Dimensional Benchmark Problems Research Articles

A first‐order hyperbolic arbitrary Lagrangian Eulerian conservation formulation for non‐linear solid dynamics

SummaryThe paper introduces a computational framework using a novel Arbitrary Lagrangian Eulerian (ALE) formalism in the form of a system of first‐order conservation laws. In addition to the usual material and spatial configurations, an additional referential (intrinsic) configuration is introduced in order to disassociate material particles from mesh positions. Using isothermal hyperelasticity as a starting point, mass, linear momentum and total energy conservation equations are written and solved with respect to the reference configuration. In addition, with the purpose of guaranteeing equal order of convergence of strains/stresses and velocities/displacements, the computation of the standard deformation gradient tensor (measured from material to spatial configuration) is obtained via its multiplicative decomposition into two auxiliary deformation gradient tensors, both computed via additional first‐order conservation laws. Crucially, the new ALE conservative formulation will be shown to degenerate elegantly into alternative mixed systems of conservation laws such as Total Lagrangian, Eulerian and Updated Reference Lagrangian. Hyperbolicity of the system of conservation laws will be shown and the accurate wave speed bounds will be presented, the latter critical to ensure stability of explicit time integrators. For spatial discretisation, a vertex‐based Finite Volume method is employed and suitably adapted. To guarantee stability from both the continuum and the semi‐discretisation standpoints, an appropriate numerical interface flux (by means of the Rankine–Hugoniot jump conditions) is carefully designed and presented. Stability is demonstrated via the use of the time variation of the Hamiltonian of the system, seeking to ensure the positive production of numerical entropy. A range of three dimensional benchmark problems will be presented in order to demonstrate the robustness and reliability of the framework. Examples will be restricted to the case of isothermal reversible elasticity to demonstrate the potential of the new formulation.

A Weighted-Least-Squares Meshless Model for Non-Hydrostatic Shallow Water Waves

In this paper, an explicit time marching procedure for solving the non-hydrostatic shallow water equation (SWE) problems is developed. The procedure includes a process of prediction and several iterations of correction. In these processes, it is essential to accurately calculate the spatial derives of the physical quantities such as the temporal water depth, the average velocities in the horizontal and vertical directions, and the dynamic pressure at the bottom. The weighted-least-squares (WLS) meshless method is employed to calculate these spatial derivatives. Though the non-hydrostatic shallow water equations are two dimensional, on the focus of presenting this new time marching approach, we just use one dimensional benchmark problems to validate and demonstrate the stability and accuracy of the present model. Good agreements are found in the comparing the present numerical results with analytic solutions, experiment data, or other numerical results.

Nonlocal operator method for the Cahn-Hilliard phase field model

In this paper we propose a Nonlocal Operator Method (NOM) for the solution of the Cahn-Hilliard (CH) equation exploiting the higher order continuity of the NOM. The method is derived based on the method of weighted residuals and implemented in 2D and 3D. Periodic boundary conditions and solid-wall boundary conditions are considered. For these boundary conditions, the highest order in the NOM scheme is 2 and 3, respectively. The proposed NOM makes use of variable support domains allowing for adaptive refinement. The generalized α-method is employed for time integration and the Newton-Raphson method to iterate nonlinearity. The performance of the proposed method is demonstrated for several two and three dimensional benchmark problems. We also implemented a CH equation with 6th order partial differential derivative and studied the influence of higher order coefficients on the pattern evolution of the phase field.

High order central WENO-Implicit-Explicit Runge Kutta schemes for the BGK model on general polygonal meshes

In this work, a family of high order accurate Central Weighted ENO (CWENO) finite volume schemes for the solution of nonlinear kinetic equation of relaxation type is presented. After discretization of the velocity space by using a discrete ordinate approach, the space reconstruction is realized by integration over conformal arbitrary shaped closed space control volumes in a CWENO fashion. Compared to other WENO methods on unstructured meshes, in the method here presented, the total stencil size is the minimum possible and the linear weights can be arbitrarily chosen. These two aspects make their use for kinetic equations and the practical implementation on general unstructured meshes particularly interesting. The full discretization is then obtained by combining the previous phase-space approximation with an Implicit-Explicit Runge Kutta high order time discretization which guarantees stability, accuracy and preservation of the asymptotic state. In particular, to guarantee in the finite volume framework space accuracy higher than two, a new class of IMEX methods has been set into place and its properties have been studied. The formal order of accuracy is numerically measured for different regimes, computational performances of the proposed class of methods are tested on several standard two dimensional benchmark problems for kinetic equations. The novel methods are finally applied to a prototype engineering problem consisting in a supersonic flow around a NACA 0012 airfoil. In our computations we employ up to ≈325 millions of degrees of freedom and 256 GB of RAM run on 128 cores with Fortran-MPI providing evidence that the above schemes are suitable for implementation on parallel distributed memory supercomputers.

A mapping-function-free WENO-M scheme with low computational cost

The classical WENO-JS scheme developed by Jiang and Shu [5] suffers accuracy loss near a critical point in smooth region. An extra mapping process was introduced by Henrick et al. [6] to overcome this problem with significantly increased computational cost, known as WENO-M scheme. In order to reduce the cost brought by the mapping process, a pre-discrete mapping method is proposed in this article. The cost of this new method is not only low, but also independent of the complexity of mapping function. Numerical experiments with one- and two- dimensional benchmark problems are conducted to demonstrate the effectiveness of this new method. Compared to the original method, the cost is reduced by 52%.

An efficient variable interdependency-identification and decomposition by minimizing redundant computations for large-scale global optimization

Although many variable decomposition methods for cooperative co-evolution (CC) have been proposed, researches on scalable and efficient decomposition have rarely been done, particularly, for the large-scale global optimization (LSGO) problems. In this paper, we propose an efficient variable interdependency identification and decomposition method, called EVIID. Different from existing studies focusing on only limited scale efficient or accurate variable decomposition, our purpose is to develop a scalable variable decomposition method with high efficiency and accuracy even on very high-dimensional problems. EVIID utilizes three core strategies: a binary variable space search, a dynamic perturbation caching, and a pre-variable sorting. Their synergy effect enables scalable and efficient variable decomposition without sacrificing decomposition accuracy by pruning many redundant computations required to identify interdependencies among decision variables. In comprehensive experiments, EVIID showed highly scalable decomposition ability on 1000 to 10,000 dimensional benchmark problems compared against the state-of-the-art variable decomposition methods. Moreover, when EVIID was embedded into practical CC frameworks, it showed good optimization performance and also fast convergence.

Calculation of [formula omitted] modes of the multi-group neutron transport equation using the discrete ordinates and Finite Difference Method

The method explained in this paper solves the steady-state of the neutron transport equation for 1D and 2D systems modeled with Cartesian geometry, by using the Discrete Ordinates method SN for the angular discretization and the Finite Difference Method for the spatial discretization. The method applies the multi-group approach for any energy discretization, including upscattering terms. The method solves the steady-state equation by solving a generalized eigenvalue problem by means of a Krylov-Schur method. One of the main advantages of the method is the capability to calculate multiple eigenfunctions. The Discrete Ordinates methodology is used for the angular discretization, which uses a simple formulation involving the angles and direction cosines. The spatial discretization with Finite Difference Method is selected for its simplicity. The method is validated with several one-dimensional benchmark problems and four two dimensional benchmark problems. The results show good agreement with respect to the reference results for all the cases studied.

An efficient approximate moment method for multi-dimensional population balance models – Application to virus replication in multi-cellular systems

Many particulate processes in process and bioprocess engineering can be described with multi-dimensional population balances. Approximate moment methods are frequently used for their solution. In the present paper a new approach is presented, which is particular efficient when the number of internal coordinates is high. It combines the direct quadrature method of moments with monomial cubatures. With the new method the computational effort increases only polynomially, in the simplest case even only linearly with the number of internal coordinates, compared to an exponential increase for the well known Gausssian cubatures. The technique is evaluated for a five dimensional benchmark problem describing virus replication in continuous cell cultures. Furthermore, the algorithm is applied to analyze influenza virus replication in genetically modified cell lines.

Analysis of the convergence properties for a non-linear implicit Equilibrium Flux Method using Quasi Newton–Raphson and BiCGStab techniques

In this work, an investigation is carried out into the implicit formulation of the Equilibrium Flux Method (EFM) applied to the numerical solution of the Euler Equations for an ideal inviscid gas. The discretization employs a non-linear Finite Volume Method (FVM) approach which requires the solution to a system of non-linear equations, which is solved using various modifications to the Quasi-Newton–Raphson method. The core of the analysis presented here lies in the investigation of the convergence properties of the BiConjugate Stabilized (BiCGStab) method used to solve the JΔx=R where R is our vector of residuals computed directly using the Euler Equations, Δx is the increment to our estimated solution x for our system and J is the Jacobian of the residual functions. The increment to our solution Δx is modified to ensure the solution remains bound and finite. Results are shown for multiple one dimensional and two dimensional benchmark problems, with convergence properties of both the Newton–Raphson and BiCGStab procedures shown. Several conclusions may be drawn from the results shown: (a) increasing CFL numbers result in increasing dispersion errors, (b) higher order treatment of temporal derivatives results in lower condition numbers, resulting in fewer BiCGstab iterations per Newton–Raphson iteration, (c) higher order treatment of spatial derivatives results in higher condition numbers, hence requiring additional BiCGstab iterations, and (d) preconditioning using a simple Jacobi preconditioner significantly reduces the number of BiCGstab iterations required to obtain a solution.

High order exactly divergence-free Hybrid Discontinuous Galerkin Methods for unsteady incompressible flows

In this paper we present an efficient discretization method for the solution of the unsteady incompressible Navier–Stokes equations based on a high order (Hybrid) Discontinuous Galerkin formulation. The crucial component for the efficiency of the discretization method is the distinction between stiff linear parts and less stiff non-linear parts with respect to their temporal and spatial treatment.Exploiting the flexibility of operator-splitting time integration schemes we combine two spatial discretizations which are tailored for two simpler sub-problems: a corresponding hyperbolic transport problem and an unsteady Stokes problem.For the hyperbolic transport problem a spatial discretization with an Upwind Discontinuous Galerkin method and an explicit treatment in the time integration scheme is rather natural and allows for an efficient implementation. The treatment of the Stokes part involves the solution of linear systems. In this case a discretization with Hybrid Discontinuous Galerkin methods is better suited. We consider such a discretization for the Stokes part with two important features: H(div)-conforming finite elements to guarantee exactly divergence-free velocity solutions and a projection operator which reduces the number of globally coupled unknowns. We present the method, discuss implementational aspects and demonstrate the performance on two and three dimensional benchmark problems.

A neutronic benchmark specification and COMET solution for the Advanced Burner Test Reactor

This work describes a stylized three dimensional benchmark problem based on Argonne National Laboratory’s conceptual Advanced Burner Test Reactor design. This reactor is a sodium cooled fast reactor designed to burn recycled fuel to generate power while transmuting transuranic waste. The specification includes heterogeneity at both the assembly and core levels while the geometry and material compositions are both fully described. Material nuclear data is provided in the form of a set of 15 group macroscopic cross sections which were developed so that the benchmark specification could be used for transport code method verification.Using this benchmark and 15 group cross sections, the Coarse Mesh Transport Method (COMET) code was verified against the Monte Carlo code MCNP. Results were generated for three separate core cases: control rods out, near critical, and control rods in. Eigenvalue comparison results between COMET and MCNP are within 50pcm for all three cases, well within two standard deviations of the stochastic uncertainty of the solution methods. The fission density results are highly accurate with a pin fission density average discrepancy between COMET and MCNP of less than 0.5% for each model.

A multi-layer line search method to improve the initialization of optimization algorithms

We introduce a novel metaheuristic methodology to improve the initialization of a given deterministic or stochastic optimization algorithm. Our objective is to improve the performance of the considered algorithm, called core optimization algorithm, by reducing its number of cost function evaluations, by increasing its success rate and by boosting the precision of its results. In our approach, the core optimization is considered as a sub-optimization problem for a multi-layer line search method. The approach is presented and implemented for various particular core optimization algorithms: Steepest Descent, Heavy-Ball, Genetic Algorithm, Differential Evolution and Controlled Random Search. We validate our methodology by considering a set of low and high dimensional benchmark problems (i.e., problems of dimension between 2 and 1000). The results are compared to those obtained with the core optimization algorithms alone and with two additional global optimization methods (Direct Tabu Search and Continuous Greedy Randomized Adaptive Search). These latter also aim at improving the initial condition for the core algorithms. The numerical results seem to indicate that our approach improves the performances of the core optimization algorithms and allows to generate algorithms more efficient than the other optimization methods studied here. A Matlab optimization package called “Global Optimization Platform” (GOP), implementing the algorithms presented here, has been developed and can be downloaded at: http://www.mat.ucm.es/momat/software.htm

Analysis of the second order accurate uniform equilibrium flux method and its graphics processing unit acceleration

The extension of the Uniform Equilibrium Flux Method (UEFM) to second order accuracy in space is presented. This extension is made possible through the recasting of the original UEFM flux expressions from a volumetric flux to a surface flux, allowing for reconstruction through a Taylor series expansion of the resulting split surface fluxes at the cell interfaces. By doing so, we avoid the difficulties associated with integration of the gradient terms over velocity and physical space as required by the original UEFM fluxes. Analysis of the dissipative qualities of the renewed direction split UEFM flux expressions demonstrate that the numerical dissipation is a function of Mach number, with increasing amounts of dissipation present with increasing Mach numbers. Following this analysis, the higher order UEFM fluxes are applied to large scale parallel computation using Graphics Processing Units, or GPUs, through the use of CUDA. The vector split nature of the UEFM fluxes are well suited to GPU computation due to the high degree of locality. This parallelization is performed using a cell-based parallel paradigm through the creation of several key CUDA kernels for the calculation of split fluxes, gradient of split fluxes and state related computations. The algorithm is executed entirely on the GPU device, with the host remaining idle during the computation stage. The GPU accelerated UEFM algorithm is then applied to the solution of several two dimensional benchmark problems. Speedup of approximately 200 and 171 times for first order accuracy and second order accuracy respectively is demonstrated when using an Nvidia Tesla C2075 computing GPU compared to that of a single core of an Intel Xeon E5-2760 CPU.

A comparative study of penalization and phase field methods for the solution of the diffusion equation in complex geometries

We present a comparative study of penalization and phase field methods for the solution of the diffusion equation in complex geometries embedded using simple Cartesian meshes. The two methods have been widely employed to solve partial differential equations in complex and moving geometries for applications ranging from solid and fluid mechanics to biology and geophysics. Their popularity is largely due to their discretization on Cartesian meshes thus avoiding the need to create body-fitted grids. At the same time, there are questions regarding their accuracy and it appears that the use of each one is confined by disciplinary boundaries. Here, we compare penalization and phase field methods to handle problems with Neumann and Robin boundary conditions. We discuss extensions for Dirichlet boundary conditions and in turn compare with methods that have been explicitly designed to handle Dirichlet boundary conditions. The accuracy of all methods is analyzed using one and two dimensional benchmark problems such as the flow induced by an oscillating wall and by a cylinder performing rotary oscillations. This comparative study provides information to decide which methods to consider for a given application and their incorporation in broader computational frameworks. We demonstrate that phase field methods are more accurate than penalization methods on problems with Neumann boundary conditions and we present an error analysis explaining this result.

A discrete shuffled frog optimization algorithm

The shuffled frog leaping (SFL) optimization algorithm has been successful in solving a wide range of real-valued optimization problems. In this paper we present a discrete version of this algorithm and compare its performance with a SFL algorithm, a binary genetic algorithm (BGA), and a discrete particle swarm optimization (DPSO) algorithm on seven low dimensional and five high dimensional benchmark problems. The obtained results demonstrate that our proposed algorithm, i.e. the DSFL, outperforms the BGA and the DPSO in terms of both success rate and speed. On low dimensional functions and for large values of tolerance the DSFL is slower than the SFL, but their success rates are equal. Part of this slowness could be attributed to the extra bits used for data coding. By increasing number of variables and the required precision of answer, the DSFL performs very well in terms of both speed and success rate. For high dimensional problems, for intrinsically discrete problems, also when the required precision of answer is high, the DSFL is the most efficient method.

Simplified two and three dimensional HTTR benchmark problems

To assess the accuracy of diffusion or transport methods for reactor calculations, it is desirable to create heterogeneous benchmark problems that are typical of whole core configurations. In this paper we have created two and three dimensional numerical benchmark problems typical of high temperature gas cooled prismatic cores. Additionally, a single cell and single block benchmark problems are also included. These problems were derived from the HTTR start-up experiment. Since the primary utility of the benchmark problems is in code-to-code verification, minor details regarding geometry and material specification of the original experiment have been simplified while retaining the heterogeneity and the major physics properties of the core from a neutronics viewpoint. A six-group material (macroscopic) cross section library has been generated for the benchmark problems using the lattice depletion code HELIOS. Using this library, Monte Carlo solutions are presented for three configurations (all-rods-in, partially-controlled and all-rods-out) for both the 2D and 3D problems. These solutions include the core eigenvalues, the block (assembly) averaged fission densities, local peaking factors, the absorption densities in the burnable poison and control rods, and pin fission density distribution for selected blocks. Also included are the solutions for the single cell and single block problems.

Performance Evaluation of Multiagent Genetic Algorithm

Zhong et al. (2004 [IEEE Trans. on Systems, Man and Cybernetics (Part B), 34: 1128---1141]) proposed the multiagent genetic algorithm (MAGA) in their publication titled "A multiagent genetic algorithm for global numerical optimization". The MAGA exploits the known characteristics of some benchmark functions to achieve outstanding results. For example, the MAGA exploits the fact that all variables have the same numerical value at the global optimum and the same upper and lower bounds to solve several 100 dimensional and 1000 dimensional benchmark problems with high precision requiring on average 7000 and 16,000 function evaluations respectively. In this paper, we evaluate the performance of the MAGA experimentally1 and demonstrate that the performance of the MAGA significantly deteriorates when the relative positions of the variables at the global optimal point are shifted with respect to the search ranges.

On the solution of a minimum compliance topology optimisation problem by optimality criteria without a priori volume constraint specification

The paper presents a topology optimisation method based on optimality criteria for total potential energy maximisation with a volume constraint. The final volume of the optimal structural configuration has not to be specified a priori and is directly controlled by the stress, displacement or stiffness constraints defined at the design problem layout phase. The proposed method leads to the identification of well defined structures characterised by a small number of discrete elements with intermediate material properties within a limited number of iterations. The results obtained by solving several two dimensional benchmark problems are shown.

An Efficient Sequential Learning Algorithm for Growing and Pruning RBF (GAP-RBF) Networks

This paper presents a simple sequential growing and pruning algorithm for radial basis function (RBF) networks. The algorithm referred to as growing and pruning (GAP)-RBF uses the concept of "Significance" of a neuron and links it to the learning accuracy. "Significance" of a neuron is defined as its contribution to the network output averaged over all the input data received so far. Using a piecewise-linear approximation for the Gaussian function, a simple and efficient way of computing this significance has been derived for uniformly distributed input data. In the GAP-RBF algorithm, the growing and pruning are based on the significance of the "nearest" neuron. In this paper, the performance of the GAP-RBF learning algorithm is compared with other well-known sequential learning algorithms like RAN, RANEKF, and MRAN on an artificial problem with uniform input distribution and three real-world nonuniform, higher dimensional benchmark problems. The results indicate that the GAP-RBF algorithm can provide comparable generalization performance with a considerably reduced network size and training time.

Wavelets for electronic structure calculations

Molecular electronic structure calculations have a multi‐scale character through the presence of a set of singularities corresponding to atomic nuclei, and thus there exists a potential to improve the efficiency of these calculations using fast wavelet transform techniques. We report on the development of a one dimensional prototype benchmark problem of sufficient complexity to capture the features of 3‐D problems that are being solved today in quantum electronics calculations. Theoretical estimates of decay across scales and spatial distribution of wavelet coefficients for the solutions of the 1‐D and 3‐D problems are derived and verified experimentally. Equivalence in a multi‐resolution context of the solutions of the 1‐D prototype and the 3‐D problem is established.