Flow transitions and combined free and forced convective heat transfer in rotating curved channels: The case of positive rotation

The simultaneous effects of curvature, rotation and heating/cooling in channel flow complicate the flow and heat transfer characteristics beyond those observed in the channels with simple curvature or rotation. The phenomena encountered are examined for steady, hydrodynamically and thermally fully developed flow in square channels. The governing equations are solved numerically by using a finite-volume method. Certain hitherto unknown flow patterns are found. And the results show both the nature of the flow transition and the effect of this transition on the distributions of temperature, friction factor and Nusselt number in a square channel. © 1996 American Institute of Physics.published_or_final_versio

Visualization of flows in curved channels with a moderate or high rotation speed

Flows in channels with streamwise curvature and spanwise rotation are visualized in terms of end-view near the exit of the test sections through injecting smoke into the flows. Two test sections are used, i.e. the rectangular channels with the aspect ratio of 1 and 10, respectively. The work focuses on visualization of Dean and Coriolis vortices under the effects of secondary instabilities and flows in the region with a relatively high rotation speed. The results show that the secondary instabilities cause the Dean and Coriolis vortices oscillating in various forms and the flows at high rotation speeds are controlled by the secondary instabilities rather than the primary instability. In particular, the secondary instabilities lead the flows to be unsteady and turbulent somewhat like the bursting flow in the turbulent boundary layers. © 1997 OPA (Overseas Publishers Association) Amsterdam B.V.published_or_final_versio

Uniform tail asymptotics for the stochastic present value of aggregate claims in the renewal risk model

Consider an insurer who is allowed to make risk-free and risky investments. The price process of the investment portfolio is described as a geometric Lévy process. We study the tail probability of the stochastic present value of future aggregate claims. When the claim-size distribution is of Pareto type, we obtain a simple asymptotic formula which holds uniformly for all time horizons. The same asymptotic formula holds for the finite-time and infinite-time ruin probabilities. Restricting our attention to the so-called constant investment strategy, we show how the insurer adjusts his investment portfolio to maximize the expected terminal wealth subject to a constraint on the ruin probability. © 2009 Elsevier B.V. All rights reserved.postprin

FABRIC: A National-Scale Programmable Experimental Network Infrastructure

FABRIC is a unique national research infrastructure to enable cutting-edge and exploratory research at-scale in networking, cybersecurity, distributed computing and storage systems, machine learning, and science applications. It is an everywhere-programmable nationwide instrument comprised of novel extensible network elements equipped with large amounts of compute and storage, interconnected by high speed, dedicated optical links. It will connect a number of specialized testbeds for cloud research (NSF Cloud testbeds CloudLab and Chameleon), for research beyond 5G technologies (Platforms for Advanced Wireless Research or PAWR), as well as production high-performance computing facilities and science instruments to create a rich fabric for a wide variety of experimental activities

Insights from Modeling the 3D Structure of New Delhi Metallo-β-Lactamse and Its Binding Interactions with Antibiotic Drugs

New Delhi metallo-beta-lactamase (NDM-1) is an enzyme that makes bacteria resistant to a broad range of beta-lactam antibiotic drugs. This is because it can inactivate most beta-lactam antibiotic drugs by hydrolyzing them. For in-depth understanding of the hydrolysis mechanism, the three-dimensional structure of NDM-1 was developed. With such a structural frame, two enzyme-ligand complexes were derived by respectively docking Imipenem and Meropenem (two typical beta-lactam antibiotic drugs) to the NDM-1 receptor. It was revealed from the NDM-1/Imipenem complex that the antibiotic drug was hydrolyzed while sitting in a binding pocket of NDM-1 formed by nine residues. And for the case of NDM-1/Meropenem complex, the antibiotic drug was hydrolyzed in a binding pocket formed by twelve residues. All these constituent residues of the two binding pockets were explicitly defined and graphically labeled. It is anticipated that the findings reported here may provide useful insights for developing new antibiotic drugs to overcome the resistance problem

Effect of non-vacuum thermal annealing on high indium content InGaN films deposited by pulsed laser deposition

InGaN films with 33% and 60% indium contents were deposited by pulsed laser deposition (PLD) at a low growth temperature of 300 °C. The films were then annealed at 500-800 °C in the non-vacuum furnace for 15 min with an addition of N(2) atmosphere. X-ray diffraction results indicate that the indium contents in these two films were raised to 41% and 63%, respectively, after annealing in furnace. In(2)O(3) phase was formed on InGaN surface during the annealing process, which can be clearly observed by the measurements of auger electron spectroscopy, transmission electron microscopy and x-ray photoelectron spectroscopy. Due to the obstruction of indium out-diffusion by forming In(2)O(3) on surface, it leads to the efficient increment in indium content of InGaN layer. In addition, the surface roughness was greatly improved by removing In(2)O(3) with the etching treatment in HCl solution. Micro-photoluminescence measurement was performed to analyze the emission property of InGaN layer. For the as-grown InGaN with 33% indium content, the emission wavelength was gradually shifted from 552 to 618 nm with increasing the annealing temperature to 800 °C. It reveals the InGaN films have high potential in optoelectronic applications

Modelling of nonlinear stochastic dynamical systems using neurofuzzy networks

Though nonlinear stochastic dynamical system can be approximated by feedforward neural networks, the dimension of the input space of the network may be too large, making it to be of little practical importance. The Nonlinear Autoregressive Moving Average model with eXogenous input (NARMAX) is shown to be able to represent nonlinear stochastic dynamical system under certain conditions. As the dimension of the input space is finite, it can be readily applied in practical application. It is well known that the training of recurrent networks using gradient method has a slow convergence rate. In this paper, a fast training algorithm based on the Newton-Raphson method for recurrent neurofuzzy network with NARMAX structure is presented. The convergence and the uniqueness of the proposed training algorithm are established. A simulation example involving a nonlinear dynamical system corrupted with the correlated noise and a sinusoidal disturbance is used to illustrate the performance of the proposed training algorithm.published_or_final_versio

Online fault detection and isolation of nonlinear systems

This paper describes an online fault detection scheme for a class of nonlinear dynamic systems with modelling uncertainty and inaccessible states. Only the inputs and outputs of the system can be measured. The faults are assumed to be functions of the state, instead of the output and the input of the system. A nonlinear online approximator using dynamic recurrent neural network is utilised to monitor the faults in the system. The construction and the learning algorithm of the online approximator are presented. The stability, robustness and sensitivity of the fault detection scheme under certain assumptions are analysed. An example demonstrates the efficiency of the proposed fault detection scheme.published_or_final_versio

Fault estimation for a class of nonlinear dynamical systems

In this paper, model based fault estimation for a class of nonlinear dynamical systems is investigated. The state of the system is assumed unavailable, and a nonlinear observer is used to estimate the state. In the observer, neurofuzzy network is used as the approximator to estimate faults. The network is trained on-line and the convergence of the proposed learning algorithm is established. Abrupt fault and incipient fault are analyzed in the paper and they can be estimated accurately using neurofuzzy network with the proposed learning algorithm.published_or_final_versio