Somnath De

The two-dimensional unsteady laminar flow past a slotted cylinder facilitated with fluid injection is studied to examine the mixing behavior between upstream air and the fluid injected from the cylinder-slots for different slot angular positions (0°–90°). Additionally, wake dynamics and the resulting variations in key hydrodynamic quantities resulting from fluid–fluid interaction are analyzed. Simulations are primarily conducted for an injected fluid and air velocity ratio (Vr) of 1, with additional cases up to 3 to assess the impact of injection momentum. Pressure, velocity, and vorticity contours are used to analyze wake dynamics, while mass-fraction and mixture-fraction fields are examined to check mixing characteristics. Furthermore, the recurrence quantification analysis (RQA) is used to understand the underlying flow dynamics. For Vr=1, two distinct wake characteristics are identified. At low injection angles (0°–20°), the wake remains symmetric with the absence of vortex shedding, resulting in minimal lift and drag. Beyond 20°, vortex shedding emerges for all configurations. For 0°–20° cases, the flow exhibits a narrow mixing layer and high downstream mass-fraction variance, indicating weak mixing. Mixing is enhanced at higher injection angles (60°–90°), as indicated by a wider mixing layer and the lowest downstream variance. However, intensified vortex shedding for 60°–90° cases leads to stronger wake oscillations and higher hydrodynamic forces. RQA of the lift coefficient shows stable equilibrium at low injection angles, transitioning to periodic dynamics at higher angles. Despite the few dynamical variations, similar mixing and hydrodynamic trends persist for all velocity ratios considered.

The inverse diffusion flame (IDF) can experience thermoacoustic instability due to variations in power input or flow conditions. However, the dynamical transitions in IDF that lead to this instability when altering control parameters have not been thoroughly investigated. In this study, we explore the control parameters through two different approaches and employ multifractal detrended fluctuation analysis to characterize the transitions observed prior to the onset of thermoacoustic instability in the inverse diffusion flame. Our findings reveal a loss of multifractality near the region associated with thermoacoustic instability, which suggests a more ordered behavior. We determine that the singularity exponent, the width of the multifractal spectrum, and the Hurst exponent are reliable indicators of thermoacoustic instability and serve as effective classifiers of dynamical states in inverse diffusion flames.

The occurrence of high-frequency (>1000 Hz) thermoacoustic instability (TAI) sustained by mutual feedback among the acoustic field, heat release rate oscillations, and hydrodynamic oscillations poses severe challenges to the operation and structural integrity of rocket engines. Hence, quantifying the differing levels of feedback between these variables can help uncover the underlying mechanisms behind such high-frequency TAI, enabling redesign of combustors to mitigate TAI. However, so far, no concrete method exists to decipher the varying levels of mutual feedback during high-frequency TAI. In the present study, we holistically investigate the mutual influence based on the spatiotemporal directionality among acoustic pressure, heat release rate, and hydrodynamic and thermal oscillations during TAI of a single-element rocket engine combustor. Using symbolic transfer entropy, we identify the spatiotemporal direction of feedback interactions between those primary variables when acoustic waves significantly emerge during TAI. We unveil the influence of vorticity dynamics at the fuel collar (or the propellant splitter plate) as the primary stimulant over the heat release rate fluctuations to rapidly amplify the amplitude of the acoustic field. Furthermore, depending on the quantification of the degree of the mutual information (i.e., the net direction of information), we identify the switches in dominating the thermoacoustic driving between the variables during TAI, each representing a distinct mechanism of a thermoacoustic state. Additionally, from this quantification, we analyze the relative dominance of the variables and rank-order the mutual feedback according to their impact on driving TAI.

The occurrence of high frequency (>1000 Hz) thermoacoustic instability (TAI) sustained by mutual feedback among the acoustic field, heat release rate oscillations, and hydrodynamic oscillations poses severe challenges to the operation and structural integrity of rocket engines. Hence, quantifying the differing levels of feedback between these variables can help uncover the underlying mechanisms behind such high frequency TAI, enabling redesign of combustors to mitigate TAI. However, so far, no concrete method exists to decipher the varying levels of mutual feedback during high-frequency TAI. In the present study, we holistically investigate the mutual influence based on the spatiotemporal directionality among acoustic pressure, heat release rate, hydrodynamic and thermal oscillations during TAI of a single-element rocket engine combustor. Using symbolic transfer entropy (STE), we identify the spatiotemporal direction of feedback interactions between those primary variables when acoustic waves significantly emerge during TAI. We unveil the influence of vorticity dynamics at the fuel collar (or the propellant splitter plate) as the primary stimulant over the heat release rate fluctuations to rapidly amplify the amplitude of the acoustic field. Further, depending on the quantification of the degree of the mutual information (i.e., the net direction of information), we identify the switches in dominating the thermoacoustic driving between the variables during TAI, each representing a distinct mechanism of a thermoacoustic state. Additionally, from this quantification, we analyze the relative dominance of the variables and rank-order the mutual feedback according to their impact on driving TAI.

In chemical industries, multiphase flows in a bubble column reactor are frequently observed. The nonlinearity associated with bubble hydrodynamics, such as bubble–bubble and bubble–liquid interactions, gives rise to complex spatiotemporal patterns with increased gas or liquid velocities, which are extremely difficult to model and predict. In the current study, we propose a new, computationally efficient recurrence-based approach involving the angular separation between suitably defined state vectors and implement it on the experimental multiphase flow variables. The experimental dataset that consists of image frames obtained using a high-speed imaging system is generated by varying air and water flow rates in a bubble column reactor setup. The recurrence plots using the new approach are compared with those derived from conventional recurrence, considering standard benchmark problems. Further, using the recurrence plots and recurrence quantification from the new recurrence methodology, we discover a transition from a high recurrence state to a complex regime with very low recurrence for an increase in airflow rate. Determinism exhibits a rise for the decrease in airflow rate. A sharp decline in determinism and laminarity, signifying the quick shift to complex dynamics, is more prominent for spatial recurrence than temporal recurrence, indicating that the rise in airflow rate significantly impacts the spatial location of bubbles. We identify three regimes that appeared as distinct clusters in the determinism–laminarity plane. The bubbly regime, characterized by high values of determinism and laminarity, is separated by an intermediate regime from the slug flow regime, which has low determinism and laminarity.

Modern lean premixed combustors are operated in ultra-lean mode to conform to strict emission norms. However, this causes the combustors to become prone to lean blowout (LBO). Online monitoring of combustion dynamics may help to avoid LBO and help the combustor run more safely and reliably. Previous studies have suggested various techniques to early predict LBO in single-burner combustors. In contrast, early detection of LBO in multi-burner combustors has been little explored to date. Recent studies have discovered significantly different combustion dynamics between multi-burner combustors and single-burner combustors. In the present paper, we show that some well-established early LBO detection techniques suitable for single-burner combustor are less effective in early detecting LBO in multi-burner combustors. To resolve this, we propose a novel tool, topological data analysis (TDA), for real-time LBO prediction in a wide range of combustor configurations. We find that the TDA metrics are computationally cheap and follow monotonic trends during the transition to LBO. This indicates that the TDA metrics can be used to fine-tune the LBO safety margin, which is a desirable feature from practical implementation point of view. Furthermore, we show that the sublevel set TDA metrics show approximately monotonic changes during the transition to LBO even with low sampling-rate signals. Sublevel set TDA is computationally inexpensive and does not require phase-space embedding. Therefore, TDA can potentially be used for real-time monitoring of combustor dynamics with simple, low-cost, and low sampling-rate sensors.

Cyclones are among the most hazardous extreme weather events on Earth. In certain scenarios, two co-rotating cyclones in close proximity to one another can drift closer and completely merge into a single cyclonic system. Identifying the dynamic transitions during such an interaction period of binary cyclones and predicting the complete merger (CM) event are challenging for weather forecasters. In this work, we suggest an innovative approach to understand the evolving vortical interactions between the cyclones during two such CM events (Noru–Kulap and Seroja–Odette) using time-evolving induced velocity-based unweighted directed networks. We find that network-based indicators, namely, in-degree and out-degree, quantify the changes in the interaction between the two cyclones and are excellent candidates to classify the interaction stages before a CM. The network indicators also help to identify the dominant cyclone during the period of interaction and quantify the variation of the strength of the dominating and merged cyclones. Finally, we show that the network measures also provide an early indication of the CM event well before its occurrence.