Dispersion analysis of numerical wave propagation and its computational consequences

We present in this paper a comparison of the dispersion properties for several finite-difference approximations of the acoustic wave equation. We investigate the compact and staggered schemes of fourth order accuracy in space and of second order or fourth order accuracy in time. We derive the computational cost of the simulation implied by a precision criterion on the numerical simulation (maximum allowed error in phase or group velocity). We conclude that for moderate accuracy the staggered scheme of second order in time is more efficient, whereas for very precise simulation the compact scheme of fourth order in time is a better choice. The comparison increasingly favors the lower order staggered scheme as the dimension increases. In three dimensional simulation, the cost of extremely precise simulation with any of the schemes is very large, whereas for simulation of moderate precision the staggered scheme is the least expensive.     

Unconditionally Optimal Error Estimates of a Linearized Galerkin Method for Nonlinear Time Fractional Reaction–Subdiffusion Equations

This paper is concerned with unconditionally optimal error estimates of linearized Galerkin finite element methods to numerically solve some multi-dimensional fractional reaction–subdiffusion equations, while the classical analysis for numerical approximation of multi-dimensional nonlinear parabolic problems usually require a restriction on the time-step, which is dependent on the spatial grid size. To obtain the unconditionally optimal error estimates, the key point is to obtain the boundedness of numerical solutions in the \(L^\infty \)-norm. For this, we introduce a time-discrete elliptic equation, construct an energy function for the nonlocal problem, and handle the error summation properly. Compared with integer-order nonlinear problems, the nonlocal convolution in the time fractional derivative causes much difficulties in developing and analyzing numerical schemes. Numerical examples are given to validate our theoretical results.     

Time-varying high-gain observers for numerical differentiation

In this paper, we propose high-gain numerical differentiators for estimating the higher derivatives of a given signal. We consider time varying high-gain vectors converging exponentially to the high-gain vectors introduced by F. Esfandiari and K.H. Khalil (1992) in an earlier paper. The dynamics of these time-varying high-gain vectors can be chosen in order to achieve specific objectives, such as peaking attenuation and low sensitivity with respect to noise disturbance. In particular, we show that the numerical differentiator introduced in an earlier paper avoids the peaking phenomenon in the sense of H.J. Sussmann and P.V. Kokotovic (1991), i.e., there is no unbounded overshoot of the error estimate during the initial times. We also propose another numerical differentiator which filters the reference signal with respect to a very simple quadratic cost.     

Towards optimal allocation of computer resources: Trade-offs between uncertainty quantification,discretization and model reduction

The computational complexity of numerical models can be broken down into contributions ranging from spatial, temporal and stochastic resolution, e.g., spatial grid resolution, time step size and number of repeated simulations dedicated to quantify uncertainty. Controlling these resolutions allows keeping the computational cost at a tractable level whilst still aiming at accurate and robust predictions. The objective of this work is to introduce a framework that optimally allocates the available computational resources in order to achieve highest accuracy associated with a given prediction goal. Our analysis is based on the idea to jointly consider the discretization errors and computational costs of all individual model dimensions (physical space, time, parameter space). This yields a cost-to-error surface which serves to aid modelers in finding an optimal allocation of the computational resources (ORA). As a pragmatic way to proceed, we propose running small cost-efficient pre-investigations in order to estimate the joint cost-to-error surface, then fit underlying complexity and error models, decide upon a computational design for the full simulation, and finally to perform the designed simulation at near-optimal costs-to-accuracy ratio. We illustrate our approach with three examples from subsurface hydrogeology and show that the computational costs can be substantially reduced when allocating computational resources wisely and in a situation-specific and task-specific manner. We conclude that the ORA depends on a multitude of parameters, assumptions and problem-specific features and, hence, ORA needs to be determined carefully prior to each investigation.     

A comparison of some model order reduction methods for fast simulation of soft tissue response using the point collocation-based method of finite spheres

In this paper we develop the point collocation-based method of finite spheres (PCMFS) to simulate the viscoelastic response of soft biological tissues and evaluate the effectiveness of model order reduction methods such as modal truncation (MT), Hankel optimal model and truncated balanced realization (TBR) techniques for PCMFS. The PCMFS is a physics-based meshfree numerical technique for real time simulation of surgical procedures. Since computational speed has a significant role in simulation of surgical procedures, model order reduction methods have been compared for relative gains in efficiency and computational accuracy. Of these methods, TBR results in the highest accuracy with an average error which is within 3.37% of the full model while MT results in the highest efficiency with a computational cost reduction of 54.2% compared to the full model.     

Estimation with lossy measurements: jump estimators for jump systems

In this paper, we consider estimation with lossy measurements. This problem can arise when measurements are communicated over wireless channels. We model the plant/measurement loss process as a Markovian jump linear system. While the time-varying Kalman estimator (TVKE) is known to be optimal, we introduce a simpler design, termed a jump linear estimator (JLE), to cope with losses. A JLE has predictor/corrector form, but at each time selects a corrector gain from a finite set of precalculated gains. The motivation for the JLE is twofold. The computational burden of the JLE is less than that of the TVKE and the estimation errors expected when using JLE provide an upper bound for those expected when using TVKE. We then introduce a special class of JLE, termed finite loss history estimators (FLHE), which uses a canonical gain selection logic. A notion of optimality for the FLHE is defined and an optimal synthesis method is given. The proposed design method is compared to TVKE in a simulation study.     

A Survey of Model Reduction by Balanced Truncation and Some New Results

Balanced truncation is one of the most common model reduction schemes. In this note, we present a survey of balancing related model reduction methods and their corresponding error norms, and also introduce some new results. Five balancing methods are studied: (1) Lyapunov balancing, (2) stochastic balancing, (3) bounded real balancing, (4) positive real balancing and (5) frequency weighted balancing. For positive real balancing, we introduce a multiplicative-type error bound. Moreover, for a certain subclass of positive real systems, a modified positive-real balancing scheme with an absolute error bound is proposed. We also develop a new frequency-weighted balanced reduction method with a simple bound on the error system based on the frequency domain representations of the system gramians. Two numerical examples are illustrated to verify the efficiency of the proposed methods.     

Dispersion and Dissipation Error in High-Order Runge-Kutta Discontinuous Galerkin Discretisations of the Maxwell Equations

Different time-stepping methods for a nodal high-order discontinuous Galerkin discretisation of the Maxwell equations are discussed. A comparison between the most popular choices of Runge-Kutta (RK) methods is made from the point of view of accuracy and computational work. By choosing the strong-stability-preserving Runge-Kutta (SSP-RK) time-integration method of order consistent with the polynomial order of the spatial discretisation, better accuracy can be attained compared with fixed-order schemes. Moreover, this comes without a significant increase in the computational work. A numerical Fourier analysis is performed for this Runge-Kutta discontinuous Galerkin (RKDG) discretisation to gain insight into the dispersion and dissipation properties of the fully discrete scheme. The analysis is carried out on both the one-dimensional and the two-dimensional fully discrete schemes and, in the latter case, on uniform as well as on non-uniform meshes. It also provides practical information on the convergence of the dissipation and dispersion error up to polynomial order 10 for the one-dimensional fully discrete scheme.     

Hermitian one-step numerical integration algorithm for structural dynamic equations

Single parametric hermitian finite difference operators are employed to derive an unconditionally stable one-step direct integration algorithm family for structural dynamic equations. Accuracy is considered in terms of local truncation error and exponential truncation error corresponding to the period dispersion, numerical damping and spectral radius. This algorithm family presents computational effort similar to Newmark's method for the lumped model.     

Dispersion analysis and computational efficiency of elastic lattice methods for seismic wave propagation

Discrete particle methods or elastic lattice methods represent a 3D elastic solid by a series of interconnected springs arranged on a regular lattice. Generally, these methods only consider nearest neighbour interactions, i.e. they are first-order in space. These interconnected springs interacted through a force term (Hooke's Law for an elastic body), which when viewed on a macroscopic scale provide a numerical solution for the elastodynamic wave equations. Along with solving the elastodynamic wave equations these schemes are capable of simulating elastic static deformation. However, as these methods rely on nearest neighbour interactions they suffer from more pronounced numerical dispersion than traditional continuum methods. By including a new force term, the numerical dispersion can be reduced while keeping the flexibility of the nearest neighbour interaction rule. We present results of simulations where the additional force term reduces the numerical dispersion and increases the accuracy of the elastic lattice method solution. The computational efficiency and parallel scaling of this method on multiple processors is compared with a finite-difference solution to assess the computational cost of using this approach for simulating seismic wave propagation. We also show the applicability of this method to modelling seismic propagation in a complex Earth model.     

Optimal Strong-Stability-Preserving Time-Stepping Schemes with Fast Downwind Spatial Discretizations

In the field of strong-stability-preserving time discretizations, a number of researchers have considered using both upwind and downwind approximations for the same derivative, in order to guarantee that some strong stability condition will be preserved. The cost of computing both the upwind and downwind operator has always been assumed to be double that of computing only one of the two. However, in this paper we show that for the weighted essentially non-oscillatory method it is often possible to compute both these operators at a cost that is far below twice the cost of computing only one. This gives rise to the need for optimal strong-stability-preserving time-stepping schemes which take into account the different possible cost increments. We construct explicit linear multistep schemes up to order six and explicit Runge–Kutta schemes up to order four which are optimal over a range of incremental costs     

Certified Reduced Basis Methods for Parametrized Elliptic Optimal Control Problems with Distributed Controls

In this paper, we consider the efficient and reliable solution of distributed optimal control problems governed by parametrized elliptic partial differential equations. The reduced basis method is used as a low-dimensional surrogate model to solve the optimal control problem. To this end, we introduce reduced basis spaces not only for the state and adjoint variable but also for the distributed control variable. We also propose two different error estimation procedures that provide rigorous bounds for the error in the optimal control and the associated cost functional. The reduced basis optimal control problem and associated a posteriori error bounds can be efficiently evaluated in an offline–online computational procedure, thus making our approach relevant in the many-query or real-time context. We compare our bounds with a previously proposed bound based on the Banach–Ne?as–Babu?ka theory and present numerical results for two model problems: a Graetz flow problem and a heat transfer problem. Finally, we also apply and test the performance of our newly proposed bound on a hyperthermia treatment planning problem.     

An Adaptive SDG Method for the Stokes System

Staggered grid techniques are attractive ideas for flow problems due to their more enhanced conservation properties. Recently, a staggered discontinuous Galerkin method is developed for the Stokes system. This method has several distinctive advantages, namely high order optimal convergence as well as local and global conservation properties. In addition, a local postprocessing technique is developed, and the postprocessed velocity is superconvergent and pointwisely divergence-free. Thus, the staggered discontinuous Galerkin method provides a convincing alternative to existing schemes. For problems with corner singularities and flows in porous media, adaptive mesh refinement is crucial in order to reduce the computational cost. In this paper, we will derive a computable error indicator for the staggered discontinuous Galerkin method and prove that this indicator is both efficient and reliable. Moreover, we will present some numerical results with corner singularities and flows in porous media to show that the proposed error indicator gives a good performance.     

A full discretisation of the time-dependent Boussinesq (buoyancy) model with nonlinear viscosity

In this article, we study the time dependent Boussinesq (buoyancy) model with nonlinear viscosity depending on the temperature. We propose and analyze first and second order numerical schemes based on finite element methods. An optimal a priori error estimate is then derived for each numerical scheme. Numerical experiments are presented that confirm the theoretical accuracy of the discretization.     

The infrastructure efficiency of cellular wireless networks

Providing downlink wireless coverage is expensive and represents a dominant variable cost for mobile communication operators. It is vital that operators select base station locations so that efficiency is achieved, with high coverage relative to total cost of the selected operational base stations. However, efficiency in this context has not previously received explicit analysis. In this paper, we explicitly study cell plan infrastructure efficiency. We determine the density of macro cells which gives maximal coverage at minimal cost, modelling an irregular dispersion of candidate base station locations with varying procurement costs. An empirical investigation is undertaken consisting of 585 experiments using 45 synthesised test problems. The results provide evidence indicating the optimal relative size for inter-cell overlap. This is a new and important observation.We introduce and assess the marginal cost of service coverage. This represents the lowest rate at which infrastructure cost must increase to facilitate higher levels of service coverage. Two important conclusions are drawn. Firstly, the results quantify a significant advantage from increasing candidate site density. Secondly, marginal cost emerges as a powerful concept for analysing the impact of investment. We observe common trends in the behaviour of this function, which quantify a rapid diminishing return (in terms of service coverage) for additional infrastructure expenditure. Finally we consider the spectral implications of increased cell density. As cell overlap increases, we determine the additional span of channels needed to satisfy signal-to-interference requirements for service area coverage. We explain how these results are of practical use in cellular network planning.     

On the simulation of wave propagation with a higher-order finite volume scheme based on Reproducing Kernel Methods

In this work we study the dispersion and dissipation characteristics of a higher-order finite volume method based on Moving Least Squares approximations (FV-MLS), and we analyze the influence of the kernel parameters on the properties of the scheme. Several numerical examples are included. The results clearly show a significant improvement of dispersion and dissipation properties of the numerical method if the third-order FV-MLS scheme is used compared with the second-order one. Moreover, with the explicit fourth-order Runge–Kutta scheme the dispersion error is lower than with the third-order Runge–Kutta scheme, whereas the dissipation error is similar for both time-integration schemes. It is also shown than a CFL number lower than 0.8 is required to avoid an unacceptable dispersion error.     

Efficient purchaser incentive when dealing with suppliers implementing continuous improvement plans

This paper presents incentive schemes in the framework of a collaborative purchasing cost reduction process with a supplier implementing a continuous improvement plan. Using a stochastic decision process formulation, we analyze the structure of the optimal policy and characterize its numerical robustness through numerical applications solved by dynamic programming. Then, we analyze two purchaser incentive schemes observed in practice. First, we describe some theoretical properties of the policies associated with these two schemes (schemes I and II) and show that these policies exhibit nonoptimal structures. Second, we estimate the quantitative loss for typical parameter values and, in particular, we show that for certain businesses this loss is significant. Then, we propose two easy‐to‐implement improvements (schemes III and IV), which result in near‐optimal solutions and a significant impact on purchasing cost performances.     

Quantifying quantum correlations in fermionic systems using witness operators

We present a method to quantify quantum correlations in arbitrary systems of indistinguishable fermions using witness operators. The method associates the problem of finding the optimal entanglement witness of a state with a class of problems known as semidefinite programs, which can be solved efficiently with arbitrary accuracy. Based on these optimal witnesses, we introduce a measure of quantum correlations which has an interpretation analogous to the Generalized Robustness of entanglement. We also extend the notion of quantum discord to the case of indistinguishable fermions, and propose a geometric quantifier, which is compared to our entanglement measure. Our numerical results show a remarkable equivalence between the proposed Generalized Robustness and the Schliemann concurrence, which are equal for pure states. For mixed states, the Schliemann concurrence presents itself as an upper bound for the Generalized Robustness. The quantum discord is also found to be an upper bound for the entanglement.     

Petascale large eddy simulation of jet engine noise based on the truncated SPIKE algorithm

With the emergence of petascale computing platforms, high-fidelity computational aeroacoustics (CAA) simulation has become a feasible, robust and accurate tool that complements theoretical and empirical approaches in the prediction of sound levels generated by aircraft airframes and engines. Differentiating itself from the broader discipline of computational fluid dynamics, CAA is particularly challenging as it demands high accuracy, good spectral resolution, and low dispersion and diffusion errors from the underlying numerical methods. Large eddy simulation based on space-implicit high-order compact finite difference schemes has been shown to meet such stringent requirements. In this paper, we discuss a new, scalable parallelization scheme with a three-dimensional computational space partitioning. Unlike many traditional multiblock computational fluid dynamics (CFD) methods, our partitioning is non-overlapping. We use the truncated SPIKE algorithm to solve the governing equations accurately and limit one-sided biased differentiation to just the physical boundaries. We present experimental performance data collected on Kraken and Ranger, two near-petascale computing platforms.     

A Multi-Level Mixed Element Method for the Eigenvalue Problem of Biharmonic Equation

In this paper, we discuss approximating the eigenvalue problem of biharmonic equation. We first present an equivalent mixed formulation which admits natural nested discretization. Then, we present multi-level finite element schemes by implementing the algorithm as in Lin and Xie (Math Comput 84:71–88, 2015) to the nested discretizations on a series of nested grids. The multi-level mixed scheme for the biharmonic eigenvalue problem possesses optimal convergence rate and optimal computational cost. Both theoretical analysis and numerical verifications are presented.