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3 edition of Parallel computing using a Lagrangian formulation found in the catalog.

Parallel computing using a Lagrangian formulation

Parallel computing using a Lagrangian formulation

  • 159 Want to read
  • 26 Currently reading

Published by National Aeronautics and Space Administration, For sale by the National Technical Information Service in [Washington, DC], [Springfield, Va .
Written in

  • Parallel computers.,
  • Lagrange equations.

  • Edition Notes

    StatementMay-Fun Liou and Ching Yuen Loh.
    SeriesNASA technical memorandum -- 104446.
    ContributionsLoh, Ching-Yuen., United States. National Aeronautics and Space Administration.
    The Physical Object
    Pagination1 v.
    ID Numbers
    Open LibraryOL15300374M

    Multi-disciplinary design optimization (MDO) is a field of engineering that uses optimization methods to solve design problems incorporating a number of disciplines. It is also known as multidisciplinary system design optimization (MSDO). MDO allows designers to incorporate all relevant disciplines simultaneously. series by Torsten Fliessbach. This is the book I started learning mechanics with and especially for people unfamiliar with the subject it gives a good, but slowly-paced introduction. The third and final book I based this lecture on, is the first part of an even more famous series - Theoretical Physics by Landau and Lifschitz. These lecture books.

    Q&A for active researchers, academics and students of physics. Stack Exchange network consists of Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.. Visit Stack Exchange. bound-based optimization algorithm applies Lagrangian decomposition to (1) generate tight lower bounds by exploiting the structure of the problem and (2) enable parallel computing of subsystems and use of efficient dual methods. We apply the approach to two important product design applications: (1) product family optimization with a fixed-.

    It proposes a fast, accurate and scalable Graphic Processing Unit (GPU)-based implementation of the total Lagrangian FEM using implicit time integration for dynamic nonlinear deformation analysis. This is a general formulation valid for large deformations and strains and can account for . A GPU-accelerated Linear Assignment Problem (LAP) solver is leveraged in concert with the Lagrangian scheme for further speed-up. We also implemented a multi-GPU variant of this algorithm which maintains a good speedup profile, when tested on problems with 31 billion variables, on up to GPUs.

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Parallel computing using a Lagrangian formulation Download PDF EPUB FB2

Get this from a library. Parallel computing using a Lagrangian formulation. [May-Fun Liou; Ching Y Loh; United States. National Aeronautics and Space Administration.].

This paper adopts a new Lagrangian formulation of the Euler equation for the calculation of two dimensional supersonic steady flow.

The Lagrangian formulation represents the inherent parallelism of the flow field better than the common Eulerian formulation and offers a competitive alternative on parallel computers.

The implementation of the Lagrangian formulation on the Thinking Machines Author: May-Fun Liou, Ching Yuen Loh. Unit commitment (UC) has been used in the vertically integrated electric industry for scheduling units to meet the demand at minimum costs.

In this paper, the authors describe a parallel implementation of the Lagrangian relaxation algorithm. The algorithm has been implemented in Matlab. Today, both the progress made in parallel computing and the semi-deterministic Lagrangian concept allow to overcome this computational cost issue, as will be demonstrated in the present paper.

This requires however an optimum parallel efficiency of the Lagrangian solver, as well as a careful control of statistical by: 3. We present a novel method for simulation of flows with dynamic interfaces based on a Lagrangian flow formulation and dynamic finite element meshes.

Dynamic interfaces are naturally resolved through. In this section, a parallel implementation of the Lagrangian decomposition method is proposed so as to gain computational efficiency in the resolution of problems such as model S2FVPP; see Problem ().A serial implementation of Lagrangian decomposition using the subgradient method for dealing with two-stage stochastic mixed 0–1 models was presented and proposed in Escudero and.

consistency constraints. As parallel computing becomes more common, it is desirable to have separable subproblems in ATC so that each subproblem can be solved concurrently to increase computational throughput. Among the existing implementation methods, the augmented Lagrangian approach is known to have the most stable convergence properties.

I attempted to start to figure that out in the mids, and no such book existed. It still doesn’t exist. When I was asked to write a survey, it was pretty clear to me that most people didn’t read surveys (I could do a survey of surveys).

So wha. () General Formulation of Second-Order Semi-Lagrangian Methods for Convection-Diffusion Problems. Abstract and Applied Analysis() An accurate anisotropic adaptation method for solving the level set advection equation. Today, the use of modern high-performance computing (HPC) systems, such as clusters equipped with graphics processing units (GPUs), allows solving problems with resolutions unthinkable only a decade ago.

The demand for high computational power is certainly an issue when simulating free-surface flows. However, taking the advantage of GPU’s parallel computing techniques, simulations involving.

dents who study this material, we felt the need for a book which presents a slightly more abstract (mathematical) formulation of the kinematics, dynamics, and control of robot manipulators.

The current book is an attempt to provide this formulation not just for a single robot but also. the equations. In general, the safest method for solving a problem is to use the Lagrangian method and then double-check things with F = ma and/or ¿ = dL=dt if you can.

| At this point it seems to be personal preference, and all academic, whether you use the Lagrangian method or the F = ma method. The two methods produce the same equations. We can use the same Lagrangian as before: LHx, y, pL = x2 +y2 + pHx+y-2L but with the additional restriction that p § 0.

Now, as long as x+y-2 ¥ 0, the player who controls p can't do anything: making p more negative is disadvantageous, since it decreases the Lagrangian. The analytical methods of mechanics, clearly stated in Lagrange’s book () [], were alternative formulations to Newton-Euler’s equations of motion (EoM).Although initially the interest was more theoretical, these methods proved their usefulness when the.

proposing an alternative Lagrangian relaxation for the optimal power flow problem and develop a parallel implementation of the algorithm [23]. A review paper for the application of high performance computing in power systems planning and operations is presented by Falcao [24].

Paper Contributions The existing work on parallel computing for. This thesis is concerned with computational aspects of complex nonlinear deformation analysis problems with an emphasis on the speed of response using a parallel computing philosophy.

It proposes a fast, accurate and scalable Graphic Processing Unit (GPU)-based implementation of the total Lagrangian FEM using implicit time integration for. Parallel Computing –]. We embed this accelerated dual-ascent algorithm in a parallel branch-and-bound scheme and conduct extensive computational experiments on single and multiple GPUs, using problem instances with up to 42 facilities from the quadratic assignment problem library (QAPLIB).

We use the Total Lagrangian (TL) formulation (Horton et al., ; Miller et al., ) where all the calculations refer to the initial configuration of the analysed continuum. All derivatives. We discuss the advantages and drawbacks of several model tools and strategies, namely Gaussian, Lagrangian, Eulerian and CFD models.

We especially focus on several recent advances in this multidisciplinary research field, like parallel computing using graphical processing units, or adaptive mesh refinement. Wei Leng is an assistant professor of Academy of Mathematics and Systems Science, Chinese Academy of Science.

Leng obtained his Ph.D. from the Academy of Mathematics and Systems Science, Chinese Academy of Sciences in Leng's primary research fields are numerical solutions of partial differential equations, parallel computing, and numerical computation in geophysics.

An approach to parallel solution of an Eulerian-Lagrangian model of dilute gas-solid flows is presented. Using Lagrangian treatments for the dispersed phase, one of the principal computational challenges comes in models in which inner-particle interactions are taken into account.The problem of computing a smooth invariant manifold for a finite-dimensional dynamical system is considered.

In this paper, it is assumed that the manifold can be parameterized over a torus in ter.The book is a comprehensive and theoretically sound treatment of parallel and distributed numerical methods. It focuses on algorithms that are naturally suited for massive parallelization, and it explores the fundamental convergence, rate of convergence, communication, and synchronization issues associated with such algorithms.