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Topology optimization is a method used for developing optimized geometric designs by distributing material pixels in a given design space that maximizes a chosen quantity of interest (QoI) subject to constraints. The purpose of this study is to develop a problem-agnostic automatic differentiation (AD) framework to compute sensitivities of the QoI required for density distribution-based topology optimization in an unstructured co-located cell-centered finite volume framework. Using this AD framework, the authors develop and demonstrate the topology optimization procedure for multi-dimensional steady-state heat conduction problems.

Topology optimization is performed using the well-established solid isotropic material with penalization approach. The method of moving asymptotes, a gradient-based optimization algorithm, is used to perform the optimization. The sensitivities of the QoI with respect to design variables, required for optimization algorithm, are computed using a discrete adjoint method with a novel AD library named residual automatic partial differentiator (Rapid).

Topologies that maximize or minimize relevant quantities of interest in heat conduction applications are presented. The efficacy of the technique is demonstrated using a variety of realistic heat transfer applications in both two and three dimensions, in conjugate heat transfer problems with finite conductivity ratios and in non-rectangular/non-cuboidal domains.

In contrast to most published work which has either used finite element methods or Cartesian finite volume methods for transport applications, the topology optimization procedure is developed in a general unstructured finite volume framework. This permits topology optimization for flow and heat transfer applications in complex design domains such as those encountered in industry. In addition, the Rapid library is designed to provide a problem-agnostic pathway to automatically compute all required derivatives to machine accuracy. This obviates the necessity to write new code for finding sensitivities when new physics are added or new cost functions are considered and permits general-purpose implementations of topology optimization for complex industrial applications.

Negative Poisson’s ratio (NPR) material has huge potential applications in various industrial fields. However, lower Young’s modulus due to the porous form limits its further applications. Based on the topology optimization technique, this paper aims to optimize the structure consisting two isotropic porous materials with positive Poisson’s ratio (PPR) and NPR and void.

Under prescribed dual-volume fraction constraints, the structural compliance is taken as the objective. Young’s modulus and Poisson’s ratio are, respectively, interpolated and expressed with Lamé’s parameters for easier programming. Accordingly, the sensitivities can be derived through the chain rule. Several two- and three-dimensional illustrative examples are presented to demonstrate the capability and effectiveness of the proposed approach. The influences of Poisson’s ratios, volume fractions and Young’s moduli on the optimized results are investigated.

For NPR materials having unique load responses, the resulting topologies of PPR and NPR materials have distinct material distributions in comparison of the results from two PPR materials. Furthermore, it is observed that higher structural stiffness can be achieved from the hybrid of PPR and NPR materials than that obtained from the structures made of individual constituent materials.

A topology optimization methodology is proposed to design structures composed of PPR and NPR materials.

The purpose of this paper is to show an efficient method for the detection of signs of early lung cancer. Various image processing algorithms are presented for different types of lesions, and a scheme is proposed for the combination of results.

A computer aided detection (CAD) scheme was developed for detection of lung cancer. It enables different lesion enhancer algorithms, sensitive to specific lesion subtypes, to be used simultaneously. Three image processing algorithms are presented for the detection of small nodules, large ones, and infiltrated areas. The outputs are merged, the false detection rate is reduced with four separated support vector machine (SVM) classifiers. The classifier input comes from a feature selection algorithm selecting from various textural and geometric features. A total of 761 images were used for testing, including the database of the Japanese Society of Radiological Technology (JSRT).

The fusion of algorithms reduced false positives on average by 0.6 per image, while the sensitivity remained 80 per cent. On the JSRT database the system managed to find 60.2 per cent of lesions at an average of 2.0 false positives per image. The effect of using different result evaluation criteria was tested and a difference as high as 4 percentage points in sensitivity was measured. The system was compared to other published methods.

The study described in the paper proves the usefulness of lesion enhancement decomposition, while proposing a scheme for the fusion of algorithms. Furthermore, a new algorithm is introduced for the detection of infiltrated areas, possible signs of lung cancer, neglected by previous solutions.

The tremendous growth of wireless applications and the demand for high data rate, the spectrum utilization improvement has been the most crucial challenges for wireless communication. Adapting cognitive radio with orthogonal frequency division multiplexing or offset quadrature amplitude modulation (OFDM/OQAM) improves the spectrum and energy efficiencies.

Thus, it overcomes the spectral leakage problem at the transmitter side and leads to less interference from secondary user (SUs) to primary user (PUs) and between the SUs in cognitive radio technology. The benefit of exploiting pulse shape filtering in the OFDM/OQAM is to not only eliminate the requirement of the guard bands but also reduce the out of band energy transmission, which also improves the spectral isolation from the neighboring systems. But the high peak to average power ratio (PAPR) phenomenon is a common issue in the majority of the multicarrier modulation systems and thus OFDM/OQAM is no exception in this case.

Therefore, this paper aims to examine the effect of integrating the Walsh–Hadamard Transform (WHT) on the power spectral density and investigates the problem of PAPR in the WHT/OQAM system.

Thus, it overcomes the spectral leakage problem at the transmitter side and leads to less interference from SUs to PUs and between the SUs in cognitive radio technology. The benefit of exploiting pulse shape filtering in the OFDM/OQAM is to not only eliminate the requirement of the guard bands but also reduce the out of band energy transmission, which also improves the spectral isolation from the neighboring systems. But the high PAPR phenomenon is a common issue in the majority of the multicarrier modulation systems thus OFDM/OQAM is no exception in this case.

The purpose of this study is to introduce a novel method for the measurement of electromagnetic material parameters.

The main idea behind the approach is the fact that for slabs with elongated shapes, the intensity of the backscattered field and the electromagnetic resonance frequency corresponding to the length of the sample are dependent on the conductivity of the sample’s material.

It is shown that for a known scattered field and resonance frequency, it is possible to formulate an inverse problem as to the calculation of the conductivity of the sample’s material at the considered frequencies. To investigate the applicability of the method, demonstrative experiments are performed during which the micro-Doppler effect is used to increase the measurement accuracy. The idea is extended to the case of anisotropic samples, with slight modifications proposed to the experimental setup in the case of significant anisotropy in the investigated material.

The measurement method may prove useful for the investigation of the high-frequency conductive properties of certain materials of interest.

To the best of the authors’ knowledge, this is the first time the use of the micro-Doppler effect is proposed for the purpose of the measurement of material parameters.

The real challenges in online crack detection testing based on guided waves are random noise as well as narrow-band coherent noise; and to achieve efficient structural health assessment methodology, magnificent extraction of noise and analysis of the signals are essential. The purpose of this paper is to provide optimal noise filtering technique for Lamb waves in the diagnosis of structural singularities.

Filtration of time-frequency information of guided elastic waves through the noisy signal is investigated in the present analysis using matched filtering technique which “sniffs” the signal buried in noise and most favorable mother wavelet based denoising methods. The optimal wavelet function is selected using Shannon’s entropy criterion and verified by the analysis of root mean square error of the filtered signal.

Wavelet matched filter method, a newly developed filtering technique in this work and which is a combination of the wavelet transform and matched filtering method, significantly improves the accuracy of the filtered signal and identifies relatively small damage, especially in enormously noisy data. A comparative study is also performed using the statistical tool to know acceptability and practicability of filtered signals for guided wave application.

The proposed filtering techniques can be utilized in online monitoring of civil and mechanical structures. The algorithm of the method is easy to implement and found to be successful in accurately detecting damage.

Although many techniques have been developed over the past several years to suppress random noise in Lamb wave signal but filtration of interferences of wave modes and boundary reflection is not in a much matured stage and thus needs further investigation. The present study contains detailed information about various noise filtering methods, newly developed filtration technique and their efficacy in handling the above mentioned issues.

The purpose of this paper is to show how a system of differential equations of one‐dimensional transient cooling heat conduction of different multi‐layer slabs has been solved numerically. A simple deterministic filtering matrix has been developed to remove errors involved in the experimental temperature measurements.

The system of differential equations is solved through Crank‐Nicolson method using a developed computer code. The developed matrix is based on the available information about the system and is strong enough to detect and remove errors from the measured temperatures.

The filtering algorithm is very straightforward and easy to implement and needs to be developed once for a given system.

This paper shows how the governing equations of transient heat conduction of multi‐layer slabs have been solved through Crank‐Nicolson method using a developed computer code. The code is user friendly and solves a large system of simultaneous differential equations for any given composite slab configurations. Furthermore, a matrix filter has been suggested to remove experimental errors. The filter is based on the available information about the considered system. The developed matrix filter is shown to be a powerful technique to detect the noisy data and correct them while intelligent enough not to harm good data. The filtering algorithm is very straightforward and easy to implement and needs to be developed once for a given system.

The purpose of this paper is to discuss the combined effects of the deterministic surface roughness and porous structure on the lubrication property of the multi-layer bearing.

Digital filtering technique and Kozeny-Carman equation are used to simulate the random Gauss surface and the internal pore structure of the porous bearing, respectively. Effects of surface morphology, structure and pores on the lubrication property are discussed by using the finite difference method.

Results show that the lubrication performance of the multi-layer bearing increased with the increase of the surface roughness. Also, the transverse surface is better than that of the longitudinal surface. Moreover, lubricating property is getting worse with the increase of the height of each layer and the porosity. The lower permeability surface is beneficial to improve the lubrication performance when the total porosity is certain.

The effect of the Gauss roughness parameters on the detail of lubrication performance are analysed, such as the migration of the oil film rupture point position, the expansion of the pressure distribution region and the fluctuation of the pressure distribution curve with the roughness parameters. The combined effects of surface roughness, multi-layer structure and the internal pore parameters on the hydrodynamic behaviours of multi-layer porous bearing are analysed. This work is beneficial for the analysis of the tribological property and the structural design of multi-layer bearing.

The paper aims to propose an artificial neural network (ANN) in conjunction with spectral domain formulation for fast and accurate determination of the resonant frequency and quality factor of circular microstrip antenna printed on isotropic or anisotropic substrate. This neurospectral approach reduces the problem complexity.

The moment method implemented in the spectral domain provides good accuracy but its computational cost is high due to the evaluation of the slowly decaying integrals and the iterative nature of the solution process. The paper introduces the electromagnetic knowledge combined with ANN in the analysis of circular microstrip antenna on isotropic or uniaxially anisotropic substrate to reduce the complexity of the spectral approach and to minimize the CPU time necessary to obtain the numerical results.

The resonant frequency results obtained from the neural model are in very good agreement with the experimental and theoretical results available in the literature. Finally, numerical results for the substrate anisotropy effect on the resonant frequency, quality factor and radiation pattern are also presented.

The paper develops fast and accurate model based on ANN technique to calculate the resonant frequencies and quality factors of circular microstrip antennas. ANN is used to model the relationship between the parameters of the microstrip antenna and the resonant frequencies and quality factors obtained from the spectral domain approach. This relatively simple model allows designers to predict accurately the resonant frequencies and quality factors for a given design without having to develop or run the spectral method codes themselves. The main advantages of the method are: less computing time than the spectral model, results with accuracy equivalent to that of full-wave models and cost effectiveness, since the client can use a simple PC for implementation. Another advantage of the proposed ANN model is that it takes into account the uniaxial anisotropy in the substrate without increasing the network size. This is done by combining ANN with electromagnetic knowledge.

Large eddy simulation (LES) is widely used in prediction of turbulent flow. The purpose of this paper is to propose a new dynamic mixed nonlinear subgrid‐scale (SGS) model (DMNM), in order to improve LES precision of complex turbulent flow, such as flow including separation or rotation.

The SGS stress in DMNM consists of scale‐similarity part and eddy‐viscosity part. The scale‐similarity part is used to describe the energy transfer of scales that are close to the cut‐off explicitly. The eddy‐viscosity part represents energy transfer of the other scales between smaller than grid‐filter size and larger than grid‐filter size. The model is demonstrated through two examples; one is channel flow and another is surface‐mounted cube flow. The computed results are compared with prior experimental data, and the behavior of DMNM is analyzed.

The proposed model has the following characteristics. First, DMNM exhibits significant flexibility in self‐calibration of the model coefficients. Second, it does not require alignment of the principal axes of the SGS stress tensor and the resolved strain rate tensor. Third, since both the rotating part and scale‐similarity part are considered in the new model, flow with rotation and separation is easily simulated. Compared with the prior experimental data, DMNM gives more accurate results in both examples.

The SGS model DMNM proposed in the paper could capture the detail vortex characteristics more accurately. It has the advantage in simulation of complex flow, including more separations.