IUTAM Symposium on Turbulent Mixing and Combustion : proceedings of the IUTAM symposium held in Kingston, Ontario, Canada, 3-6 June 2001
Bibliographic record
Abstract
IUTAM Opening Address T. Tatsumi. Session 1: Turbulence and Mixing. Mixing as an Aggregation Process E. Villermaux. Downstream Evolution of the Most Energetic Pod Modes in the Mixing Layer of a High Reynolds Number Axisymmetric Jet E. Jung, et al. Micro Structure and Lagrangian Statistics of the Scalar Field with a Mean Gradient in Isotropic Turbulence G. Brethouwer, et al. Dynamics & Control of Jets in Crossflow P.N. Blossey, et al. Characteristics of Supercritical Transitional Temporal Mixing Layers N. Okong'o, B. Bellan. On Accurate Experimental Measurements of the Dynamics of Large Scale Structures in Turbulent Flows D. Ewing, S. Woodward. Mixing in a Cross-Jet Enhanced by a Coaxial Annular Synthetic Jet L. Sigurdson, J. Diep. Session 2: Turbulent Mixing with Combustion. Challenges in Turbulent Mixing with Combustion P.E. Dimotakis. One-Dimensional Stochastic Simulation of Advection-Diffusion-Reaction Couplings in Turbulent Combustion J.C. Hewson, et al. Compressibility Effects on the Scalar Mixing in Reacting Homogeneous Turbulence D. Livescu, C.K. Madnia. The Interaction of Scalar Mixing and Heat Release in a Reacting Shear Layer C. Pantano, et al. Influence of Differential Diffusion on Local Equilibrium and Super-Equilibrium Combustion in Turbulent Non-Premixed Flames O. Gicquel, et al. Diffusion Edge-Flame Quenching J. Boulanger, L. Vervisch. A Model Description of the Effects of Variable Fuel-Air Mixture Composition on Turbulent Flame Propagation H. Jerome, A. Trouve. Session 3: Modeling Scalars. Challenges in Modeling Scalars in Turbulence and LES C. Meneveau, et al. PDF of Temperature Fluctuations in Uniformly Sheared Turbulence M. Ferchichi, S. Tavoularis. A MathematicalPrototype to Validate LES Strategies for Turbulent Flames A. Bourlioux, et al. Study of Mixing in Swirling Turbulent Jets R.Z. Szasz, et al. Session 4: DNS/LES of Flames. LES of Partially Premixed Combustion L. Vervisch, et al. Nonpremixed Turbulent Flames Investigated with Direct Numerical Simulation using Detailed Chemistry R. Hilbert, D. Thevenin. Counter-Gradient Scalar Transport in Large Eddy Simulation of Turbulent Premixed Flames S. Tullis, R.S. Cant. Local Flame Structure in Hydrogen-Air Turbulent Premixed Flames M. Tanahashi, et al. Three-Dimensional Direct Numerical Simulations of Turbulent Flames Using Realistic Chemistry Modeling D. Thevenin, et al. Simulation of a Buoyancy-Driven Jet Diffusion Flame J. Charentenay, et al. Visualization of the Fuel Stripping Mechanism For Wake-Stabilized Diffusion Flames in a Crossflow M.R. Johnson, L.W. Kostiuk. Session 5: Control. Progress in Control of Mixing and Large Eddy Simulation of Turbulent Combustion P. Moin. Large Eddy Simulation of a Non-Premixed Turbulent Burner Using a Dynamically Thickened Flame Model J.P. Legier, et al. Characterisation of an Air-Blast Injection Device With Forced Periodic Entries F. Giuliani, et al. Numerical Analysis of Hydrogen/Air Jet Diffusion Flame Y. Mizobuchi, et al. Turbulent Mixing With Sprays R. Ben-Dakhlia, et al. Large Eddy Simulations of Combustion Instabilities in a Swirled Combustor S. Ducruix, et al. An Experimental Investigation of Mixing and Combustion Characteristics on the Can-Type Micro Combustor With a Multi-Jet Baffle Plate H. Choi, et al. Turbulent Structures, Mixing and Entrainment in Jets With Tabs Identified Using the Discriminant Method S. McIlwain, et al. Session
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.070 | 0.033 |
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".