27 Incorporating Uncertainty in the Physical Substructure During Hybrid Substructuring 239 Fig. 27.3 All FRFs obtained by evaluating at 500 sample points: (a) the true system model; and (b) the metamodel Full RTHS tests to validate the proposed method will be conducted in the Shock and Vibration Laboratory at the University of Connecticut. The physical substructure is a Lord Corporation MR damper (Model RD-1005-3). This damper is attached to a servo-hydraulic actuator system which consists of a Quincy-Ortman Cylinder with a MOOG servo-valve. The actuator is controlled with a Parker Hannifin Corporation analog controller (Model 23-7030). A PCB force sensor (Model 208C04) will be used to measure the damper force. The dynamic equations of the numerical substructure will be solved by a Speedgoat performance real-time target machine. The Speedgoat machine will also provide displacement commands to the servohydraulic actuator system. A Data Physics SignalCalc Mobilyzer dynamic signal analyzer will be used to collect numerical and physical data and compute the signal power spectral densities, frequency response functions, and coherence functions automatically. References 1. Blakeborough, A., Williams, M.S., Darby, A.P., Williams, D.M.: The development of real-time substructure testing. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 359(1786), 1869–1891 (2001) 2. Abbiati, G., Schöbi, R., Sudret, B., Stojadinovic, B.: Structural reliability analysis using deterministic hybrid simulations and adaptive kriging metamodeling. In: 16th World Conference on Earthquake Engineering, paper no. 595 (2017) 3. Ligeikis, C., Freeman, A., Christenson, R.: Assessing structural reliability at the component test stage using real-time hybrid substructuring. In: 36th International Modal Analysis Conference (2018) 4. Kwok, N.M., Ha, Q.P., Nguyen, T.H., Li, J., Samali, B.: A novel hysteretic model for magnetorheological fluid dampers and parameter identification using particle swarm optimization. Sensors Actuators A Phys. 132(2), 441–451 (2006) 5. Yaghoubi, V., Marelli, S., Sudret, B., Abrahamsson, T.: Sparse polynomial chaos expansions of frequency response functions using stochastic frequency transformation. Probab. Eng. Mech. 48, 39–58 (2017) 6. Marelli, S., Sudret, B.: UQLab: A framework for uncertainty quantification in Matlab. In: 2nd International Conference on Vulnerability, Risk Analysis and Management (ICVRAM2014), pp. 2554–2563 (2014)
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