WAVELET ADAPTIVE MULTIRESOLUTION REPRESENTATION: APPLICATIONS TO VISCOUS MULTISCALE FLOW SIMULATIONS

2006 ◽  
Vol 04 (02) ◽  
pp. 333-343 ◽  
Author(s):  
YEVGENII A. RASTIGEJEV ◽  
SAMUEL PAOLUCCI
Keyword(s):  
Degrees Of Freedom ◽  
Computational Cost ◽  
Reynolds Numbers ◽  
Great Ability ◽  
Driven Cavity ◽  
Multiresolution Representation ◽  
Order Of Magnitude ◽  
Comparable Accuracy ◽  
Solution Accuracy ◽  
Low Computational Cost

We present a new wavelet-based adaptive multiresolution representation (WAMR) algorithm for the numerical solution of multiscale evolution problems. Key features of the algorithm are fast procedures for grid rearrangement, computation of derivatives, as well as the ability to minimize the degrees-of-freedom for a prescribed solution accuracy. To demonstrate the efficiency and accuracy of the algorithm, we use it to solve the two-dimensional benchmark problem of incompressible fluid-flow in a lid-driven cavity at large Reynolds numbers. The numerical experiments demonstrate the great ability of the algorithm to adapt to different scales at different locations and at different times so as to produce accurate solutions at low computational cost. Specifically, we show that solutions of comparable accuracy as the benchmarks are obtained with more than an order of magnitude reduction in degrees-of-freedom.

Multiscale Calculations Using an Adaptive Wavelet Algorithm

2002 ◽  
Author(s):  
Yevgenii A. Rastigejev ◽  
Samuel Paolucci
Keyword(s):  
Degrees Of Freedom ◽  
Computational Cost ◽  
Reynolds Numbers ◽  
Adaptive Wavelet ◽  
Great Ability ◽  
Driven Cavity ◽  
Multiresolution Representation ◽  
Order Of Magnitude ◽  
Comparable Accuracy ◽  
Solution Accuracy

We present a new wavelet-based adaptive multiresolution representation (WAMR) algorithm for the numerical solution of multiscale evolution problems. Key features of the algorithm are fast procedures for grid rearrangement, computation of derivatives, as well as the ability to minimize the degrees of freedom for a prescribed solution accuracy. To demonstrate the efficiency and accuracy of the algorithm, we use it to solve the two-dimensional benchmark problem of incompressible fluid-flow in a lid-driven cavity at large Reynolds numbers. The numerical experiments demonstrate the great ability of the algorithm to adapt to different scales at different locations and at different times so as to produce accurate solutions at low computational cost. Specifically, we show that solutions of comparable accuracy as the benchmarks are obtained with more than an order of magnitude reduction in degrees of freedom.

A 3D Complexity-Adaptive Approach to Exploit Sparsity in Elastic Wave Propagation

Geophysics ◽  
2021 ◽  
pp. 1-64
Author(s):  
Claudia Haindl ◽  
Kuangdai Leng ◽  
Tarje Nissen-Meyer
Keyword(s):  
Degrees Of Freedom ◽  
Computational Cost ◽  
Parameter Tuning ◽  
Performance Limits ◽  
Absorbing Boundary ◽  
Adaptive Approach ◽  
Order Of Magnitude ◽  
Speed Up ◽  
Complex Settings ◽  
Close Fit

We present an adaptive approach to seismic modeling by which the computational cost of a 3D simulation can be reduced while retaining resolution and accuracy. This Azimuthal Complexity Adaptation (ACA) approach relies upon the inherent smoothness of wavefields around the azimuth of a source-centered cylindrical coordinate system. Azimuthal oversampling is thereby detected and eliminated. The ACA method has recently been introduced as part of AxiSEM3D, an open-source solver for global seismology. We employ a generalization of this solver which can handle local-scale Cartesian models, and which features a combination of an absorbing boundary condition and a sponge boundary with automated parameter tuning. The ACA method is benchmarked against an established 3D method using a model featuring bathymetry and a salt body. We obtain a close fit where the models are implemented equally in both solvers and an expectedly poor fit otherwise, with the ACA method running an order of magnitude faster than the classic 3D method. Further, we present maps of maximum azimuthal wavenumbers that are created to facilitate azimuthal complexity adaptation. We show how these maps can be interpreted in terms of the 3D complexity of the wavefield and in terms of seismic resolution. The expected performance limits of the ACA method for complex 3D structures are tested on the SEG/EAGE salt model. In this case, ACA still reduces the overall degrees of freedom by 92% compared to a complexity-blind AxiSEM3D simulation. In comparison with the reference 3D method, we again find a close fit and a speed-up of a factor 7. We explore how the performance of ACA is affected by model smoothness by subjecting the SEG/EAGE salt model to Gaussian smoothing. This results in a doubling of the speed-up. ACA thus represents a convergent, versatile and efficient method for a variety of complex settings and scales.

Implementation of Low Computational Cost Nonlinear Formulation for Multibody Railroad Vehicle Systems

2007 ◽  
Author(s):  
Graham G. Sanborn ◽  
Jason R. Heineman ◽  
Ahmed A. Shabana
Keyword(s):  
Degrees Of Freedom ◽  
Computational Cost ◽  
Companion Paper ◽  
Computer Interface ◽  
Model Parameters ◽  
Nonlinear Formulation ◽  
Vehicle Systems ◽  
Railroad Applications ◽  
Railroad Vehicle ◽  
Low Computational Cost

In a companion paper [1], a low computational cost nonlinear formulation was presented to for the analysis of the forces of long trains. The formulation allows systematically changing the number of degrees of freedom of each rail car and includes the force inputs that are typically found in railroad applications. In this paper, the implementation of this nonlinear formulation is described. A computer interface is also developed in order to allow the user to operate a virtual train model in real time and also change the model parameters and car connectivity conditions. Numerical results are presented in order to demonstrate the use of the formulation proposed and its computer implementation.

scholarly journals GEOMETRICAL EVALUATION OF RECTANGULAR FIN MOUNTED IN LATERAL SURFACE OF LID-DRIVEN CAVITY FORCED CONVECTIVE FLOWS

2019 ◽  
Vol 18 (2) ◽  
pp. 98
Author(s):  
E. D. dos Santos ◽  
P. M. Rodrigues ◽  
L. A. Isoldi ◽  
J. F. Prolo Filho ◽  
L. A. O. Rocha ◽  
...  
Keyword(s):  
Aspect Ratio ◽  
Thermal Performance ◽  
Degrees Of Freedom ◽  
Reynolds Numbers ◽  
Cavity Surface ◽  
Square Cavity ◽  
Driven Cavity ◽  
Convective Flows ◽  
Constructal Design ◽  
Volume Method

In this work, it is investigated the geometric effect of rectangular fin inserted in a lid-driven square cavity over thermal performance of laminar, incompressible, steady and forced convective flows. This study is performed by applying Constructal Design to maximize the heat transfer between the fin and the cavity flow. For that, the problem is subjected to two constraints: area of the cavity and area of rectangular fin, and two degrees of freedom: height/length ratio of rectangular fin (H1/L1) and its position in upstream surface of the cavity (S/A1/2). It is considered here some fixed parameters, as the ratio between the fin and cavity areas (ϕ = 0.05), the aspect ratio of the cavity dimensions (H/L = 1.0) and Prandtl number (Pr = 0.71). The fin aspect ratio (H1/L1) was varied for three different placements of the fin at the upstream cavity surface (S/A1/2 = 0.1, 0.5 and 0.9) which represents a lower, intermediate and upper positions of the fin. The effects of the fin geometry over the spatial-averaged Nusselt number ( ) is investigated for three different Reynolds numbers (ReH = 10, 102 and 103). The conservation equations of mass, momentum and energy were numerically solved with the Finite Volume Method. Results showed that both degrees of freedom (H1/L1 and S/A1/2) had a strong influence over , mainly for higher magnitudes of Reynolds number. Moreover, the best thermal performance is reached when the fin is placed near the upper surface of the cavity for an intermediate ratio between height and length of rectangular fin, more precisely when (S/A1/2)o = 0.9 and (H1/L1)oo = 2.0.

A Low Computational Cost Nonlinear Formulation for Multibody Railroad Vehicle Systems

2007 ◽  
Author(s):  
Graham G. Sanborn ◽  
Jason R. Heineman ◽  
Ahmed A. Shabana
Keyword(s):  
Coordinate System ◽  
Degrees Of Freedom ◽  
Dynamic Models ◽  
Computational Cost ◽  
Space Curve ◽  
The Body ◽  
Degree Of Freedom ◽  
Arbitrary Body ◽  
Resistance Forces ◽  
Low Computational Cost

In this investigation, a multibody system formulation for the nonlinear dynamics of railroad vehicles is developed. This formulation, which permits developing simplified models for the forces acting on rail cars, allows the analysis of long trains at a low computational cost. In the dynamic models developed using the formulation proposed in this investigation, each rail car can be represented as a single rigid body. The configurations of the bodies in a train model are defined with respect to trajectory coordinate systems which follow a space curve whose geometry is defined at a preprocessing stage. In the formulation presented in this study, the number of degrees of freedom of an arbitrary body can be varied from one to six degrees of freedom. The principal degree of freedom of an arbitrary body is the arc length along the space curve. This degree of freedom defines the location of the origin and the orientation of the body trajectory coordinate system. The other five degrees of freedom define the location and orientation of the body with respect to the body trajectory coordinate system. The nonlinear equations of motion of the bodies in a train model are developed using the three-dimensional Newton-Euler equations. These equations are then expressed in terms of the trajectory coordinates and their derivatives. To this end, a velocity transformation is obtained by expressing the Cartesian and angular velocities of the bodies in terms of the time derivatives of the trajectory coordinates. Various force element models particular to rail cars are developed in this study. These forces include tractive effort, and air brake and dynamic brake forces, as well as a model of available wheel-rail adhesion. Additionally, various types of couplers are formulated as force elements, allowing the modeling of connections between cars. Resistance forces are also modeled in order to be able to simulate rolling, curve, and air resistance forces that may act on the cars during the train operations.

Verification of Length Scale Effects on Solution Accuracy of Hybrid RANS-LES Methods

2016 ◽  
Author(s):  
Gaurav Kumar ◽  
Harish Gopalan ◽  
Dominic Chandar ◽  
Vinh-Tan Nguyen ◽  
Ashoke De
Keyword(s):  
Bluff Body ◽  
Scale Effects ◽  
Computational Cost ◽  
Separated Flow ◽  
Reynolds Numbers ◽  
Test Problems ◽  
Length Scale ◽  
High Reynolds ◽  
Solution Accuracy ◽  
Bluff Body Flows

Hybrid RANS-LES methods are gaining popularity for the simulation of the complex bluff body flows at high Reynolds numbers due to their reduced computational cost and good accuracy. A number of such methods have been proposed in the literature. Each of these methods have enjoyed varying degree of success for different applications. One of the most important parameter which determines the switching between near-wall RANS region and off-body LES region is the length scale parameter. This parameter can be grid based or physics based and numerous choices exist for defining this parameter. This study proposes to investigate the effect of this parameter on the size of the RANS and LES regions and also on the solution accuracy. Four test problems are chosen covering attached, mildly separated and massively separated flow regimes. Results will help us to identify length scale definitions to be used for different flow scenarios.

Driving Torsion Scans with Wavefront Propagation

2020 ◽  
Author(s):  
Yudong Qiu ◽  
Daniel Smith ◽  
Chaya Stern ◽  
mudong feng ◽  
Lee-Ping Wang
Keyword(s):  
Potential Energy ◽  
Force Field ◽  
Degrees Of Freedom ◽  
Computational Cost ◽  
Initial Guess ◽  
Field Development ◽  
Force Field Development ◽  
Wavefront Propagation ◽  
Open Source Software Package

<div>The parameterization of torsional / dihedral angle potential energy terms is a crucial part of developing molecular mechanics force fields.</div><div>Quantum mechanical (QM) methods are often used to provide samples of the potential energy surface (PES) for fitting the empirical parameters in these force field terms.</div><div>To ensure that the sampled molecular configurations are thermodynamically feasible, constrained QM geometry optimizations are typically carried out, which relax the orthogonal degrees of freedom while fixing the target torsion angle(s) on a grid of values.</div><div>However, the quality of results and computational cost are affected by various factors on a non-trivial PES, such as dependence on the chosen scan direction and the lack of efficient approaches to integrate results started from multiple initial guesses.</div><div>In this paper we propose a systematic and versatile workflow called \textit{TorsionDrive} to generate energy-minimized structures on a grid of torsion constraints by means of a recursive wavefront propagation algorithm, which resolves the deficiencies of conventional scanning approaches and generates higher quality QM data for force field development.</div><div>The capabilities of our method are presented for multi-dimensional scans and multiple initial guess structures, and an integration with the MolSSI QCArchive distributed computing ecosystem is described.</div><div>The method is implemented in an open-source software package that is compatible with many QM software packages and energy minimization codes.</div>

scholarly journals Nebula: ultra-efficient mapping-free structural variant genotyper

2021 ◽  
Author(s):  
Parsoa Khorsand ◽  
Fereydoun Hormozdiari
Keyword(s):  
Large Scale ◽  
Structural Variants ◽  
Sequencing Technologies ◽  
Generic Framework ◽  
Common Genetic Variants ◽  
Order Of Magnitude ◽  
Complex Events ◽  
Comparable Accuracy ◽  
Using Data ◽  
Computational Resources

Abstract Large scale catalogs of common genetic variants (including indels and structural variants) are being created using data from second and third generation whole-genome sequencing technologies. However, the genotyping of these variants in newly sequenced samples is a nontrivial task that requires extensive computational resources. Furthermore, current approaches are mostly limited to only specific types of variants and are generally prone to various errors and ambiguities when genotyping complex events. We are proposing an ultra-efficient approach for genotyping any type of structural variation that is not limited by the shortcomings and complexities of current mapping-based approaches. Our method Nebula utilizes the changes in the count of k-mers to predict the genotype of structural variants. We have shown that not only Nebula is an order of magnitude faster than mapping based approaches for genotyping structural variants, but also has comparable accuracy to state-of-the-art approaches. Furthermore, Nebula is a generic framework not limited to any specific type of event. Nebula is publicly available at https://github.com/Parsoa/Nebula.

scholarly journals Computing Expectiles Using k-Nearest Neighbours Approach

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 645
Author(s):  
Muhammad Farooq ◽  
Sehrish Sarfraz ◽  
Christophe Chesneau ◽  
Mahmood Ul Hassan ◽  
Muhammad Ali Raza ◽  
...  
Keyword(s):  
Computational Cost ◽  
Real Life ◽  
Distance Measures ◽  
Computational Time ◽  
High Dimensional ◽  
Test Error ◽  
Nearest Neighbours ◽  
Comparable Performance ◽  
Asymmetric Least Squares ◽  
Low Computational Cost

Expectiles have gained considerable attention in recent years due to wide applications in many areas. In this study, the k-nearest neighbours approach, together with the asymmetric least squares loss function, called ex-kNN, is proposed for computing expectiles. Firstly, the effect of various distance measures on ex-kNN in terms of test error and computational time is evaluated. It is found that Canberra, Lorentzian, and Soergel distance measures lead to minimum test error, whereas Euclidean, Canberra, and Average of (L1,L∞) lead to a low computational cost. Secondly, the performance of ex-kNN is compared with existing packages er-boost and ex-svm for computing expectiles that are based on nine real life examples. Depending on the nature of data, the ex-kNN showed two to 10 times better performance than er-boost and comparable performance with ex-svm regarding test error. Computationally, the ex-kNN is found two to five times faster than ex-svm and much faster than er-boost, particularly, in the case of high dimensional data.

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