A novel recurrence-based approach for investigating multiphase flow dynamics in bubble column reactors.

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.

Lean blowout detection using topological data analysis.

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.

Study of interaction and complete merging of binary cyclones using complex networks.

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.

Anti-Vegf therapy leads to an improvement in grade of retinal pigment epithelium alterations on single layer retinal pigment epithelium map in diabetic macular edema.

PURPOSE: In bovine retinal pigment epithelium (RPE) cells, increased secretion of vascular endothelial growth factor (VEGF) has a positive linear association with proliferation of RPE. Spectral domain optical coherence tomography (SD-OCT) based improvement in grades of topographic retinal pigment epithelium alterations (RPE-A), were evaluated after intravitreal anti-VEGF therapy, in diabetic macular edema (DME), for the first time. METHODS: A tertiary care center-based, prospective study. Forty-four consecutive patients, 40-65 years of age with type 2 diabetes mellitus (DM) with DME, were administered three doses of anti-VEGF therapy at monthly intervals. Pre- and post-intervention SD-OCT was done and central sub field thickness (CST), cube average thickness (CAT) and topographic grades of RPE-A were assessed using single layer RPE map (SL-RPE) as; Grade 0: No alterations, Grade 1: Alteration in two quadrants, Grade 2: Alteration in more than two quadrants. RESULTS: CST decreased from 354.2 ± 16.0 µm pre-intervention to 233.2 ± 7.9 µm post-intervention. CAT reduced from 340.6 ± 6.5 µm pre-intervention to 274.1 ± 5.1 µm post-intervention. Significant improvement in grades of RPE-A pre- v/s post-intervention were observed. (Grade 0: 0 v/s 39; Grade 1: 17 v/s 3; Grade 2: 27 v/s 2) (p < 0.001). CONCLUSION: Anti-VEGF therapy is associated with an improvement in grades of RPE-A in DME.The study was registered with the Clinical Trial Registry of India (CTRI/2019/03/018135).

Early detection of lean blowout using recurrence network for varying degrees of premixedness.

Lean premixed combustors are highly susceptible to lean blowout flame instability, which can cause a fatal accident in aircrafts or expensive shutdown in stationary combustors. However, the lean blowout limit of a combustor may vary significantly depending on a number of variables that cannot be controlled in practical situations. Although a large literature exists on the lean blowout phenomena, a robust strategy for early lean blowout detection is still not available. To address this gap, we study a relatively unexplored route to lean blowout using a nonlinear dynamical tool, the recurrence network. Three recurrence network parameters: global efficiency, average degree centrality, and global clustering coefficient are chosen as metrics for an early prediction of the lean blowout. We observe that the characteristics of the time series near the lean blowout limit are highly dependent on the degree of premixedness in the combustor. Still, for different degrees of premixedness, each of the three recurrence network metrics increases during transition to lean blowout, indicating a shift toward periodicity. Thus, qualitatively, the recurrence network metrics show similar trends for different degrees of premixing showing their robustness. However, the sensitivities and absolute trends of the recurrence network metrics are found to be significantly different for highly premixed and partially premixed configurations. Thus, the results indicate that prior knowledge about (i) the degree of premixedness and (ii) the route to lean blowout may be required for accurate early prediction of the lean blowout. We show that the visible structural changes in the recurrence network can be linked to the changes in the recurrence network metrics, helping to better understand the dynamical transition to lean blowout. We observe the power law degree distribution of the recurrence network to break down close to the lean blowout limit due to the intermittent dynamics in the near-LBO regime.

Increased serum cortisol is associated with alterations in cross-sectional and topographic OCT parameters in diabetic retinopathy: a preliminary study.

PURPOSE: Cortisol, a steroid hormone, plays an essential role in metabolic processes of diabetes mellitus. This study for the first time evaluated the association of serum cortisol with spectral domain optical coherence tomography (SD-OCT)-based cross-sectional and topographic parameters with severity of diabetic retinopathy (DR). METHODS: A tertiary care center-based preliminary study was undertaken. Fourteen consecutive cases of DR and fifteen healthy controls were included. Cases were graded according to ETDRS classification: non-proliferative DR (NPDR, n = 8) and proliferative DR (PDR, n = 6). All study subjects underwent complete ophthalmological evaluation. Serum cortisol was analyzed using chemiluminescence microparticle assay method. Central subfield thickness (CST), cube average thickness (CAT), cube volume (CV), retinal nerve fiber layer (RNFL) thickness, disorganization of inner retinal layers (DRIL), grade of retinal photoreceptor ellipsoid zone (EZ) disruption and grade of retinal pigment epithelium (RPE) alterations were evaluated using SD-OCT. Statistical analysis was done using ANOVA and Pearson's correlation analysis. RESULTS: Mean serum cortisol levels (µg/dL) were NPDR = 11.59 ± 0.42, PDR = 14.50 ± 0.26 and controls = 8.22 ± 0.77. With increasing severity of DR, mean CST, CAT, CV showed positive correlation, whereas mean RNFL thickness showed negative correlation with serum cortisol levels (p < 0.01). DRIL, EZ disruption and RPE alterations showed positive correlation with serum cortisol levels (p < 0.001). CONCLUSION: Serum cortisol levels are significantly associated with severity of DR and correlate positively with CST, CAT, CV, DRIL, EZ disruption and RPE alterations and negatively with RNFL thickness.

Recurrence network analysis exploring the routes to thermoacoustic instability in a Rijke tube with inverse diffusion flame.

Inverse diffusion flame (IDF) is a reliable low NOx technology that is suitable for various industrial applications including gas turbines. However, a confined IDF may exhibit thermoacoustic instability, a kind of dynamic instability, which is characterized by catastrophically large amplitude pressure oscillations. Transition to such instability for an inverse diffusion flame is less explored compared to other types of flame. In the present study, thermoacoustic instability in a Rijke tube with IDF is achieved by varying air flow rate and input power independently, and the onset of thermoacoustic instability is examined using the framework of recurrence network (RN). During the transition to thermoacoustic instability, we find new routes and two new intermediate states, here referred to as "amplitude varying aperiodic oscillations" and "low amplitude limit cycle-like oscillations." Furthermore, we show that recurrence network analysis can be used to identify the dynamical states during the transition to thermoacoustic instability. We observe an absence of a single characteristic scale, resulting in a non-regular network even during thermoacoustic instability. Furthermore, the degree distributions of RN during combustion noise do not obey a single power law. Thus, scale-free nature is not exhibited during combustion noise. In short, recurrence network analysis shows significant differences in the topological information during combustion noise and thermoacoustic instability for IDF with those for premixed flames, reported earlier.

Sequential restoration of external limiting membrane and ellipsoid zone after intravitreal anti-VEGF therapy in diabetic macular oedema.

BACKGROUND/OBJECTIVES: To study the mechanism of restoration of retinal photoreceptor ellipsoid zone (EZ), after intravitreal bevacizumab (IVB) therapy, in diabetic macular oedema (DMO). SUBJECTS/METHODS: Forty-four consecutive patients aged 40-65 years having type 2 diabetes mellitus (DM) with DMO were prospectively recruited for IVB therapy. It comprised of three doses (1.25 mg in 0.05 ml) of IVB at monthly intervals. Patients with other ocular and systemic diseases affecting retinal vessels and earlier ophthalmological interventions were excluded. Visual acuity (logMAR VA) was recorded. Spectral domain optical coherence tomography (SD-OCT) was performed pre and post intervention. Central sub-foveal thickness (CST) and grades of disorganization of retinal inner layers (DRIL), external limiting membrane (ELM) and EZ were assessed. Data were statistically analysed on SPSS software. Clinical trials registry: CTRI/2019/03/018135. RESULTS: Mean logMAR VA decreased after IVB therapy from 1.78 ± 0.07 pre-intervention to 0.42 ± 0.05 post intervention (p < 0.001). Similarly, CST reduced from 354.23 ± 15.0 µm pre-intervention to 233.18 ± 7.88 µm post intervention (p < 0.001). Among qualitative variables, DRIL decreased from 93.2% pre-intervention to 13.6% post intervention. Likewise, global ELM disruption reduced from 81.8 to 9.1% and global EZ disruption reduced from 79.5 to 11.4%. ELM restoration preceded EZ restoration. CONCLUSION: Anti-VEGF therapy restores the barrier effect of ELM. It causes ELM to restore first followed by EZ restoration in DMO.

Application of recurrence quantification analysis for early detection of lean blowout in a swirl-stabilized dump combustor.

Lean blowout (LBO) is a serious issue in modern gas turbine engines that operate in a lean (premixed) mode to follow the stringent emission norms. When an engine operates with a lean fuel-air mixture, the flame becomes unstable and is at times carried out of the combustion chamber by the unburnt flow. Thus, the sudden loss of the flame, known as lean blowout, leads to fatal accidents in aircrafts and loss of production in power plants. Therefore, an in-depth analysis of lean blowout is necessary as the phenomenon involves complex interactions between flow dynamics and chemical kinetics. For understanding the complex dynamics of this phenomenon, recurrence analysis can be a very useful method. In the current study, we observe a transition to LBO as the global fuel-air ratio is reduced from stoichiometric condition and perform recurrence quantification analysis (RQA) with the CH∗ chemiluminescence data obtained experimentally. The extent of fuel-air mixing is varied with an objective of developing some robust early predictors of LBO that would work over a wide range of premixing. We find some RQA measures, such as determinism, laminarity, and trapping time, which show distinctive signature toward LBO and thereby can be used as early predictors of LBO for both premixed and partially premixed flames. Our analysis shows that the computational time for laminarity and trapping time is relatively less. However, computational time for those measures depends upon the dynamics of the combustor, size of the data taken, and choice of recurrence threshold.