Test analysis and numerical simulation of noise from forage crushers

Zhang, Baoyuan; Zhai, Zhigang; Xuejian, Lun; Cui, Hongmei; Lan, Yuezheng

doi:10.1016/j.apacoust.2022.108873

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…Therefore, this value can be negligible for the final noise superposition results. That is, the effect of background noise in the actual measurement can be ignored [35].…”

Section: Background Noisementioning

confidence: 99%

Experimental Study on the Noise Evolution of a Horizontal Axis Icing Wind Turbine Based on a Small Microphone Array

Sun

Cui

et al. 2022

Sustainability

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In recent years, the global energy mix is shifting towards sustainable energy systems due to the energy crisis and the prominence of ecological climate change. Wind energy resources are abundant in cold regions, and wind turbines are increasingly operating in cold regions with wet natural environments, increasing the risk of wind turbine blade icing. To address the problem of noise source distribution and the frequency characteristic variation of wind turbines in natural icing environments, this paper uses a 112-channel microphone array to acquire the acoustic signals of a horizontal axis wind turbine with a diameter of 2.45m. Using the beamforming technique, the wind turbine noise evolution law characteristics under natural icing environment were studied by field experiments, and the noise source distribution and noise increase in different frequency bands under different icing mass and positions and different angles of attack were analyzed in detail. The results show that under the leading-edge and windward-side icing, the noise source gradually moves toward the blade tip along the spanwise direction with the increase in ice mass. In addition, the total sound pressure level at 460 r/min, 520 r/min, 580 r/min, and 640 r/min are increased by 0.82 dB, 0.85 dB, 0.91 dB, and 0.95 dB, respectively for the leading-edge icing condition in comparison with the uniform icing over the windward side of the blade.

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“…Therefore, this value can be negligible for the final noise superposition results. That is, the effect of background noise in the actual measurement can be ignored [35].…”

Section: Background Noisementioning

confidence: 99%

Experimental Study on the Noise Evolution of a Horizontal Axis Icing Wind Turbine Based on a Small Microphone Array

Sun

Cui

et al. 2022

Sustainability

Self Cite

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“…The absence of consideration for the optimization method in the process of multiobjective optimization in the aforementioned studies could compromise the authenticity of the optimization results. Zhang and colleagues [10] employed the acoustic-solid coupling method to numerically predict the combined air and mechanical noise of the forage crusher, enabling the identification of the primary noise sources. The feasibility of the numerical prediction method for the coupled noise of the forage crusher was verified, and the prediction results were validated through tests, establishing their credibility.…”

Section: Introductionmentioning

confidence: 99%

Simulation Analysis and Multiobjective Optimization of Pulverization Process of Seed-Used Watermelon Peel Pulverizer Based on EDEM

Mou,

Wan,

et al. 2024

Agriculture

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To enhance the utilization of seed-used watermelon peel and mitigate environmental pollution, a hammer-blade seed-used watermelon peel crusher was designed and manufactured, and its structure and working parameters were optimized. Initially, the seed-used watermelon peel crusher and seed-used watermelon peel model were constructed, and the model’s parameters were calibrated. Subsequently, the discrete element method (EDEM2022) was employed to investigate the effects of spindle speed (MSS), the number of hammers (NCB), and feeding volume (FQ) on the pulverizing process. Multivariate nonlinear regression prediction models were developed for the percentage of pulverized particle size less than 8 mm (Psv), pulverizing efficiency (Ge), and power density (Ppd), followed by the analysis of influencing factors and prediction models using ANOVA. The multiobjective optimization of the prediction model utilizing the improved hybrid metacellular genetic algorithm CellDE resulted in solutions of 90.02%, 89.57%, and 8.35 × 10−3 t/(h-kw) for Psv-opt, Ge-opt, and Ppd-opt, respectively. The corresponding optimal interaction values of MSS, NCB, and FQ were determined to be 1500 r/min, 108, and 150 kg/min. Finally, a prototype test was conducted by combining the optimal factor interaction values, yielding statistically calculated values of 96.63%, 92.37%, and 7.76 × 10−3 t/(h-kw) for Psv-pr, Ge-pr, and Ppd-pr, respectively. The results indicate that the optimized values of Psv-opt, Ge-opt, and Ppd-opt models have an error of less than 8% compared to the statistically calculated values of the prototype test and outperform the values of Psv-ori, Ge-ori, and Ppd-ori obtained under the original parameters.

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Numerical prediction and optimization of aerodynamic noise of straw crushers by considering the straw-crushing process

Zhao,

Zhai,

Gao

et al. 2024

Physics of Fluids

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Straw crops are struck and broken into soft filaments by the high-speed rotating hammers of straw crushers, which disturb the internal airflow field and generate much noise during the operation of straw crushers. To accurately estimate and reduce the aerodynamic noise of straw crushers at the design stage, in this study, first, the coupling method of the discrete element method, bonded-particle model, and computational fluid dynamics were used to obtain the acoustics source data. Next, the Ffowcs Williams–Hawkins theory and the indirect boundary element method were used to predict the aerodynamic noise generated during the straw crushing process. The multi-island genetic algorithm was used to optimize the aerodynamic noise of straw crushers. The results indicate that the simulated and measured total sound pressure levels (TSPLs) at the outlet and inlet differed by 1.43 and 2.12 dB(A), respectively. Additionally, aerodynamic noise at the inlet appears to be primarily influenced by the sound pressure level at the excitation fundamental frequency, while noise at the outlet is primarily influenced by the sound pressure level at the double frequency. Higher sound pressure levels were mainly concentrated at the fundamental frequency and its lower harmonic frequencies, and the sound pressure level gradually decreased with the increase in the frequency. After optimization, the aerodynamic noise TSPL at the inlet decreased from 100.87 to 88.58 dB(A) and at the outlet decreased from 102.26 to 89.62 dB(A). This study provides a methodological reference for aerodynamic noise prediction and the design of low-noise straw crushers.

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Test analysis and numerical simulation of noise from forage crushers

Cited by 4 publications

References 9 publications

Experimental Study on the Noise Evolution of a Horizontal Axis Icing Wind Turbine Based on a Small Microphone Array

Experimental Study on the Noise Evolution of a Horizontal Axis Icing Wind Turbine Based on a Small Microphone Array

Simulation Analysis and Multiobjective Optimization of Pulverization Process of Seed-Used Watermelon Peel Pulverizer Based on EDEM

Numerical prediction and optimization of aerodynamic noise of straw crushers by considering the straw-crushing process

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