Prediction of Droplet Statistics in Sprays Using Deep Neural Networks
Bibliographic record
Abstract
Predicting droplet statistics in sprays is essential for improving efficiency, minimizing losses, and enhancing reliability in various propulsion and power systems and associated subsystems. However, accurately and efficiently predicting the random behavior of spray droplets in practical applications remains challenging due to their large numbers and diverse characteristics. Design-stage estimations often fail to capture operational changes, while unplanned variations in system conditions and injector wear and/or partial clogging can negatively impact spray performance. Nonetheless, advances in industrial digitalization—such as digital twins and AI/machine learning (ML)—offer transformative opportunities for the development of intelligent systems. In this context, the present study demonstrates the development of a multi-output deep neural network (DNN) for the simultaneous prediction of the size and velocity of spray droplets. Such data-driven models can be directly employed in edge-computing devices for real-time control and fault diagnostics in high-precision spray systems. Additionally, they can be integrated with physics-based models to develop digital twins for spray systems. Their predictions can be assimilated into Lagrangian particle tracking simulations to enable the numerical analysis and design optimization of spray systems without the need for resolving the complete range of spatiotemporal scales involved in the high-fidelity simulation of the breakup/atomization of liquid jets and sheets.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".