arXiv subjects

Deanna A. Lacoste

Publications and source records attributed to Deanna A. Lacoste.

4 recordsLinked to original sources

arXiv2026

Deep Learning-Based Characterization of Detonation-Cell Size Distributions in Soot-Foil Records

The geometric size and regularity of detonation cells are key physical parameters for characterizing detonation waves. Traditional manual measurement of soot foils is time-consuming and subjective, while existing computer vision techniques often exhibit poor generalization on real experimental images with high noise, blurred boundaries, and severe overlapping. To address this, we propose a novel method for automated recognition and high-order feature extraction of detonation cells based on deep learning instance segmentation (Mask R-CNN). By constructing a custom heterogeneous dataset (numerical simulations and physical experiments) and integrating transfer learning, the model achieves accurate pixel-level mask prediction within highly noisy flow fields. Results indicate high pixel-level agreement in benchmark validations and strong robustness against noise in complex real-world soot foils. Predicted average cell sizes agree well with manual measurements, yielding relative errors under 2% and 3.5% for regular and irregular conditions, respectively. Sensitivity ablation experiments confirm the model's scale adaptability and guided the establishment of a standardized preprocessing paradigm for appropriate image patching. Overcoming the limitation of extracting only global average sizes, this model achieves automated tracking of the transient spatial evolution of cell sizes along the propagation direction. Furthermore, it quantitatively extracts high-order regularity features, such as the irregularity index (RI) and standard deviation of cell deflection angles, demonstrating consistency with theoretical expectations. The proposed method enhances the efficiency and objectivity of statistical analysis, providing a powerful data extraction tool for experimental and numerical soot foils.

eess.IV↗

arXiv2026

Toward Quantitative Electric-Field Measurements of Inception Clouds in Nanosecond Discharges Using E-FISH Assisted by Machine Learning

This study investigates the spatio-temporal evolution of the electric field during the early stages of a nanosecond positive corona discharge in atmospheric-pressure air by combining time-resolved E-FISH measurements, machine-learning-assisted field inversion (based on a recently developed operator-learning model), and iCCD optical emission imaging. The objective is to quantitatively characterize the electric field in the vicinity of the high-voltage electrode during inception and the transition toward streamer formation. By averaging over a large number of discharge events and operating in a regime where the discharge remains statistically axisymmetric, the proposed approach enables reconstruction of the electric-field profiles with nanosecond resolution. The results show a rapid increase of the field during the first nanoseconds, followed by the formation of a shell-like structure exhibiting the highest reduced fields prior to destabilization. The reconstructed reduced electric-field magnitude reaches peak values in the range of approximately 230-270 Td, with an estimated uncertainty of about 20-30% associated with calibration and profile-shape effects. These values correspond to the regime where electron-impact excitation and photoionization processes become highly efficient, consistent with the observed transition from a stable inception cloud to streamer destabilization. After the onset of streamer branching, increasing asymmetry limits the applicability of the inversion, and the reconstructed fields represent averaged contributions rather than the local field at individual streamer heads. The methodology thus identifies the conditions under which quantitative E-field mapping is reliable and establishes a framework for extending electric-field diagnostics to the inception phase of nanosecond atmospheric discharges.

physics.plasm-ph↗

arXiv2025

A unified fluid model for nonthermal plasmas and reacting flows

This work presents a unified fluid modeling framework for reacting flows coupled with nonthermal plasmas (NTPs). Building upon the gas-plasma kinetics solver, ChemPlasKin, and the CFD library, OpenFOAM, the integrated solver, reactPlasFOAM, allows simulation of fully coupled plasma-combustion systems with versatility and high performance. By simplifying the governing equations according to the dominant physical phenomena at each stage, the solver seamlessly switches between four operating modes: streamer, spark, reacting flow, and ionic wind, using coherent data structures. Unlike conventional streamer solvers that rely on pre-tabulated or fitted electron transport properties and reaction rates as functions of the reduced electric field or electron temperature, our approach solves the electron Boltzmann equation (EBE) on the fly to update the electron energy distribution function (EEDF) at the cell level. This enables a high-fidelity representation of evolving plasma chemistry and dynamics by capturing temporal and spatial variations in mixture composition and temperature. To improve computational efficiency for this multiscale, multiphysics system, we employ adaptive mesh refinement (AMR) in the plasma channel, dynamic load balancing for parallelization, and time-step subcycling for fast and slow transport processes. The solver is first verified against six established plasma codes for positive-streamer simulations and benchmarked against Cantera for a freely propagating hydrogen flame, then applied to three cases: (1) spark discharge in airflow; (2) streamer propagation in a premixed flame; and (3) flame dynamics under non-breakdown electric fields. These applications validate the model's ability to predict NTP properties such as fast heating and radical production and demonstrate its potential to reveal two-way coupling between plasma and combustion.

physics.comp-ph↗

arXiv2024

ChemPlasKin: a general-purpose program for unified gas and plasma kinetics simulations

This work introduces ChemPlasKin, a freely accessible solver optimized for zero-dimensional (0D) simulations of chemical kinetics of neutral gas in non-equilibrium plasma environments. By integrating the electron Boltzmann equation solver, CppBOLOS, with the open-source combustion library, Cantera, at the source code level, ChemPlasKin computes time-resolved evolution of species concentration and gas temperature in a unified gas-plasma kinetics framework. The model allows high fidelity predictions of both chemical thermal effects and plasma-induced heating, including fast gas heating and slower vibrational-translational relaxation processes. Additionally, a new heat loss model is developed for nanosecond pulsed discharges, specifically within pin-pin electrode configurations. With its versatility, ChemPlasKin is well-suited for a wide range of applications, from plasma-assisted combustion (PAC) to fuel reforming. In this paper, the reliability, accuracy and efficiency of ChemPlasKin are validated through a number of test problems, demonstrating its utility in advancing gas-plasma kinetic studies.

physics.plasm-ph↗