Special Issue on the 2022 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 2022 Fluids Engineering Division Summer Meeting (FEDSM). The FED conference organizers were Professor Philipp Epple (chair) and Professor Kamran Siddiqui (co-chair), whose efforts helped the community assemble in the first in-person FED meeting since the pandemic. The conference was successful due to the hard work and dedication of the conference chairs, technical committee chairs, symposia organizers and other FED volunteers. The 2022 FEDSM was held August 3–5, 2022 at the Intercontinental Toronto Center, Toronto, ON, Canada and included three plenary speakers plus technical presentations and 145 attendees.The Special Issue is pleased to include the review paper by Professor Efstathios (Stathis) Michaelides (Texas Christian University) and Professor Zhigang Feng (University of Texas at San Antonio) titled “Drag Coefficients of Non-spherical and Irregularly-Shaped Particles.” The other paper topics include cardiovascular flow in the aorta, boundary layer response to plasma actuators, thermally stratified turbulent boundary layer flows, flapping foils, and unsteady propulsion.The Special Issue also presents the winners of the Flow Visualization competition, organized by Prof. Philipp Epple. The competition is an initiative that begin in 2017 and was expanded to include both still images and videos. The 2022 FEDSM winners in the video category were:In closing, my sincerest gratitude is extended to JFE Associate Editors, Professor Costanza Aricò, Professor Arindam Banerjee, Professor Luigi P. M. Colombo, Professor Philipp Epple, Professor Pierre Sullivan, and Professor Qianhong Wu, who shared in the responsibilities. I would also like to thank 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, Ms. Tamiko Fung, and Ms. Jenna Seyer. Finally, thank you to our colleagues who served as reviewers and of course the authors.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.339 | 0.230 |
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".