Particles mass flow rate and concentration measurement using electrostatic sensor

In many industries where flow parameters measurement is essential to control manufacturing process, the use of a reliable, cost effective and high accuracy instrument is an important issue. Appropriate measurement method and design leads to improvement of pneumatic conveyors operation and process efficiency. This paper present an instrumentation design based on passive charge detection using a single electrostatic sensor. Two different sensor electrodes are applied to show the flexibility of electrostatic sensor application. A time domain signal processing algorithm is developed to measurement of mass flow rate and concentration profile from acquired electrical charge signal. The findings is led to a low cost and high accuracy design, the experimental test results of the design shows less than ±5% error between measured parameters and reference reading acquired from the manual weighing

On Lagerstrom’s Model of Slow Incompressible Viscous Flow

The model discussed is a nonlinear boundary value problem which contains a parameter $\varepsilon$ that models the Reynolds number. The matched asymptotic expansions, an inner “Stokes” expansion valid near the inner boundary and an outer “Oseen” expansion valid away from it, that describe the solutions of the model problem for $\varepsilon$ small are extended. Numerical calculations show that these matched expansions have only a small range of usefulness, with the addition of further terms generally causing a worse, rather than better, approximation at moderate values of $\varepsilon$. Far better results are achieved when a single expansion, the outer expansion, is used throughout. The additional terms that have been calculated then consistently give improved approximations for all $\varepsilon$. It is also rigorously proved that an iterative method of solution of the model equation based on the outer “Oseen” approximation, converges for all $\varepsilon$ to a unique solution.\ud The results presented here for Lagerstrom’s model suggest that iterative improvement of the Oseen expansion may be an effective method of approximation of viscous flows at moderate Reynolds number

A method for solving systems of non-linear differential equations with moving singularities

We present a method for solving a class of initial valued, coupled, non-linear differential equations with `moving singularities' subject to some subsidiary conditions. We show that this type of singularities can be adequately treated by establishing certain `moving' jump conditions across them. We show how a first integral of the differential equations, if available, can also be used for checking the accuracy of the numerical solution.Comment: 9 pages, 7 eps figures, to appear in Comput. Phys. Co

A New Numerical Procedure for Determination of Effective Elastic Constants in Unidirectional Composite Plates

In this paper a composite plate with similar unidirectional fibers is considered. Assuming orthotropic structure, theory of elasticity is used for investigating the stress concentration. Also, complex variable functions are utilized for solving the plane stress problems. Then the effective characteristics of this plate are studied numerically by using ANSYS software. In this research a volume element of fibers in square array is considered. In order to investigate the numerical finite element modeling, the modeling of a quarter unit cell is considered. For determining the elasticity coefficients, stress analysis is performed for considered volume with noting to boundary conditions. Effective elasticity and mechanical properties of composite which polymer epoxy is considered as its matrix, are determined theoretically and also by the proposed method in this paper with finite element method. Finally, the variations of mechanical properties with respect to fiber-volume fraction are studied

ADAPTIVE METHOD TO PREDICT AND TRACK UNKNOWN SYSTEM BEHAVIORS USING RLS AND LMS ALGORITHMS

This study investigates the ability of recursive least squares (RLS) and least mean square (LMS) adaptive filtering algorithms to predict and quickly track unknown systems. Tracking unknown system behavior is important if there are other parallel systems that must follow exactly the same behavior at the same time. The adaptive algorithm can correct the filter coefficients according to changes in unknown system parameters to minimize errors between the filter output and the system output for the same input signal. The RLS and LMS algorithms were designed and then examined separately, giving them a similar input signal that was given to the unknown system. The difference between the system output signal and the adaptive filter output signal showed the performance of each filter when identifying an unknown system. The two adaptive filters were able to track the behavior of the system, but each showed certain advantages over the other. The RLS algorithm had the advantage of faster convergence and fewer steady-state errors than the LMS algorithm, but the LMS algorithm had the advantage of less computational complexity

Non-Rigid Registration Via Intelligent Adaptive Feedback Control

Preserving features or local shape characteristics of a mesh using conventional non-rigid registration methods is always difficult, as the preservation and deformation are competing with each other. The challenge is to find a balance between these two terms in the process of the registration, especially in presence of artefacts in the mesh. We present a non-rigid Iterative Closest Points (ICP) algorithm which addresses the challenge as a control problem. An adaptive feedback control scheme with global asymptotic stability is derived to control the stiffness ratio for maximum feature preservation and minimum mesh quality loss during the registration process. A cost function is formulated with the distance term and the stiffness term where the initial stiffness ratio value is defined by an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based predictor regarding the source mesh and the target mesh topology, and the distance between the correspondences. During the registration process, the stiffness ratio of each vertex is continuously adjusted by the intrinsic information, represented by shape descriptors, of the surrounding surface as well as the steps in the registration process. Besides, the estimated process-dependent stiffness ratios are used as dynamic weights for establishing the correspondences in each step of the registration. Experiments on simple geometric shapes as well as 3D scanning datasets indicated that the proposed approach outperforms current methodologies, especially for the regions where features are not eminent and/or there exist interferences between/among features, due to its ability to embed the inherent properties of the surface in the process of the mesh registration.</p

Particle size measurement using electrostatic sensor through spatial filtering method

Particle size measurement is important in powder and particle industries in which the particle size affects the productivity and efficiency of the machine, for example, in coal-fired power plants. An electrostatic sensor detects the electric charge from dry particles moving in a pipeline. Analysis of the detected signal can provide useful information about the particle velocity, mass flow rate, concentration and size. Using electrostatic sensors, previous researches studied particle sizing using magnitude dependent analysis which is a highly conditional method where the results can be affected by other parameters such as particle mass flow rate, velocity and concentration. This research proposes a magnitude independent analysis for particle sizing in the frequency domain called spatial filtering method. The solution was started by modeling and analysis of the charge induced to the ring electrode using finite-element analysis to find the sensitivity of electrode. A mathematical model was provided to compute particle position on the radial axis of the electrode and then a new technique was proposed to extract a single particle size from the calculated particle radial position. To validate the proposed method experimentally, a sensor was designed and five test particles ranging from 4 mm to 14 mm were selected for measurement. The results show a 0.44 mm estimation error between the estimated and expected results. The results also show that the method is promising for the establishment of a reliable and cost-effective solid particle sizing system