Analysis of dominant flow structures and their flow dynamics in chemical process equipment using snapshot proper orthogonal decomposition technique

Snapshot proper orthogonal decomposition (POD) technique has been applied to reveal the dominant flow structures, their dynamics and length scales in six widely used industrial equipments (stirred tank, bubble column, Taylor-Couette flow (annual contactor), ultrasonic reactor, jet reactor, and channel flow). The variation in length scale of structures within an equipment, with change in its operating conditions (Reynolds number and power input) or change in its geometric configuration (sparger and impeller designs), has been brought out in this work. The planar data set for POD analysis was obtained from particle image velocimetry (PIV) and large eddy simulation (LES) studies. The dominant spatial topology was analyzed by using the velocity and vorticity POD modes. The modes have revealed the following flow structures: the ascending streaks and bursts in channel flow, the vortex tube and leading edge vortices in jets, the irregular small chaotic vortices in Taylor-Couette flow, the variation in plume oscillation and flow structures in the vortical region of bubble column resulting from changes in sparger design, the high intensity vortices near the source of ultrasound in the ultrasonic reactor and the effect of impeller designs on dominant flow structures and near blade vortices in the stirred tank. The length scales of structures are obtained by applying image processing on the spatial modes. The dynamics of these flow structures in each of the items of equipment is captured by reconstructing the flow field using appropriate spatial and temporal modes that contribute to these structures. Further, a unique attempt has been made to correlate the length scale distribution with the mixing time.     

Elastic response of a carbon nanotube fiber reinforced polymeric composite: A numerical and experimental study

Premature failure due to low mechanical properties in the transverse direction to the fiber constitutes a fundamental weakness of fiber reinforced polymeric composites. A solution to this problem is being addressed through the creation of nanoreinforced laminated composites where carbon nanotubes are grown on the surface of fiber filaments to improve the matrix-dominated composite properties. The carbon nanotubes increase the effective diameter of the fiber and provide a larger interface area for the polymeric matrix to wet the fiber. A study was conducted to numerically predict the elastic properties of the nanoreinforced composites. A multiscale modeling approach and the Finite Element Method were used to evaluate the effective mechanical properties of the nanoreinforced laminated composite. The cohesive zone approach was used to model the interface between the nanotubes and the polymer matrix. The elastic properties of the nanoreinforced laminated composites including the elastic moduli, the shear modulus, and the Poisson’s ratios were predicted and correlated with iso-strain and iso-stress models. An experimental program was also conducted to determine the elastic moduli of the nanoreinforced laminated composite and correlate them with the numerical values.     

Role of cell polarity dynamics and motility in pattern formation due to contact-dependent signalling

A key challenge in biology is to understand how spatio-temporal patterns and structures arise during the development of an organism. An initial aggregate of spatially uniform cells develops and forms the differentiated structures of a fully developed organism. On the one hand, contact-dependent cell–cell signalling is responsible for generating a large number of complex, self-organized, spatial patterns in the distribution of the signalling molecules. On the other hand, the motility of cells coupled with their polarity can independently lead to collective motion patterns that depend on mechanical parameters influencing tissue deformation, such as cellular elasticity, cell–cell adhesion and active forces generated by actin and myosin dynamics. Although modelling efforts have, thus far, treated cell motility and cell–cell signalling separately, experiments in recent years suggest that these processes could be tightly coupled. Hence, in this paper, we study how the dynamics of cell polarity and migration influence the spatiotemporal patterning of signalling molecules. Such signalling interactions can occur only between cells that are in physical contact, either directly at the junctions of adjacent cells or through cellular protrusional contacts. We present a vertex model which accounts for contact-dependent signalling between adjacent cells and between non-adjacent neighbours through long protrusional contacts that occur along the orientation of cell polarization. We observe a rich variety of spatiotemporal patterns of signalling molecules that is influenced by polarity dynamics of the cells, relative strengths of adjacent and non-adjacent signalling interactions, range of polarized interaction, signalling activation threshold, relative time scales of signalling and polarity orientation, and cell motility. Though our results are developed in the context of Delta–Notch signalling, they are sufficiently general and can be extended to other contact dependent morpho-mechanical dynamics.     

Comparison of Coal Ash Particle Size Distributions from Berner and Dekati Low Pressure Impactors

This article presents the differential mass size distributions of coal combustion particulate matter (PM) determined with the Berner low-pressure impactor (BLPI, Hauke Model 25-4/0.015) and a newer generation of low pressure impactor, the Dekati low-pressure impactor (DLPI, Dekati Ltd Model 6281). The collection characteristics of the BLPI and DLPI are compared and cutoff diameters are calculated. Samples were collected in the post-combustion zone of a 19 kW vertical downflow combustor from two coal types. Both BLPI and DLPI represent a tri-modal distribution and give statistically similar characterizations of the coal ash particle size distribution. Distributions generated from DLPI data have higher fractions of submicron particles compared to those generated from BLPI data. The DLPI's two additional stages may provide greater resolution in the submicron region than the BLPI.     

Cooperative bathymetry-based localization using low-cost autonomous underwater vehicles

We present a cooperative bathymetry-based localization approach for a team of low-cost autonomous underwater vehicles (AUVs), each equipped only with a single-beam altimeter, a depth sensor and an acoustic modem. The localization of the individual AUV is achieved via fully decentralized particle filtering, with the local filter’s measurement model driven by the AUV’s altimeter measurements and ranging information obtained through inter-vehicle communication. We perform empirical analysis on the factors that affect the filter performance. Simulation studies using randomly generated trajectories as well as trajectories executed by the AUVs during field experiments successfully demonstrate the feasibility of the technique. The proposed cooperative localization technique has the potential to prolong AUV mission time, and thus open the door for long-term autonomy underwater.     

Robust underwater obstacle detection and collision avoidance

A robust obstacle detection and avoidance system is essential for long term autonomy of autonomous underwater vehicles (AUVs). Forward looking sonars are usually used to detect and localize obstacles. However, high amounts of background noise and clutter present in underwater environments makes it difficult to detect obstacles reliably. Moreover, lack of GPS signals in underwater environments leads to poor localization of the AUV. This translates to uncertainty in the position of the obstacle relative to a global frame of reference. We propose an obstacle detection and avoidance algorithm for AUVs which differs from existing techniques in two aspects. First, we use a local occupancy grid that is attached to the body frame of the AUV, and not to the global frame in order to localize the obstacle accurately with respect to the AUV alone. Second, our technique adopts a probabilistic framework which makes use of probabilities of detection and false alarm to deal with the high amounts of noise and clutter present in the sonar data. This local probabilistic occupancy grid is used to extract potential obstacles which are then sent to the command and control (C2) system of the AUV. The C2 system checks for possible collision and carries out an evasive maneuver accordingly. Experiments are carried out to show the viability of the proposed algorithm.     

Minimum Speed Seeking Control for Nonhovering Autonomous Underwater Vehicles

Most autonomous underwater vehicles (AUVs) are propelled by a single thruster, use elevators and rudders as control surfaces, and are torpedo‐shaped. Furthermore, they are positively buoyant to facilitate recovery during an emergency. For this class of nonhovering AUVs, there is a minimum speed at which the AUV must travel for stable depth control. Otherwise, the extra buoyancy will bring the AUV up to the surface when the fin loses its effectiveness at low speeds. Hence, we develop a novel algorithm such that the AUV is automatically controlled to travel at its minimum speed while maintaining a constant depth. This capability is important in a number of practical scenarios, including underwater loitering with minimum energy consumption, underwater docking with minimum impact, and high‐resolution sensing at minimum speed. First, we construct a depth dynamic model to explain the mechanism of the minimum speed, and we show its relationship with the buoyancy, the righting moment, and the fin's effectiveness of the AUV. Next, we discuss the minimum speed seeking problem under the framework of extremum seeking. We extend the framework by introducing a new definition of steady‐state mapping that imposes new structure on the seeking algorithm. The proposed algorithm employs a fuzzy inference system, which is driven by the real‐time measurements of pitch error and elevator deflection. The effectiveness of the algorithm in seeking the minimum speed is validated in both simulations and field experiments.     

Comparative accounts of probiotic characteristics of Bacillus spp. isolated from food wastes

Bacillus strains JHT3, DET6 and DET9 were selectively isolated from food wastes. These isolates exhibited various degrees of essential probiotic qualities and varied level of susceptibility patterns against tested antibiotics. Spores of DET9 elucidated best tolerance against simulated gastro-acidic conditions whereas DET6 showed best steadiness against simulated intestinal conditions. DET6 exhibited better antimicrobial activity than JHT3 and DET9 against unsafe organisms viz Staphylococcus aureus, Micrococcus flavus, Proteus vulgaris, Salmonella typhi and Escherichia coli. Susceptibility of these isolates to antibiotics decreases the illustration to offer resistance determinants to other organisms if administered in the form of probiotic preparations. JHT3, DET6 and DET9 showed high homology with Bacillus megaterium, (98%) Bacillus subtilis (99%) and Bacillus thuringiensis, respectively, using partial 16S r-RNA gene sequencing. Biochemical characterizations have supported the results of partial 16S r-RNA gene sequencing for JHT3 and DET6 but did not for DET9 and revealed its innovation. These isolates exhibited zero mortality of fishes in a 60 days trial, when fishes (Surfi tetra) were challenged up to 100 ppm cell concentration, with their daily diet.     

Production of hyaluronic acid from Streptococcus zooepidemicus MTCC 3523 and its wound healing activity

Exopolysaccharide produced and purified from Streptococcus zooepidemicus MTCC 3523 was identified as hyaluronic acid (HA) based on IR and NMR spectroscopy while its Mw was found to be 5.38 × 10(5)Da. HA produced passed bacterial endotoxin test and showed significant wound healing activity in Wistar rats on 12th and 16th day.