Investigação matemática e numérica dos mecanismos de geração de ruído em escoamentos cisalhantes livres
Bibliographic record
Abstract
This thesis aims to broaden the knowledge of the physical mechanisms responsible for transforming the energies of turbulent flows into acoustic energy, as well as to identify and interpret the terms of the equations that model the generation of aerodynamic noise. In this sense, the work contributes by presenting the decomposition of the tensor of Lighthill in six terms and interpreting them in order to understand the physics modeled by each one. The contribution of the work also occurs in the development of numerical methods based on finite differences optimized for the solution of the flows of the type of temporal mixing layer and of the spatial mixing layer. Numerical methods were optimized using Genetic Algorithms to reduce numerical dispersion and diffusion errors, thus obtaining a seven-point stencil for calculating spatial derivatives and a four-stage stencil for calculating time derivatives, both with optimized coefficients. The main challenges encountered in the implementation of the computational code were the non-reflexive boundary conditions based on the Navier-Stokes characteristic equations that, because they have particularities related to each domain boundary, makes it difficult to implement them generically. Finally, the investigation of the acoustic field generated by the temporal and spatial mixing layer flows and their relations with the development of fluid dynamics is presented. From the point of view of the sound production, it is observed that the vorticity equations are one of the main mechanisms of sound generation in low Mach number flows, the term that is the model of the net kinetic energy and the term connected to the modeling of the rotational movement of the fluid, both of which originate in the Lighthill tensor, which contribute most to the total noise measured in the distant field of the two types of flows studied.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".