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Balaji Raghavan
Researcher at Intelligence and National Security Alliance
Publications - 37
Citations - 798
Balaji Raghavan is an academic researcher from Intelligence and National Security Alliance. The author has contributed to research in topics: Shape optimization & Topology optimization. The author has an hindex of 15, co-authored 36 publications receiving 591 citations. Previous affiliations of Balaji Raghavan include Institut national des sciences appliquées de Rennes & University of Technology of Compiègne.
Papers
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Effect of different types of fibers on the microstructure and the mechanical behavior of Ultra-High Performance Fiber-Reinforced Concretes
TL;DR: In this paper, the effect of adding different types of fibers on the microstructure and the mechanical behavior of cementitious composites, in particular on UHPC, was investigated by using a uni-axial compression test which combines the gas permeability and the acoustic emission (AE) measurement.
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Towards a space reduction approach for efficient structural shape optimization
TL;DR: This paper presents a two-level a posteriori mesh parameterization approach for the design domain geometry that uses Principal Component Analysis and Diffuse Approximation to replace the geometry-based variables with the smallest set of variables needed to represent an admissible shape for a chosen precision.
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Identification of material properties using indentation test and shape manifold learning approach
TL;DR: In this paper, the authors proposed an approach based solely on the 3D indentation imprint shape measured after indenter withdrawal, rather than relying on the minimization of the pointwise discrepancy between the experimental and simulated indentation curve.
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On the study of mystical materials identified by indentation on power law and Voce hardening solids
Liang Meng,Liang Meng,Piotr Breitkopf,Balaji Raghavan,Gérard Mauvoisin,Olivier Bartier,Xavier Hernot +6 more
TL;DR: In this paper, Chen et al. investigated the existence of a one-to-one correspondence between a material's elastoplastic properties and its indentation responses, with particular emphasis on the residual imprint.
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On-the-fly model reduction for large-scale structural topology optimization using principal components analysis
TL;DR: The authors propose a projection-based reduced-order modeling approach using proper orthogonal decomposition for the construction of a reduced basis for the FE solution during the optimization, using a small number of previously obtained and stored solutions.