Design of a single-input system for specified roots using output feedback

A method is given for choosing feedbacks from an output vector of smaller dimension than the state vector in such a way that a subset of the roots assume desired values. A sufficient condition for arbitrary placement of as many roots as there are feedbacks is proved. It is also proved that a root corresponding to a mode which is either uncontrollable or unobservable cannot be altered. It may be desired to place more roots than there are feedbacks, and it is shown that the method can be extended to give an approximate solution of this problem.     

Topology optimization of flow domains using the lattice Boltzmann method

We consider the optimal design of two- (2D) and three-dimensional (3D) flow domains using the lattice Boltzmann method (LBM) as an approximation of Navier-Stokes (NS) flows. The problem is solved by a topology optimization approach varying the effective porosity of a fictitious material. The boundaries of the flow domain are represented by potentially discontinuous material distributions. NS flows are traditionally approximated by finite element and finite volume methods. These schemes, while well established as high-fidelity simulation tools using body-fitted meshes, are effected in their accuracy and robustness when regular meshes with zero-velocity constraints along the surface and in the interior of obstacles are used, as is common in topology optimization. Therefore, we study the potential of the LBM for approximating low Mach number incompressible viscous flows for topology optimization. In the LBM the geometry of flow domains is defined in a discontinuous manner, similar to the approach used in material-based topology optimization. In addition, this non-traditional discretization method features parallel scalability and allows for high-resolution, regular fluid meshes. In this paper, we show how the variation of the porosity can be used in conjunction with the LBM for the optimal design of fluid domains, making the LBM an interesting alternative to NS solvers for topology optimization problems. The potential of our topology optimization approach will be illustrated by 2D and 3D numerical examples.     

Design optimization with back-propagation neural networks

A methodology with back-propagation neural network models is developed to explore the artificial neural nets (ANN) technology in the new application territory of design optimization. This design methodology could go beyond the Hopfield network model, Hopfield and Tank (1985), for combinatorial optimization problems In this approach, pattern classification with back-propagation network, the most demonstrated power of neural networks applications, is utilized to identify the boundaries of the feasible and the infeasible design regions. These boundaries enclose the multi-dimensional space within which designs satisfy all design criteria. A feedforward network is then incorporated to perform function approximation of the design objective function. This approximation is performed by training the feedforward network with objective functions evaluated at selected design sets in the feasible design regions. Additional optimum design sets in the classified feasible regions are calculated and included in the successive training sets to improve the function mapping. Iteration is continued until convergent criteria are satisfied. This paper demonstrates that the artificial neural nets technology provides a global perspective of the entire design space with good and near optimal solutions. ANN can indeed be a potential technology for design optimization.     

Topology optimization of channel flow problems

This paper describes a topology design method for simple two-dimensional flow problems. We consider steady, incompressible laminar viscous flows at low-to-moderate Reynolds numbers. This makes the flow problem nonlinear and hence a nontrivial extension of the work of Borrvall and Petersson (2003).Further, the inclusion of inertia effects significantly alters the physics, enabling solutions of new classes of optimization problems, such as velocity-driven switches, that are not addressed by the earlier method. Specifically, we determine optimal layouts of channel flows that extremize a cost function which measures either some local aspect of the velocity field or a global quantity, such as the rate of energy dissipation. We use the finite element method to model the flow, and we solve the optimization problem with a gradient-based math-programming algorithm that is driven by analytical sensitivities. Our target application is optimal layout design of channels in fluid network systems. Using concepts borrowed from topology optimization of compliant mechanisms in solid mechanics, we introduce a method for the synthesis of fluidic components, such as switches, diodes, etc.     

Characterization of DNA hybridization kinetics in a microfluidic flow channel

In this investigation we report on the influence of volumetric flow rate, flow velocity, complementary DNA concentration, height of a microfluidic flow channel and time on DNA hybridization kinetics. A syringe pump was used to drive Cy3-labeled target DNA through a polydimethylsiloxane (PDMS) microfluidic flow channel to hybridize with immobilized DNA from the West Nile Virus. We demonstrate that a reduction of channel height, while keeping a fixed volumetric flow rate or a fixed flow velocity, enhances mass transport of target DNA to the capture probes. Compared to a passive hybridization, the DNA hybridization in the microfluidic flow channel generates higher fluorescence intensities for lower concentration of target DNA during the same fixed period of time. Within a fixed 2 min time period the fastest DNA hybridization at a 50 pM concentration of target DNA is achieved with a continuous flow of target DNA at the highest flow rate and the lowest channel height.     

Effects of channel surface finish on blood flow in microfluidic devices

The behaviour of blood flow in relation to microchannel surface roughness has been investigated. Special attention was focused on the techniques used to fabricate the microchannels and on the apparent viscosity of the blood as it flowed through these microchannels. For the experimental comparison of smooth and rough surface channels, each channel was designed to be 10 mm long and rectangular in cross-section with aspect ratios of ≥100:1 for channel heights of 50 and 100 μm. Polycarbonate was used as the material for the device construction. The shims, which created the heights of the channels, were made of polyethylene terephthalate. Surface roughnesses of the channels were varied from R _z of 60 nm to 1.8 μm. Whole horse blood and filtered water were used as the test fluids and differential pressures ranged from 200 to 5,000 Pa. The defibrinated horse blood was treated further to prevent coagulation. The results indicate that a surface roughness above an unknown value lowers the apparent viscosity of blood dramatically due to boundary effects. Furthermore, the roughness seemed to influence both water and whole blood almost equally. A set of design rules for channel fabrication is also presented in accordance with the experiments performed.     

Optimal design of microfluidic networks using biologically inspired principles

From the earliest of times, Man has sought to replicate ideas that have evolved naturally in plants and animals. Understanding and extracting these “natural” design strategies has opened up a whole new field of research known as biomimetics. Designs formulated using biologically inspired principles range from novel surface treatments that mimic physiological processes to geometrical optimization for improving the performance of a system. In this paper, we will show how biomimetic principles based on the laws that govern biological vascular trees can be used to design artificial microfluidic distribution systems. The study focuses specifically on microfluidic manifolds composed of constant-depth rectangular- or trapezoidal-sectioned channels, as these geometries can readily be fabricated using standard micro-fabrication techniques. We will show that it is possible to introduce a prescribed element of flow control into the system by carefully selecting the branching parameter that governs the change in channel dimension at each bifurcation.     

Design optimization of micromixer with square-wave microchannel on compact disk microfluidic platform

This paper presents a passive micromixer on a compact disk (CD) microfluidic platform that performs plasma mixing function. The driving force of CD microfluidic platform including, the centrifugal force due to the system rotation, the Coriolis force as a function of the rotation angular frequency and velocity of liquid. Numerical simulations are performed to investigate the flow characteristics and mixing performance of three CD microfluidic mixers with square-wave, curved and zig-zag microchannels, respectively. Of the three microchannels, the square-wave microchannel is found to yield the best mixing performance, and is therefore selected for design optimization. Four CD microfluidic micromixers incorporating square-wave PDMS microchannels with different widths in the x- and y-directions are fabricated using conventional photolithography techniques. The mixing performance of the four microchannels is investigated both numerically and experimentally. The results show that given an appropriate specification of the microchannel geometry and a CD rotation speed of 2,000 rpm, a mixing efficiency of more than 93 % can be obtained within 5 s.     

Design of poiseuille flow controllers using the method of inequalities

This paper investigates the use of the method of inequalities （MoI） to design output-feedback compensators for the problem of the control of instabilities in a laminar plane Poiseuille flow. In common with many flows, the dynamics of streamwise vortices in plane Poiseuille flow are very non-normal. Consequently, small perturbations grow rapidly with a large transient that may trigger nonlinearities and lead to turbulence even though such perturbations would, in a linear flow model, eventually decay. Such a system can be described as a conditionally linear system. The sensitivity is measured using the maximum transient energy growth, which is widely used in the fluid dynamics community. The paper considers two approaches. In the first approach, the MoI is used to design low-order proportional and proportional-integral （PI） controllers. In the second one, the MoI is combined with McFarlane and Glover＇s H∞ loop-shaping design procedure in a mixed-optimization approach.     

Fabrication of microfluidic networks with integrated electrodes

In this paper a method is presented for the fabrication of micro-channel networks in glass with integrated and insulated gate electrodes to control the zeta-potential at the insulator surface and therewith the electro-osmotic flow (EOF). The fabrication of the electrodes is a sequence of photolithography, etching and thin film deposition steps on a glass substrate, followed by chemical mechanical polishing (CMP) and subsequently direct thermal bonding to a second glass plate to form closed micro-channels. Plasma enhanced chemical vapor deposition (PECVD) SiO₂-layers as insulating material between the electrodes and micro-channels and different electrode materials are examined with respect to a high bonding temperature to obtain an optimal insulating result. A CMP process for the reduction of the SiO₂ topography and roughness is studied and optimized in order to obtain a surface that is smooth enough to be directly bondable to a second glass plate.     

Design optimization of circular antenna arrays using Taguchi method

This paper presents the application of Taguchi method (TM) to design optimization of non-uniform circular antenna array (CAA) for suppression of sidelobe levels (SLLs). TM, a robust design approach, takes signal-to-noise ratio and orthogonal array tools from the statistical design of experiments. These tools allow instead of full factorial parametric analysis minimize the design parameters; thus, increase the convergence speed and generate more accurate solutions. TM is used to determine an optimal set of amplitudes and positions of CAA for 8, 10, and 12 elements. Comparison of the results of the TM with those of latest meta-heuristic algorithms in the literature reveals that the CAA design with TM provides the best SLL reduction performance in all cases.

     

Simultaneous assay of platelet adhesion at multiple shear rates within a single microfluidic channel

Cardiovascular diseases are currently the major causes of mortality in the world, especially in developed nations. As a predominant one, thrombosis is the platelet aggregation induced by a high shear rate. Platelet aggregation assay can clarify the occurrence mechanism of thrombosis, as well as be used as an important tool in the clinical diagnosis, personalized treatment, and screening of anticoagulants. Thus, relevant studies attracted considerable attention. As an important step in platelet aggregation, platelet adhesion and its detection also attract intensive concern. Thus, some analytical methods have been developed for platelet adhesion assay, and the impact of shear rate is one of the focuses. Compared with other devices, biosensors can give a more accurate result within a shorter time. Furthermore, some biosensors can achieve real-time analysis. However, only one or several shear rates can be tested at the same time, which may decrease the analytical efficiency. Meanwhile, in most cases, only the average platelet adhesion effect within a reactor is detected, and the impact of the distribution of shear rates is improperly neglected. In this study, a microfluidic device with a single channel is designed and fabricated for platelet adhesion assay. When the platelet-rich plasma flows through the collagen-modified sensing surface of the channel bottom, the interaction between platelets and collagen molecules on the entire surface can be simultaneously monitored by using a surface plasmon resonance imaging (SPRi) system. A gradient of the shear rate (0–546 s^-1) could be formed within the channel by choosing a suitable depth-to-width ratio (1:5), so platelet adhesion at multiple shear rates could be monitored simultaneously. This method enables the measurement of the adhesion process of unlabeled platelets on the entire sensing surface, in vitro, at multiple shear rates. Such a system can obtain more accurate platelet adhesion result at a given shear rate than traditional methods. Furthermore, in an individual operation, platelet adhesion can be repeatedly tested at multiple points with an equal shear rate, so a much higher analytical efficiency can also be achieved.     

A flow topology optimization method for steady state flow using transient information of flow field solved by lattice Boltzmann method

A topology optimization method for fluid flow using transient information is proposed. In many conventional methods, the design domain is updated using steady state information which is obtained after solving the flow field equations completely. Hence we must solve the flow field at each iterative which leads to high computational cost. In contrast, the proposed method updates the design domain using transient information of flow field. Hence the flow field is solved only once. The flow field is solved by lattice Boltzmann method (LBM). It is found that, by using LBM, the flow field is stably computed even though the design domain drastically changes during the computation. The design domain is updated according to sensitivity analysis. In many conventional methods, the sensitivity of objective functionals under lattice Boltzmann equations is obtained using additional adjoint equations. However, in the proposed method, the sensitivity is explicitly formulated and computed without using adjoint variables. In this paper, we show some numerical examples for low Reynolds number flows. The results demonstrate good convergence property in small computation time.     

Implications of variable fluid resistance caused by start-up flow in microfluidic networks

Electrical circuit analogies are often used to design microfluidic systems because they simplify device design, providing simple relationships between fluid flow rate, driving forces, and channel dimensions. However, such approximations often significantly overestimate flow rates in situations where start-up energy losses from establishing kinetic head are similar in magnitude to the energy required to overcome viscous shear stresses, as is often the case within complex microfluidic networks. These reduced flows can be more accurately predicted within an electrical analogy framework that accounts for the nonlinear flow resistance generated on the transient regime of start-up flow. In this work, standard flow resistance expressions are modified to account for such effects, and the onset of nonlinear resistance is predicted by a dimensionless parameter, $\xi = Re\frac{D}{L},$ which is dependent on the Reynolds number and the channel length. As a demonstration, variable fluid resistance is shown to dramatically affect the flow performance of common microfluidic units such as T-junctions and serpentine channels, and the change in performance is accurately predicted. Experimental and theoretical analysis of T-junctions further shows that variable flow resistance causes the ratio of flows through the junction to converge toward unity with respect to an increasing total flow rate. In addition, serpentine channels are shown to exaggerate these start-up effects, owing to compounded energetic demand associated with changing a flow’s direction. As a result, serpentine channels cause the ratio of flow rates exiting a T-junction to diverge from unity with respect to an increasing flow rate.     

Perturbation analysis and optimization of stochastic flow networks

We consider a stochastic fluid model of a network consisting of several single-class nodes in tandem and perform perturbation analysis for the node queue contents and associated event times with respect to a threshold parameter at the first node. We then derive infinitesimal perturbation analysis (IPA) derivative estimators for loss and buffer occupancy performance metrics with respect to this parameter and show that these estimators are unbiased. We also show that the estimators depend only on data directly observable from a sample path of the actual underlying discrete event system, without any knowledge of the stochastic characteristics of the random processes involved. This renders them computable in online environments and easily implementable for network management and optimization. This is illustrated by combining the IPA estimators with standard gradient based stochastic optimization methods and providing simulation examples.     

Weights optimization for multi-instance multi-label RBF neural networks using steepest descent method

Multi-instance multi-label learning (MIML) is an innovative learning framework where each sample is represented by multiple instances and associated with multiple class labels. In several learning situations, the multi-instance multi-label RBF neural networks (MIMLRBF) can exploit connections between the instances and the labels of an MIML example directly, while most of other algorithms cannot learn that directly. However, the singular value decomposition (SVD) method used to compute the weights of the output layer will cause augmented overall error in network performance when training data are noisy or not easily discernible. This paper presents an improved approach to learning algorithms used for training MIMLRBF. The steepest descent (SD) method is used to optimize the weights after they are initialized by the SVD method. Comparing results employing diverse learning strategies shows interesting outcomes as have come out of this paper.     

A heuristic method for learning Bayesian networks using discrete particle swarm optimization

Bayesian networks are a powerful approach for representing and reasoning under conditions of uncertainty. Many researchers aim to find good algorithms for learning Bayesian networks from data. And the heuristic search algorithm is one of the most effective algorithms. Because the number of possible structures grows exponentially with the number of variables, learning the model structure from data by considering all possible structures exhaustively is infeasible. PSO (particle swarm optimization), a powerful optimal heuristic search algorithm, has been applied in various fields. Unfortunately, the classical PSO algorithm only operates in continuous and real-valued space, and the problem of Bayesian networks learning is in discrete space. In this paper, two modifications of updating rules for velocity and position are introduced and a Bayesian networks learning based on binary PSO is proposed. Experimental results show that it is more efficient because only fewer generations are needed to obtain optimal Bayesian networks structures. In the comparison, this method outperforms other heuristic methods such as GA (genetic algorithm) and classical binary PSO.     

Design optimization of supersonic jet pumps using high fidelity flow analysis

Supersonic jet pumps are simple devices with no moving parts, where a high velocity (primary) flow is used to pump a second fluid. In this paper, Computational Fluid Dynamics (CFD) is combined with an optimization framework in order to develop a tool for the rapid generation of jet pump designs. A key feature of the problem formulation is the transformation of the jet pump design parameters in terms of geometric ratios. This approach dramatically reduces the number of unrealistic designs covered by the Design of Experiments. Optimal Latin Hypercubes for surrogate model building and model validation points are constructed using a permutation genetic algorithm and design points are evaluated using CFD. Surrogate models of primary and entrained flow rates are built using a Moving Least Squares approach. A series of optimizations for various pump sizes are performed using a genetic algorithm and Sequential Quadratic Programming, with responses calculated from the surrogates. This approach results in a set of optimized designs, from which pumps for a wide range of flow rates can be interpolated.     

Control of serial microfluidic droplet size gradient by step-wise ramping of flow rates

This paper describes a method to control and detect droplet size gradient by step-wise flow rate ramping of water-in-oil droplets in a microfluidic device. The droplets are generated in a cross channel device with two oil inlets and a water inlet. The droplet images are captured and analyzed in a time sequence in order to quantify the droplet generation frequency. It is demonstrated that by controlling the ramping of the oil flow rates it is possible to manipulate the ramping of droplet sizes. Increasing or decreasing of droplet sizes is achieved for a step-wise triangular ramping profile of the oil flow rate. The dynamic behavior of droplets due to the step-wise flow pulses is investigated. Uniform linear size ramping of water-in-oil droplets from 73 to 83 μm in diameter is generated with an oil flow ramping range from 1 to 11 μL/min in a minimum of five steps while water flow rate is held constant at 2 μL/min.