Fast neighbor lists for adaptive-resolution particle simulations

Awile, Omar; Büyükkeçeci, Ferit; Reboux, Sylvain; Sbalzarini, Ivo F.

doi:10.1016/j.cpc.2012.01.003

Cited by 25 publications

(21 citation statements)

References 29 publications

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“…Here we assume the same configuration as in a benchmark test in a two-dimensional rectangular region (Fig. 2a), combining large node-spacing zone (Zone I) and small node-spacing zone (Zone II) connected on the right to Zone I (Awile et al, 2012). The node spacing for each zone is constant at h I and h II , respectively, and the size ratio is defined as λ = h I /h II .…”

Section: Bubble Mesh Methodsmentioning

confidence: 99%

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Adjoint-based shape optimization of fin geometry for heat transfer enhancement in solidification problem

Morimoto

Kinoshita

Suzuki

2016

JTST

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In the present study, an adjoint-based shape-optimization method is formulated for heat transfer enhancement in liquid-solid phase change problems, in which heat conduction is dominant. In the present shape-optimization scheme, extended heat transfer surfaces with constant wall temperature are allowed to deform based on variational information of a cost functional, which is obtained from the physical temperature and adjoint enthalpy fields. In the computation of the developed scheme with an enthalpy-based formulation, a meshless local Petrov-Galerkin (MLPG) method is implemented for dealing with the complex boundary shape. For high-resolution analyses, a bubble-mesh method for boundary-fitted node arrangement in a well-controlled manner is employed, combined with a high-efficiency searching algorithm for choosing the neighboring bubbles interacted with each other. In the shape evolution process for different initial fin shapes, it is demonstrated that, within a certain range of the initial state, the present shape-optimization scheme leads to tree-like fin shapes that achieve the temperature field with global similarity.

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Section: Bubble Mesh Methodsmentioning

confidence: 99%

“…We reduce the computational time by introducing an adaptive resolution (AR) cell list (Awile et al, 2012). In the AR cell lists, an adaptive tree data structure is employed, and each node is assigned to one of the different tree levels that are divided according to the cutoff radius.…”

Section: Bubble Mesh Methodsmentioning

confidence: 99%

Adjoint-based shape optimization of fin geometry for heat transfer enhancement in solidification problem

Morimoto

Kinoshita

Suzuki

2016

JTST

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“…Figure 4.12a shows the times spent in each step of the simulation algorithm. The overall scaling of the computational cost appears linear with the total number of particles in the simulation, but has an upper bound of O(N log N ) due to the adaptive tree used in the neighbor-list algorithm [3]. In the benchmarks presented here, particle selforganization (step 4 in the algorithm in §3.2.2) is done every 10 time steps and represents 36% of the total CPU time 2 .…”

Section: Curvature-driven Surface Refinementmentioning

confidence: 99%

“…Of the time spent for the particles to self-organize, 57% comes from constructing the DC-PSE operators (step 4(a)) for evaluating the monitor function and for interpolating the particle intensities to the adapted particle positions. 38% of the self-organization time is spent constructing neighbor lists (step 4(e)-iii) using the algorithm of Awile et al [3]. Insertion/removal of particles and gradient descent on the self-organization energy (step 4(e) without 4(e)-iii), jointly account for the remaining 5% of the self-organization time (1.8% of the total simulation time).…”

Section: Curvature-driven Surface Refinementmentioning

confidence: 99%

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A self-organizing Lagrangian particle method for adaptive-resolution advection–diffusion simulations

Reboux

Schrader

Sbalzarini

2012

Journal of Computational Physics

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We present a novel adaptive-resolution particle method for continuous parabolic problems. In this method, particles self-organize in order to adapt to local resolution requirements. This is achieved by pseudo forces that are designed so as to guarantee that the solution is always well sampled and that no holes or clusters develop in the particle distribution. The particle sizes are locally adapted to the length scale of the solution. Differential operators are consistently evaluated on the evolving set of irregularly distributed particles of varying sizes using discretization-corrected operators. The method does not rely on any global transforms or mapping functions. After presenting the method and its error analysis, we demonstrate its capabilities and limitations on a set of two-and three-dimensional benchmark problems. These include advection-diffusion, the Burgers equation, the Buckley-Leverett five-spot problem, and curvature-driven level-set surface refinement. AbstractWe present a novel adaptive-resolution particle method for continuous parabolic problems. In this method, particles self-organize in order to adapt to local resolution requirements. This is achieved by pseudo forces that are designed so as to guarantee that the solution is always well sampled and that no holes or clusters develop in the particle distribution. The particle sizes are locally adapted to the length scale of the solution. Differential operators are consistently evaluated on the evolving set of irregularly distributed particles of varying sizes using discretization-corrected operators. The method does not rely on any global transforms or mapping functions. After presenting the method and its error analysis, we demonstrate its capabilities and limitations on a set of two-and three-dimensional benchmark problems. These include advection-diffusion, the Burgers equation, the Buckley-Leverett five-spot problem, and curvature-driven level-set surface refinement.

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A meshless point collocation treatment of transient bioheat problems

Bourantas

Loukopoulos

Burganos

et al. 2014

Numer Methods Biomed Eng

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A meshless numerical method is proposed for the solution of the transient bioheat equation in two and three dimensions. The Pennes bioheat equation is extended in order to incorporate water evaporation, tissue damage, and temperature-dependent tissue properties during tumor ablation. The conductivity of the tissue is not assumed constant but is treated as a local function to simulate local variability due to the existence of usually unclear interfacing of healthy and pathological segments. In this way, one avoids the need for accurate identification of the boundaries between pathological and healthy regions, which is a typical problem in medical practice, and sidesteps, evidently, the corresponding mathematical treatment of such boundaries, which is usually a tedious procedure with some inevitable degree of approximation. The numerical results of the new method for test applications of the bioheat transfer equation are validated against analytical predictions and predictions of other numerical methods. 3D simulations are presented that involve the modeling of tumor ablation and account for metabolic heat generation, blood perfusion, and heat ablation using realistic values for the various parameters. An evaluation of the effective medium approximation to homogenize conductivity fields for use with the bioheat equation is also provided.

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Fast neighbor lists for adaptive-resolution particle simulations

Cited by 25 publications

References 29 publications

Adjoint-based shape optimization of fin geometry for heat transfer enhancement in solidification problem

Adjoint-based shape optimization of fin geometry for heat transfer enhancement in solidification problem

A self-organizing Lagrangian particle method for adaptive-resolution advection–diffusion simulations

A meshless point collocation treatment of transient bioheat problems

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