Measurement of the acoustical impedance of a black box at low frequencies using a shorter impedance tube

For the experimental evaluation of the acoustical impedance of a termination by the impedance-tube method at low frequencies, the length of the impedance tube is a problem. In the present paper, the method of exact analysis of standing waves developed by the authors for the stationary medium as well as for mean flow, has been extended for measurement of the acoustical impedance of a termination at low frequencies. The values of the tube attenuation factor and the wave number at the low frequency of interest are established from the experiment conducted, with the given impedance tube, at a higher frequency. Then, exciting the tube at the desired low frequency it is sufficient to measure sound pressure at three differenct locations (not necessarily the minima) in order to evaluate reflection coefficient and hence the impedance of the termination at that frequency.

CFD analysis of high frequency miniature pulse tube refrigerators for space applications with thermal non-equilibrium model

High frequency, miniature, pulse tube cryocoolers are extensively used in space applications because of their simplicity. Parametric studies of inertance type pulse tube cooler are performed with different length-to-diameter ratios of the pulse tube with the help of the FLUENT (R) package. The local thermal non-equilibrium of the gas and the matrix is taken into account for the modeling of porous zones, in addition to the wall thickness of the components. Dynamic characteristics and the actual mechanism of energy transfer in pulse are examined with the help of the pulse tube wall time constant. The heat interaction between pulse tube wall and the oscillating gas, leading to surface heat pumping, is quantified. The axial heat conduction is found to reduce the performance of the pulse tube refrigerator. The thermal non-equilibrium predicts a higher cold heat exchanger temperature compared to thermal equilibrium. The pressure drop through the porous medium has a strong non-linear effect due to the dominating influence of Forchheimer term over that of the linear Darcy term at high operating frequencies. The phase angle relationships among the pressure, temperature and the mass flow rate in the porous zones are also important in determining the performance of pulse tuberefrigerator.

Field Emission Properties of Carbon Nanotube Arrays with Defects and Impurities

It has been found experimentally that the results related to the collective field emission performance of carbon nanotube (CNT) arrays show variability. The emission performance depends on the electronic structure of CNTs (especially their tips). Due to limitations in the synthesis process, production of highly pure and defect free CNTs is very difficult. The presence of defects and impurities affects the electronic structure of CNTs. Therefore, it is essential to analyze the effect of defects on the electronic structure, and hence, the field emission current. In this paper, we develop a modeling approach for evaluating the effect of defects and impurities on the overall field emission performance of a CNT array. We employ a concept of effective stiffness degradation for segments of CNTs, which is due to structural defects. Then, we incorporate the vacancy defects and charge impurity effects in our Green's function based approach. Simulation results indicate decrease in average current due to the presence of such defects and impurities.

An adaptive drug delivery design using neural networks for effective treatment of infectious diseases: A simulation study

An adaptive drug delivery design is presented in this paper using neural networks for effective treatment of infectious diseases. The generic mathematical model used describes the coupled evolution of concentration of pathogens, plasma cells, antibodies and a numerical value that indicates the relative characteristic of a damaged organ due to the disease under the influence of external drugs. From a system theoretic point of view, the external drugs can be interpreted as control inputs, which can be designed based on control theoretic concepts. In this study, assuming a set of nominal parameters in the mathematical model, first a nonlinear controller (drug administration) is designed based on the principle of dynamic inversion. This nominal drug administration plan was found to be effective in curing "nominal model patients" (patients whose immunological dynamics conform to the mathematical model used for the control design exactly. However, it was found to be ineffective in curing "realistic model patients" (patients whose immunological dynamics may have off-nominal parameter values and possibly unwanted inputs) in general. Hence, to make the drug delivery dosage design more effective for realistic model patients, a model-following adaptive control design is carried out next by taking the help of neural networks, that are trained online. Simulation studies indicate that the adaptive controller proposed in this paper holds promise in killing the invading pathogens and healing the damaged organ even in the presence of parameter uncertainties and continued pathogen attack. Note that the computational requirements for computing the control are very minimal and all associated computations (including the training of neural networks) can be carried out online. However it assumes that the required diagnosis process can be carried out at a sufficient faster rate so that all the states are available for control computation.

An adiabatic model for a two-stage double-inlet pulse tube refrigerator

A numerical modelling technique for predicting the detailed performance of a double-inlet type two-stage pulse tube refrigerator has been developed. The pressure variations in the compressor, pulse tube, and reservoir were derived, assuming the stroke volume variation of the compressor to be sinusoidal. The relationships of mass flowrates, volume flowrates, and temperature as a function of time and position were developed. The predicted refrigeration powers are calculated by considering the effect of void volumes and the phase shift between pressure and mass flowrate. These results are compared with the experimental results of a specific pulse tube refrigerator configuration and an existing theoretical model. The analysis shows that the theoretical predictions are in good agreement with each other.

Time Domain Characteristics of Electrical Measures for a Piezoelectric Thin Film to Identify Defects in the Substrate

A reduced 3D continuum model of dynamic piezoelectricity in a thin-film surface-bonded to the substrate/host is presented in this article. While employing large area flexible thin piezoelectric films for novel applications in device/diagnostics, the feasibility of the proposed model in sensing the surface and/or sub-surface defects is demonstrated through simulations - which involve metallic beams with cracks and composite beam with delaminations of various sizes. We have introduced a set of electrical measures to capture the severity of the damage in the existing structures. Characteristics of these electrical measures in terms of the potential difference and its spatial gradients are illustrated in the time domain. Sensitivity studies of the proposed measures in terms of the defected areas and their region of occurence relative to the sensing film are reported. The simulations' results for electrical measures for damaged hosts/substrates are compared with those due to undamaged hosts/substrates, which show monotonicity with high degree of sensitivity to variations in the damage parameters.

Classification of Indian meteorological stations using cluster and fuzzy cluster analysis, and Kohonen artificial neural networks

The present study deals with the application of cluster analysis, Fuzzy Cluster Analysis (FCA) and Kohonen Artificial Neural Networks (KANN) methods for classification of 159 meteorological stations in India into meteorologically homogeneous groups. Eight parameters, namely latitude, longitude, elevation, average temperature, humidity, wind speed, sunshine hours and solar radiation, are considered as the classification criteria for grouping. The optimal number of groups is determined as 14 based on the Davies-Bouldin index approach. It is observed that the FCA approach performed better than the other two methodologies for the present study.

Experimental studies on a two stage pulse tube cryocooler reaching 2.5K

A two stage Pulse Tube Cryocooler (PTC) is designed and fabricated which reaches a no-load temperature of 2.5K in the second stage and similar to 60 K in the first stage respectively. The system provides a cooling power of similar to 250 mW at 5K in the second stage. Stainless steel meshes (size 200) and lead (Pb) granules are used as the first stage regenerator materials and combination of Pb with Er3Ni / HoCu2 are used as the second stage regenerator materials. The system operates at 1.6 Hz using a 6 kW water cooled helium compressor. Studies conducted by varying the dimensions of Pulse Tubes and regenerators show that the dimensions of the Pulse Tubes are more critical to the performance of the Cryocooler than those of the regenerators. Experimental studies show that the optimum volume ratios of Er3Ni to Pb and HoCu2 to Pb in the second stage regenerator should be 3:2 and 2:3 respectively for the best performance. Further, systems with HoCu2 performed better than those with Er3Ni. The theoretical analysis of the system has been carried out using a simple isothermal model. The experimentally measured cooling powers are in good agreement with the theoretical predictions.

LOX separation studies using cryogenic vortex tube

In-flight collection of air, pre-cooling, liquefaction and separation of liquid oxygen (LOX) are key technologies for futuristic launch vehicles, Vortex tube technology is one of the few potential technologies for this application. Extensive studies have been carried out on straight and conical vortex tubes for developing vortex tube technology for high purity LOX separation. Studies show that 12mm. diameter conical vortex tube with L/D of 10 could achieve LOX purity of similar to 96% with separation efficiency of similar to 14% indicating that it is not possible to obtain both high LOX purity and high separation efficiency simultaneously in a single vortex tube. However, it is possible to achieve both high LOX purity and separation efficiency by staging of vortex tubes. LOX purity of 96% and separation efficiency of similar to 73.5% has been achieved for second stage vortex tube supplied with pre-cooled air having 60% oxygen purity. LOX purity has been further increased to 97% by applying controlled heating power over liquid oxygen flowing discharge surface of the vortex tube.

Rotorcraft parameter estimation using radial basis function neural network

Increased emphasis on rotorcraft performance and perational capabilities has resulted in accurate computation of aerodynamic stability and control parameters. System identification is one such tool in which the model structure and parameters such as aerodynamic stability and control derivatives are derived. In the present work, the rotorcraft aerodynamic parameters are computed using radial basis function neural networks (RBFN) in the presence of both state and measurement noise. The effect of presence of outliers in the data is also considered. RBFN is found to give superior results compared to finite difference derivatives for noisy data. (C) 2010 Elsevier Inc. All rights reserved.

Investigations of a two-stage pulse tube cryocooler operating down to 2.5K

A two-stage pulse tube cryocooler (PTC) which produces a no-load temperature of similar to 2.5 K in its second stage at an operating frequency of 1.6 Hz has been designed and fabricated. The second stage of the system provides a refrigeration power of similar to 250 mW at 5.0 K. The system uses stainless steel meshes (mesh size 200) along with lead (Pb) granules and combinations of Pb, Er3Ni, and HoCu2 as the first and second stage regenerator materials, respectively. Experimental studies have been carried out on different pulse tube configurations by varying the dimensions of the pulse tubes and regenerators to arrive at the best one, which leads to the lowest no-load second stage cold head temperature. Using this configuration, detailed experimental studies have been conducted by varying the volume percentage ratios of the second stage regenerator materials such as HoCu2, Er3Ni, and Pb (with an average grain size of similar to 250 mu m). This article presents the results of our experimental studies on cryocoolers with the regenerator material arranged in layered structures. Comparative studies have also been presented for specific cases where the regenerator materials are arranged as a homogeneous mixture in the second stage. The experimental results clearly indicate that the design of PTCs should use only layered structures of regenerator materials and not homogenous mixtures.

Neural network approach for fault location in unbalanced distribution networks with limited measurements

This paper presents an Artificial Neural Network (ANN) approach for locating faults in distribution systems. Different from the traditional Fault Section Estimation methods, the proposed approach uses only limited measurements. Faults are located according to the impedances of their path using a Feed Forward Neural Networks (FFNN). Various practical situations in distribution systems, such as protective devices placed only at the substation, limited measurements available, various types of faults viz., three-phase, line (a, b, c) to ground, line to line (a-b, b-c, c-a) and line to line to ground (a-b-g, b-c-g, c-a-g) faults and a wide range of varying short circuit levels at substation, are considered for studies. A typical IEEE 34 bus practical distribution system with unbalanced loads and with three- and single- phase laterals and a 69 node test feeder with different configurations are considered for studies. The results presented show that the proposed approach of fault location gives close to accurate results in terms of the estimated fault location.

Robust/optimal temperature profile control using neural networks

An approximate dynamic programming (ADP) based neurocontroller is developed for a heat transfer application. Heat transfer problem for a fin in a car's electronic module is modeled as a nonlinear distributed parameter (infinite-dimensional) system by taking into account heat loss and generation due to conduction, convection and radiation. A low-order, finite-dimensional lumped parameter model for this problem is obtained by using Galerkin projection and basis functions designed through the 'Proper Orthogonal Decomposition' technique (POD) and the 'snap-shot' solutions. A suboptimal neurocontroller is obtained with a single-network-adaptive-critic (SNAC). Further contribution of this paper is to develop an online robust controller to account for unmodeled dynamics and parametric uncertainties. A weight update rule is presented that guarantees boundedness of the weights and eliminates the need for persistence of excitation (PE) condition to be satisfied. Since, the ADP and neural network based controllers are of fairly general structure, they appear to have the potential to be controller synthesis tools for nonlinear distributed parameter systems especially where it is difficult to obtain an accurate model.

Design of a FIR filter for image restoration using principal component neural networks

The neural network finds its application in many image denoising applications because of its inherent characteristics such as nonlinear mapping and self-adaptiveness. The design of filters largely depends on the a-priori knowledge about the type of noise. Due to this, standard filters are application and image specific. Widely used filtering algorithms reduce noisy artifacts by smoothing. However, this operation normally results in smoothing of the edges as well. On the other hand, sharpening filters enhance the high frequency details making the image non-smooth. An integrated general approach to design a finite impulse response filter based on principal component neural network (PCNN) is proposed in this study for image filtering, optimized in the sense of visual inspection and error metric. This algorithm exploits the inter-pixel correlation by iteratively updating the filter coefficients using PCNN. This algorithm performs optimal smoothing of the noisy image by preserving high and low frequency features. Evaluation results show that the proposed filter is robust under various noise distributions. Further, the number of unknown parameters is very few and most of these parameters are adaptively obtained from the processed image.