Turbulent scalar transfer modeling in reacting flows
Bibliographic record
Abstract
Turbulence modeling is a major factor, affecting the precision of current numerical simulations, particularly for reacting flows. It is also one of the principal unsolved problems in physics today. In the last five decades, much effort has been devoted to the development of turbulent momentum transfer models. However, researches on turbulent scalar transportation issues are limited, particularly for reacting flows. In almost all turbulent reacting flow RANS (Reynolds-averaged Navier-Stokes) simulations, the Reynolds analogy concept has been used to model turbulent scalar transfers since the 1970s. With this concept, the turbulent Prandtl/Schmidt number is used to calculate the turbulent scalar transfers in flow fields based on the momentum transfer that is modeled by a selected turbulence model. In this chapter, the rationale and limitation of the Reynolds analogy are analyzed and validated against two benchmarking cases, a turbulent jet diffusion flame and a model diffusion flame combustor. The former represents a simple boundary-type flow, while the latter involves complex flow phenomena (shear layers, wall boundary layers, separations, recirculation zones, reattachments and their interactions) which are relevant to many practical combustion systems. The effects of turbulent Prandtl/Schmidt numbers on the flow fields of the jet flame and model combustor have been numerically studied with selected turbulence, combustion and radiation models. In comparison with comprehensive experimental databases, it is found that for both cases, the flow features and magnitudes of mean velocity fields are well predicted, particularly for the jet flame case, and the turbulent Prandtl/Schmidt number has insignificant effect on the velocity fields. In addition, for the combustor case, the turbulence kinetic energy and shear stress distributions are also reasonably well predicted. The proper prediction of velocity fields (or momentum transfers) provides a prerequisite for adequate evaluation of the Reynolds analogy concept or the effect of turbulent Prandtl/Schmidt numbers on the temperature fields of the reacting flows. In contrast, the turbulent Prandtl/Schmidt number shows significant effects on the temperature fields, particularly for the temperature profiles in the outer layer region of the jet flame and the downstream region of the model combustor. This is also true for the temperature profile along the combustor wall. Jet diffusion flames seem simple; however, they pose challenges to numerical simulations. The discrepancies of temperature distributions in the upstream outer layer region of the jet flame are observed by the authors and other researchers, and the anticipated reasons are three-fold, incapability to predict the local laminarization phenomenon, the limitations of Reynolds analogy, and insufficient effort in the development of turbulent scalar transfer models. For the Prandtl/Schmidt numbers considered, 0.45-1.2, the value of 0.85 can provide acceptable results for the temperature distributions along the jet centerline and at the downstream cross-sections. For the model combustor configuration and operating conditions, the optimal Prandtl/Schmidt number for temperature prediction inside the combustor is 0.5 for all three combustion models, and it varies from 0.35 to 0.55 for the combustor wall temperature prediction. With the optimal value of 0.50, the velocity and temperature fields are reasonably well predicted, except in some local regions. The present work suggests that for reliable temperature prediction in turbulent reacting flows without tuning turbulent Prandtl/Schmidt numbers, the Reynolds analogy concept should be improved and new approaches should be investigated. © 2010 Nova Science Publishers, Inc. All rights reserved.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".