs were Dr. Takeo Kajishima (Osaka University) and Dr. Genta Kawahara (Osaka University) representing JSME, Dr. Kamran Siddiqui (University of Western Ontario) and Dr. Marianne Francois (Los Alamos National Laboratory) representing ASME, and Dr. Han Seo Ko (Sungkyunkwan University) and Dr. Gwang Hoon Rhee (University of Seoul) representing KSME.The AJKFluids 2023 conference was held in Osaka, Japan, from July 9–13, 2023, at the Osaka International Convention Center. The special issue dedicated to this conference includes 12 papers invited based on their conference presentations and extended abstracts. The collection of topics includes both experimental and numerical studies on multiphase flow, turbomachinery, turbulent fluid motion, and applications with additive manufacturing. I am grateful for the assistance of the FED executive committee members who helped review the extended abstracts and select the paper contributions. Special thanks to Professor Philipp Epple, Dr. Marianne Francois, and Professor Ning Zhang.The Special Issue also presents the winners of the Flow Visualization competition, organized by Professor Philipp Epple. The 2023 winners in the video category were as follows:In closing, my sincerest gratitude is extended to our colleagues who served as reviewers, and of course the authors, whose contributions comprise the special issue. I would also like to acknowledge the JFE guest editors, Dr. Xiang Yang and Dr. Deify Law, who shared in the responsibilities to support the special issue. We are indebted to the JFE editorial assistant, Ms. Colette Montague, who helped ensure timely processing, and the support of the ASME staff, especially Ms. Beth Darchi, Ms. Erica Hodge, and Ms. Tamiko Fung.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.278 | 0.163 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".