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This paper aims to propose a nonlinear model for aeroelastic aircraft that can predict the flight parameters throughout the investigated flight envelopes.

A system identification method based on the support vector machine (SVM) is developed and applied to the nonlinear dynamics of an aeroelastic aircraft. In the proposed non-parametric gray-box method, force and moment coefficients are estimated based on the state variables, flight conditions and control commands. Then, flight parameters are estimated using aircraft equations of motion. Nonlinear system identification is performed using the SVM network by minimizing errors between the calculated and estimated force and moment coefficients. To that end, a least squares algorithm is used as the training rule to optimize the generalization bound given for the regression.

The results confirm that the SVM is successful at the aircraft system identification. The precision of the SVM model is preserved when the models are excited by input commands different from the training ones. Also, the generalization of the SVM model is acceptable at non-trained flight conditions within the trained flight conditions. Considering the precision and generalization of the model, the results indicate that the SVM is more successful than the well-known methods such as artificial neural networks.

In this paper, both the simulated and real flight data of the F/A-18 aircraft are used to provide aeroelastic models for its lateral-directional dynamics.

This paper proposes a non-parametric system identification method for aeroelastic aircraft based on the SVM method for the first time. Up to the author’s best knowledge, the SVM is not used for the aircraft system identification or the aircraft parameter estimation until now.

Water/Al₂O₃ nanofluid with volume fractions of 0, 0.3 and 0.06 was investigated inside a rectangular microchannel. Jet injection of nanofluid was used to enhance the heat transfer under a homogeneous magnetic field with the strengths of Ha = 0, 20 and 40. Both slip velocity and no-slip boundary conditions were used.

The laminar flow was studied using Reynolds numbers of 1, 10 and 50. The results showed that in creep motion state, the constricted cross section caused by fluid jet is not observable and the rise of axial velocity level is only because of the presence of additional size of the microchannel. By increasing the strength of the magnetic field and because of the rise of the Lorentz force, the motion of fluid layers on each other becomes limited.

Because of the limitation of sudden changes of fluid in jet injection areas, the magnetic force compresses the fluid to the bottom wall, and this behavior limits the vertical velocity gradients. In the absence of a magnetic field and under the influence of the velocity boundary layer, the fluid motion has more variations. In creeping velocities of fluid, the presence or absence of the magnetic field does not have an essential effect on Nusselt number enhancement.

In lower velocities of fluid, the effect of the jet is not significant, and the thermal boundary layer affects the entire temperature field. In this case, for Hartmann numbers of 40 and 0, changing the Nusselt number on the heated wall is similar.

This paper aims to propose a new nonlinear function estimation fuzzy system as a novel statistical approach to estimate nanofluids’ thermal conductivity.

A fuzzy system having a product inference engine, a singleton fuzzifier, a center average defuzzifier and Gaussian membership functions is proposed for this purpose.

Results indicate that the proposed fuzzy system can predict the thermal conductivity of Al₂O₃/paraffin nanofluid with appropriate precision and generalization and it also outperforms the classic interpolation methods.

A new nonlinear function estimation fuzzy system was introduced as a novel statistical approach to estimate nanofluids’ thermal conductivity for the first time.

This paper aims to simulate the nanofluid forced convection in a microchannel. According to the results, at high Reynolds numbers and higher nanofluid volume fractions, an increase in the rib height and slip coefficient further improved the heat transfer rate. The ribs also affect the flow physics depending on the Reynolds number so that the slip velocity decreases with increasing the nanofluid volume fraction and rib height.

Forced heat transfer of the water–copper nanofluid is numerically studied in a two dimensional microchannel. The effects of the slip coefficient, Reynolds number, nanofluid volume fraction and rib height are investigated on the average Nusselt number, slip velocity on the microchannel wall and the performance evaluation criterion.

In contrast, the slip velocity increases with increasing the Reynolds number and slip coefficient. Afterwards, a non-parametric function estimation is performed relying on the artificial neural network.

Finally, the Genetic Algorithm was used to establish a set of optimal decision parameters for the problem

This paper aims to present a black-box fuzzy system identification method coupled with genetic algorithm optimization approach to predict the mixture thermal conductivity at dissimilar temperatures and nanoparticle concentrations, in the examined domains.

WO3 nanoparticles are dispersed in the deionized water to produce a homogeneous mixture at various nanoparticles mass fractions of 0.1, 0.5, 1.0 and 5.0 Wt.%.

The results depicted that the models not only have satisfactory precision, but also have acceptable accuracy in dealing with non-trained input values.

The transmission electron microscopy is applied to measure the mean diameters, shape and morphology of the dry nanoparticles. Moreover, the stability of nanoparticles inside the water is evaluated by using zeta potential and dynamic light scattering (DLS) tests. Then, the prepared nanofluid thermal conductivity is presented at different values of temperatures and concentrations.

This paper aims to present a new method for identification of some flight modes, including natural and non-standard modes, and extraction of their characteristics, directly from measurements of flight parameters in the time domain.

The Hilbert-Huang transform (HHT), as a novel prevailing tool in the signal analysis field, is used to attain the purpose. The study shows that the HHT has superior potential capabilities to improve the airplane flying quality analysis and to conquer some drawbacks of the classical method in flight dynamics.

The proposed method reveals the existence of some non-standard modes with small damping ratios at non-linear flight regions and obtains their characteristics.

The paper examines only airplane longitudinal dynamics. Further research is needed regarding lateral-directional dynamic modes and coupling effects of the longitudinal and lateral modes.

Application of the proposed method to the flight test data may result in real-time flying quality analysis, especially at the non-linear flight regions.

First, to utilize the empirical mode decomposition (EMD) capabilities in real time, a local-online algorithm is introduced which estimates the signal trend by the Savitzky-Golay sifting process and eliminates it from the signal in the EMD algorithm. Second, based on the local-online EMD algorithm, a systematic method is proposed to identify flight modes from flight parameters in the time domain.

The routing algorithm is one of the most important components in designing a network-on-chip (NoC). An effective routing algorithm can cause better performance and throughput, and thus, have less latency, lower power consumption and high reliability. Considering the high scalability in networks and fault occurrence on links, the more the packet reaches the destination (i.e. to cross the number of fewer links), the less the loss of packets and information would be. Accordingly, the proposed algorithm is based on reducing the number of passed links to reach the destination.

This paper presents a high-performance NoC that increases telecommunication network reliability by passing fewer links to destination. A large NoC is divided into small districts with central routers. In such a system, routing in large routes is performed through these central routers district by district.

By reducing the number of links, the number of routers also decreases. As a result, the power consumption is reduced, the performance of the NoC is improved, and the probability of collision with a faulty link and network latency is decreased.

The simulation is performed using the Noxim simulator because of its ability to manage and inject faults. The proposed algorithm, XY routing, as a conventional algorithm for the NoC, was simulated in a 14 × 14 network size, as the typical network size in the recent works.

The purpose of this paper is to develop an inverse network data envelopment analysis (INDEA) model to solve resource allocation problems.

The authors estimate inputs’ variations based on outputs so that the efficiencies of decision-making unit under evaluation (DMU_o) and other decision-making units (DMUs) are constant.

The new INDEA model is developed to allocate resources such that inputs are not increased while efficiency scores of all DMUs remain constant. Furthermore, the authors obtain new combinations of inputs and outputs, together with a growth in efficiency score of DMU_o such that efficiency scores of other DMUs are not changed. A case study is provided.

This paper proposes INDEA model to estimate inputs (outputs) without changing efficiency scores of DMUs.