Bibliographic record
Abstract
Overview Computational fluid dynamics (CFD) is a numerical tool for solving equations of fluid mechanics. CFD is a relatively recent development that has become an indispensable tool in the last two decades. It was developed originally for aeronautical uses but now pervades all disciplines involving flow phenomena, such as medical, natural sciences, and engineering applications. The built-in codes of the CFD software are algorithms of numerical solutions for the fluid-mechanics equations. Flow fields that were previously difficult to solve by analytical means – and, in some situations, impossible – are now accessible by means of CFD. Today, the aircraft industry uses CFD during the conceptual study phase. There are limitations in obtaining accurate results, but research continues in academic and industrial circles to improve prediction. This chapter aims to familiarize newly initiated readers with the scope of CFD in configuring aircraft geometry (those already exposed to the subject may skip this chapter). This is not a book about CFD; therefore, this chapter does not present a rigorous mathematical approach but rather an overview. CFD is a subject that requires considerable knowledge in fluid mechanics and mathematics. CFD is introduced late in undergraduate studies, when students have mastered the prerequisites. Commercial CFD tools are menu-driven and it is possible to quickly become proficient, but interpreting the results thus obtained requires considerable experience in the subject. An accurate 3D model of an aircraft in CAD significantly reduces preprocessing time.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.063 | 0.039 |
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".