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Analysis of two spaced aluminum plates against hypervelocity impact of a spherical aluminum projectile was performed to find an optimum thickness ratio of two plates. The smooth particle hydrodynamics (SPH) scheme used here is especially effective since the major features of debris clouds between two plates are successfully captured. For systematical analysis the extent of debris clouds is first correlated as a function of sphere diameter to front plate thickness ratio, with which a potential optimum front plate thickness range is identified. Then a critical sphere diameter causing failure of rear wall is examined while keeping the total thickness of two plates constant. In fact an optimum thickness ratio exists and is examined as a function of the size combination of the sphere diameter and plate thicknesses. The debris cloud expansion correlated optimum thickness ratio study provides a good insight on the hypervelocity impact onto spaced target system.

Smoothed Particle Hydrodynamics (SPH) is fast emerging as a practically useful computational simulation tool for a wide variety of engineering problems. SPH is also gaining popularity as the back bone for fast and realistic animations in graphics and video games. The Lagrangian and mesh-free nature of the method facilitates fast and accurate simulation of material deformation, interface capture, etc. Typically, particle-based methods would necessitate particle search and locate algorithms to be implemented efficiently, as continuous creation of neighbor particle lists is a computationally expensive step. Hence, it is advantageous to implement SPH, on modern multi-core platforms with the help of High-Performance Computing (HPC) tools. In this work, the computational performance of an SPH algorithm is assessed on multi-core Central Processing Unit (CPU) as well as massively parallel General Purpose Graphical Processing Units (GP-GPU). Parallelizing SPH faces several challenges such as, scalability of the neighbor search process, force calculations, minimizing thread divergence, achieving coalesced memory access patterns, balancing workload, ensuring optimum use of computational resources, etc. While addressing some of these challenges, detailed analysis of performance metrics such as speedup, global load efficiency, global store efficiency, warp execution efficiency, occupancy, etc. is evaluated. The OpenMP and Compute Unified Device Architecture$(CUDA)$ parallel programming models have been used for parallel computing on Intel Xeon$(R)$ E5-$2630$ multi-core CPU and NVIDIA Quadro M$4000$ and NVIDIA Tesla p$100$ massively parallel GPU architectures. Standard benchmark problems from the Computational Fluid Dynamics (CFD) literature are chosen for the validation. The key concern of how to identify a suitable architecture for mesh-less methods which essentially require heavy workload of neighbor search and evaluation of local force fields from neighbor interactions is addressed.