Parallelization of Numerical Conjugate Heat Transfer Analysis in Parallel Plate Channel Using OpenMP

被引:13
作者
Afzal, Asif [1 ]
Ansari, Zahid [2 ]
Ramis, M. K. [1 ]
机构
[1] Visvesvaraya Technol Univ, PA Coll Engn, Dept Mech Engn, Belagavi, Mangaluru, India
[2] Visvesvaraya Technol Univ, PA Coll Engn, Dept Comp Sci & Engn, Belagavi, Mangaluru, India
关键词
Parallelization; FVM code; OpenMP; Fluid flow; Speedup; Parallel efficiency; FLUID; IMPLEMENTATION; OPTIMIZATION; COMPUTATIONS; SIMULATIONS; STRATEGIES; MODEL; FLOW;
D O I
10.1007/s13369-020-04640-1
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Conjugate heat transfer and fluid flow is a common phenomenon occurring in parallel plate channels. Finite volume method (FVM) formulation-based semi-implicit pressure linked equations algorithm is a common technique to solve the Navier-Stokes equation for fluid flow simulation in such phenomena, which is computationally expensive. In this article, an indigenous FVM code is developed for numerical analysis of conjugate heat transfer and fluid flow, considering different problems. The computational time spent by the code is found to be around 90% of total execution time in solving the pressure (P) correction equation. The remaining time is spent onU,Vvelocity, and temperature (T) functions, which use tri-diagonal matrix algorithm. To carry out the numerical analysis faster, the developed FVM code is parallelized using OpenMP paradigm. All the functions of the code (U,V,T, andP) are parallelized using OpenMP, and the parallel performance is analyzed for different fluid flow, grid size, and boundary conditions. Using nested and without nested OpenMP parallelization, analysis is done on different computing machines having different configurations. From the complete analysis, it is observed that flow Reynolds number (Re) has a significant impact on the sequential execution time of the FVM code but has a negligible role in effecting speedup and parallel efficiency. OpenMP parallelization of the FVM code provides a maximum speedup of up to 1.5 for considered conditions.
引用
收藏
页码:8981 / 8997
页数:17
相关论文
共 44 条
[21]  
Lehmkuhl O., 2007, LECT NOTES COMPUTATI, V67, P275
[22]   Review on thermal management systems using phase change materials for electronic components, Li-ion batteries and photovoltaic modules [J].
Ling, Ziye ;
Zhang, Zhengguo ;
Shi, Guoquan ;
Fang, Xiaoming ;
Wang, Lei ;
Gao, Xuenong ;
Fang, Yutang ;
Xu, Tao ;
Wang, Shuangfeng ;
Liu, Xiaohong .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2014, 31 :427-438
[23]  
Mattson T., 1999, SUP 99 C
[24]   A hybrid MPI-OpenMP scheme for scalable parallel pseudospectral computations for fluid turbulence [J].
Mininni, Pablo D. ;
Rosenberg, Duane ;
Reddy, Raghu ;
Pouquet, Annick .
PARALLEL COMPUTING, 2011, 37 (6-7) :316-326
[25]   Exploring Shared-memory Optimizations for an Unstructured Mesh CFD Application on Modern Parallel Systems [J].
Mudigere, Dheevatsa ;
Sridharan, Srinivas ;
Deshpande, Anand ;
Park, Jongsoo ;
Heinecke, Alexander ;
Smelyanskiy, Mikhail ;
Kaul, Bharat ;
Dubey, Pradeep ;
Kaushik, Dinesh ;
Keyes, David .
2015 IEEE 29TH INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM (IPDPS), 2015, :723-732
[26]  
Niedermeier CA, 2018, 6 EUR C COMP MECH EC, P1
[27]   Recent progress and challenges in exploiting graphics processors in computational fluid dynamics [J].
Niemeyer, Kyle E. ;
Sung, Chih-Jen .
JOURNAL OF SUPERCOMPUTING, 2014, 67 (02) :528-564
[28]   Parallelized structural topology optimization and CFD coupling for design of aircraft wing structures [J].
Oktay, E. ;
Akay, H. U. ;
Merttopcuoglu, O. .
COMPUTERS & FLUIDS, 2011, 49 (01) :141-145
[29]   Analysis and implementation of a parallelization strategy on a Navier-Stokes solver for shear flow simulations [J].
Passoni, G ;
Cremonesi, P ;
Alfonsi, G .
PARALLEL COMPUTING, 2001, 27 (13) :1665-1685
[30]  
Patankar S, 2018, Numerical Heat Transfer and Fluid Flow, DOI DOI 10.1201/9781482234213/NUMERICAL-HEAT-TRANSFER-FLUID-FLOW-SUHAS-PATANKAR