Spray Characterization of a Preheated Bio-Oil Surrogate at Elevated Pressures
Bibliographic record
Abstract
Abstract Atomization plays an important role in the gasification or combustion of bio-oils, where the atomizer parameters need to be properly controlled to efficiently atomize a highly viscous liquid at elevated pressures with imparting the least amount of kinetic energy to the discharged droplets because of evaporation and chemical reaction constraints. With a focus on bio-oil deployments in microgas turbines (MGTs), an aqueous surrogate of a preheated bio-oil, injected from an original equipment manufacturer (OEM) twin-fluid atomizer, is used in the present study for spray size and velocity measurements at elevated pressures. The experiments were conducted in High Pressure Spray Facility of the National Research Council of Canada (NRC) using various optical diagnostics including laser sheet imaging (LSI), phase Doppler anemometry (PDA), and laser diffraction (LD). A scaling strategy was adopted to conserve the ranges of gas-to-liquid momentum flux ratio, M, at different working pressures, P. Over the range of conditions studied, it is found out that the cone angle of sprays is insensitive to P, but they decrease with increasing M. For a constant value of M, droplet mean diameters increase and their corresponding velocities decrease with increasing P, attributed to the effect of gas-to-liquid density ratio on the primary breakup of a liquid jet in a coaxial gas stream. Therefore, to estimate the Sauter mean diameter of spray droplets, D32, a correlation previously reported in the literature is modified by including the effect of system air density at elevated pressures, and a novel correlation is proposed based on four dimensionless groups, namely, gas Weber number and gas-to-liquid momentum flux ratio, density ratio, and viscosity ratio. The detailed results obtained in the present study could be used to define the optimal parameters required for twin-fluid atomization of high viscosity liquids with various atomization gases under realistic operating conditions and to enhance the capabilities of their numerical simulations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".