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

8. Subset Selection in Regression (Monographs on Statistics and Applied Probability, no. 40). By A. J. Miller. ISBN 0 412 35380 6. Chapman and Hall, London, 1990. 240 pp. £25.00.

Subset Selection in Regression

Inspired by the unique and efficient wound healing processes in biological systems, several approaches to develop synthetic polymers that can repair themselves with complete, or nearly complete, autonomy have recently been developed. This review aims to survey the rapidly expanding field of self-healing polymers by reviewing the major successful autonomic repairing mechanisms developed over the last decade. Additionally, we discuss several issues related to transferring these self-healing technologies from the laboratory to real applications, such as virgin polymer property changes as a result of the added healing functionality, healing in thin films v. bulk polymers, and healing in the presence of structural reinforcements.

Self-healing polymers and composites

The firefly algorithm has become an increasingly important tool of Swarm Intelligence that has been applied in almost all areas of optimization, as well as engineering practice. Many problems from various areas have been successfully solved using the firefly algorithm and its variants. In order to use the algorithm to solve diverse problems, the original firefly algorithm needs to be modified or hybridized. This paper carries out a comprehensive review of this living and evolving discipline of Swarm Intelligence, in order to show that the firefly algorithm could be applied to every problem arising in practice. On the other hand, it encourages new researchers and algorithm developers to use this simple and yet very efficient algorithm for problem solving. It often guarantees that the obtained results will meet the expectations.

A comprehensive review of firefly algorithms

Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference.Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literatureProvides a theoretical understanding as well as practical implementation hintsProvides a step-by-step introduction to each algorithm

Nature-Inspired Optimization Algorithms

Because a structure’s modal parameters (natural frequencies and mode shapes) are affected by structural damage, finite- element model updating techniques are often applied to locate and quantify structural damage. However, the dynamic behavior of a structure can only be observed in a narrow knowledge space, which usually causes nonuniqueness and ill-posedness in the damage detection problem formulation. Thus, advanced optimization techniques are a necessary tool for solving such a complex inverse problem. Furthermore, a preselection process of the most significant damage parameters is helpful to improve the efficiency of the damage detection procedure. A new approach, which combines a parameter subset selection process with the application of damage functions is proposed herein to accomplish this task. Starting with a simple 1D beam, this paper first demonstrates several essential concepts related to the proposed model updating approach. A more advanced example considering a 2D model is then considered. T...

Improved Damage Localization and Quantification Using Subset Selection

In this paper, a novel hybrid heat source model is developed considering the different absorption mechanisms for porous and dense state materials, and an effective absorptivity is adapted to the proposed model to analyze the melting mode transition. The proposed model can predict the melt pool characteristics including the melt pool dimensions and the melting modes in the selective laser melting (SLM) process. The problem is formulated using the heat transfer equation considering the phase transition and the degree of consolidation based on the phase-field approach. The single-track scans of 316 L stainless steel for both the non-powder case and the powder case are simulated to validate the model and the obtained results are in good agreement with the experimentally measured melt pool dimensions (mean error within 6%). Furthermore, the melting modes (conduction and keyhole) can be distinguished based on the predicted melt pool morphology and the degree of vaporization with the proposed model, which provides insight to define the optimized process boundary. It is also found that the keyhole mode melting is more sensitive to the change of the process parameter than the conduction mode melting.

A novel heat source model for analysis of melt Pool evolution in selective laser melting process

A predictive mechanoluminescence transduction model for thin-film SrAl2O4:Eu2+, Dy3+ (SAOED) stress sensor

In this paper, a vibration-based testing methodology to assess fatigue behavior of a metallic structure is presented. To minimize the testing duration, the test setup is designed for a base-excited multiple-specimen arrangement driven in a high-frequency resonant mode, which allows completion of fatigue testing in an accelerated period. The shaker operates in closed-loop control with dynamic specimen response feedback provided by a scanning laser vibrometer. A test coordinator function is developed to synchronize the shaker controller and the laser vibrometer and complete the closed-loop scheme: the test coordinator monitors structural health of the test specimens throughout the test period, recognizes change in specimen dynamic behavior due to fatigue crack initiation, terminates test progression, and acquires test data in an orderly manner. The test methodology is demonstrated with cantilever specimens that are clasped on the shaker armature with specially-designed clamp fixtures. Experimental stress evaluation is carried out to verify the specimen stress predictions. A successful application of the experimental methodology is demonstrated by validation tests with Al 6061-T6 aluminum specimens subjected to fully-reversed bending stress.

Development of a Closed-Loop High-Cycle Resonant Fatigue Testing System

An improved parameter subset selection method based on a dynamic residual force measure is proposed for damage localization. Often the original subset selection using the fundamental modal characteristics requires a single damage location and the use of many smaller model updating processes for detecting multiple damage locations. A new approach to subset selection proposed herein is demonstrated to be efficient for identifying multiple damaged elements. Cases in which damage detection must be achieved using incomplete measurements of the structural responses are also considered. Modal expansion is incorporated to deal with this challenge. An advantage of employing this new approach is that it can reliably identify multiple damage locations. The effect of measurement noise on the performance of the proposed subset selection method has also been investigated. Through a series of illustrative examples considering different types of structures, the proposed method is demonstrated and shown to be very efficient in detecting multiple damage locations. Copyright © 2008 John Wiley & Sons, Ltd.

Gun Jin Yun

Papers

Improved Damage Localization and Quantification Using Subset Selection

A novel heat source model for analysis of melt Pool evolution in selective laser melting process

A predictive mechanoluminescence transduction model for thin-film SrAl2O4:Eu2+, Dy3+ (SAOED) stress sensor

Development of a Closed-Loop High-Cycle Resonant Fatigue Testing System

A parameter subset selection method using residual force vector for detecting multiple damage locations