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Year 2022

Year 2019

Year 2010

  • 1D Model Callibration on the Basis on 3D calculations for Tesla Turbine

    The paper presents the system of equations for axisymmetriclaminar flow, after averaging, through the width of interdisk slit ofTesla turbine. Coefficients were introduced, as a result ofaveraging, that improve the efficiency of 1D model. The minimalnumber of such coefficients was determined. The 1D modelmakes it possible to attain analytical solutions to an accuracylimited by these coefficients. Calibration of 1D model depends...

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  • 3D seafloor reconstruction using data from side scan and synthetic aperture sonar

    Side scan and synthetic aperture sonars are widely used imaging systems in the underwater environment. They are relatively cheap and easy to deploy, in comparison with more powerful sensors, like multibeam echosounders. Although side scan and synthetic aperture sonars does not provide seafloor bathymetry directly, their records are finally related to seafloor images. Moreover, the analysis of such images performed by human eye...

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Year 2018

Year 2009

Year 2011

Year 2006

Year 2017

Year 2016

Year 2021

Year 2014

Year 2023

  • 3D Metamaterial Ultra-Wideband Absorber for curved surface
    • M. Norouzi
    • S. Jarchi
    • M. Miab
    • M. Esfandiari
    • A. Lalbakhsh
    • S. Kozieł
    • S. Reisenfeld
    • G. Moloudian

    - Scientific Reports - Year 2023

    This paper proposes a three-dimensional metamaterial absorber based on a resistive film patch array to develop a low-cost, lightweight absorber for curved surfaces. An excellent absorption over a large frequency band is achieved through two different yet controllable mechanisms; In the first mechanism, a considerable attenuation in the wave power is achieved via graphite resistive films. The absorption is then intensified through...

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  • A Bayesian regularization-backpropagation neural network model for peeling computations
    • S. Gouravaraju
    • J. Narayan
    • R. Sauer
    • S. S. Gautam

    - JOURNAL OF ADHESION - Year 2023

    A Bayesian regularization-backpropagation neural network (BRBPNN) model is employed to predict some aspects of the gecko spatula peeling, viz. the variation of the maximum normal and tangential pull-off forces and the resultant force angle at detachment with the peeling angle. K-fold cross validation is used to improve the effectiveness of the model. The input data is taken from finite element (FE) peeling results. The neural network...

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Year 2020

Year 2013

Year 2008

Year 2012