Reconciliation of Rotordynamic Models With Experimental Data

Abstract

A computationally efficient strategy is presented for adjusting analytic rotordynamic models to make them consistent with experimental data. The approach permits use of conventional rotordynamic models derived using finite element methods in conjunction with conventional plant identification models derived from impact or sine sweep testing in a transfer function or influence coefficient format. The underlying assumption is that the predominant uncertainties in engineered models occur at discrete points as effects like shrink fits, seal coefficients or foundation interactions. Further, it is assumed that these unmodeled or poorly modeled effects are essentially linear (at least within the testing and expected operating domains). Matching is accomplished by deriving a dynamic model for these uncertain effects such that the resulting composite model has a transfer function which matches that obtained experimentally. The derived augmentations are computationally compatible with the original rotor model and valid for stability or forced response predictions. Further, computation of this augmentation is accomplished using well developed and widely disseminated tools for modern control. Background theory and a complete recipe for the solution are supported by a number of examples.

Estimation and Active Damping of Unbalance Forces in Jeffcott-Like Rotor-Bearing Systems

“…Then, the asymptotic convergence of the tracking error e ω to zero can be achieved selecting the design parameters α 0,ω and α 1,ω such as the characteristic polynomial associated to tracking error dynamics in closed loop (19) given by…”

“…The mathematical description of rotating systems allows the possibility to predict their dynamic behavior and to use this information for the design of control algorithms in order to preserve the desired stability and dynamic performance. The accuracy of a model is determined by comparing its response and the response of the real system to the same input signal [19]. Rotor systems are subjected, in an intrinsic way, to endogenous disturbances, centrifugal forces by the inevitable unbalance phenomenon.…”

“…Some other authors have proposed the problem solution in a lumped parameters approach. Maslen et al (2002) developed a method to analytically adjust the models of rotor systems to make them consistent with experimental data under the assumption that the predominant uncertainties in the models occur at discrete points, from effects like seal coefficients or foundation interactions. The purpose of the method is to modify the engineering model such that the output of the model matches the experimental data in frequency domain.…”

Model Validation for Damage Identification and Determination of Local Damage Dynamics

“…Model reconciliation was first proposed by Maslen et al [5] which used the approach of model-based identification, i.e., comparing the response of a nominal model and the experimentally identified system, and then controlling the nominal model to minimize the difference between the two responses. Two important assumptions are utilized.…”

Active Unbalance Control of Rotor Systems Using On‐Line Algebraic Identification Methods

“…The mathematical description of mechanical systems allows the possibility predict of their dynamic behavior and using this information for design and synthesis of robust control algorithms in order to preserve the desired stability and dynamic performance . In rotordynamics the accuracy of a model is determined by comparing the response of the model and the response of the real system to the same input signal and some of the most important aspects are the presence of parameter uncertainty, exogenous and synchronous disturbances, noise, and unmodelled dynamics . Therefore, the identification and control techniques are important for modeling, simulation, and real‐time operation of rotor‐bearing systems.…”