Jinshuai Bai

Bio: Jinshuai Bai is an academic researcher from Queensland University of Technology. The author has contributed to research in topics: Computer science & Smoothed-particle hydrodynamics. The author has an hindex of 2, co-authored 3 publications receiving 17 citations.

Effective enhancement of a carbon nanothread on the mechanical properties of the polyethylene nanocomposite

Abstract: The mechanical performance of nanomaterial-reinforced polymer nanocomposites is a prerequisite for their engineering implementations, which is largely determined by the interfacial load transfer efficiency. This work investigates the role of the nanofillers via molecular dynamics simulations under different deformation scenarios, mimicking a maximum and minimum load transfer scenario from the polymer matrix. On the basis of the polyethylene (PE) nanocomposite reinforced by a new nanofiller-carbon nanothread (NTH), we find that the loading conditions dominantly determine its enhancement effect on the mechanical properties of the PE nanocomposite. Under tensile deformation, the ultimate tensile strength of the PE nanocomposite receives around 61 to 211% increment when the filler deforms simultaneously with the PE matrix. However, such enhancement is largely suppressed when the NTH is deforming nonsimultaneously. Similar results are observed from the compressive deformation. Specifically, both morphology and functionalization are found to alter the enhancement effect from the NTH fillers, while also relying on the loading directions. Overall, this work provides an in-depth understanding of the role of the nanofiller. The observations signify the importance of establishing effective load transfer at the interface, which could benefit the design and fabrication of high-performance polymer nanocomposites.

Fast parallel algorithms for short-range molecular dynamics

Abstract: 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.

The best features of diamond nanothread for nanofibre applications

Abstract: Carbon fibres have attracted interest from both the scientific and engineering communities due to their outstanding physical properties Here we report that recently synthesized ultrathin diamond nanothread not only possesses excellent torsional deformation capability, but also excellent interfacial load-transfer efficiency Compared with (10,10) carbon nanotube bundles, the flattening of nanotubes is not observed in diamond nanothread bundles, which leads to a high-torsional elastic limit that is almost three times higher Pull-out tests reveal that the diamond nanothread bundle has an interface transfer load of more than twice that of the carbon nanotube bundle, corresponding to an order of magnitude higher in terms of the interfacial shear strength Such high load-transfer efficiency is attributed to the strong mechanical interlocking effect at the interface These intriguing features suggest that diamond nanothread could be an excellent candidate for constructing next-generation carbon fibres

Current Trends in Fluid Research in the Era of Artificial Intelligence: A Review

Abstract: Computational methods in fluid research have been progressing during the past few years, driven by the incorporation of massive amounts of data, either in textual or graphical form, generated from multi-scale simulations, laboratory experiments, and real data from the field. Artificial Intelligence (AI) and its adjacent field, Machine Learning (ML), are about to reach standardization in most fields of computational science and engineering, as they provide multiple ways for extracting information from data that turn into knowledge, with the aid of portable software implementations that are easy to adopt. There is ample information on the historical and mathematical background of all aspects of AI/ML in the literature. Thus, this review article focuses mainly on their impact on fluid research at present, highlighting advances and opportunities, recognizing techniques and methods having been proposed, tabulating, and testing some of the most popular algorithms that have shown significant accuracy and performance on fluid applications. We also investigate algorithmic accuracy on several fluid datasets that correspond to simulation results for the transport properties of fluids and suggest that non-linear, decision tree-based methods have shown remarkable performance on reproducing fluid properties.