Numerical Simulation Analysis Mathematics of Fluid Mechanics for Semiconductor Circuit Breaker

Liu, Danjuan; He, Wei

doi:10.2478/amns.2021.2.00024

Cited by 7 publications

(2 citation statements)

References 13 publications

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“…From this, the amount of memory and calculation required for LU decomposition is calculated, and the memory required to save non-zero elements after LU decomposition is ensured, and the position of non-zero elements is recorded. In order to perform symbolic decomposition efficiently, the concept of column elimination tree is used to classify the problem as a path exploration problem of efficient graphs, thus making high-speed computation possible [11]. The actual LU factorization is performed with the amount of memory guaranteed during symbolic factorization.…”

Section: Multi-core and Multi-threaded Parallel Solver Pardisomentioning

confidence: 99%

Computer big data modeling system based on finite element mathematical equation simulation

Xiong

Nie

Bayatti

2022

Applied Mathematics and Nonlinear Sciences

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In the traditional computer analysis system, the equipment is cumbersome and takes up space, and is gradually being eliminated by the market. In view of the characteristics of small size, large throughput and high precision of computer systems in the era of big data, in order to solve the phenomenon of swell disasters induced by high-speed moving landslides, debris flows and glaciers impacting water bodies in enclosed waters, a mathematical equation based on finite element was developed. The sparse linear multi-thread parallel solving system of the group, using the sparse linear equation solver implemented on the shared memory machine provided by Intel MKL, completed the design of the high-performance finite element analysis program for surge. The theoretical solution and numerical calculation results of vertical dam failure and laboratory experimental results show that the normalized error between the theoretical value calculated by the calculation system designed in this paper and the actual value obtained from the experiment is only 5.2%~6.8% In between, it is verified that the system has rapid reliability and practical engineering significance.

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Section: Multi-core and Multi-threaded Parallel Solver Pardisomentioning

confidence: 99%

Computer big data modeling system based on finite element mathematical equation simulation

Xiong

Nie

Bayatti

2022

Applied Mathematics and Nonlinear Sciences

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“…Simulating continuous systems in discrete computers usually requires the numerical solution of ordinary differential equations (ODEs), which is usually performed using numerical integration methods [1][2][3][4]. Software implementing numerical integration methods is called an ODE solver.…”

Section: Introductionmentioning

confidence: 99%

Parallelization of Runge–Kutta Methods for Hardware Implementation

et al. 2022

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Parallel numerical integration is a valuable tool used in many applications requiring high-performance numerical solvers, which is of great interest nowadays due to the increasing difficulty and complexity in differential problems. One of the possible approaches to increase the efficiency of ODE solvers is to parallelize recurrent numerical methods, making them more suitable for execution in hardware with natural parallelism, e.g., field-programmable gate arrays (FPGAs) or graphical processing units (GPUs). Some of the simplest and most popular ODE solvers are explicit Runge–Kutta methods. Despite the high implementability and overall simplicity of the Runge–Kutta schemes, recurrent algorithms remain weakly suitable for execution in parallel computers. In this paper, we propose an approach for parallelizing classical explicit Runge–Kutta methods to construct efficient ODE solvers with pipeline architecture. A novel technique to obtain parallel finite-difference models based on Runge–Kutta integration is described. Three test initial value problems are considered to evaluate the properties of the obtained solvers. It is shown that the truncation error of the parallelized Runge–Kutta method does not significantly change after its known recurrent version. A possible speed up in calculations is estimated using Amdahl’s law and is approximately 2.5–3-times. Block diagrams of fixed-point parallel ODE solvers suitable for hardware implementation on FPGA are given.

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A novel analytical approach to micro-polar nanofluid thermal analysis in the presence of thermophoresis, Brownian motion and Hall currents

et al. 2022

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The present study analyzed micro-polar nanofluid in a rotating system between two parallel plates with electric and magnetic fields. The fluid flow study was performed in a steady state. The governing equations of the present issue are considered coupled and nonlinear equations with proper similar variables. Numerical and new semi-analytical methods have been employed to solve the problem to define the exactness of the results. The influence of physical parameters governing the problem is investigated and illustrated in detail in the diagram. Results show that velocity profile and micro-rotation velocity increased when the magnetic parameter increased. Furthermore, the velocity is increased by increasing the rotation parameter. Also, in the case of the temperature profile, the Reynolds and Schmidt number has an inverse effect, and Prandtl Number and Brownian motion have a direct effect. Other results indicate that concentration value declines by increasing the Thermophoretic parameter and Reynolds number. Results compare to the prior research display good accuracy and efficiency.

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Numerical Simulation Analysis Mathematics of Fluid Mechanics for Semiconductor Circuit Breaker

Cited by 7 publications

References 13 publications

Computer big data modeling system based on finite element mathematical equation simulation

Computer big data modeling system based on finite element mathematical equation simulation

Parallelization of Runge–Kutta Methods for Hardware Implementation

A novel analytical approach to micro-polar nanofluid thermal analysis in the presence of thermophoresis, Brownian motion and Hall currents

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