A novel generation of 3D SAR-based passive micromixer: efficient mixing and low pressure drop at low Reynolds number

Abstract This study introduces a novel generation of 3D splitting and recombination (SAR) passive micromixer with microstructures placed on the top and bottom floors of microchannels called a ‘chain mixer’. Both experimental verification and numerical analysis of the flow structure of this type of passive micromixer have been performed to evaluate the mixing performance and pressure drop of the microchannel, respectively. We propose here two types of chain mixer—chain 1 and chain 2—and compare their mixing performance and pressure drop with other micromixers, T-, O- and tear-drop micromixers. Experimental tests carried out in the laminar flow regime with a low Reynolds number range, 0.083 Re 4.166, and image-based techniques are used to evaluate the mixing efficiency. Also, the computational fluid dynamics code, ANSYS FLUENT-13.0 has been used to analyze the flow and pressure drop in the microchannel. Experimental results show that the chain and tear-drop mixer’s efficiency is very high because of the SAR process: specifically, an efficiency of up to 98% can be achieved at the tested Reynolds number. The results also show that chain mixers have a lower required pressure drop in comparison with a tear-drop micromixer

The Cascade Neo-Fuzzy Architecture and its Online Learning Algorithm

In the paper learning algorithm for adjusting weight coefficients of the Cascade Neo-Fuzzy Neural Network (CNFNN) in sequential mode is introduced. Concerned architecture has the similar structure with the Cascade-Correlation Learning Architecture proposed by S.E. Fahlman and C. Lebiere, but differs from it in type of artificial neurons. CNFNN consists of neo-fuzzy neurons, which can be adjusted using high-speed linear learning procedures. Proposed CNFNN is characterized by high learning rate, low size of learning sample and its operations can be described by fuzzy linguistic “if-then” rules providing “transparency” of received results, as compared with conventional neural networks. Using of online learning algorithm allows to process input data sequentially in real time mode

Dynamics of a class A nonlinear mirror mode-locked laser

Using a delay differential equation model we study theoretically the dynamics of a unidirectional class-A ring laser with a nonlinear amplifying loop mirror. We perform linear stability analysis of the CW regimes in the large delay limit and demonstrate that these regimes can be destabilized via modulational and Turing-type instabilities, as well as by an instability leading to the appearance of square-waves. We investigate the formation of square-waves and mode-locked pulses in the system. We show that mode-locked pulses are asymmetric with exponential decay of the trailing edge in positive time and faster-than-exponential (super-exponential) decay of the leading edge in negative time. We discuss asymmetric interaction of these pulses leading to a formation of harmonic mode-locked regimes.Comment: 9 pages

Analysis of the Wedge Method of Generating Guided Waves

The “wedge” method of generating guided waves in isotropic layers was analyzed both theoretically and experimentally by Viktorov et. al, in 1965 [1]. The main parts of the work were later reproduced in Viktorov’s now famous book on Rayleigh and Lamb waves [2]. Of several detailed observations made in these investigations, one was that: For optimal generation of a mode of a given wavenumber, k, the angle of the wedge should be “in the neighborhood” of the Snell’s law angle, θ i = sin−1(k/k w), where k w represents the wavenumber of the wave in the wedge[2]. Such a choice of incident angle was being used by experimentalists utilizing Lamb waves for nondestructive evaluation purposes [3–5] even before Viktorov’s analysis. The use of such an angle no doubt arose from the theory of (infinite) plane wave reflection/refraction at planar interfaces. In those cases, which are strictly of academic interest or for approximating real experimental conditions, Snell’s law holds exactly as a result of satisfaction of boundary conditions along the entire (infinite) interface

Refractory times for excitable dual state quantum dot laser neurons

Excitable photonic systems show promise for ultrafast analog computation, several orders of magnitude faster than biological neurons. Optically injected quantum dot lasers display several excitable mechanisms with dual state quantum lasers recently emerging as true all or none excitable artificial neurons. For use in applications, deterministic triggering is necessary and this has previously been demonstrated in the literature. In this work we analyse the crucially important \emph{refractory time} for this dual state system, which defines the minimum possible time between distinct pulses in any excitable pulse train. Ultrashort times on the order of 1~ns are obtained suggesting potential use where ultrafast analog computing is desired

The Cascade Orthogonal Neural Network

In the paper new non-conventional growing neural network is proposed. It coincides with the Cascade- Correlation Learning Architecture structurally, but uses ortho-neurons as basic structure units, which can be adjusted using linear tuning procedures. As compared with conventional approximating neural networks proposed approach allows significantly to reduce time required for weight coefficients adjustment and the training dataset size

Traveling wave modeling, simulation and analysis of quantum-dot mode-locked semiconductor lasers

We analyze the dynamics of a mode-locked quantum-dot edge-emitting semiconductor laser consisting of reversely biased saturable absorber and forward biased amplifying sections. To describe spatial non-uniformity of laser parameters, optical fields and carrier distributions we use the traveling wave model, which takes into account carrier exchange processes between wetting layer and quantum dots. A comprehensive parameter study and an optical mode analysis of operation regimes are presented