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.

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.

Dynamics of a single-phase natural circulation system under harmonic excitation.

A natural circulation system (NCS) has a broad application in the sector of thermal engineering, for example, in nuclear reactors due to its capability in transferring heat without any support of external power or a mechanical device. NCSs, although designed for steady operation, have been found to exhibit dynamical behavior, such as periodic oscillations, depending on the input heater power. The heater power, otherwise assumed to be steady, can be fluctuating in practice. The present study focuses on the dynamics of a single-phase square NCS, which exhibits periodic dynamics and is subjected to an external harmonic perturbation (chosen as the simplest form of fluctuation) over the steady heater power. The objective of the study is to study the dynamics of NCS under external (harmonic) disturbances using the framework of forced synchronization. Toward that, a 1D transient model of a square-shaped NCS is simulated with harmonically forced input power. The transition in the nature of synchronization between mass flux oscillations and the external forcing has been characterized by varying the frequency and amplitude of external perturbation independently. We find different nonlinear effects of the harmonic forcing, such as exhibition of quasiperiodic dynamics, frequency pulling/pushing, and frequency locking or forced synchronization. The aforementioned characterization techniques open up an avenue for detailed analysis of the more complex type of fluctuating input heat in real systems.

Pool Boiling Amelioration by Aqueous Dispersion of Silica Nanoparticles.

Non-metallic oxide nanofluids have recently attracted interest in pool boiling heat transfer (PBHT) studies. Research work on carbon and silica-based nanofluids is now being reported frequently by scholars. The majority of these research studies showed improvement in PBHT performance. The present study reports an investigation on the PBHT characteristics and performance of water-based silica nanofluids in the nucleate boiling region. Sonication-aided stable silica nanofluids with 0.0001, 0.001, 0.01, and 0.1 particle concentrations were prepared. The stability of nanofluids was detected and confirmed via visible light absorbance and zeta potential analyses. The PBHT performance of nanofluids was examined in a customized boiling pool with a flat heating surface. The boiling characteristics, pool boiling heat transfer coefficient (PBHTC), and critical heat flux (CHF) were analyzed. The effects of surface wettability, contact angle, and surface roughness on heat transfer performance were investigated. Bubble diameter and bubble departure frequency were estimated using experimental results. PBHTC and CHF of water have shown an increase due to the nanoparticle inclusion, where they have reached a maximum improvement of ≈1.33 times over that of the base fluid. The surface wettability of nanofluids was also enhanced due to a decrease in boiling surface contact angle from 74.1° to 48.5°. The roughness of the boiling surface was reduced up to 1.5 times compared to the base fluid, which was due to the nanoparticle deposition on the boiling surface. Such deposition reduces the active nucleation sites and increases the thermal resistance between the boiling surface and bulk fluid layer. The presence of the dispersed nanoparticles caused a lower bubble departure frequency by 2.17% and an increase in bubble diameter by 4.48%, which vigorously affects the pool boiling performance.

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.

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.

Leveraging Wettability Engineering to Develop Three-Layer DIY Face Masks From Low-Cost Materials.

With the rapid spread of COVID-19 worldwide, the demand for appropriate face masks in the market has also skyrocketed. To ease strain on the supply of masks to the essential healthcare sector, it has become imperative that ordinary people rely more on home-made masks that can be easily put together using commonly available materials, while at the same time performing reasonably at arresting the ingress or egress of airborne droplets. Here, we propose a simple do-it-yourself (DIY) method for preparing a three-layered face mask that deploys two hydrophobic polypropylene nonwoven layers interspaced with a hydrophilic cellulosic cloth. The first hydrophobic layer, facing the user, allows high-momentum droplets (e.g., expelled by a sneeze or cough) to pass through and get absorbed in the next hydrophilic layer, thereby keeping the skin in contact with the mask dry and comfortable. The third (outermost) hydrophobic layer prevents penetration of the liquids from the middle layer to the outside, and also arrests any airborne droplets on its exterior. Simple tests show that our masks perform better in arresting the droplet transmission as compared to surgical masks available in the market.

Enhanced bone regeneration with carbon nanotube reinforced hydroxyapatite in animal model.

In order to improve the inherently poor mechanical properties of hydroxyapatite (HAp) and to increase its feasibility as load bearing implant material, in the present investigation, functionalised (HFC1 and HFC2) and non-functionalized (HC1 and HC2) multi-walled carbon nanotubes were used as reinforcing material with HAp. Significant improvement with respect to fracture toughness, flexural strength and impact strength of the composites was noticed. In vitro biological properties of HAp-carbon nanotube (CNT) biocomposites have also favored uniform and systematic apatite growth on their surface. Subsequently, in vivo osseous ingrowth at bone defect of rabbit femur was evaluated and compared using radiology, push out test, fluorochrome labeling, histology and scanning electron microscopy after 2 and 4 months respectively. The results demonstrated growth of web like soft callus from the host bone towards the implant, ensuring strong host bone interaction. Toxicological studies of the liver and kidney cells exhibited no abnormality, thereby confirming non-toxicity of the CNT in the animal body. Host-implant biomechanical strength showed high interfacial strength of the composites, indicating their high potentials to be used for bone remodeling applications.