فهرست مطالب

Journal of Theoretical and Applied Vibration and Acoustics
Volume:8 Issue: 2, Summer & Autumn 2022

  • تاریخ انتشار: 1401/08/20
  • تعداد عناوین: 2
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  • Acoustical Analysis of a Structure with an Auxetic Honeycomb and Internal Resonator
    Mostafa Khosroupour Arabi *, Roohollah Talebitooti Pages 0-0
    Metamaterials are manmade materials used due to their different properties originating from their geometry. Sound attenuation is a property, which depends on structure geometry; the small structures could reduce sound propagation within a mid-high frequency. To change the range of sound propagated in metamaterial from high-frequency to low-frequency, however, internal resonators could be used due to their rotational vibration having highly effect on sound attenuation. In this paper, an acoustical analysis is done on a hexachiral lattice structure with an internal resonator showing the ability to decrease sound wave propagation among the structure in the low-frequency range and causes corresponding bandgaps in this frequency range (1-4500 Hz). Geometry parameters that can affect the width and range of bandgaps are studied including the radius of the resonator, the thickness of the ligament, and the distance between each node. However, the radius of the resonator has a positive impact on the ability of attenuation, but the distance between each node has a more negative impact on the bandgap. Optimization is done on the geometric parameters considering the weight of the structure, which leads us to the construction of light structures capable of reducing sound propagation.
    Keywords: Metamaterial, Auxetic Honeycomb Structures, Internal Resonator, Band Gap
  • Mohamad Gohari *, Abbas Pak, Masoud Kazemi Pages 1-20
    Engine failure is a significant issue for drivers, often requiring substantial experience to identify and troubleshoot effectively. Repairing the engine based on probable causes and uncertainties can be time-consuming and costly. Recently, AI models, particu+
    larly those based on Artificial Neural Networks (ANN), have been developed and gained popularity in fault diagnosis. This paper considers two common faults in internal combustion engines - cylinder misfire and complete cylinder failure - caused by ignition system issues. An Artificial Neural Network fed by Statistical features (SANN) is employed to distinguish these two faults. The SANN was trained on statistical features derived from vibration signals and achieved an accuracy of over 90%. Thus, SANN can classify the fault generated by the ignition system. This model was further validated using a different engine as a second case, demonstrating its ability to predict fault types with acceptable accuracy. In fact, the SANN could find a malfunction of the engine mounted on a car perfectly. This capability enables operators to accurately identify the type of fault, allowing for more precise and efficient repairs. Therefore, the proposed method is well-suited for troubleshooting ignition system malfunctions and diagnosing related issues via a reliable fault detection model
    Keywords: Malfunction Of Ignition System, Artificial Intelligent Model, Statistical Features, Internal Combustion Engine