Liquid Film Coating a Fiber as a Model System for the Formation of Bound States in Active Dispersive-Dissipative Nonlinear Media

We analyze the coherent-structure interaction and the formation of bound states in active dispersivedissipative nonlinear media using a viscous film coating a vertical fiber as a prototype. The coherent structures in this case are droplike pulses that dominate the evolution of the film.We study experimentally the interaction dynamics and show evidence for formation of bound states. A theoretical explanation is provided through a coherent-structures theory of a simple model for the flow

Computational analysis of shock-induced flow through stationary particle clouds

We investigate the shock-induced flow through random particle arrays using particle-resolved Large Eddy Simulations for different incident shock wave Mach numbers, particle volume fractions and particle sizes. We analyze trends in mean flow quantities and the unresolved terms in the volume averaged momentum equation, as we vary the three parameters. We find that the shock wave attenuation and certain mean flow trends can be predicted by the opacity of the particle cloud, which is a function of particle size and particle volume fraction. We show that the Reynolds stress field plays an important role in the momentum balance at the particle cloud edges, and therefore strongly affects the reflected shock wave strength. The Reynolds stress was found to be insensitive to particle size, but strongly dependent on particle volume fraction. It is in better agreement with results from simulations of flow through particle clouds at fixed mean slip Reynolds numbers in the incompressible regime, than with results from other shock wave particle cloud studies, which have utilized either inviscid or two-dimensional approaches. We propose an algebraic model for the streamwise Reynolds stress based on the observation that the separated flow regions are the primary contributions to the Reynolds stress.Comment: 33 pages, 23 figures, 3 table

Liquid film coating a fiber as a model system for the formation of bound states in active dispersive-dissipative nonlinear media

We analyze the coherent-structure interaction and the formation of bound states in active dispersivedissipative nonlinear media using a viscous film coating a vertical fiber as a prototype. The coherent structures in this case are droplike pulses that dominate the evolution of the film.We study experimentally the interaction dynamics and show evidence for formation of bound states. A theoretical explanation is provided through a coherent-structures theory of a simple model for the flow

Higher-order photon correlations in pulsed photonic crystal nanolasers

We report on the higher-order photon correlations of a high-$\beta$ nanolaser under pulsed excitation at room temperature. Using a multiplexed four-element superconducting single photon detector we measured g$^{(n)}(\vec{0})$ with $n$=2,3,4. All orders of correlation display partially chaotic statistics, even at four times the threshold excitation power. We show that this departure from coherence and Poisson statistics is due to the quantum fluctuations associated with the small number of dipoles and photons involved in the lasing process

Increasing the efficiency of optimized v-sba-15 catalysts in the selective oxidation of methane to formaldehyde by artificial neural network modelling

The present study investigates the possibility of improving the selective oxidation of methane to formaldehyde over V-SBA-15 catalysts in two different ways. In a classical approach of catalyst optimization, the in situ synthesis of V-SBA-15 catalysts was optimized with regard to the applied pH value. Among the set of catalysts synthesized, a higher amount of incorporated vanadium, a higher content of polymeric VOx species as well as a less ordered structure of the support material were observed by increasing the pH values from 2.0 to 3.0. An optimum in performance during the selective oxidation of methane to formaldehyde with respect to activity and selectivity was found over V-SBA-15 prepared at a pH value of 2.5. With this knowledge, we have now evaluated the possibilities of reaction control using this catalyst. Specifically, artificial neural network modelling was applied after the collection of 232 training samples for obtaining insight into the influence of different reaction parameters (temperature; gas hourly space velocity (GHSV); and concentration of O2, N2 and H2O) onto methane conversion and selectivity towards formaldehyde. This optimization of reaction conditions resulted in an outstanding high space-time yield of 13.6 kgCH2O·kgcat·h−1. © 2020 by the authors. Licensee MDPI, Basel, Switzerland

Molecular Dynamics Study of Solid-Liquid Heat Transfer and Passive Liquid Flow

High heat flux removal is a challenging problem in boilers, electronics cooling, concentrated photovoltaic and other power conversion devices. Heat transfer by phase change is one of the most efficient mechanisms for removing heat from a solid surface. Futuristic electronic devices are expected to generate more than 1000 W/cm2 of heat. Despite the advancements in microscale and nanoscale manufacturing, the maximum passive heat flux removal has been ~300 W/cm2 in pool boiling. Such limitations can be overcome by developing nanoscale thin-film evaporation based devices, which however require a better understanding of surface interactions and liquid vapor phase change process. Evaporation based passive flow is an inspiration from the transpiration process that happens in trees. If we can mimic this process and develop heat removal devices, then we can develop efficient cooling devices. The existing passive flow based cooling devices still needs improvement to meet the future demands. To improve the efficiency and capacity of these devices, we need to explore and quantify the passive flow happening at nanoscales. Experimental techniques have not advanced enough to study these fundamental phenomena at the nanoscale, an alternative method is to perform theoretical study at nanoscales. Molecular dynamics (MD) simulation is a widely accepted powerful tool for studying a range of fundamental and engineering problems. MD simulations can be utilized to study the passive flow mechanism and heat transfer due to it. To study passive flow using MD, apart from the conventional methods available in MD, we need to have methods to simulate the heat transfer between solid and liquid, local pressure, surface tension, density, temperature calculation methods, realistic boundary conditions, etc. Heat transfer between solid and fluids has been a challenging area in MD simulations, and has only been minimally explored (especially for a practical fluid like water). Conventionally, an equilibrium canonical ensemble (NVT) is simulated using thermostat algorithms. For research in heat transfer involving solid liquid interaction, we need to perform non equilibrium MD (NEMD) simulations. In such NEMD simulations, the methods used for simulating heating from a surface is very important and must capture proper physics and thermodynamic properties. Development of MD simulation techniques to simulate solid-liquid heating and the study of fundamental mechanism of passive flow is the main focus of this thesis. An accurate surface-heating algorithm was developed for water which can now allow the study of a whole new set of fundamental heat transfer problems at the nanoscale like surface heating/cooling of droplets, thin-films, etc. The developed algorithm is implemented in the in-house developed C++ MD code. A direct two dimensional local pressure estimation algorithm is also formulated and implemented in the code. With this algorithm, local pressure of argon and platinum interaction is studied. Also, the surface tension of platinum-argon (solid-liquid) was estimated directly from the MD simulations for the first time. Contact angle estimation studies of water on platinum, and argon on platinum were also performed. A thin film of argon is kept above platinum plate and heated in the middle region, leading to the evaporation and pressure reduction thus creating a strong passive flow in the near surface region. This observed passive liquid flow is characterized by estimating the pressure, density, velocity and surface tension using Eulerian mapping method. Using these simulation, we have demonstrated the fundamental nature and origin of surface-driven passive flow. Heat flux removed from the surface is also estimated from the results, which shows a significant improvement can be achieved in thermal management of electronic devices by taking advantage of surface-driven strong passive liquid flow. Further, the local pressure of water on silicon di-oxide surface is estimated using the LAMMPS atomic to continuum (ATC) package towards the goal of simulating the passive flow in water

Human action recognition with 3D convolutional neural networks

Convolutional neural networks (CNNs) adapt the regular fully-connected neural network (NN) algorithm to facilitate image classification. Recently, CNNs have been demonstrated to provide superior performance across numerous image classification databases including large natural images (Krizhevsky et al., 2012). Furthermore, CNNs are more readily transferable between different image classification problems when compared to common alternatives. The extension of CNNs to video classification is simple and the rationale behind the components of the model are still applicable due to the similarity between image and video data. Previous CNNs have demonstrated good performance upon video datasets, however have not employed methods that have been recently developed and attributed improvements in image classification networks. The purpose of this research to build a CNN model that includes recently developed elements to present a human action recognition model which is up-to-date with current trends in CNNs and current hardware. Focus is applied to ensemble models and methods such as the Dropout technique, developed by Hinton et al. (2012) to reduce overfitting, and learning rate adaptation techniques. The KTH human action dataset is used to assess the CNN model, which, as a widely used benchmark dataset, facilitates the comparison between previous work performed in the literature. Three CNNs are built and trained to provide insight into design choices as well as allow the construction of an ensemble model. The final ensemble model achieved comparative performance to previous CNNs trained upon the KTH data. While the inclusion of new methods to the CNN model did not result in an improvement on previous models, the competitive result provides an alternative combination of architecture and components to other CNN models