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

An overview of the extended/generalized finite element method (GEFM/XFEM) with emphasis on methodological issues is presented. This method enables the accurate approximation of solutions that involve jumps, kinks, singularities, and other locally non-smooth features within elements. This is achieved by enriching the polynomial approximation space of the classical finite element method. The GEFM/XFEM has shown its potential in a variety of applications that involve non-smooth solutions near interfaces: Among them are the simulation of cracks, shear bands, dislocations, solidification, and multi-field problems. Copyright © 2010 John Wiley & Sons, Ltd.

The extended/generalized finite element method: An overview of the method and its applications

Review of State of Art of Smart Structures and Integrated Systems

A strain sensor has been fabricated from a polymer nanocomposite with multiwalled carbon nanotube (MWNT) fillers. The piezoresistivity
of this nanocomposite strain sensor has been investigated based on an improved three-dimensional (3D) statistical resistor network
model incorporating the tunneling effect between the neighboring carbon nanotubes (CNTs), and a fiber reorientation model. The
numerical results agree very well with the experimental measurements. As compared with traditional strain gauges, much higher sensitivity
can be obtained in the nanocomposite sensors when the volume fraction of CNT is close to the percolation threshold. For a small
CNT volume fraction, weak nonlinear piezoresistivity is observed both experimentally and from numerical simulation. The tunneling
effect is considered to be the principal mechanism of the sensor under small strains.

Tunneling effect in a polymer/carbon nanotube nanocompositestrain sensor

The paper provides a state of the art review of guided wave based structural health monitoring (SHM). First, the fundamental concepts of guided wave propagation and its implementation for SHM is explained. Following sections present the different modeling schemes adopted, developments in the area of transducers for generation, and sensing of wave, signal processing and imaging technique, statistical and machine learning schemes for feature extraction. Next, a section is presented on the recent advancements in nonlinear guided wave for SHM. This is followed by section on Rayleigh and SH waves. Next is a section on real-life implementation of guided wave for industrial problems. The paper, though briefly talks about the early development for completeness,. is primarily focussed on the recent progress made in the last decade. The paper ends by discussing and highlighting the future directions and open areas of research in guided wave based SHM.

https://iopscience.iop.org/article/10.1088/0964-1726/25/5/053001/pdf

Guided wave based structural health monitoring: A review

This paper provides an overview of research in Aerospace structures in India by premier academic institutions and research laboratories. The research programs have been closely influenced by the aerospace flight vehicle programs in India. Research funding was available based on the design requirements and the Indian researchers covered a wade spectrum of research areas. The major sponsor of aerospace structures research has been Aeronautics R&D Board and in space related research the major sponsor has been Indian Space Research organization. This paper highlights some of the major contributions. Aerospace Structures research in India has been supported at the premier academic Institutions covering Indian Institute of Science (IISc), Indian Institutes of Technology (IITs) at Chennai, Mumbai, Kanpur and Kharagpur, and also the Departments of Aerospace Engineering and Madras Institute of Technology, Anna University among several other universities. Within these academic institutions, individual engineering departments like Aerospace, Mechanical and Civil have contributed to the overall development. Among the Research laboratories, the CSIR Laboratories had extensive sponsored R&D projects with the maximum interaction with National Aerospace Laboratories (NAL), Bangalore, and Structural Research Engineering Center (SERC), Chennai. The major organization which sponsored the projects has been Aeronautics R&D Board. The design and production agencies have also sponsored their project/need based R&D from time to time. In all, quite a significant work of a high caliber was carried out in the past four decades and the results are extensively published in open literature. This paper presents a review of certain parts of the work which is primarily published and help built a broad theme of research behind the ongoing and recently initiated programs. The research reviewed presented here can be divided into the followings: 1. Structural Integrity studies covering Fracture Mechanics, Joints and Non-Destructive Testing 2. Composite Structures including analytical and manufacturing methods 3. Computational Structural Mechanics primarily covering Finite Element Methods 4. Broad spectrum of topics covering Impact Mechanics, Patch Repairs, interface cracks etc. These are the areas covered under structures with wide coverage on Structural Dynamics, Materials Engineering, Structural health Monitoring and Rotor Dynamics which are reported elsewhere. The work is generally coordinated by a large monitoring committee which encourages Scientists to initiate new areas, phase out well researched topics and take up challenging fields at the frontiers of science. The authors compiled as much as they could collect and put together in the time available within the time constraints of the scientists in various institutions. II. Fracture Mechanics and NDT Studies A major effort at the Indian Institute of Science (IISc), Bangalore resulted in fatigue and fracture software including crack initiation and crack growth phases. Besides the fatigue analysis based on stress- and strain- life approaches, the program utilized special methods developed for estimation of strain-energy-release rates for crack growth analysis. Modified Virtual Crack Closure Integral (MVCCI) technique was developed for regular and singular two- and three- dimension finite element analysis of cracked domains. This has been integrated with the software and applications of this software extended to de-laminations in composites, cracks around riveted and bolted joints and

Overview of Aerospace Structures Research in India

Several concepts have been developed in the recent years for nanomaterial based integrated MEMS platform in order to accelerate the process of biological sample preparation followed by selective screening and identification of target molecules. In this context, there exist several challenges which need to be addressed in the process of electrical lysis of biological cells. These are due to (i) low resource settings while achieving maximal lysis (ii) high throughput of target molecules to be detected (iii) automated extraction and purification of relevant molecules such as DNA and protein from extremely small volume of sample (iv) requirement of fast, accurate and yet scalable methods (v) multifunctionality toward process monitoring and (vi) downward compatibility with already existing diagnostic protocols. This paper reports on the optimization of electrical lysis process based on various different nanocomposite coated electrodes placed in a microfluidic channel. The nanocomposites are synthesized using different nanomaterials like Zinc nanorod dispersion in polymer. The efficiency of electrical lysis with various different electrode coatings has been experimentally verified in terms of DNA concentration, amplification and protein yield. The influence of the coating thickness on the injection current densities has been analyzed. We further correlate experimentally the current density vs. voltage relationship with the extent of bacterial cell lysis. A coupled multiphysics based simulation model is used to predict the cell trajectories and lysis efficiencies under various electrode boundary conditions as estimated from experimental results. Detailed in-situ fluorescence imaging and spectroscopy studies are performed to validate various hypotheses.

Charge injection through nanocomposite electrode in microfluidic channel for electrical lysis of biological cells

A novel numerical integration technique over arbitrary polygons

A Novel Active Vibration Control Design Methodology using Viscoelastic Constitutive Model

A vibration data-driven structural defect identification and classification technique is developed using frequency response under random excitation and a hierarchical neural network. A system of artificial neural networks (ANNs) is trained using finite element simulation-based synthetic data to reduce the need for many sensor measurements required otherwise. Principal component analysis (PCA) is employed to compress the high dimensionality of the vibration response data and eliminate the noise effect in the training and testing. Frequency responses data dimension for the structure with defects such a crack from stress concentration, rivet hole expansion, and attached foreign object mass such as ice accumulation in aircraft wing or fuselage are reduced using PCA and fed to a classifier network. The probabilistic decision output from the classifier network and the compressed data are then fed to the next levels of estimator networks, where each network is dedicated to the individual type of defect for the estimation of the defect parameters corresponding to that class of defect. The methodology is applied to a stiffened panel structure. The cracks and rivet hole expansions are introduced in the rivet line of the stiffener, and the foreign object mass is attached to the panel surface. The results show that it is possible to classify the defects and further estimate the defect parameters with good accuracy and reliability. It was observed that the damage classification network had an accuracy of roughly 95%. The damage localization network for crack as well as rivet expansion had average absolute error of around 2. The damage severity network was also able to perform well with a mean absolute error of about 0.34 for crack length detection and 0.22 for expanded rivet damage. However, the damage localization and severity prediction networks were quite challenging to train in the presence of multiple damages and need further development in the network architecture.

https://downloads.hindawi.com/journals/schm/2023/3555133.pdf

D. Roy Mahapatra

Papers

Overview of Aerospace Structures Research in India

Charge injection through nanocomposite electrode in microfluidic channel for electrical lysis of biological cells

A novel numerical integration technique over arbitrary polygons

A Novel Active Vibration Control Design Methodology using Viscoelastic Constitutive Model

Hierarchical Neural Network and Simulation Based Structural Defect Identification and Classification