[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.12989/sss.2016.17.2.231

Closed-form optimum tuning formulas for passive Tuned Mass Dampers under benchmark excitations

Salvi, Jonathan (Universita di Bergamo, Dipartimento di Ingegneria e Scienze Applicate)
Rizzi, Egidio (Universita di Bergamo, Dipartimento di Ingegneria e Scienze Applicate)

Publication Information

Smart Structures and Systems / v.17, no.2, 2016 , pp. 231-256 More about this Journal

Abstract

This study concerns the derivation of optimum tuning formulas for a passive Tuned Mass Damper (TMD) device, for the case of benchmark ideal excitations acting on a single-degree-of-freedom (SDOF) damped primary structure. The free TMD parameters are tuned first through a non-linear gradient-based optimisation algorithm, for the case of harmonic or white noise excitations, acting either as force on the SDOF primary structure or as base acceleration. The achieved optimum TMD parameters are successively interpolated according to appropriate analytical fitting proposals, by non-linear least squares, in order to produce simple and effective TMD tuning formulas. In particular, two fitting models are presented. The main proposal is composed of a simple polynomial relationship, refined within the fitting process, and constitutes the optimum choice. A second model refers to proper modifications of literature formulas for the case of an undamped primary structure. The results in terms of final (interpolated) optimum TMD parameters and of device effectiveness in reducing the structural dynamic response are finally displayed and discussed in detail, showing the wide and ready-to-use validity of the proposed optimisation procedure and achieved tuning formulas. Several post-tuning trials have been carried out as well on SDOF and MDOF shear-type frame buildings, by confirming the effective benefit provided by the proposed optimum TMD.

Keywords

Tuned Mass Damper (TMD); harmonic excitation; white noise excitation; tuning formulas; optimisation; nonlinear least squares; fitting models;

Citations & Related Records

Times Cited By KSCI : 6 (Citation Analysis)

Reference
Cited By KSCI

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