High-speed droplet impingement on dry and wetted substrates
Why this work is in the frame
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Bibliographic record
Abstract
High-speed droplet impact is of great interest to power generation and aerospace industries due to the accrued cost of maintenance in steam and gas turbines. The repetitive impacts of liquid droplets onto rotor blades, at high relative velocities, result in blade erosion, which is known as liquid impingement erosion (LIE). Experimental and analytical studies in this field are limited due to the complexity of the droplet impact at such conditions. Hence, numerical analysis is a very powerful and affordable tool to investigate the LIE phenomenon. In this regard, it is crucial to understand the hydrodynamics of the impact in order to identify the consequent solid response before addressing the LIE problem. The numerical study of the droplet impingement provides the transient pressure history generated in the liquid. Determining the transient behavior of the substrate, in response to the pressure force exerted due to the droplet impact, would facilitate engineering new types of surface coatings that are more resistant to LIE. To that end, quantifying the impact pressure of compressible liquid droplets impinged at very high velocities, up to 500 m/s, on rigid solid substrates and liquid films is the main objective of the present work. A wide range of scenarios that commonly arise in the LIE problem are considered, i.e., droplet sizes between 200 µm and 1000 μm, impact velocities ranging from 100 m/s to 500 m/s, and liquid film thicknesses of 0 µm–200 μm. The maximum pressure exerted on the solid surface due to the droplet impact is calculated for both dry and wetted substrates. The results obtained from compressible fluid modeling are compared to those of other numerical studies and analytical correlations, available in the open literature. New correlations are developed for maximum impact pressure on rigid solids and liquid films that can be used to characterize the solid stress and estimate the lifetime of the material by carrying out the fatigue analysis.
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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 it