Dynamical energy analysis

Last updated

Dynamical energy analysis (DEA) [1] is a method for numerically modelling structure borne sound and vibration in complex structures. It is applicable in the mid-to-high frequency range and is in this regime computational more efficient than traditional deterministic approaches (such as finite element and boundary element methods). In comparison to conventional statistical approaches such as statistical energy analysis (SEA), [2] DEA provides more structural details and is less problematic with respect to subsystem division. The DEA method predicts the flow of vibrational wave energy across complex structures in terms of (linear) transport equations. These equations are then discretized and solved on meshes.

Contents

Key point summary of DEA

Introduction

This image describes the range of applicability of dynamical energy analysis (DEA) in comparison to statistical energy analysis (SEA) and the finite element method (FEM). Horizontal axis is frequency, vertical axis is complexity of the structure. Dynamical energy analysis mid freq gap.png
This image describes the range of applicability of dynamical energy analysis (DEA) in comparison to statistical energy analysis (SEA) and the finite element method (FEM). Horizontal axis is frequency, vertical axis is complexity of the structure.

Simulations of the vibro-acoustic properties of complex structures (such as cars, ships, airplanes,...) are routinely carried out in various design stages. For low frequencies, the established method of choice is the finite element method (FEM). But high frequency analysis using FEM requires very fine meshes of the body structure to capture the shorter wavelengths and therefore is computational extremely costly. Furthermore the structural response at high frequencies is very sensitive to small variations in material properties, geometry and boundary conditions. This makes the output of a single FEM calculation less reliable and makes ensemble averages necessary furthermore enhancing computational cost. Therefore at high frequencies other numerical methods with better computational efficiency are preferable.

The statistical energy analysis (SEA) [2] has been developed to deal with high frequency problems and leads to relatively small and simple models. However, SEA is based on a set of often hard to verify assumptions, which effectively require diffuse wave fields and quasi-equilibrium of wave energy within weakly coupled (and weakly damped) sub-systems.

One alternative to SEA is to instead consider the original vibrational wave problem in the high frequency limit, leading to a ray tracing model of the structural vibrations. [note 1] The tracking of individual rays across multiple reflection is not computational feasible because of the proliferation of trajectories. Instead, a better approach is tracking densities of rays propagated by a transfer operator. This forms the basis of the Dynamical Energy Analysis (DEA) method introduced in reference. [3] DEA can be seen as an improvement over SEA where one lifts the diffusive field and the well separated subsystem assumption. One uses an energy density which depends both on position and momentum. DEA can work with relatively fine meshes where energy can flow freely between neighboring mesh cells. This allows far greater flexibility for the models used by DEA in comparison to the restriction imposed by SEA. No remodeling as for SEA is necessary as DEA can use meshes created for a FE analysis. As a result, finer structural details than SEA can be resolved by DEA.

Method

The implementation of DEA on meshes is called Discrete Flow Mapping (DFM). We will here briefly describe the idea behind DFM, for details see the references [1] [3] [4] [5] [6] [7] below. Using DFM it is possible to compute vibro-acoustic energy densities in complex structures at high frequencies, including multi-modal propagation and curved surfaces. DFM is a mesh based technique where a transfer operator is used to describe the flow of energy through boundaries of subsystems of the structure; the energy flow is represented in terms of a density of rays , that is, the energy flux through a given surface is given through the density of rays passing through the surface at point with direction . Here, parametrises the surface and is the direction component tangential to the surface. In what follows, the surfaces is represented by the union of all boundaries of the mesh cells of the FE mesh describing the car floor. The density , with phase space coordinate , is transported from one boundary to the adjacent boundary intersection via the boundary integral operator

 

 

 

 

(1)

where is the map determining where a ray starting on a boundary segment at point with direction passes through another boundary segment, and is a factor containing damping and reflection/transmission coefficients (akin to the coupling loss factors in SEA). It also governs the mode conversion probabilities in the case of both in-plane and flexural waves, which are derived from wave scattering theory (see [8] ). This allows DEA to take curvature and varying material parameters into account. Equation ( 1 ) is a way to write ray tracing across one single mesh cell in terms of an integral equation transferring an energy density from one surface to an adjacent surface.

In a next step, the transfer operator ( 1 ) is discretised using a set of basis functions of the phase space. Once the matrix has been constructed, the final energy density on the boundary phase-space of each element is given in terms of the initial density by the solution of a linear system of the form

 

 

 

 

(2)

The initial density models some source distribution for vibrational excitations, for example the engine in ship. Once the final density (describing the energy density on all cell boundaries) has been computed, the energy density at any location inside the structure may be computed as a post-processing step.

Concerning the terminology, there is some ambiguity concerning the terms "Discrete Flow Mapping(DFM)" and "Dynamical Energy Analysis". To some extent, one can use one term in place of the other. For example, consider a plate. In DFM, one would subdivide the plate into many small triangles and propagate the flow of energy from triangle to (neighbouring) triangle. In DEA, one would not subdivide the plate, but use some high order basis functions (both in position and momentum) on the boundary of the plate. But in principle it would be admissible to describe both procedures as either DFM or DEA.

Examples

This image compares the results from Dynamical Energy Analysis (DEA) with that of frequency averaged FEM. Shown is the kinetic energy distribution resulting from a point excitation on a carfloor panel on a logarithmic color scale. Dynamical energy analysis example carfloor panel.png
This image compares the results from Dynamical Energy Analysis (DEA) with that of frequency averaged FEM. Shown is the kinetic energy distribution resulting from a point excitation on a carfloor panel on a logarithmic color scale.

As an example application, a simulation [9] [10] of a carfloor panel is shown here. A point excitation at 2500 Hz with 0.04 hysteretic damping was applied. The results from a frequency averaged FEM simulation are compared with a DEA simulation (for DEA, no frequency averaging is necessary). The results also show a good quantitative agreement. In particular, we see the directional dependence of the energy flow, which is predominantly in the horizontal direction as plotted. This is caused by several horizontally extended out-of-plane bulges. It is only in the lower right part of the panel, with negligible energy content, that deviations between the FEM and DFM predictions are visible. The total kinetic energy given by the DFM prediction is within 12% of the FEM prediction. For more details, see the cited works.

This image shows the result of a DEA simulation on a model of a Yanmar tractor. Shown is the out-of-plane acceleration on a logarithmic color scale for a frequency of 1000 Hz. Dynamical energy analysis example tractor.png
This image shows the result of a DEA simulation on a model of a Yanmar tractor. Shown is the out-of-plane acceleration on a logarithmic color scale for a frequency of 1000 Hz.

As a more applied example, the result of a DEA simulation [11] on a Yanmar tractor model (body in blue: chassis/cabin steel frame and windows) is shown here to the left. In the cited work, the numerical DEA results are compared with experimental measurements at frequencies between 400 Hz and 4000 Hz for an excitation on the back of the gear casing. Both results agree favorably. The DEA simulation can be extended to predict the sound pressure level at driver's ear.

Notes

  1. Well known examples for this mechanism are the transition from quantum mechanics to classical mechanics and the transition from electromagnetic wave dynamics to light rays.

Related Research Articles

<span class="mw-page-title-main">Fluid dynamics</span> Aspects of fluid mechanics involving flow

In physics and engineering, fluid dynamics is a subdiscipline of fluid mechanics that describes the flow of fluids—liquids and gases. It has several subdisciplines, including aerodynamics and hydrodynamics. Fluid dynamics has a wide range of applications, including calculating forces and moments on aircraft, determining the mass flow rate of petroleum through pipelines, predicting weather patterns, understanding nebulae in interstellar space and modelling fission weapon detonation.

<span class="mw-page-title-main">Speed of sound</span> Speed of sound wave through elastic medium

The speed of sound is the distance travelled per unit of time by a sound wave as it propagates through an elastic medium. At 20 °C (68 °F), the speed of sound in air is about 343 metres per second, or one kilometre in 2.91 s or one mile in 4.69 s. It depends strongly on temperature as well as the medium through which a sound wave is propagating. At 0 °C (32 °F), the speed of sound in air is about 331 m/s.

<span class="mw-page-title-main">Gravity wave</span> Wave in or at the interface between fluids where gravity is the main equilibrium force

In fluid dynamics, gravity waves are waves generated in a fluid medium or at the interface between two media when the force of gravity or buoyancy tries to restore equilibrium. An example of such an interface is that between the atmosphere and the ocean, which gives rise to wind waves.

<span class="mw-page-title-main">Computational fluid dynamics</span> Analysis and solving of problems that involve fluid flows

Computational fluid dynamics (CFD) is a branch of fluid mechanics that uses numerical analysis and data structures to analyze and solve problems that involve fluid flows. Computers are used to perform the calculations required to simulate the free-stream flow of the fluid, and the interaction of the fluid with surfaces defined by boundary conditions. With high-speed supercomputers, better solutions can be achieved, and are often required to solve the largest and most complex problems. Ongoing research yields software that improves the accuracy and speed of complex simulation scenarios such as transonic or turbulent flows. Initial validation of such software is typically performed using experimental apparatus such as wind tunnels. In addition, previously performed analytical or empirical analysis of a particular problem can be used for comparison. A final validation is often performed using full-scale testing, such as flight tests.

<span class="mw-page-title-main">Finite volume method</span>

The finite volume method (FVM) is a method for representing and evaluating partial differential equations in the form of algebraic equations. In the finite volume method, volume integrals in a partial differential equation that contain a divergence term are converted to surface integrals, using the divergence theorem. These terms are then evaluated as fluxes at the surfaces of each finite volume. Because the flux entering a given volume is identical to that leaving the adjacent volume, these methods are conservative. Another advantage of the finite volume method is that it is easily formulated to allow for unstructured meshes. The method is used in many computational fluid dynamics packages. "Finite volume" refers to the small volume surrounding each node point on a mesh.

<span class="mw-page-title-main">Internal wave</span> Type of gravity waves that oscillate within a fluid medium

Internal waves are gravity waves that oscillate within a fluid medium, rather than on its surface. To exist, the fluid must be stratified: the density must change with depth/height due to changes, for example, in temperature and/or salinity. If the density changes over a small vertical distance, the waves propagate horizontally like surface waves, but do so at slower speeds as determined by the density difference of the fluid below and above the interface. If the density changes continuously, the waves can propagate vertically as well as horizontally through the fluid.

Topology optimization (TO) is a mathematical method that optimizes material layout within a given design space, for a given set of loads, boundary conditions and constraints with the goal of maximizing the performance of the system. Topology optimization is different from shape optimization and sizing optimization in the sense that the design can attain any shape within the design space, instead of dealing with predefined configurations.

<span class="mw-page-title-main">Smoothed-particle hydrodynamics</span> Method of hydrodynamics simulation

Smoothed-particle hydrodynamics (SPH) is a computational method used for simulating the mechanics of continuum media, such as solid mechanics and fluid flows. It was developed by Gingold and Monaghan and Lucy in 1977, initially for astrophysical problems. It has been used in many fields of research, including astrophysics, ballistics, volcanology, and oceanography. It is a meshfree Lagrangian method, and the resolution of the method can easily be adjusted with respect to variables such as density.

<span class="mw-page-title-main">Computational electromagnetics</span> Branch of physics

Computational electromagnetics (CEM), computational electrodynamics or electromagnetic modeling is the process of modeling the interaction of electromagnetic fields with physical objects and the environment.

Computational aeroacoustics is a branch of aeroacoustics that aims to analyze the generation of noise by turbulent flows through numerical methods.

Ewald summation, named after Paul Peter Ewald, is a method for computing long-range interactions in periodic systems. It was first developed as the method for calculating electrostatic energies of ionic crystals, and is now commonly used for calculating long-range interactions in computational chemistry. Ewald summation is a special case of the Poisson summation formula, replacing the summation of interaction energies in real space with an equivalent summation in Fourier space. In this method, the long-range interaction is divided into two parts: a short-range contribution, and a long-range contribution which does not have a singularity. The short-range contribution is calculated in real space, whereas the long-range contribution is calculated using a Fourier transform. The advantage of this method is the rapid convergence of the energy compared with that of a direct summation. This means that the method has high accuracy and reasonable speed when computing long-range interactions, and it is thus the de facto standard method for calculating long-range interactions in periodic systems. The method requires charge neutrality of the molecular system in order to accurately calculate the total Coulombic interaction. A study of the truncation errors introduced in the energy and force calculations of disordered point-charge systems is provided by Kolafa and Perram.

<span class="mw-page-title-main">Meshfree methods</span> Methods in numerical analysis not requiring knowledge of neighboring points

In the field of numerical analysis, meshfree methods are those that do not require connection between nodes of the simulation domain, i.e. a mesh, but are rather based on interaction of each node with all its neighbors. As a consequence, original extensive properties such as mass or kinetic energy are no longer assigned to mesh elements but rather to the single nodes. Meshfree methods enable the simulation of some otherwise difficult types of problems, at the cost of extra computing time and programming effort. The absence of a mesh allows Lagrangian simulations, in which the nodes can move according to the velocity field.

The finite-difference frequency-domain (FDFD) method is a numerical solution method for problems usually in electromagnetism and sometimes in acoustics, based on finite-difference approximations of the derivative operators in the differential equation being solved.

Pressure-correction method is a class of methods used in computational fluid dynamics for numerically solving the Navier-Stokes equations normally for incompressible flows.

Statistical energy analysis (SEA) is a method for predicting the transmission of sound and vibration through complex structural acoustic systems. The method is particularly well suited for quick system level response predictions at the early design stage of a product, and for predicting responses at higher frequencies. In SEA a system is represented in terms of a number of coupled subsystems and a set of linear equations are derived that describe the input, storage, transmission and dissipation of energy within each subsystem. The parameters in the SEA equations are typically obtained by making certain statistical assumptions about the local dynamic properties of each subsystem. These assumptions significantly simplify the analysis and make it possible to analyze the response of systems that are often too complex to analyze using other methods.

In fluid dynamics, Airy wave theory gives a linearised description of the propagation of gravity waves on the surface of a homogeneous fluid layer. The theory assumes that the fluid layer has a uniform mean depth, and that the fluid flow is inviscid, incompressible and irrotational. This theory was first published, in correct form, by George Biddell Airy in the 19th century.

<span class="mw-page-title-main">Hydrodynamic stability</span>

In fluid dynamics, hydrodynamic stability is the field which analyses the stability and the onset of instability of fluid flows. The study of hydrodynamic stability aims to find out if a given flow is stable or unstable, and if so, how these instabilities will cause the development of turbulence. The foundations of hydrodynamic stability, both theoretical and experimental, were laid most notably by Helmholtz, Kelvin, Rayleigh and Reynolds during the nineteenth century. These foundations have given many useful tools to study hydrodynamic stability. These include Reynolds number, the Euler equations, and the Navier–Stokes equations. When studying flow stability it is useful to understand more simplistic systems, e.g. incompressible and inviscid fluids which can then be developed further onto more complex flows. Since the 1980s, more computational methods are being used to model and analyse the more complex flows.

Miniaturizing components has always been a primary goal in the semiconductor industry because it cuts production cost and lets companies build smaller computers and other devices. Miniaturization, however, has increased dissipated power per unit area and made it a key limiting factor in integrated circuit performance. Temperature increase becomes relevant for relatively small-cross-sections wires, where it may affect normal semiconductor behavior. Besides, since the generation of heat is proportional to the frequency of operation for switching circuits, fast computers have larger heat generation than slow ones, an undesired effect for chips manufacturers. This article summaries physical concepts that describe the generation and conduction of heat in an integrated circuit, and presents numerical methods that model heat transfer from a macroscopic point of view.

Structural acoustics is the study of the mechanical waves in structures and how they interact with and radiate into adjacent media. The field of structural acoustics is often referred to as vibroacoustics in Europe and Asia. People that work in the field of structural acoustics are known as structural acousticians. The field of structural acoustics can be closely related to a number of other fields of acoustics including noise, transduction, underwater acoustics, and physical acoustics.

A density meter, also known as a densimeter, is a device that measures the density. Density is usually abbreviated as either or . Typically, density either has the units of or . The most basic principle of how density is calculated is by the formula:

References

  1. 1 2 Bajars, J.; Chappell, D.J.; Hartmann, T.; Tanner, G. (2017). "Improved Approximation of Phase-Space Densities on Triangulated Domains Using Discrete Flow Mapping with p-Refinement". Journal of Scientific Computing. 72 (3): 1290–1312. doi: 10.1007/s10915-017-0397-8 .
  2. 1 2 Lyon, R.H.; DeJong, R.G. (1995). Theory and Application of Statistical Energy Analysis. Butterworth-Heinemann.
  3. 1 2 Tanner, G. (2009). "Dynamical energy analysis—Determining wave energy distributions in vibro-acoustical structures in the high-frequency regime". Journal of Sound and Vibration. 320 (4–5): 1023–1038. arXiv: 0803.1791 . Bibcode:2009JSV...320.1023T. doi:10.1016/j.jsv.2008.08.032. S2CID   222175617.
  4. Chappell, D.J.; Tanner, G. (2013). "Solving the stationary Liouville equation via a boundary element method". Journal of Computational Physics. 234: 487–498. arXiv: 1202.4754 . Bibcode:2013JCoPh.234..487C. doi:10.1016/j.jcp.2012.10.002. S2CID   18791626.
  5. Chappell, D.J.; Tanner, G.; Giani, G. (2012). "Boundary element dynamical energy analysis: A versatile method for solving two or three dimensional wave problems in the high frequency limit". Journal of Computational Physics. 231 (18): 6181–6191. arXiv: 1202.4416 . Bibcode:2012JCoPh.231.6181C. doi:10.1016/j.jcp.2012.05.028. S2CID   12930689.
  6. Chappell, D.J.; Tanner, G.; Löchel, D.; Søndergaard, N. (2013). "Discrete flow mapping: transport of phase space densities on triangulated surfaces". Proc. R. Soc. A. 469 (2155): 20130153. arXiv: 1303.4249 . Bibcode:2013RSPSA.46930153C. doi:10.1098/rspa.2013.0153. S2CID   61520644.
  7. Chappell, D.J.; Löchel, D.; Søndergaard, N.; Tanner, G. (2014). "Dynamical energy analysis on mesh grids: A new tool for describing the vibro-acoustic response of complex mechanical structures" (PDF). Wave Motion. 51 (4): 589–597. doi:10.1016/j.wavemoti.2014.01.004.
  8. Langley, R.S.; Heron, K.H. (1990). "Elastic wave transmission through plate/beam junctions". J. Sound Vib. 143 (2): 241–253. Bibcode:1990JSV...143..241L. doi:10.1016/0022-460X(90)90953-W.
  9. Hartmann, Timo; Tanner, Gregor; Xie, Gang; Chappell, David; Bajars, Janis (2016). Modelling of high-frequency structure-borne sound transmission on FEM grids using the Discrete Flow Mapping technique. MoVic-RASD 2016. doi: 10.1088/1742-6596/744/1/012237 .
  10. Hartmann, Timo; Xie, Gang; Bajars, Janis; Chappell, David; Tanner, Gregor (2016). Vibro-acoustic energy flow through spot-welds in Dynamical Energy Analysis (PDF). Internoise 2016. Archived from the original (PDF) on 2017-03-25. Retrieved 2017-03-08.
  11. Hartmann, Timo; Satoshi, Morita; Tanner, Gregor; Chappell, David; Chronopoulos, Dimitrios (2016). High-frequency structure-borne sound transmission on an FE mesh for a tractor model using Dynamical Energy Analysis. ISMA 2016.