Showing papers by "Herman Van der Auweraer published in 2007"

Operational modal analysis for estimating the dynamic properties of a stadium structure during a football game

Abstract: During a football game, the ambient vibrations at the roof of a football stadium were recorded. A very large data set consisting of 4 hours of data, sampled at 80 Hz, is available. By a data reduction procedure, the complete data set could be analysed at once in a very short time. The data set was also split in shorter segments corresponding to certain events before, during and after the game to investigate the influence of varying operational conditions on the dynamic properties.

Removing disturbing harmonics in operational modal analysis

Abstract: In Operational Modal Analysis applications, it is assumed that the structure is excited by white noise. However, in some cases, the operational vibration data are acquired while rotating equipment is active in the background or while it is even the main source of excitation. The structural responses will then consists of a broadband response from which the structural modes can be determined and additional harmonic response at discrete frequencies, which are disturbing the parameter identification process. Sometimes, the harmonic response is dominating and the Operational Modal Analysis methods only find poles at these harmonic frequencies. Therefore, it is desired to try to remove the disturbing harmonics from the data before applying Operational Modal Analysis. In this paper, a method that serves this purpose will be discussed. If the fundamental frequency of the disturbing harmonics is not known, it will be estimated by applying a “tacho-less rpm extraction” procedure. Using the (possibly fluctuating) rpm, the data can be converted to the angle domain and, then time (or better: angle) synchronous averaging is applied to remove the harmonics. This procedure will be illustrated using simulated data as well as real industrial operational data from an in-flight helicopter test and from a running large diesel engine.

Virtual Prototyping for Sound Quality Design of Automobiles

Abstract: During the last few years, promising research results have been obtained for smart materials and active control concepts. In order to bring these research results to real-world applications, related design processes have to become a part of the complete product creation process. This requires that product functional performance simulation models, which are the cornerstone of today’s design process, must support advanced materials, active systems, actuators, sensors and controls and integrate these into system level virtual prototype models. To go from acoustic design to sound quality design, the actual temporal and spectral signal structures from the controlled sound need to be optimized to meet sound quality targets.

Automatic modal analysis: reality or myth?

Abstract: The increasing use of modal analysis as a standard tool means that both experienced and inexperienced analysts are faced with new challenges: uncertainty about the accuracy of results, inconsistency between estimates of different operators, the tedious task of selecting obvious poles in a stabilization diagram and the time-consuming iterations required to validate a modal model. Therefore, it is no surprise that considerable research efforts are spent to overcome these difficulties. A few years ago, the PolyMAX modal parameter estimation method was introduced which makes the modal parameter estimation process much easier by better discriminating spurious from physical poles. Nevertheless, the route to automation still requires discrimination methods to distinguish physical from mathematical poles, in particular in the case of high-order and/or highly damped structures. This paper discusses an approach for automating the modal parameter estimation process and its industrial validation. 1 INTRODUCTION: THE CHALLENGE OF INDUSTRIAL MODAL ANALYSIS The vibration and acoustical behavior of a mechanical structure is determined by its dynamic characteristics. This dynamic behavior is typically described with a linear system model. The inputs to the system are forces (“loads”), the outputs the resulting displacements or accelerations. System poles usually occur in complex conjugate pairs, corresponding to structural vibration “modes”. The pole’s imaginary part relates to the resonance frequency and the real part to the damping. Structural damping is typically very low (a few percent of the critical damping). The system’s eigenvectors, expressed in the basis of the structural coordinates correspond to characteristic vibration patterns or “mode shapes”. System identification from input-output measurements yields the modal model parameters [1][2]. This approach is now a standard part of the mechanical product engineering process. Several constraints however make the system identification process for structural dynamics largely different from this in electrical engineering or process control. A review of the specific challenges of system identification for structural dynamics is given in [3]. A key issue is the difficulty of selecting the correct model order and the corresponding validation of the obtained system poles. First of all, a continuous structure has an infinite number of modes. In practice, the analyst is interested only in a limited number of these, up to a certain frequency or only in a certain frequency band. Still, model orders over 100 are no exception. Furthermore, while some of the modes are separated in resonance frequency, others may be very close leading to highly overlapping responses. The standard approach of selecting a model order and then deriving the corresponding poles is in general not applicable and over-specification of the model order is needed. Finally, the size of the problem often requires more than 1000 responses to be processed (e.g. a car body is discretized by over 500 nodes, measured in 3 directions), and this using large data segments to reduce the measurement noise. The consequence of these constraints is that classical system identification approaches, extracting the parameters of a discrete-time statespace model or of an ARMA model directly from the sampled input-output data, are often not practical or not feasible. Specific procedures are hence needed for modal analysis.

Simulation-Based Optimization of an Active Noise and Vibration Control Solution

Abstract: Active control appears a feasible solution to many noise and vibration problems. To bring present research results to industrial use, the related design approach must become part of the industrial product creation process. This requires the product’s CAE models to support the specific aspects related to advanced materials, actuators, sensors and control. To be of practical use in solving industrial problems, a constraint is that the simulations must as much as possible make use of standard available simulation tools such as major FE/BE and MultiBody Simulation (MBS) codes, 1-D simulation tools etc. The most challenging element hereto is to link the different worlds of 1-D control simulation and 3-D geometrybased structural/vibro-acoustic simulation into system-level models. The paper addresses as main approach the reduction of the structural & vibro-acoustic models into a 1-D state space representation. Alternative approaches such as the integration of control in FE and the co-simulation between structural and control models are also briefly addressed. The applied approach is demonstrated on the InMAR “Concrete Car”, where the optimization of the control approach for an active firewall solution is performed, taking into account multi-objective design criteria. The research is executed in the context of the EC-FP6 Integrated Project InMAR. 1. THE MODELING PROBLEM Shortening development cycles, reducing design costs and at the same time improving product performances requires that the correct design decisions are made as early as possible in the design process. In recent years, we have seen major progress hereto, based on the extensive use of a virtual prototyping approach, which allows to optimize the product behavior in all its aspects even before that the first physical prototypes are available. The cornerstones of such approach are performance simulation models, the parameters of which are derived in multiattribute optimization schemes. Also for design solutions making use of smart systems technology, the use of such simulation-based optimization approach is a prerequisite to identify the optimal configuration and approach. Typical design choices include the selection between structural or acoustic control, the location and number of actuators and sensors, the selection of the correct material and dimensional parameters and the selection of the controller algorithms and settings. A schematic view of an actively controlled mechanical structure is shown in Fig. 1 [1]. The basic parts that are to be included in the model are the structure itself (including where appropriate acoustic cavity and vibro-acoustic effects), mechanical and electronic parts of the actuators and sensors and related circuits and finally of course the controller.