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Research on time-frequency multi-scale characteristics of high-power giant magnetostrictive underwater transducers
Husheng Li1,2, Bing Gao2, Zhixing He2
1School of Energy and Electrical Engineering, Hunan Institute of Science and Technology, Yueyang 414000, China.
Abstract:
High-power giant magnetostrictive underwater transducers are integral to underwater active sonar detection systems due to their high energy density, rapid dynamic response, and significant output force. However, these transducers exhibit complex nonlinear dynamic hysteresis behavior, which is influenced by the coupling of electric, magnetic, mechanical, and acoustic fields. This complexity presents considerable challenges in accurately characterizing their output properties. To address this issue, a comprehensive equivalent circuit model considering the hysteresis nonlinearity has been developed to accurately represent the time-frequency characteristics of the transducer. Initially, the proposed model utilizes an analytical equation to calculate both the bias magnetic field and the AC-driven magnetic field, thereby facilitating the analysis of the magnetic field distribution within the high-power giant magnetostrictive underwater transducer (HGMUT). Subsequently, an enhanced Preisach hysteresis model is employed to characterize the dynamic magnetic-mechanical strain relationship of the giant magnetostrictive material rods. Following this, a dynamic equation is established to ascertain the output displacement and force of the transducer. Moreover, a comprehensive equivalent circuit that includes mechanical-acoustic coupling is constructed to analyze the frequency domain transmitting current response and the time-domain acoustic signal of the transducer. Finally, a prototype of the high-power transducer has been successfully fabricated and tested, achieving a resonant frequency of approximately 1 kHz and a maximum transmitting current response of 187 dB. The experimental results indicate that the proposed model aligns closely with the experimental data, effectively capturing and predicting the output time-frequency characteristics of the transducer.
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