arXiv subjects

Omar K Matar

Publications and source records attributed to Omar K Matar.

4 recordsLinked to original sources

arXiv2026

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 by this setting because both the liquid--gas interface and the underlying adaptive discretization evolve across time and geometries. We introduce a geometry-conditioned latent surrogate trained on 797 two-phase nozzle simulations that addresses this by encoding the AMR cell-density field, rather than the full multi-channel flow state, as a compact proxy for where the solver concentrates resolution. From this representation, the model reconstructs transient density evolution and nozzle geometry, and a lightweight second stage recovers the remaining flow variables. On held-out simulations, the method accurately captures key interface dynamics while reducing inference time to 0.045 seconds per trajectory, corresponding to a speed-up of more than $6\times10^4$ relative to Basilisk CFD. These results suggest that AMR refinement structure can serve as a compact and learnable representation for geometry-conditioned surrogate modeling of transient two-phase flows.

physics.flu-dyn↗

arXiv2024

Machine Learning-Assisted Discovery of Flow Reactor Designs

Additive manufacturing has enabled the fabrication of advanced reactor geometries, permitting larger, more complex design spaces. Identifying promising configurations within such spaces presents a significant challenge for current approaches. Furthermore, existing parameterisations of reactor geometries are low-dimensional with expensive optimisation limiting more complex solutions. To address this challenge, we establish a machine learning-assisted approach for the design of the next-generation of chemical reactors, combining the application of high-dimensional parameterisations, computational fluid dynamics, and multi-fidelity Bayesian optimisation. We associate the development of mixing-enhancing vortical flow structures in novel coiled reactors with performance, and use our approach to identify key characteristics of optimal designs. By appealing to the principles of flow dynamics, we rationalise the selection of novel design features that lead to experimental plug flow performance improvements of 60% over conventional designs. Our results demonstrate that coupling advanced manufacturing techniques with `augmented-intelligence' approaches can lead to superior design performance and, consequently, emissions-reduction and sustainability.

cs.CE↗

arXiv2023

Multi-Fidelity Data-Driven Design and Analysis of Reactor and Tube Simulations

The development of new manufacturing techniques such as 3D printing have enabled the creation of previously infeasible chemical reactor designs. Systematically optimizing the highly parameterized geometries involved in these new classes of reactor is vital to ensure enhanced mixing characteristics and feasible manufacturability. Here we present a framework to rapidly solve this nonlinear, computationally expensive, and derivative-free problem, enabling the fast prototype of novel reactor parameterizations. We take advantage of Gaussian processes to adaptively learn a multi-fidelity model of reactor simulations across a number of different continuous mesh fidelities. The search space of reactor geometries is explored through an amalgam of different, potentially lower, fidelity simulations which are chosen for evaluation based on weighted acquisition function, trading off information gain with cost of simulation. Within our framework we derive a novel criteria for monitoring the progress and dictating the termination of multi-fidelity Bayesian optimization, ensuring a high fidelity solution is returned before experimental budget is exhausted. The class of reactor we investigate are helical-tube reactors under pulsed-flow conditions, which have demonstrated outstanding mixing characteristics, have the potential to be highly parameterized, and are easily manufactured using 3D printing. To validate our results, we 3D print and experimentally validate the optimal reactor geometry, confirming its mixing performance. In doing so we demonstrate our design framework to be extensible to a broad variety of expensive simulation-based optimization problems, supporting the design of the next generation of highly parameterized chemical reactors.

cs.CE↗

arXiv2020

Single-Bubble Dynamics in Nanopores: Transition Between Homogeneous and Heterogeneous Nucleation

When applying a voltage bias across a thin nanopore, localized Joule heating can lead to single bubble nucleation, offering a unique platform for studying nanoscale bubble behavior, which is still poorly understood. Accordingly, we investigate bubble nucleation and collapse inside solid-state nanopores filled with electrolyte solutions and find that there exists a clear correlation between homo/heterogeneous bubble nucleation and the pore diameter. As the pore diameter is increased from 280 nm to 525 nm, the nucleation regime transitions from predominantly periodic homogeneous nucleation to a non-periodic mixture of homogeneous and heterogeneous nucleation. A transition barrier between the homogeneous and heterogeneous nucleation regimes is defined by considering the relative free-energy costs of cluster formation. A thermodynamic model considering the transition barrier and contact-line pinning on curved surfaces is constructed, which determines the possibility of heterogeneous nucleation. It is shown that the experimental bubble generation behavior is closely captured by our thermodynamic analysis, providing important information for controlling the periodic homogeneous nucleation of bubbles in nanopores.

physics.flu-dyn↗