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2022 | Buch

Handbook of Experimental Structural Dynamics

herausgegeben von: Randall Allemang, Peter Avitabile

Verlag: Springer New York

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

The SEM Handbook of Experimental Structural Dynamics stands as a comprehensive overview and reference for its subject, applicable to workers in research, product design and manufacture, and practice. The Handbook is devoted primarily to the areas of structural mechanics served by the Society for Experimental Mechanics IMAC community, such as modal analysis, rotating machinery, structural health monitoring, shock and vibration, sensors and instrumentation, aeroelasticity, ground testing, finite element techniques, model updating, sensitivity analysis, verification and validation, experimental dynamics sub-structuring, quantification of margin and uncertainty, and testing of civil infrastructure. Chapters offer comprehensive, detailed coverage of decades of scientific and technologic advance and all demonstrate an experimental perspective. Several sections specifically discuss the various types of experimental testing and common practices utilized in the automotive, aerospace, and civil structures industries.

· History of Experimental Structural Mechanics

· DIC Methods - Dynamic Photogrammetry

· LDV Methods

· Applied Digital Signal Processing

· Introduction to Spectral - Basic Measurements

· Structural Measurements - FRF

· Random and Shock Testing

· Rotating System Analysis Methods *

· Sensors Signal Conditioning Instrumentation

· Design of Modal Tests

· Experimental Modal Methods

· Experimental Modal Parameter Evaluation

· Operating Modal Analysis Methods *

· Analytical Numerical Substructuring

· Finite Element Model Correlation

· Model Updating

· Damping of Materials and Structures

· Model Calibration and Validation in Structures*

· Uncertainty Quantification: UQ, QMU and Statistics *

· Nonlinear System Analysis Methods (Experimental)

· Structural Health Monitoring and Damage Detection

· Experimental Substructure Modeling

· Modal Modeling

· Response (Impedance) Modeling

· Nonlinear Normal Mode Analysis Techniques (Analytical) *

· Modal Modeling with Nonlinear Connection Elements (Analytical)

· Acoustics of Structural Systems (VibroAcoustics) *

· Automotive Structural Testing *

· Civil Structural Testing

· Aerospace Perspective for Modeling and Validation

· Sports Equipment Testing *

· Applied Math for Experimental Structural Mechanics

* Chapter Forthcoming

Contributions present important theory behind relevant experimental methods as well as application and technology. Topical authors emphasize and dissect proven methods and offer detail beyond a simple review of the literature. Additionally, chapters cover practical needs of scientists and engineers who are new to the field. In most cases, neither the pertinent theory nor, in particular, the practical issues have been presented formally in current academic textbooks. Each chapter in the Handbook represents a ’must read’ for someone new to the subject or for someone returning to the field after an absence. Reference lists in each chapter consist of the seminal papers in the literature.

This Handbook stands in parallel to the SEM Handbook of Experimental Solid Mechanics, where this Handbook focuses on experimental dynamics of structures at a macro-scale often involving multiple components and materials where the SEM Handbook of Experimental Solid Mechanics focuses on experimental mechanics of materials at a nano-scale and/or micro-scale.

Inhaltsverzeichnis

Frontmatter

Sensors and Measurements

Frontmatter

1. Recent History of Experimental Structural Dynamics

Abstract

This chapter on the recent history of experimental structural dynamics puts much of the Handbook in a historical perspective that begins with the development of digital data methodology and computerized data processing that began in the mid-1960s. Experimental structural dynamics began much earlier with analog, single frequency data acquisition and mostly visual data processing that began in the 1800s with the rail and marine industries, particularly when the steam engine impacted those technologies. The analog, single frequency data acquisition methodology, continued in the automotive and the aircraft industries in the first half of the 1900s. This Handbook mostly chronicles data acquisition and processing methods that began more recently, in the mid-1960s, with the advent of the Fourier transform, analog to digital data conversion, and digital minicomputers to the present time period. The Handbook also discusses many methods and techniques in use during the 1960s and 1970s that utilize experimentally derived models, both linear and nonlinear, to calibrate and validate corresponding analytical models. Part of this discussion includes the issue of the varying dimensionality of the number of degrees of freedom (DOF) between experimental and analytical models. This chapter also discusses the researchers and educators that were part of the development of the experimental structural dynamics methodology in the 1960s to 1980s that led to the current technical state of the art. This discussion includes the identification of researchers and educators that were instrumental to the Society for Experimental Mechanics (SEM) in the development of this area of interest within the Society over the last 50 years.

R. J. Allemang

2. Sensors and their Signal Conditioning for Dynamic Acceleration, Force, Pressure, and Sound Applications

Abstract

The continued demand for efficiency, reliability, and lower operating costs together with the general increase in awareness of the effects of vibration and noise in the workplace has demanded a better understanding of the causes and characteristics of the vibration of machines and structures in many industries. This, coupled with advances in transducer technology, electronics, and signal processing power, has led to a wealth of commercial products for performing structural dynamics measurements.

Structural dynamics is the response of a structural system to dynamically imposed loads. Undesirable responses can cause suboptimum performance or structural failure. Understanding the relationships between forces and responses may involve in-service operational measurements or the creation of simulated environments in the laboratory. Analytical modeling verified by experimental modal analysis is a frequently employed tool to understand and/or adjust structural dynamics before the introduction of a product into service.

Successful testing depends on the accurate measurement of the dynamic force and pressure loading encountered by the structure as well as the response of the structure. The common electromechanical transducers that are used for these measurements are pressure transducers and microphones, load cells or force transducers, strain gages, and accelerometers. The signals from these devices are amplified and filtered in a way that preserves their fidelity over the required bandwidth and amplitude levels. Proper use depends on wise selection, informed by the knowledge of the required data and the limitations of the products. This chapter provides a guide to the sensing technologies and electronics available to make these measurements and their use.

Gary Foss, Jessica Meloy, Mark Valentino, Patrick Walter

3. Laser Doppler Vibrometry Measurements in Structural Dynamics

Abstract

Laser vibrometry is a powerful tool for measurement of vibration on a variety of structures. Lasers do not mass-load or otherwise change the dynamics of a structure, and so they have enabled measurements from surfaces that are too light, delicate, hot, etc. to allow conventional surface mounted sensors. The position of the measurement point can also be changed readily. Thus, laser vibrometry has also allowed acquisition of measurements over a dense grid of points, to more completely characterize a structure its deformation shapes, the evolution of stress waves, or the identification of structural damages than it might be feasible with other methods. The chapter is divided in two sections: the first one is intended to provide an insight about the theory behind laser Doppler vibrometry (LDV), while the second section aims at giving an overview of the different types of laser Doppler vibrometers that have been developed so far. The chapter is not intended to give a comprehensive discussion of laser Doppler vibrometry, but it provides sufficient details about potentials, issues, and best practice approaches for successfully exploiting such technique in structural dynamics testing. References are provided to direct the interested reader to more detailed information as well as to examples of application cases.

Paolo Chiariotti, Christian Rembe, Paolo Castellini, Matt Allen

4. Applied Digital Signal Processing

Abstract

This chapter contains a broad discussion of digital signal processing techniques as applied to the solution of mechanical problems, primarily by analyzing the vibration responses of a machine or structure. Such responses are always a combination of a set of excitation functions and structural response or transfer functions, and the aim of the analyst is usually to separate them and learn their characteristics, for purposes such as structural analysis, primarily concerned with changes in the latter, and machine condition monitoring and diagnostics, primarily concerned with changes in the former, but possibly in both. Since a number of other chapters are mainly concerned with structural analysis, the reader is referred to those for some specialized treatments.

The chapter first introduces a number of idealized signal types, including their definitions and basic analysis methods, then gives a guide to the optimum choice of such models to apply in practical situations, such as for modal analysis and condition monitoring.

A very important section deals with the two types of blind separation required for complete analysis; first the separation of the various independent sources acting on the machine or structure, and then the identification of the different transfer functions by which the responses to these different sources are modified at the various measurement points. Topics include separation by filtering, blind extraction, blind deconvolution, and separation of responses to different sources, including those distinguished by different characteristics (e.g. deterministic or random), or by virtue of statistical independence.

There is a comprehensive discussion of analysis in different domains, or domain pairs, such as time, frequency, and joint time-frequency, but also the recognition that with variable speed machines, it is often best to represent “time” as rotation angle, with corresponding "frequency" in terms of harmonic order. A topic that has become much more important in recent years is the recognition that many machine signals are stochastic, but with random carrier signals that are modulated by deterministic modulation functions, usually linked to machine speed, which can be extracted and identified, even though seemingly hidden in normal signal representations. With constant speed machines, such signals are “cyclostationary”, but with varying speed are termed “cyclo-non-stationary”.

Most of these approaches are demonstrated by applying them to three quite different, but very important examples of machine diagnostics, namely for rolling element bearings, gears and reciprocating machines and engines.

Finally, a topic which is not widely known, cepstrum analysis, is presented in some detail, because of its very powerful properties in both source separation and structural analysis, with examples of application to machine diagnostics and modal analysis.

Robert B. Randall, Jerome Antoni, Pietro Borghesani

5. Introduction to Spectral and Correlation Analysis: Basic Measurements and Methods

Abstract

Spectral analysis is one of the most important tools used in experimental structural dynamics. This can be partly explained by the fact that the output of a linear system in the frequency domain, at each frequency, is equal to the product of the input spectrum at that frequency and the frequency response at the same frequency. For random vibrations, correlation functions and their frequency counterparts, spectral densities, are the tools used to describe the frequency content of the vibrations. In this chapter, we start by briefly describing the essential properties of linear systems. After this, we describe the three classes of signals: periodic, random, and transient signals, and for each signal class, we define a spectrum to describe its frequency content. We then go on to describe the discrete Fourier transform, DFT, since this is by far the most common tool to compute spectra, by the fast Fourier transform (FFT) algorithm. In this context, leakage and time windowing are explained, after which we go into detail on how to compute the spectra for each type of signal. Two different methods are described: Welch’s method, based on averaging several shorter DFT blocks, and the periodogram-based method which relies on making one, long DFT and then averaging adjacent frequency bins. Finally, also correlation function estimates are described using the same two techniques.

Anders Brandt, Stefano Manzoni

6. Frequency Response Function Estimation

Abstract

For current approaches to experimental modal analysis, the frequency response function is the most important measurement to be made. This chapter develops the frequency response function from the perspective of experimentally measured system excitations and responses. Experimental measurement and numerical processing techniques are presented that allow minimization of the impact of measurement noise and signal processing errors.

A. W. Phillips, R. J. Allemang

7. Random Vibration and Mechanical Shock

Abstract

All physical systems are exposed to structural dynamic environments, including random vibration or mechanical shock, or both. These environments can cause structural or component failure. The capability to analyze dynamic response is critical not only for purposes of response prediction and design, but also for specification of random vibration and shock tests. This chapter develops the ideas and the mathematics underlying the structural dynamics of linear single-degree-of-freedom and multiple-degree-of-freedom structures, random processes, random vibration, mechanical shock, random vibration testing, and mechanical shock testing. Examples are provided and many recommendations are given for the performance of random vibration and shock tests.

Thomas L. Paez, Norman F. Hunter, David O. Smallwood

8. DIC and Photogrammetry for Structural Dynamic Analysis and High-Speed Testing

Abstract

This chapter provides an overview and some important considerations to be made when making optical and stereophotogrammetry measurements on structures for dynamic applications. In particular, the chapter focuses on leveraging those measurements to perform digital image correlation (DIC) to extract dynamic parameters (e.g., strain, deflection, operating shapes, and mode shapes). Structural dynamic testing and analysis in the context of performing optical measurements is described. Information on optical high rate testing is also presented along with lessons learned and best practices.

Christopher Niezrecki, Phillip L. Reu, Javad Baqersad, Daniel P. Rohe

Modal Model Development

Frontmatter

9. Design of Modal Tests

Abstract

This chapter examines a number of issues that require consideration when a modal test is being planned or designed. As with any engineering procedure, a modal test needs to be designed; otherwise, objectives may not be fulfilled or time and effort may be poorly used. The issues discussed in this chapter include the purpose of the test, excitation considerations, response measurements, support conditions, measurement quality criteria, and considerations for model validation. When a modal test is to be performed to validate a finite element model, one needs to design the test so that the resulting measurements will provide the data required for the correlation of modeling results with those from the test. From a correlation perspective, one would like to select the response locations to allow a definitive, one-to-one correspondence between the measured modes and the predicted modes. Further, the excitation must be designed to excite all the modes of interest at a sufficient level so that the modal estimation algorithms can accurately extract the modal parameters.

Thomas Carne, Ralph Brillhart, Daniel Kammer, Kevin Napolitano

10. Experimental Modal Analysis Methods

Abstract

This chapter provides the basis and background of all experimental modal analysis (EMA) methods that have been developed over the last fifty years. In this context, modal parameters refer to complex valued modal frequencies, complex valued modal vectors and complex valued modal scaling. The chapter focusses on modal parameter estimation (MPE) methods that have been or are commercially available but includes many related MPE methods that have been developed and presented in research journals and articles as well. The methods are mostly based upon experimentally measured frequency response function (FRF) or impulse response function (IRF) data. MPE methods that are fundamentally single input, single output (SISO) methods finding one single mode are included through modern multiple input, multiple output (MIMO) methods that find all modal parameters for all modes simultaneously (in one or two passes). Discussion includes the theoretical background of all methods along with the kernel equations for each method. The mathematical development utilizes a central concept of matrix coefficient polynomials that provide the basis of the unified matrix polynomial approach (UMPA). Basic definitions are included as concepts are developed and a complete set of historical references is provided.

R. J. Allemang, D. L. Brown

11. Experimental Modal Parameter Evaluation Methods

Abstract

Modern experimental modal analysis (EMA) methods provide a number of modal parameter solutions based upon different models, different model orders and different numerical processing of the redundant data and/or results. Evaluation of the modal parameter solutions provides a way of obtaining a single unique set of modal parameters that best represents the measured experimental data. The early portion of this chapter is a review of some of the experimental modal analysis (EMA) methods covered in detail in Chap. 10, “Experimental Modal Analysis Methods” in this handbook. This is followed by presenting a number of numerical tools that are used in connection with the EMA methods to evaluate and validate the number of modal parameters that can be estimated from a multiple input, multiple output (MIMO) set of measured data. Some tools like complex and multivariate mode indication functions (CMIF and MvMIF) can be used to determine the model order and/or number of modal frequencies that can be estimated from the experimental data. These tools can be applied independent of the EMA method that is used and are particularly useful when close or repeated modal frequencies are present in the experimental data. Additionally, various consistency diagrams, pole surface plots and modal parameter clustering methods are defined that become part of, and enhance, the EMA method used to estimate the modal parameters. Finally, the last portion of this chapter overviews methods that are primarily post processing tools to evaluate and validate the modal parameters that have been estimated. Methods include techniques for normalizing, conditioning and presenting the modal vectors, like the modal vector complexity plot (MVCP) along with techniques for using the estimated modal vectors to estimate other functions like the enhanced frequency response function (eFRF) which can be used to validate the physical validity of the estimated modal vectors. Orthogonality of modal vectors along with consistency of modal vectors, as measured by the modal assurance criterion (MAC), also falls into this category of evaluation and validation tools that are applied after the modal parameters have been estimated. The chapter finishes with a brief example of how several of the evaluation and validation tools can be combined into an autonomous modal parameter estimation method.

R. J. Allemang, A. W. Phillips

12. Damping of Materials and Structures

Abstract

Damping is a phenomenon that can be observed in connection with all kind of materials: solid, liquid, or gaseous. Any kind of time-dependent change in stresses or strains of the material results in a loss of mechanical energy, which in most cases is transformed into thermal energy. However, all the other mechanisms such as the conversion into electrical energy or any kind of radiation over the system’s boundaries play a role. Typical observations that can be made in connection with damping are the occurrence of creep and relaxation processes or hysteresis curves in the case of cyclic loadings. The overall damping is influenced by a variety of mechanisms, especially for structures assembled from different components.

No matter whether the presence of damping is sought or should be avoided in technical applications, for any kind of tuning or optimization of a system under consideration, a basic understanding of the underlying physics is needed. This is especially true if calculations or simulations have to be run in order to predict the dynamical behavior of a system.

This chapter intends to introduce the reader into the subject and provide an extensive overview on the different aspects of damping regarding the fundamentals, mathematical, and numerical models as well as experimental techniques for the detection of damping properties. It shall give an overview of the state of knowledge and experience gathered in various fields of application and research. For further information, the reader is referred to various publications and textbooks whenever needed. This chapter is organized as follows: Sect. 1 provides an extensive overview on the topic, the classification of damping phenomena, and some remarks on computer-based programs. Section 2 refers to the damping of solids, while Sect. 3 extends the view on structures assembled from different components. Section 4 deals with different mathematical models toward the description of damping and relevant numerical approaches. Experimental techniques for the detection of the damping parameters needed for calculations are described in Sect. 5. This includes possible instrumentation as well as analytical methods. Finally, in Sect. 6, an application of the whole subject covering the detection of damping properties, its mathematical representation, and parameter identification along with a numerical simulation is presented as an example. Conclusions from this chapter are drawn in Sect. 7.

Lothar Gaul, André Schmidt

13. Modal Analysis of Nonlinear Mechanical Systems

Abstract

The objective of this chapter is to introduce nonlinear normal modes (NNMs) to structural dynamicists who are not acquainted with them. Specifically, this chapter describes how the concept of modes can be extended to the nonlinear case. It also describes, in simple terms, the fundamental properties of NNMs, including frequency-energy dependence, harmonics, bifurcation, and stability.

G. Kerschen, A. F. Vakakis

Analytical/Experimental Modeling Applications

Frontmatter

14. Substructuring Concepts and Component Mode Synthesis

Abstract

In this chapter, the basic principle of model reduction for linear models in structural dynamics is explained. In particular, the principle of substructuring is outlined following different common approaches.

Daniel Rixen

15. Finite Element Model Correlation

Abstract

Correlation of a finite element model with test data is commonly performed. In order to perform these correlation studies, the finite element model may require reduction due to the large size of the model, or the test data may be expanded to the size of the finite element model. Model reduction and model expansion techniques are presented first. Correlation tools typically deployed are then presented. Some additional commentary related to the test data and the correlation process is also provided to give insight into some of the issues that must be faced.

Peter Avitabile, Michael Mains

16. Model Updating

Abstract

The term “model updating” describes the process of adjusting the parameters of a finite element model in order that its predictions, in terms of eigenvalues and eigenvectors, are in agreement with measurements obtained by modal testing. The sensitivity method described in this chapter has been implemented numerous times in commercial codes and applied successfully in industry. It has become a mature technology in regular use in the automotive and aerospace industries worldwide. However, there are various subtleties surrounding the application of model updating that are discussed here for the benefit of potential users. Firstly there must be an awareness of the frequency range in which the updated model is to be applied. The available data is generally insufficient to define the system parameters without the use of additional information provided by regularization. And the choice of parameters is of critical importance: it is not only a matter of choosing sensitive parameters; they should also be chosen as part of an engineering understanding of the dynamics of the system. Careful choice of parameters, together with regularization, will lead to validated models that predict the behavior of the system beyond the scope of the original test data.

John E. Mottershead, Michael Link, Michael I. Friswell, Carsten Schedlinski

17. Nonlinear System Analysis Methods

Abstract

Practical techniques for experimentally detecting and characterizing system nonlinearities are demonstrated through a test bed consisting of a composite panel, both with and without a disbond, undergoing an electrodynamic shaker excitation. Techniques for detecting and characterizing system nonlinearities are applied to force and response data collected from this test bed, and then models are identified for those nonlinearities. Both time and frequency domain techniques are utilized, and the underlying theory and experimental requirements for each technique are discussed. References to the literature are provided throughout the chapter for more in-depth discussion of the techniques.

Janette J. Meyer, Raymond M. Bond, Douglas E. Adams

18. Structural Health Monitoring and Damage Identification

Abstract

Structural dynamics is fundamentally concerned with the design, operation, and understanding of physical structures. A significant concern in the management of these, often very high-value, assets, is their state of health. When a structure sustains damage, this can have an extremely negative effect on its availability, and this will have serious implications for profitability and also the safety of any human operators or occupants. It is therefore important to implement some means of monitoring structural health so that incipient damage can be detected and remedial actions can be taken before negative consequences occur. The pertinent damage identification methodology for engineering structures is Structural Health Monitoring (SHM). This chapter presents an overview of SHM, with particular reference to implementations based on monitoring structural vibrations and waves. The main philosophy under discussion here is data-based SHM, where diagnostics are based on the interpretation of measured data directly, without recourse to physics-based models. The main technologies for carrying out data-based SHM are statistical pattern recognition and machine learning, and the chapter gives some background on these methods and provides some case studies to illustrate their use. One of the main approaches to damage detection is novelty detection, where one develops a statistical model of measured features from the undamaged structure of interest, and monitors subsequent data to see if there are deviations from the model, indicative of damage. A serious problem with this approach is that it is prone to false alarms if there are benign changes to the data, like operational or environmental variations. Such benign changes – referred to here as confounding influences, need to be compensated for, if the SHM system is to be reliable and error-free (as far as possible). The chapter considers how confounding influences arise, and how they can be removed in the data-driven context by data normalization. Finally, the chapter concludes with some discussion of how physics-based models can still have a potentially useful role in data-driven SHM.

R. Fuentes, E. J. Cross, P. A. Gardner, L. A. Bull, T. J. Rogers, R. J. Barthorpe, H. Shi, N. Dervilis, C. R. Farrar, K. Worden

19. Experimental Dynamic Substructures

Abstract

This chapter deals with experimental dynamic substructures which are reduced order models that can be coupled with each other or with finite element derived substructures to estimate the system response of the coupled substructures. A unifying theoretical framework in the physical, modal or frequency domain is reviewed with examples. The major issues that have hindered experimental based substructures are addressed. An example is demonstrated with the transmission simulator method that overcomes the major historical difficulties. Guidelines for the transmission simulator design are presented.

Randall L. Mayes, Matthew S. Allen

20. Structural Dynamics Modification and Modal Modeling

Abstract

The structural dynamics modification (SDM) method, also called eigenvalue modification, was first commercialized in the earlier 1980s by a software company founded by the authors. It has proved to be a useful engineering tool for providing a quick look at the influence of physical modifications to a mechanical structure on its modes of vibration.

SDM can be used with experimental mode shapes as well as analytical mode shapes. It provides meaningful results even when used with a few mode shapes and combined experimental and analytical mode shape data. This chapter also provides background information on the many details to be considered when acquiring experimental mode shapes. Finally, the example provided shows that SDM gives very accurate results when both translational and rotational joint stiffnesses are used to model the attachment of a rib stiffener to a plate.

Mark Richardson, David Formenti

21. Toward Robust Response Models: Theoretical and Experimental Issues

Abstract

The purpose of building a reliable response model requires awareness and understanding of the theoretical and practical problems and pitfalls that may be encountered along the process. Those issues have to deal with many different aspects of structural dynamics that will be tackled in this chapter. First of all, it is necessary to frame the problem in the context of structural dynamics and modal analysis; then, it is crucial to discuss the substructuring/coupling problem, especially when facing a very complex structure, which needs to be studied by parts. This leads us inevitably to the difficult task of measuring angular responses, that is, to measure the response at rotational degrees of freedom. A comprehensive discussion on this matter is given here.

Not only due to the lack of information regarding rotational measurements, a constant issue is the incompleteness of the experimental data and its expansion to the numerical model size or the condensation/reduction of the numerical model to the experimental one. A significant discussion on this subject is also given in this chapter.

Finally, the more recently developed transmissibility theory for N degrees of freedom allows for the estimation of unmeasured frequency response functions, contributing to the building of robust response models.

Nuno Maia, António Urgueira, Raquel Almeida, Tiago Silva

22. Linear Modal Substructuring with Nonlinear Connections

Abstract

Large finite element models predominate the structural dynamic community, and inclusion of discrete nonlinear connections further complicates the models. The need for highly reduced order, computationally efficient nonlinear models is the focus of this work. System models generated from highly reduced order components interconnected with nonlinear connection elements to predict accurate system response with efficient approaches is identified. In addition, expansion from the reduced order model to the full space of the original finite element model is shown with no degradation in the predicted full-field response for both displacement and strain for nonlinear dynamic environments.

Peter Avitabile

Applications and Miscellaneous Topics

Frontmatter

23. Civil Structural Testing

Abstract

Civil engineering structures are especially complex due to their size, geometric and physical uniqueness, intrinsic nonlinearity, and also due to the variations of their properties as a function of environmental or operational loadings and support conditions. While overloading can produce severe but recoverable changes in modal properties, in the order of 30%, environmental conditions like temperature and humidity can produce state changes, particularly in supporting soils and boundary conditions, that have been observed to impose changes of 50% on modal properties without the presence of damage. Some of these variations are abrupt while others are slow, yet regardless of their speed they impose challenges on the experimental setups and identifications techniques. The basic civil structural instrumentation is mainly based on accelerometers and displacements sensors, being their characteristics described in the present chapter. The environmental excitations are in general nonstationary and input excitations are usually not measured. So, response is generally analyzed using Operational Modal Analysis (OMA) techniques, even if Experimental Modal Analysis (EMA) have also been extensively used. The most commonly applied technique for identification is the Stochastic Identification algorithm in their covariance and data-driven versions. In order to observe the complexities of civil infrastructure, examples are given for buildings under environmental and earthquake loads and bridges under environmental and traffic loads. Identification and automatic tracking algorithm are presented theoretically and with examples.

Ruben Boroschek, Joao Pedro Santos

24. Aerospace Perspective for Modeling and Validation

Abstract

The present chapter on the Aerospace Perspective for Modeling and Validation offers an overview of generally accepted practices in the U.S. aerospace community, with emphasis on space launch systems and their spacecraft payloads. Inclusion of practices in the important U.S. aircraft community requires a more extensive document. In keeping the present chapter within the confines of “accepted practices,” valuable subject matter related to scientific and technical innovations that are not yet generally accepted or peer “vetted” is considered beyond the scope of the present exposition. This chapter includes two distinct areas of subject matter, namely (1) modeling and analysis practices employed in prediction of structural dynamic behavior, and (2) integrated test-analysis practices employed in model verification, validation, and updating.

Robert Coppolino

25. Applied Math for Experimental Structural Dynamics

Abstract

Explanation of the methods, techniques, and theoretical aspects of experimental structural dynamics requires a fundamental understanding of several mathematical concepts. Data acquisition and analysis and their relationship to structural dynamics theory rely on an understanding of domains and transforms. Theoretical and computational methods in structural dynamics (modeling, correlation, parameter estimation, etc.) make use of linear algebra concepts and techniques. This chapter presents the fundamentals of these concepts and methods.

Chuck Van Karsen, Andrew Barnard

Backmatter

Titel: Handbook of Experimental Structural Dynamics
herausgegeben von: Randall Allemang
Peter Avitabile
Verlag: Springer New York
Electronic ISBN: 978-1-4614-4547-0
Print ISBN: 978-1-4614-4546-3
DOI: https://doi.org/10.1007/978-1-4614-4547-0

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