In order to characterize different damage modes, real-time detection of the tensile cracking process for AZ31 magnesium alloy was performed using acoustic emission （AE） technique. Results showed that elastic deforma...In order to characterize different damage modes, real-time detection of the tensile cracking process for AZ31 magnesium alloy was performed using acoustic emission （AE） technique. Results showed that elastic deformation, plastic deformation, microcracking, stable and unstable propagation occurred during crack damage. Four damage modes were determined using AE multiparameter analysis. Dislocation motion signals with amplitudes 〈70 dB and twinning signals with 70-100 dB were found. Microcrack signal energy was concentrated from 2400 aJ to 4100 aJ, mainly at a rise time of less than 800 gs. A stable crack propagation signal had high peak to counts in the 20 to 50 range, whereas its ring count was in the 20 to 2000 range. The average frequency of unstable propagation signals was approximately 100 kHz, with duration from 2000 gs to 10s gs. The damage mechanisms and AE resources from different crack propagation steps were discussed. Various damage modes could be characterized by different AE signal parameters when they appeared simultaneously during crack propagation.展开更多
Based on 1,3-propanediol production from batch fermentation of glycerol by Klebsiella pneurnoniae, a multistage dynamic system and its parameter identification are discussed in this paper. The batch fermentation proce...Based on 1,3-propanediol production from batch fermentation of glycerol by Klebsiella pneurnoniae, a multistage dynamic system and its parameter identification are discussed in this paper. The batch fermentation process is divided into three stages exhibiting different dynamic behaviors and characteristics, from which a corresponding nonlinear multistage dynamic system is built. We then propose a parameter identification optimization model whose objective function is the average relative error. The model is solved by particle swarm optimization weighted by inertia, and the result shows that the relative error of our proposed model is 2-10%smaller than those of existing models.展开更多
基金Project(2213K3170027) supported by the Shenzhen Polytechnic Project Fund,China
文摘In order to characterize different damage modes, real-time detection of the tensile cracking process for AZ31 magnesium alloy was performed using acoustic emission （AE） technique. Results showed that elastic deformation, plastic deformation, microcracking, stable and unstable propagation occurred during crack damage. Four damage modes were determined using AE multiparameter analysis. Dislocation motion signals with amplitudes 〈70 dB and twinning signals with 70-100 dB were found. Microcrack signal energy was concentrated from 2400 aJ to 4100 aJ, mainly at a rise time of less than 800 gs. A stable crack propagation signal had high peak to counts in the 20 to 50 range, whereas its ring count was in the 20 to 2000 range. The average frequency of unstable propagation signals was approximately 100 kHz, with duration from 2000 gs to 10s gs. The damage mechanisms and AE resources from different crack propagation steps were discussed. Various damage modes could be characterized by different AE signal parameters when they appeared simultaneously during crack propagation.
基金Acknowledgments This work was supported by the National Natural Science Foundation of China （Grant No. 10871033）, ＂863＂ Program （No. 2007AA02Z208） and ＂973＂ Program （No. 2007CB71430c）.
文摘Based on 1,3-propanediol production from batch fermentation of glycerol by Klebsiella pneurnoniae, a multistage dynamic system and its parameter identification are discussed in this paper. The batch fermentation process is divided into three stages exhibiting different dynamic behaviors and characteristics, from which a corresponding nonlinear multistage dynamic system is built. We then propose a parameter identification optimization model whose objective function is the average relative error. The model is solved by particle swarm optimization weighted by inertia, and the result shows that the relative error of our proposed model is 2-10%smaller than those of existing models.
