Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

Roles of chemistry modification for laser textured metal alloys to achieve extreme surface wetting behaviors

This paper aims at providing a state-of-the-art review of an increasingly important class of joining technologies called solid-state (SS) welding, as compared to more conventional fusion welding. Among many other advantages such as low heat input, SS processes are particularly suitable for dissimilar materials joining. In this paper, major SS joining technologies such as the linear and rotary friction welding (RFW), friction stir welding (FSW), ultrasonic welding, impact welding, are reviewed, as well as diffusion and roll bonding (RB). For each technology, the joining process is first depicted, followed by the process characterization, modeling and simulation, monitoring/diagnostics/ nondestructive evaluation (NDE), and ended with concluding remarks. A discussion section is provided after reviewing all the technologies on the common critical factors that affect the SS processes. Finally, the future outlook is presented.

A State-of-the-Art Review on Solid-State Metal Joining

Performance and mechanisms of graphene oxide suspended cutting fluid in the drilling of titanium alloy Ti-6Al-4V

Motivated by the need to enhance the kerf quality during cutting of Poly(methyl methacrylate) (PMMA) sheets using pulsed CO2 laser beam, this study presents an experimental investigation and optimization of laser cutting parameters including cutting speed, assisted gas pressure, laser beam power, and sheet thickness. The kerf quality characteristics including the top kerf width, bottom kerf width, and kerf taper have been considered as the process responses and have been measured using polarized light microscope. The experiments were designed and planned using Taguchi L18 orthogonal array with a mixed design. The effects of different cutting parameters on the kerf characteristics have been statistically analyzed using analysis of variance technique (ANOVA). The obtained results revealed that any increase in cutting parameters will result in increasing the top and the bottom kerf widths, while increasing cutting speed or laser power results in increasing the kerf taper. Second order regression models have been developed to model different kerf characteristics as functions of the process parameters. Genetic algorithm (GA) has been used to select the optimal cutting parameters using the developed regression model as an objective function to minimize the kerf taper. A considerable improvement in kerf quality has been achieved and the obtained results have been verified using confirmation experiments. The application of the proposed approach is capable to reduce the kerf taper from 1.92° to 0.02° while maintaining the minimum kerf width at a reasonable value (less than 0.5 mm).

Improving laser cutting quality of polymethylmethacrylate sheet: experimental investigation and optimization

Friction stir processing (FSP) is a friction stir-based material processing method for enhancement of material microstructural and surface properties. As FSP is a multi-physics problem coupled with severe plastic deformation, material flow, heat flow, and microstructure evolution, modeling of the FSP process can be very complicated and challenging. Few research work has been reported on modeling and simulations of FSP for material modification. In this study, a computation-efficient process model is developed using ABAQUS/Explicit based on coupled Eulerian-Lagrangian (CEL) formulation to simulate FSP of aluminum alloy 5083. The three-dimensional (3D) finite element model simulates the entire process of FSP including tool plunging, dwelling, and stirring phases. Simulations are performed to evaluate the effects of tool-rotational speed and tool pin profile during the FSP process. The computational efficiency of the developed model is also evaluated in comparison with other existing models for friction stir–welding processes. FSP experiment is performed with measurements of process force and temperature for model validation. This study shows that the CEL model can be a powerful numerical tool to simulate the complex process mechanics and optimize the FSP process parameters for industrial applications.

An efficient coupled Eulerian-Lagrangian finite element model for friction stir processing

Simulating microstructure evolution of battery tabs during ultrasonic welding

Extreme wetting activities of laser-textured metal alloys have received significant interest due to their superior performance in a wide range of commercial applications and fundamental research st...

Design of Chemical Surface Treatment for Laser-Textured Metal Alloys to Achieve Extreme Wetting Behavior.

Superhydrophobic metal alloy surfaces are increasingly employed in aerospace and naval applications for anti-icing, drag reduction, self-cleaning, and high-efficiency light absorption capabilities. Emerging laser-based surface texturing methods demonstrate significant potential for manufacturing these surfaces, with the advantages of high processing precision and flexibility. In this research, superhydrophobicity is achieved on engineering metal surfaces using a novel nanosecond Laser-based High-throughput Surface Nanostructuring process. First, a high-energy nanosecond pulse laser scans the metal surface submerged in water using a large spatial increment and a fast processing speed. After that, the laser-textured surface is further treated by immersion in a chlorosilane reagent for a specific period of time. As a result of these two processes, micro- and nano-scale surface features are generated on the metal surface. These features are measured on AISI 4130 steel workpieces through scanning electron microscopy. The surface chemistry is characterized by x-ray photoelectron spectroscopy and correlated with processing conditions. The features are also compared after completion of each process step to understand their individual and cumulative effect on the textured surface. It is found that utilizing a high laser power intensity during the laser texturing process phase will significantly enhance surface nanostructuring effects after the chlorosilane treatment, resulting in feature size decrease and increase in feature density.Superhydrophobic metal alloy surfaces are increasingly employed in aerospace and naval applications for anti-icing, drag reduction, self-cleaning, and high-efficiency light absorption capabilities. Emerging laser-based surface texturing methods demonstrate significant potential for manufacturing these surfaces, with the advantages of high processing precision and flexibility. In this research, superhydrophobicity is achieved on engineering metal surfaces using a novel nanosecond Laser-based High-throughput Surface Nanostructuring process. First, a high-energy nanosecond pulse laser scans the metal surface submerged in water using a large spatial increment and a fast processing speed. After that, the laser-textured surface is further treated by immersion in a chlorosilane reagent for a specific period of time. As a result of these two processes, micro- and nano-scale surface features are generated on the metal surface. These features are measured on AISI 4130 steel workpieces through scanning electron micr...

Avik Samanta

Papers

Roles of chemistry modification for laser textured metal alloys to achieve extreme surface wetting behaviors

An efficient coupled Eulerian-Lagrangian finite element model for friction stir processing

Simulating microstructure evolution of battery tabs during ultrasonic welding

Design of Chemical Surface Treatment for Laser-Textured Metal Alloys to Achieve Extreme Wetting Behavior.

Nanostructuring of laser textured surface to achieve superhydrophobicity on engineering metal surface