Swarnendu Sen

Bio: Swarnendu Sen is an academic researcher from Jadavpur University. The author has contributed to research in topics: Heat transfer & Combustor. The author has an hindex of 25, co-authored 159 publications receiving 2303 citations. Previous affiliations of Swarnendu Sen include Indian Institute of Technology Madras & University of Illinois at Chicago.

Thermomagnetic convection in a square enclosure using a line dipole

Abstract: We have simulated thermomagnetic convection in a differentially heated square cavity with an infinitely long third dimension. The cavity is under the influence of an imposed two-dimensional magnetic field that conforms to the Maxwell’s equations. Our objective is to characterize the thermomagnetic convection in terms of the geometric length scales, magnetic fluid properties, temperature differences, and strengths of the imposed magnetic fields. Fluid motion occurs due to both the gradients of the magnetic field and the temperature. Colder fluid that has a larger magnetic susceptibility is attracted toward regions with larger field strength during thermomagnetic convection, which displaces warmer fluid of lower susceptibility. The height-averaged Nusselt number Nuav increases with increasing magnetic dipole strength and temperature but decreases with increasing fluid viscosity. Thermomagnetic heat transfer increases when the length scale decreases if the dipole strength of the source magnet is constant. Th...

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

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.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Petal Effect: A Superhydrophobic State with High Adhesive Force

Abstract: Hierarchical micropapillae and nanofolds are known to exist on the petals' surfaces of red roses. These micro- and nanostructures provide a sufficient roughness for superhydrophobicity and yet at the same time a high adhesive force with water. A water droplet on the surface of the petal appears spherical in shape, which cannot roll off even when the petal is turned upside down. We define this phenomenon as the “petal effect” as compared with the popular “lotus effect”. Artificial fabrication of biomimic polymer films, with well-defined nanoembossed structures obtained by duplicating the petal's surface, indicates that the superhydrophobic surface and the adhesive petal are in Cassie impregnating wetting state.

Numerical Heat Transfer And Fluid Flow

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