arXiv subjects

Balasundaram Mohan

Publications and source records attributed to Balasundaram Mohan.

5 recordsLinked to original sources

arXiv2026

Machine Learning based Ensemble Flame Regime Classification for Mesoscale Combustors based on Insights from Linear and Nonlinear Dynamic Analysis

Gaining insights into flame behaviour at small scales can lead to improvements in the efficiency of micro-reactors, compact power generation systems, fire safety technologies, and various other applications where combustion is confined to micro or mesoscales. Flame regimes observed in mesoscale combustors, namely Stable flame, Flames with repetitive extinction and ignition, and Propagating flame, exhibit unique dynamic characteristics that differentiate them from one another. In this study, we systematically examine the various flame regimes observed in mesoscale combustors from both dynamical and statistical standpoints. Our experimental methodology involves stabilizing a flame inside a quartz tube (an optically accessible mesoscale combustor) with an inner diameter of 5 mm. A premixed methane-air mixture is used as fuel, with its equivalence ratio and Reynolds number being the input parameters. Instantaneous OH* chemiluminescence and Acoustic pressure signals, along with high-speed flame imaging, were acquired for combustion dynamics characterization. The objective of this study is to analyze the distinct dynamical signatures associated with these observed flame regimes. For this purpose, Recurrence Quantification Analysis, followed by a Statistical-Spectral analysis, has been performed based on the experimentally acquired OH* Chemiluminescence and Acoustic pressure time-series signals. Subsequently, a stacking ensemble-based machine learning framework has been implemented for mesoscale flame regime classification based on the features extracted from the two aforementioned analyses. In addition, Continuous wavelet transform (CWT) scalograms and three-dimensional phase plots have been graphed to visually elucidate the evolution of system dynamics and the complex interaction of competing time scales in these flame regimes.

physics.flu-dyn↗

arXiv2025

Insight into the combustion dynamics of ITA-driven swirl flames

In this article, we examine the flame, flow, and acoustic coupling of Intrinsic ThermoAcoustic (ITA) driven combustion instability using a high-shear swirl injector in a model combustor. The combustor is operated using pure methane and methane-hydrogen mixture as fuels with air in a partially premixed mode. In this study, the airflow rate is varied by keeping the fuel flow rates constant, corresponding to the Reynolds number range of 8000-19000 and a fixed thermal power of 16 kW. Acoustic pressure in the combustion chamber, high-speed OH$^*$, CH$^*$ chemiluminescence images, high-speed particle image velocimetry, and steady exhaust gas temperature are measured for scrutiny. The combustor shows non-monotonic variations in the acoustic pressure amplitude for both fuels. A wider operating envelope and relatively large amplitude acoustic fluctuations are observed for the methane-hydrogen mixture compared with pure methane. Both methane and methane-hydrogen mixtures exhibit a linear increase in dominant instability frequencies with airflow, indicating the ITA-driven combustion instability. A low-order network model is developed that incorporates a simple $n-\tau$ flame response. It reproduces the observed dominant instability frequency by taking in the convective time delay from the experiments that substantiates the ITA-driven instability. Spatio-temporally resolved flame and flow dynamics show periodic axisymmetric vortex shedding from the outer shear layer and its subsequent interaction with CRZ during large amplitude acoustic oscillations. At relatively low amplitude, the vortices shed from both inner and outer shear layers convect downstream at different velocities without interacting with each other. The periodic vortex shedding process across the operating condition is further examined using a simplified model representing vortex dynamics...

physics.flu-dyn↗

arXiv2025

Effects of flame macrostructures on the combustion dynamics of novel counter-rotating radial swirl injector in a model can combustor

This study explores the flame macrostructures observed during self-sustained thermoacoustic oscillations in a model can combustor featuring a novel counter-rotating swirler. The swirler is designed to achieve high shear, distributing 60% of the airflow through the primary passage and 40% through the secondary passage, with radial fuel injection introduced via a central lance. To examine the influence of flame macrostructures on combustion instabilities, the flow expansion angles at the combustor's dump plane are systematically varied. In the present study, we consider three different flare angles comprising $90^\circ$, $60^\circ$, and $40^\circ$ for Reynolds numbers and thermal powers ranging from 10500-16800 and $10-16.2$ kW, respectively. Acoustic pressure, high-speed stereo PIV, and high-speed OH$^*$ chemiluminescence measurements are conducted to scrutinize flow and flame macrostructures during combustion instability. Large-amplitude acoustic oscillations are observed for a flare angle of $40^\circ$, accompanied by shorter flames and wider central recirculation zones. In contrast, a flare angle of $90^\circ$ results in low-amplitude acoustic oscillations characterized by longer flames and narrower central recirculation zones. The phase-averaged Rayleigh index is utilized to pinpoint regions that drive or dampen thermoacoustic instability, enabling the identification of potential strategies for its mitigation. Additionally, time-series analysis is employed to reveal the dominant acoustic modes and their interaction with heat release rate fluctuations.

physics.flu-dyn↗

arXiv2024

Effect of the blast wave interaction on the flame heat release and droplet dynamics

The study comprehensively investigates the response of a combusting droplet during its interaction with high-speed transient flow imposed by a coaxially propagating blast wave. The blast wave is generated using a miniature shock generator which facilitates wide Mach number range ($1.01 1.06$. The timescale of the flame extinction is faster (interaction with $\rm v_s$) for $M_s>1.1$. The study investigates the effect on droplet regression, flame heat release rate and flame topological evolution during the interaction. The droplet regression rate gets enhanced after the interaction with blast wave for $M_s < 1.06$, while it slowed down due to complete extinction for $M_s > 1.06$. A momentary flame heat release rate (HRR) enhancement occurs during the interaction with shock flow, and this HRR enhancement is found to be more than 8 times the nominal unforced flame HRR for $M_s > 1.1$, where rapid flame extinction occurs due to faster interaction with $\rm v_s$ ($\sim O(10^{-1})ms$). HRR enhancement has been attributed to the fuel vapor accumulation during the interaction. Furthermore, for $M_s > 1.1$), compressible vortex interaction occurs with the droplet resulting in droplet atomization. The droplet shows a wide range of atomization response modes ranging for different shock strengths. No significant effect of nanoparticle (NP) addition has been found on the flame dynamics due to the faster timescales. However, minimial effects of NP addition are observed during droplet breakup due to fluid property variation.

physics.flu-dyn↗

arXiv2024

Combustion Condition Identification using a Decision Tree based Machine Learning Algorithm Applied to a Model Can Combustor with High Shear Swirl Injector

Combustion is the primary process in gas turbine engines, where there is a need for efficient air-fuel mixing to enhance performance. High-shear swirl injectors are commonly used to improve fuel atomization and mixing, which are key factors in determining combustion efficiency and emissions. However, under certain conditions, combustors can experience thermoacoustic instability. In this study, a decision tree-based machine learning algorithm is used to classify combustion conditions by analyzing acoustic pressure and high-speed flame imaging from a counter-rotating high-shear swirl injector of a single can combustor fueled by methane. With a constant Reynolds number and varying equivalence ratios, the combustor exhibits both stable and unstable states. Characteristic features are extracted from the data using time series analysis, providing insight into combustion dynamics. The trained supervised machine learning model accurately classifies stable and unstable operations, demonstrating effective prediction of combustion conditions within the studied parameter range.

cs.LG↗