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

The energy compaction and multiresolution properties of wavelets have made the image fusion successful in combining important features such as edges and textures from source images without introducing any artifacts for context enhancement and situational awareness. The wavelet transform is visualized as a convolution of wavelet filter coefficients with the image under consideration and is computationally intensive. The advent of lifting-based wavelets has reduced the computations but at the cost of visual quality and performance of the fused image. To retain the visual quality and performance of the fused image with reduced computations, a discrete cosine harmonic wavelet (DCHWT)-based image fusion is proposed. The performance of DCHWT is compared with both convolution and lifting-based image fusion approaches. It is found that the performance of DCHWT is similar to convolution-based wavelets and superior/similar to lifting-based wavelets. Also, the computational complexity (in terms of additions and multiplications) of the proposed method scores over convolution-based wavelets and is competitive to lifting-based wavelets.

Multifocus and multispectral image fusion based on pixel significance using discrete cosine harmonic wavelet transform

A review on state of health estimations and remaining useful life prognostics of lithium-ion batteries

Multifocus and multispectral image fusion based on pixel significance using discrete cosine harmonic

The Department of Mechanical Engineering (https:// www.mccormick.northwestern.edu/mechanical/graduate) prepares graduates for careers in industry, research, and academia. Students specialize in Design/Manufacturing/Tribology, Dynamics, Control, Robotics, Neural Engineering, Simulation Driven Engineering, Solid Mechanics, Fluid Dynamics, Nanotechnology/MEMS, Bioengineering and Biomechanics, Energy and Sustainability, Engineering Design and Innovation, Product Development, Engineering Management and other disciplines.

What is Mechanical Engineering

Harmonic wavelet method towards solution of the Fredholm type integral equations of the second kind

Remaining useful life prediction for Lithium-ion batteries using fractional Brownian motion and Fruit-fly Optimization Algorithm

In the marine systems, engines represent the most important part of ships, the probability of the bearings fault is the highest in the engines, so in the bearing vibration analysis, early weak fault detection is very important for long term monitoring. In this paper, we propose a novel method to solve the early weak fault diagnosis of bearing. Firstly, we should improve the alternating direction method of multipliers (ADMM), structure of the traditional ADMM is changed, and then the improved ADMM is applied to the compressed sensing (CS) theory, which realizes the sparse optimization of bearing signal for a mount of data. After the sparse signal is reconstructed, the calculated signal is restored with the minimum entropy de-convolution (MED) to get clear fault information. Finally we adopt the sample entropy. Morphological mean square amplitude and the root mean square (RMS) to find the early fault diagnosis of bearing respectively, at the same time, we plot the Boxplot comparison chart to find the best of the three indicators. The experimental results prove that the proposed method can effectively identify the early weak fault diagnosis.

Spare optimistic based on improved ADMM and the minimum entropy de-convolution for the early weak fault diagnosis of bearings in marine systems.

In this study we use the fractional Levy stable motion (fLsm) to establish a finite iterative forecasting model with Long Range Dependent (LRD) characteristics. The LRD forecasting model considers the influence of current and past trends in stochastic sequences on future trends. We find that the discussed model can accurately forecast the trends of stochastic sequences. This fact enables us to introduce the fLsm as the fractional-order model of Levy stable motion. Self-similarity and LRD characteristics of the flsm model is introduced by using the relationship between self-similar index and the characteristic index. Thus, the order Stochastic Differential Equation (FSDE) which describes the fLsm can be obtained. The parameters of the FSDE were estimated by using a novel characteristic function method. The forecasting model with the LRD characteristics was obtained by discretization of FSDE. The Monte Carlo method was applied to demonstrate the feasibility of the forecasting model. The power load forecasting history data demonstrates the advantages of our model.

Fractional Lévy stable motion: Finite difference iterative forecasting model

Mechanical response of the carbon nanotube bundle to uniaxial and biaxial lateral compression followed by unloading is modeled under plane strain conditions. The chain model with a reduced number of degrees of freedom is employed with high efficiency. During loading, two critical values of strain are detected. Firstly, period doubling is observed as a result of the second order phase transition, and at higher compressive strain, the first order phase transition takes place when carbon nanotubes start to collapse. The loading-unloading stress-strain curves exhibit a hysteresis loop and, upon unloading, the structure returns to its initial form with no residual strain. This behavior of the nanotube bundle can be employed for the design of an elastic damper.

/pdf/elastic-damper-based-on-the-carbon-nanotube-bundle-3s80tkqk2p.pdf

Aleksey Kudreyko

Papers

Harmonic wavelet method towards solution of the Fredholm type integral equations of the second kind

Remaining useful life prediction for Lithium-ion batteries using fractional Brownian motion and Fruit-fly Optimization Algorithm

Spare optimistic based on improved ADMM and the minimum entropy de-convolution for the early weak fault diagnosis of bearings in marine systems.

Fractional Lévy stable motion: Finite difference iterative forecasting model

Elastic damper based on the carbon nanotube bundle