NUMERICAL INVESTIGATION AND PARALLEL COMPUTING FOR THERMAL TRANSPORT MECHANISM DURING NANOMACHINING

作者: Ravi R. Kumar

DOI:

关键词: Conjugate gradient methodComputationHeat transferParallel computingDomain decomposition methodsMessage Passing InterfaceGaussianSuccessive over-relaxationMathematicsThermal energy

摘要: OF THESIS NUMERICAL INVESTIGATION AND PARALLEL COMPUTING FOR THERMAL TRANSPORT MECHANISM DURING NANOMACHINING Nano-scale machining, or “Nanomachining” is a hybrid process in which the total thermal energy necessary to remove atoms from work-piece surface applied external sources. In current study, two sources: (1) localized laser beam focused micron-scale spot preheat work-piece, and (2) high-precision electron-beam emitted tips of carbon nano-tubes material via evaporation/sublimation. Macro-to-nano scale heat transfer models are discussed for understanding their capability capture its application predict transient mechanism required nano-machining. this case, transport during nano-scale machining involves both phonons (lattice vibrations) electrons; it modeled using parabolic two-step (PTS) model, accounts time lag between these carriers. A numerical algorithm developed solution PTS model based on explicit implicit finite-difference methods. Since simulation nanomachining high computational cost terms wall clock consumed, performance comparison over wide range techniques has been done devise an efficient procedure. Gauss-Seidel (GS), successive relaxation (SOR), conjugate gradient (CG), δ -form Douglas-Gunn splitting, other methods have used compare involved Use splitting 3D time-dependent equations appears be optimal especially as problem size (number spatial grid points and/or number steps) becomes large. Parallel computing implemented further reduce complete process. Domain decomposition with inter-processor communication Message Passing Interface (MPI) libraries adapted parallel computing. Performance tuning parallelization by overlapping computation. Numerical source different Gaussian distribution presented. code tested four distinct computer cluster architecture. Results obtained agree well available experimental data literature. The results self-consistent; nevertheless, they need validated experimentally.

参考文章(58)
J.M. McDonough, Tianliang Yang, M. Sheetz, Parallelization of a Modern CFD Incompressible Turbulent Flow Code Parallel Computational Fluid Dynamics 2003#R##N#Advanced Numerical Methods Software and Applications. pp. 473- 479 ,(2004) , 10.1016/B978-044451612-1/50061-5
Jim Douglas, James E. Gunn, A general formulation of alternating direction methods Numerische Mathematik. ,vol. 6, pp. 428- 453 ,(1964) , 10.1007/BF01386093
Robert Vallance, Apparao Rao, M. Menguc, Processes for nanomachining using carbon nanotubes ,(2002)
Louis A Hageman, David Matheson Young, Applied Iterative Methods ,(2004)
Jun Zhang, Jennifer J. Zhao, Iterative solution and finite difference approximations to 3D microscale heat transport equation Mathematics and Computers in Simulation. ,vol. 57, pp. 387- 404 ,(2001) , 10.1016/S0378-4754(01)00319-6
A. Graßmann, F. Peters, Experimental investigation of heat conduction in wet sand Heat and Mass Transfer. ,vol. 35, pp. 289- 294 ,(1999) , 10.1007/S002310050326