Internal friction and Young’s modulus were measured for pure iron and a high carbon steel. Temperature dependent measurements in pure iron show a strain dependent component to internal friction below the Curie temperature. It can be attributed to the magneto-mechanical damping as confirmed by amplitude dependent measurements at room temperature with and without magnetic field. The bcc to fcc phase transition on further heating is clearly marked by internal friction peaks in both iron and steel. The effects of cementite dissolution and precipitation in steel are visible on thermal cycling at even higher temperatures.
This study investigates the dual solution existence and the associated temporal stability of magnetohydrodynamic hybrid nanofluid (HNF) flow over a bidirectional shrinking sheet. The working fluid consists of an Al₂O₃–Cu/water HNF subjected to suction, magnetic field effects, and porous medium resistance. The reduced coupled ordinary differential governing equations are solved using the Galerkin weighted residual method. A temporal stability analysis based on eigenvalue formulation is conducted to distinguish physically realizable solutions from unstable ones. The effects of emerging parameters on the skin friction coefficients and the Nusselt number are examined with the concentration given on magnetic effect. The results reveal the dual solution existence in a critical range of suction values, beyond which no solutions exist, indicating boundary-layer separation. Stability analysis confirms that only the upper-branch solution is stable and physically admissible. Enhanced magnetic field strength, delay boundary-layer separation and improve thermal performance. The present results show excellent agreement with previously reported limiting cases, demonstrating the accuracy and robustness of the proposed method.
The impact of periodic mechanical vibration on the phase transitional property of VO 2 based thermal sensing thin film synthesized on AT-cut quartz has been investigated in this paper. VO 2 were found to go through phase transition due to effect of heat, and display reduction of film resistivity and transmitted infrared power at a specific transition temperature, typically around 60-70°C. But sometimes the transmitted infrared power has been observed to decrease at a significantly lower temperature compared to the similar phenomenon for resistivity reduction. In order to correctly validate the transition temperature, the derivative of the transmitted optical power signal is measured, and it has been found that this measurement is experimentally possible when the VO 2 film is subjected to a moderate to high frequency periodic mechanical vibration. It has also been observed that the variation of the mechanical vibration frequency has a direct effect on the derivative signal, which indicates the sharpness of the phase transition and magnitude of change of the transmitted infrared power depending on the width of the extrema lobe of the derivative.
This work focuses on the modeling and prediction of the optical absorption coefficient in GaAs 2D nanostructure subjected to hydrostatic pressure. The database is generated from numerical calculations describing the optical absorption of exciton confined in GaAs 2D nanostructure. The obtained data are exploited to develop predictive models based on machine learning such as Decision Tree and Gradient Boosting techniques, in order to establish an accurate relationship between the incident photon energies and the optical absorption coefficient. The quality of the proposed approaches is examined using standard statistical metrics, considering the mean absolute, squared errors, and root mean squared error, as well as the coefficient of determination. The obtained results demonstrate an excellent agreement between the predicted and numerical values, with very low errors and strong generalization capability. these approaches therefore represent an efficient alternative to conventional numerical methods for the rapid prediction of the optical properties of discoidal quantum dots and offers promising perspectives for the optimization of optoelectronic devices.
