• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1454-1459
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• 2024

Researchers have applied theoretical and CFD models for years to analyze the fluidelastic instability (FEI) of tube arrays in steam generators and other heat exchangers. The accuracy of each approach has typically been evaluated using the discrepancy between the experimental critical flow velocity and the predicted value. In the best cases, the predicted critical flow velocity was within an order of magnitude comparable to the measured one. This paper revisits the quasi-steady approach for damping controlled FEI in a normal triangular array with a pitch ratio of P/d = 1.375. The method addresses the fluidelastic frequency at the stability threshold as an input parameter for the approach. The excellent agreement between the estimated stability thresholds and the equivalent experimental results suggests that the fluidelastic frequency must be included in the quasi-steady analysis, which requires minimal computing time and experimental data. In addition, the model allows a simple time delay analysis regarding flow convective and viscous effects.

• Structural Engineering and Mechanics
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• v.76 no.1
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• pp.101-114
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• 2020

In this paper, an improved experience-based learning algorithm (EBL), termed as IEBL, is proposed to solve the nonlinear hysteretic parameter identification problem with Bouc-Wen model. A quasi-opposition-based learning mechanism and new updating equations are introduced to improve both the exploration and exploitation abilities of the algorithm. Numerical studies on a single-degree-of-freedom system without/with viscous damping are conducted to investigate the efficiency and robustness of the proposed algorithm. A laboratory test of seven lead-filled steel tube dampers is presented and their hysteretic parameters are also successfully identified with normalized mean square error values less than 2.97%. Both numerical and laboratory results confirm that, in comparison with EBL, CMFOA, SSA, and Jaya, the IEBL is superior in nonlinear hysteretic parameter identification in terms of convergence and accuracy even under measurement noise.