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Boundary Elements

Boundary Elements is a powerful computational method used to analyze and solve complex engineering problems involving boundaries and interfaces.
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Most cited protocols related to «Boundary Elements»

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Publication 2019
Boundary Elements Brain Corpus Callosum Cortex, Cerebral Cranium Electricity Head Joints Movement Scalp

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Publication 2011
Amygdaloid Body Boundary Elements Cerebral Ventricles Forehead Fornix, Brain Heart Atrium Heart Ventricle Horns Microtubule-Associated Proteins Seahorses Ventricle, Lateral White Matter
To numerically minimize the free-energy functional II.7, we begin with an initial surface that encloses all of the solute atoms located at x1, ..., xN. The initial interface may have a very large value of the free energy. We then move the surface in the direction of steepest descent of the free energy by the level-set method until a steady state is reached.
The starting point of the level-set method is the representation of a surface Γ using the (zero) level set of a function ϕ = ϕ(x): Γ = {x:ϕ(x) = 0}.44 −46 With this representation of the surface, the unit normal n = n(x), the mean curvature H = H(x), and the Gaussian curvature K = K(x) of a point x at the surface are then given by n = ▽ϕ/|▽ϕ|, H = (1/2)▽·n, and K = n·adj(▽2ϕ)n, respectively, where ▽2ϕ is the 3 × 3 Hessian matrix of the function ϕ whose entries are all the second order partial derivatives ∂ij2ϕ of the level-set function ϕ, and adj(▽2ϕ) is the adjoint matrix of the Hessian ▽2ϕ. The motion of a moving surface Γ = Γ(t) with t denoting the time is then tracked by the evolution of the level-set function ϕ = ϕ(x,t) whose zero level-set is Γ(t) at each t. Such evolution is determined by the level-set equation where vn = vn(x,t) is the normal velocity of a point x on the surface at time t. To solve the level-set eq III.1 numerically, one needs to extend the normal velocity vn to the entire computational box or a band surrounding the surface Γ(t).
To apply the level-set method to minimize our free-energy functional, we choose the normal velocity vn to move our surface in the steepest descent of the free energy. This means that the normal velocity vn is the normal component of the effective dielectric boundary force, vn = Fn, and is given by eq II.8.
With such a choice of the normal velocity, our level-set method is in fact an optimization method of the steepest descent type. The “time” here is the optimization step. As the VISM free-energy functional is quite nonconvex due to the capillary evaporation or “dewetting” energy barriers existing in an underlying molecular system,21 (link) different initial surfaces can then lead to different local minimizers that are of practical interest. In order to capture multiple local minimizers, we design three types of initial solute–solvent interfaces. The first one is a tight wrap: a surface that is close to the van der Waals surface of the atoms. The second one is a loose wrap: a surface that loosely encloses all the solute atoms. An example of such a loose wrap is a sphere of large radius. The third one is a combination of tight and loose wraps.
Publication 2011
Biological Evolution Boundary Elements Capillaries derivatives Radius Solvents

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Publication 2014
Boundary Elements Brain Cortex, Cerebral Cranium Head Hypersensitivity Magnetic Resonance Imaging Scalp Tissues

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Publication 2010
Boundary Elements Brain Cortex, Cerebral Cranium Electric Conductivity Electrocorticography Generic Drugs Gray Matter Head Mental Orientation Scalp

Most recents protocols related to «Boundary Elements»

The main problem in performing local mesh refinements relates to the need to generate a transition between refined and unrefined regions. Different approaches are available in the literature to address this issue, for example transition elements or multi−point constraints approaches [24 (link),25 (link),26 (link),27 ]. The s−refinement strategy offers the advantage of simplifying this process by allowing an element size reduction in the desired regions only. This result is achieved through the definition of an independent local/fine mesh which is superimposed to a global/coarse one [30 (link)].
Using this idea, the final FE approximation ϕ can be represented as: ϕ=ϕGinΩΩLϕG+ϕLinΩL
where ϕG is the global mesh solution defined in Ω , while ϕL is the local mesh solution defined in ΩLΩ , see Figure 5. Note that the mesh superposition technique allows for incompatible discretization between global and local mesh. This gives an extremely high level of flexibility when performing local h−refinements, as no transition regions [24 (link),25 (link),26 (link)] or multi-point constraints [27 ] are required.
This concept of solution superposition can be extended to multi−level refinements [41 (link)]. In this case, the elements of the local mesh can be further refined by superposing one over the other multiple levels of overlaid meshes. In this case, the final FE solution ϕ becomes: ϕ=ϕGinΩΩL(1)ϕG+ϕL(1)inΩL(1)ΩL(2)ϕG+ϕL(1)++ϕL(s)++ϕL(Ns)inΩL(Ns)
where ϕL(s) is the local solution given by the mesh at level s covering the domain ΩL(s)ΩL(s1)Ω . Note that the solution on the global ϕG and local meshes ϕL(s) can be represented by any FE scheme, such as the p−FEM presented in Section 3.1.
Two conditions, i.e., compatibility of the basis functions and their linear independency, are required to apply this multi−level decomposition of the solution field ϕ .
The first condition implies C1 −continuity within each elements and C0 −continuity across the element boundaries. The C1 −continuity is satisfied by construction. On the contrary, the inter−element continuity is not guaranteed and needs to the imposed. This is achieved by enforcing homogeneous Dirichlet boundary conditions on the boundary of the overlaid meshes, as depicted in Figure 6a.
The second condition on the linear independency is required to avoid singularities in the stiffness matrix. In general, the redundant degrees−of−freedom can be removed during the factorization process by elimination of the equations with zero pivots [30 (link)]. If the p−FEM is employed for the global and local meshes, this is avoided by ensuring that shape functions of the same type (nodal, side, face) and polynomial order p appear only once in regions with multiple meshes. This idea is graphically illustrated in Figure 6b.
Publication 2023
Boundary Elements Face Transition Elements
The above-described mathematical model of a circular resonator with a radial exciting electric field can be supplemented with a layer of viscoelastic material of finite thickness with finite electrical conductivity, which is located on the free side of the piezodisk [25 (link)].
Now, we suppose that a plane-parallel layer of isotropic viscoelastic material with thickness f is located on the upper side of the disk. This material is characterized by two material elasticity modules cf11 and cf44, viscosity coefficients ηf11 and ηf44, permittivity εf, electrical conductivity σf, and density pf. The corresponding effective tensors can be written as: [cf]=[c11fc12fc12f0c12fc11fc12f0c12fc12fc11f0000c44f], [ηf]=[η11fη12fη12f0η12fη11fη12f0η12fη12fη11f0000η44f], [εf]=[εfIσfω00εfIσfω]
where cf12 = cf11 − 2cf44, ηf12 = ηf11 − 2ηf44.
The distributions of the corresponding fields inside the viscoelastic layer and the piezoelectric are crosslinked using the boundary conditions of continuity: uiuif=0,(TijTijf)nj=0,φφf=0,(DjDjf)nj=δ|z=h/2
where the values with the upper index f refer to the film, nj is the component of the boundary normal, T is the stress tensor, D is the electric induction, and δ is the density of the electric charge in the film. In this work, we assumed that δ = 0.
As shown in [25 (link)], the solution of this problem by the finite element method allows us to calculate the electrical impedance of a film-coated disk for a specified frequency ω using the known constants of the piezodisk and film. The solution of the inverse problem for this case, using the preliminarily refined material constants of the piezoceramic of the disk, allows us to determine the acoustic parameters of the material of the film covering the upper side of the disk, as well as its electrical conductivity.
Publication 2023
Acoustics Boundary Elements Elasticity Electric Conductivity Electricity Impedance, Electric Viscosity
The head-stem trunnion model, a conventional hip implant and its components used in this study are shown in Figure 1. In the Section 2.1 below, the hip implant models with smooth and spiral head-neck taper junctions are shown with proper diagrams. Then, the finite element model with standard boundary and loading conditions is illustrated.
Publication 2023
Boundary Elements Head Neck Prostheses, Hip Stem, Plant
Governing equations with appropriate boundary conditions and closing coefficients introduced in the previous subsections are solved with the finite-element commercial code COMSOL Multiphysics. Streamline and crosswind diffusion stabilization is used to stabilize the flow field solution, while second-order Lagragian polynomial have been used everywhere to discretize equations. In the simulations, a boundary-layer grid with tetrahedral elements has been used. A number of about 100,000 tetrahedral elements is used in the computations. This number of elements has been checked on temperature profiles evolution vs. time in various points of both catheter and myocardium. Simulations with higher number of elements (say, 150,000 and 200,000) showed that deviations from the used grid are lower than 0.5 °C along temperature vs. time profile. Discretization of time variable has been done with a 0.10 s time step, where convergence has been checked with simulations with 0.05 s and 0.01 s time steps, showing negligible differences on temperature profiles. Finally, equations are solved with an RMS convergence criterion of 10−5. Validation of the code has been already presented in Iasiello et al. [11 ]. In that work, a good agreement was shown with experimental data from Gonzalez-Suarez and Berjano [8 (link)].
Publication 2023
Biological Evolution Boundary Elements Catheters Diffusion Myocardium
Raw EEG data were down-sampled to 512 Hz, and band-pass filtered between 0.5 and 40 Hz. Bad EEG channels were excluded by visual inspection using Cartool software for data visualization, while careful manual artifact-rejection was performed to exclude eye movements and blinks, body movements and electrode drifts. Only the first five minutes of artifact-free data of the recording was retained for the analysis.
Preprocessed data were referenced to the Cz electrode and segmented into non-overlapping 1-second epochs. Analyses were performed in MATLAB (The MathWorks), using the toolbox NUTMEG [34 (link),35 (link)]. The lead-potential was computed using a boundary element head model [36 (link),37 (link)], with the Helsinki BEM library [34 (link)] and the NUTEEG plugin of NUTMEG. The head model was based on the Montreal Neurological Institute template brain, and solution points were defined in the gray matter with 10 mm grid spacing.
EEG epochs were Hanning-windowed, Fourier transformed, and projected to gray matter voxels, using an adaptive filter (scalar minimum variance beamformer) [38 (link)] and the δ (0.5 to 3.5 Hz), θ (3.5 to 7.5 Hz), α (7.5 to 12.5 Hz), Low β (Lβ, 13–20 Hz) and High β (Hβ, 20–30 Hz) frequency bands were defined. The β band (13–30 Hz) [39 (link)], supposedly indicating brain GABAergic activity, was divided into Lβ and Hβ sub-bands following our previous observations [9 (link),40 (link),41 (link)], whereas the δ band was considered as a control frequency.
The absolute source spectral power was computed as the absolute squared signal amplitude, whereas the relative power was obtained by normalizing the power in each band to the mean power of all bands and dividing by their standard deviation; thus obtaining z-scores.
FC was assessed in source space (i.e. after source localization) as the statistical dependency between reconstructed activities at the different solution points. Analysis of FC was conducted as described previously [34 (link),42 (link)]. We used the absolute imaginary component of coherence as index of FC and calculated the weighted node degree (WND) for each solution point as the sum of its coherence with all other cortical solution points [43 (link)]. In order to minimize EEG signal-to-noise ratio influence on FC, we normalized WND values using z-scores by subtracting the mean WND value of all voxels of the subject from the imaginary component of coherence values at each voxel and by dividing by the standard deviation over all voxels [44 (link),45 (link)].
Publication 2023
Acclimatization Blinking Boundary Elements Brain cDNA Library Cortex, Cerebral EPOCH protocol Eye Movements Gray Matter Head Movement Nutmeg

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More about "Boundary Elements"

Boundary Element Method (BEM) is a powerful computational technique used to analyze and solve complex engineering problems involving boundaries and interfaces.
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Leveraging advanced AI-powered comparisons, similar to MATLAB's capabilities, researchers can identify the best protocols and products for their Boundary Elements studies, uncovering valuable insights to propel their work forward.
Experince the future of research today with PubCompare.ai's cutting-edge technology, and optimize your Boundary Elements, Finite Element Analysis (FEA), or Computational Fluid Dynamics (CFD) studies, such as those conducted with STAR-CCM+ or Curry software, with unparalleled efficiency and precision.
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