Abstract

Statistical energy analysis (SEA) can accurately describe the average vibration characteristics through system energy flow and transmission feedback. It is a powerful tool to solve the problem of high-frequency acoustics-vibration. SEA is widely used in vehicles, ships, aviation, and other transportation engineering fields. However, the expansion of SEA, based on the assumption of modal equipartition and weak coupling, is limited to the intermediate frequency. Although the SEA basic theory can be extended by relaxing the hypothesis conditions or the analysis of the medium-frequency acoustics-vibration can be carried out using the finite element method (FEM) and SEA mixing method, there are still many challenges associated with these options. To improve the basic theory of SEA and knowledge of intermediate frequency extension methods, as well as attract the attention of domestic scholars, this paper describes classical SEA and intermediate frequency extension methods. First, coupling loss factor (CLF) error propagation and parameter acquisition in classical SEA are introduced, and the three relative error calculation methods of CLF are compared. Then, the method of obtaining parameters is described from three aspects of energy transfer, input load, and modal density. Second, SEA intermediate frequency extension technology (experimental statistical energy analysis (ESEA), finite element statistical energy analysis (FE-SEA), statistical modal energy distribution analysis (SMEDA), and waveguide analysis (WGA)) are introduced. Neutron structure assembly and modeling, interval and mixed interval analysis, interval variable and mixed interval variable response are also described, so as to justify the development of a hybrid, large-scale interval algorithm. Finally, the engineering application of the above method is introduced, the limitations and shortcomings of SEA and intermediate frequency extension methods are reviewed, and unsolved problems are further discussed.

Highlights

  • Statistical energy analysis (SEA) is a statistical method for studying vibration and acoustics from an energy perspective

  • SEA method is applied to solve highfrequency acoustics-vibration problems. e calculation accuracy depends on the accuracy of its parameters, namely, modal density, internal loss factor (ILF), coupling loss factor (CLF), input power, and system energy

  • Statistical modal energy distribution analysis (SMEDA) does not assume an even distribution of modal energy and is applicable to coupling of subsystems with low mode overlap, coupling of heterogeneous subsystems, local excitation, and coupled continuous elastic systems in general [6]. is method takes the two-mode formula [38] as the theoretical basis and the input parameters of subsystems as process variables to obtain the energy distribution of each subsystem. e calculation process is shown in Figure 14. e SMEDA equation depends only on the source characteristics

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Summary

Introduction

Statistical energy analysis (SEA) is a statistical method for studying vibration and acoustics from an energy perspective. It divides a system into weakly coupled substructures and describes the state of each subsystem with power and energy. SEA is currently the most widely used method to solve high-frequency acoustics-vibration problems [1] and to obtain the acoustics-vibration characteristics of the system through feedback of energy flow and transmission to the equilibrium equation [1]. SEA can accurately describe the average vibration characteristics of the system and is a powerful tool to solve the problem of high-frequency acoustics-vibration. SEA method is applied to solve highfrequency acoustics-vibration problems. The application scope of SEA has been extended in recent

Study of the Parameters of Classical SEA
Energy Transfer
ESEA Method
FE-SEA Hybrid Method
Parameter Uncertainty
SMEDA Method
Waveguide Method
Conclusion and Reflection
SMEDA and Waveguide Method
FE-SEA Method
Interval Variables with Parameter Uncertainty
Hybrid Interval Variables with Parameter Uncertainty
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