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New AI Model Dramatically Accelerates Spray Formation Simulations

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel geometry-conditioned latent surrogate model for simulating spray formation, significantly outperforming traditional methods. This new model encodes the adaptive mesh refinement (AMR) cell-density field as a compact representation, enabling faster and more accurate predictions of transient two-phase flows. The approach reduces inference time to mere milliseconds, offering a speed-up of over 60,000 times compared to existing Basilisk CFD simulations, making it highly valuable for iterative design processes in spray nozzle development. AI

IMPACT Enables rapid, high-fidelity simulations for complex fluid dynamics, accelerating design cycles in engineering.

RANK_REASON The cluster contains an academic paper detailing a new AI model for scientific simulation.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Julius H Ramlau, Friedrich Hastedt, Tolga Birdal, Ehecatl-Antonio del R\'io Chanona, Nausheen S Basha, Omar K Matar ·

    Learning Interface Breakup: A Geometry-Conditioned Latent Surrogate for Spray Formation

    arXiv:2606.16587v1 Announce Type: cross Abstract: Designing spray nozzles requires predicting how geometry shapes transient two-phase breakup, but high-fidelity volume-of-fluid (VOF) simulations with adaptive mesh refinement (AMR) are too expensive for iterative design exploratio…

  2. arXiv cs.AI TIER_1 English(EN) · Omar K Matar ·

    Learning Interface Breakup: A Geometry-Conditioned Latent Surrogate for Spray Formation

    Designing spray nozzles requires predicting how geometry shapes transient two-phase breakup, but high-fidelity volume-of-fluid (VOF) simulations with adaptive mesh refinement (AMR) are too expensive for iterative design exploration. Standard surrogate models are also challenged b…

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