Parallel computation of a highly nonlinear Boussinesq equation model through domain decomposition

2005 ◽  
Vol 49 (1) ◽  
pp. 57-74 ◽  
Author(s):  
Khairil Irfan Sitanggang ◽  
Patrick Lynett
Keyword(s):  
Domain Decomposition ◽  
Parallel Computation ◽  
Boussinesq Equation ◽  
Equation Model ◽  
Highly Nonlinear

Parallel finite-element method using domain decomposition and Parareal for transient motor starting analysis

2019 ◽  
Vol 38 (5) ◽  
pp. 1507-1520 ◽  
Author(s):  
Yasuhito Takahashi ◽  
Koji Fujiwara ◽  
Takeshi Iwashita ◽  
Hiroshi Nakashima
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Parallel Computing ◽  
Domain Decomposition ◽  
Parallel Computation ◽  
Domain Decomposition Method ◽  
Space Time ◽  
Content Type ◽  
Parallel Performance ◽  
Element Method

Purpose This paper aims to propose a parallel-in-space-time finite-element method (FEM) for transient motor starting analyses. Although the domain decomposition method (DDM) is suitable for solving large-scale problems and the parallel-in-time (PinT) integration method such as Parareal and time domain parallel FEM (TDPFEM) is effective for problems with a large number of time steps, their parallel performances get saturated as the number of processes increases. To overcome the difficulty, the hybrid approach in which both the DDM and PinT integration methods are used is investigated in a highly parallel computing environment. Design/methodology/approach First, the parallel performances of the DDM, Parareal and TDPFEM were compared because the scalability of these methods in highly parallel computation has not been deeply discussed. Then, the combination of the DDM and Parareal was investigated as a parallel-in-space-time FEM. The effectiveness of the developed method was demonstrated in transient starting analyses of induction motors. Findings The combination of Parareal with the DDM can improve the parallel performance in the case where the parallel performance of the DDM, TDPFEM or Parareal is saturated in highly parallel computation. In the case where the number of unknowns is large and the number of available processes is limited, the use of DDM is the most effective from the standpoint of computational cost. Originality/value This paper newly develops the parallel-in-space-time FEM and demonstrates its effectiveness in nonlinear magnetoquasistatic field analyses of electric machines. This finding is significantly important because a new direction of parallel computing techniques and great potential for its further development are clarified.

scholarly journals Observer Design for a Core Circadian Rhythm Network

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Yuhuan Zhang
Keyword(s):  
Differential Equation ◽  
Circadian Rhythm ◽  
Biological Networks ◽  
Original System ◽  
Equation Model ◽  
Observer Design ◽  
Differential Equation Model ◽  
Highly Nonlinear ◽  
Input Signals ◽  
Potential Applications

The paper investigates the observer design for a core circadian rhythm network inDrosophilaandNeurospora. Based on the constructed highly nonlinear differential equation model and the recently proposed graphical approach, we design a rather simple observer for the circadian rhythm oscillator, which can well track the state of the original system for various input signals. Numerical simulations show the effectiveness of the designed observer. Potential applications of the related investigations include the real-world control and experimental design of the related biological networks.

scholarly journals SPATIOTEMPORAL DOMAIN DECOMPOSITION FOR MASSIVE PARALLEL COMPUTATION OF SPACE-TIME KERNEL DENSITY

2015 ◽  
Vol II-4/W2 ◽  
pp. 7-11 ◽  
Author(s):  
A. Hohl ◽  
E. M. Delmelle ◽  
W. Tang
Keyword(s):  
Domain Decomposition ◽  
Parallel Computation ◽  
High Performance ◽  
Parallel Implementation ◽  
Kernel Density ◽  
Great Accuracy ◽  
Space Time ◽  
Size Diversity ◽  
Data Points ◽  
Tropical Climates

Accelerated processing capabilities are deemed critical when conducting analysis on spatiotemporal datasets of increasing size, diversity and availability. High-performance parallel computing offers the capacity to solve computationally demanding problems in a limited timeframe, but likewise poses the challenge of preventing processing inefficiency due to workload imbalance between computing resources. Therefore, when designing new algorithms capable of implementing parallel strategies, careful spatiotemporal domain decomposition is necessary to account for heterogeneity in the data. In this study, we perform octtree-based adaptive decomposition of the spatiotemporal domain for parallel computation of space-time kernel density. In order to avoid edge effects near subdomain boundaries, we establish spatiotemporal buffers to include adjacent data-points that are within the spatial and temporal kernel bandwidths. Then, we quantify computational intensity of each subdomain to balance workloads among processors. We illustrate the benefits of our methodology using a space-time epidemiological dataset of Dengue fever, an infectious vector-borne disease that poses a severe threat to communities in tropical climates. Our parallel implementation of kernel density reaches substantial speedup compared to sequential processing, and achieves high levels of workload balance among processors due to great accuracy in quantifying computational intensity. Our approach is portable of other space-time analytical tests.

A Parallel Domain Decomposition Technique for Meshless Methods Applications to Large-Scale Heat Transfer Problems

2004 ◽  
Author(s):  
Eduardo Divo ◽  
Alain J. Kassab ◽  
Eric Mitteff ◽  
Luis Quintana
Keyword(s):  
Domain Decomposition ◽  
Parallel Computation ◽  
Wavelet Transforms ◽  
Large Scale ◽  
Iteration Process ◽  
Meshless Methods ◽  
Boundary Element Methods ◽  
Iteration Algorithm ◽  
Large Set ◽  
Decomposition Technique

Mesh reduction methods such as the boundary element methods, method of fundamental solutions or the so-called meshless methods all lead to fully populated matrices. This poses serious challenges for large-scale three-dimensional problems due to storage requirements and iterative solution of a large set of non-symmetric equations. Researchers have developed several approaches to address this issue including the class of fast-multipole techniques, use of wavelet transforms, and matrix decomposition. In this paper, we develop a domain-decomposition, or the artificial sub-sectioning technique, along with a region-by-region iteration algorithm particularly tailored for parallel computation to address the coefficient matrix issue. The meshless method we employ is based on expansions using radial basis functions (RBFs). An efficient physically-based procedure provides an effective initial guess of the temperatures along the sub-domain interfaces. The iteration process converges very efficiently, offers substantial savings in memory, and features superior computational efficiency. The meshless iterative domain decomposition technique is ideally suited for parallel computation. We discuss its implementation under MPI standards on a small Windows XP PC cluster. Numerical results reveal the domain decomposition meshless methods produce accurate temperature predictions while requiring a much-reduced effort in problem preparation in comparison to other traditional numerical methods.

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