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

Utilization of polymers as biomaterials has greatly impacted the advancement of modern medicine. Specifically, polymeric biomaterials that are biodegradable provide the significant advantage of being able to be broken down and removed after they have served their function. Applications are wide ranging with degradable polymers being used clinically as surgical sutures and implants. In order to fit functional demand, materials with desired physical, chemical, biological, biomechanical and degradation properties must be selected. Fortunately, a wide range of natural and synthetic degradable polymers has been investigated for biomedical applications with novel materials constantly being developed to meet new challenges. This review summarizes the most recent advances in the field over the past 4 years, specifically highlighting new and interesting discoveries in tissue engineering and drug delivery applications.

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Biomedical Applications of Biodegradable Polymers

Dynamics of structures

Hydrogels for biomedical applications.

https://escholarship.org/content/qt5188s9nk/qt5188s9nk.pdf

Cell-based tissue engineering strategies used in the clinical repair of articular cartilage.

Shock wave-loaded plates

In recent years, artificial neural networks were included in the prediction of deformations of structural elements, such as pipes or tensile specimens. Following this method, classical mechanical calculations were replaced by a set of matrix multiplications by means of artificial intelligence. This was also continued in finite element approaches, wherein constitutive equations were substituted by an artificial neural network (ANN). However, little is known about predicting complex non-linear structural deformations with artificial intelligence. The aim of the present study is to make ANN accessible to complicated structural deformations. Here, shock-wave loaded plates are chosen, which lead to a boundary value problem taking geometrical and physical non-linearities into account. A wide range of strain-rates and highly dynamic deformations are covered in this type of deformation. One ANN is proposed for the entire structural model and another ANN is developed for replacing viscoplastic constitutive equations, integrated into a finite element code, leading to an intelligent finite element. All calculated results are verified by experiments with a shock tube and short-time measurement techniques.

Marcus Stoffel

Papers

Shock wave-loaded plates

Artificial neural networks and intelligent finite elements in non-linear structural mechanics

Role of platelet-released growth factors in detoxification of reactive oxygen species in osteoblasts.

Artificial neural networks in structural dynamics: A new modular radial basis function approach vs. convolutional and feedforward topologies

Neural network based constitutive modeling of nonlinear viscoplastic structural response