Fields of Research

Research Overview

Dr. Naskar's research group works in multi-scale structural mechanics and multi-physics analysis focusing on engineered materials and structures involving the intersection of additive manufacturing, material characterization through computational design and experiments in engineering. Her group is developing methods to address long-time horizon problems and challenges of coupling scales in multiscale multiphysics material modeling. In addition, she is also working on advanced manufacturing techniques that are relevant to the fabrication of engineered materials.

Dr. Naskar's lab is primarily interested in:

Mechanics of metamaterials
Deployable and reconfigurable structures
Uncertainty Quantification
Scientific machine learning algorithm
Advanced multi-functional composites
Smart materials and structures
2D Materials and nano-heterostructures

Research Highlights

Mechanics of metamaterials

The reserach activities in this area at EMSL cover the emerging field of micro-structured materials to invent novel material microstructures leading to unprecedented and extreme mechanical properties with prospective applications

The proposed active class of elastic metamaterials brings a step-change in the on-demand mechanical performance of critically important structural components and unsupervised damage resilience for enhanced durability and sustainability.

Related Publications:

Kundu D., Naskar S., Mukhopadhyay T. (2024) Active mechanical cloaking for unsupervised damage resilience in programmable elastic metamaterials, Philosophical Transactions of the Royal Society A, 382 20230360, 2024, Royal Society

The coupled interaction of beam-level and lattice-level architectures will enhance the specific elastic properties to an extreme extent, leading to ultra-lightweight multifunctional materials for critical applications under static and dynamic environments.

Related Publications:

Kundu D., Ghuku S., Naskar S., Mukhopadhyay T. (2022) Extreme specific stiffness through interactive cellular networks in bi-level micro-topology architected metamaterials, Advanced Engineering Materials, 2201407, Wiley Publication

The proposed method brings ths new avenues for efficient optimized design of the next-generation multi-functional lattices and cellular metamaterials with highly tailored effective elastic properties.

Related Publications:

Awasthi M., Singh A., Naskar S., Mukhopadhyay T., (2024) Constitutive behavior of asymmetric multi-material honeycombs with bi-level variably-thickened composite architecture, Thin-Walled Structures, 112183, Elsevier Publication

Scientific machine learning algorithm development for physical systems

The research activities in this area at EMSL develop algorithms and architectures tailored to address computational challenges in engineering, physical, and engineering systems. the group is interested in developing methods to address long-time horizon problems and challenges of coupling scales in multiscale multiphysics structural and material modeling.

This research on explainable machine learning, mechanics & materials focuses on data-driven analysis that warrants frequent excursions among the boundaries of applied mathematics and data science for several engineering applications.

Related Publications:

Vaishali, Mukhopadhyay T., Naskar S., Dey S., On machine learning assisted data-driven bridging of FSDT and HOZT for high-fidelity uncertainty quantification of laminated composite and sandwich plates, Composite Structures, 304 116276, 2022, Elsevier Publication.
Daniell J., Kobayashi K., Naskar S., Kumar D., Chakraborty S., Alajo A., Taber E., Graham J., Alam S., Physics-Informed Multi-Stage Deep Learning Framework Development for Digital Twin-Centred State-Based Reactor Power Prediction. Preprint
Kobayashi K., Kumar D., Naskar S., Chakraborty S., Paaren K., Graham J., Alam S., Non-Intrusive Uncertainty Quantification for U3Si2 and UO2 Fuels with SiC/SiC Cladding using BISON for Digital Twin-Enabling Technology, Preprint

The work proposed an elementary-level coupling of machine learning for efficient, yet accurate mechanical analysis. Based on such machine learning-based difference mapping, we augment the elementary stiffness matrices obtained using FSDT efficiently to the equivalent of HOZT theory without any additional computational expenses (referred to here as augmented FSDT, or aFSDT).

Related Publications:

Garg A, Naskar S, Mukhopadhyay T, Elementary-level Intrusive Coupling of Machine Learning for Efficient Mechanical Analysis of Variable Stiffness Composite Laminates: A Spatially-adaptive Fidelity-sensitive Computational Framework. Engineering with Computers, Springer Publication. (In Press) Preprint version

Deployable and reconfigurable structures

The research activities in this area at EMSL cover the design and realization of novel deployable and origami structures, a type of unconventional structures capable of large shape change where the particular interest is in the underlying principles governing large geometrical transformations of these structures.

The fundamental mechanics of the proposed origami metamaterials being mostly scale-independent, this novel class of deployable shape-changing architectures can be directly transferred for several applications.

Related Publications:

Sharma A, Naskar S, Mukhopadhyay T, Tailoring the mechanical responses of metamaterial through a waterbomb-based tubular architecture. 9th European Congress on Computational Methods in Applied Sciences and Engineering, Lisboa, Portugal, June 2024.
Sharma A, Naskar S, Mukhopadhyay T, Programmable shape morphing and state-transitional deployment through second-order graded derivatives of Miura-origami architectures. Science Advances (Under review)

The proposed work developed a new class of origami-based deorbit modules to reduce the duration of time CubeSats remain in low earth orbit after their operational life.

Related Publications:

Upcoming..

Uncertauinty Quantification

The research activities in this area at EMSL cover the methodological research in Uncertainty Quantification, Machine Learning, and Stochastic Methods to understand the effects of uncertainties and random variations on the performance of materials and structures

We have developed a novel stochastic representative volume element (SRVE) based framework for incorporating the effect of spatially random damage which has been further to develop a fuzzy-based topology optimization approach for optimum design of composites.

Related Publications:

Naskar S., Mukhopadhyay T., Sriramula S. (2018) Probabilistic micromechanical spatial variability quantification in laminated composites, Composites Part B: Engineering, 151 291-325, Elsevier Publication.
Naskar S., Mukhopadhyay T., Sriramula S. (2019) Spatially varying fuzzy multi-scale uncertainty propagation in unidirectional fibre reinforced composites, Composite Structures, 209 940-967, Elsevier Publication.

We have developed algorithms and architectures tailored to address computational challenges in engineering and physical systems to address long-time challenges of coupling scales in multiscale multiphysics material modeling.

Related Publications:

Mukhopadhyay T., Naskar S., Chakraborty S., Karsh P. K., Choudhury R., Dey S. (2020) Stochastic oblique impact on composite laminates: A concise review and characterization of the essence of hybrid machine learning algorithms, Archives of Computational Methods in Engineering, 28 1731–1760, Springer Publication

Advanced multi-functional composites

The research activities in this area at EMSL are to develop new design, analysis, and testing methodologies for composite structures that can be used for advanced next-generation structures. The multi-talented researchers and PhD students are exploring new methods for designing composite structures combining numerical and analytical tools with a reduced number of experimental tests.

The developed models would help to recognize the most important material properties with respect to different shapes and orientation of reinforcements which influences the performance of system significantly. To confirm safety, robustness and sustainability of the structure, it is the most prior requirement to determine the effective properties of composites considering different parameters for the different static and structural analyses.

Related Publications:

Shingare K., Naskar S., (2021) Probing the prediction of effective properties for composite materials, European Journal of Mechanics - A/Solids, 87, 104228, Elsevier Publication

Transient low velocity impact analysis of both laminated and delaminated composite Structures.

Related Publications:

Karsh P. K., Mukhopadhyay T., Chakraborty S., Naskar S., Dey S. (2019) A hybrid stochastic sensitivity analysis for low-frequency vibration and low-velocity impact of functionally graded plates, Composites Part B: Engineering, 176 107221, Elsevier Publication.
Kumar R. R., Mukhopadhyay T., Naskar S., Pandey K. M., Dey S. (2019) Stochastic low-velocity impact analysis of sandwich plates including the effects of obliqueness and twist, Thin-Walled Structures, 145 106411, Elsevier Publication.

Smart materials and structures

Numerous disciplines, including structural dynamics, energy harvesting, structural health monitoring, etc., are addressed by the research initiatives conducted in this area at EMSL. We work to create cutting-edge and novel materials and structural engineering solutions to challenging problems. Our approach is interdisciplinary since we perform at the intersection of experimental and computational structural dynamics.

The developed framework provides investigating of the nonlinear dynamics of smart composite elastomer-based minimum energy structures with the provision of non-aligned electric and magnetic fields, leading to an actively programmable pre-stretch paradigm. The efficient framework developed here would be crucial in developing new actuators, smart devices and soft robots for a variety of advanced engineering and medical applications.

Related Publications:

Khurana A., Naskar S., Varma R. K., Mukhopadhyay T., (2023) Smart electro-magneto-viscoelastomer minimum energy structures with particle-reinforcements: Theoretical equilibrium and nonlinear dynamics of actuated configurations, International Journal of Engineering Science, 103974, Elsevier Publication

We introduce a novel concept of chiral fractal substrates in piezoelectric energy harvesters, wherein a significant improvement is noticed in the energy output along with increased frequency-band programmability. The power output of such architected and optimized energy harvesters holds the potential to serve as a reliable and sustainable alternative to conventional batteries, effectively providing a renewable source of power to energize and sustain low-power micro-electro-mechanical systems (MEMS) and devices.

Related Publications:

Mondal S., Mukhopadhyay T., Scarpa F., Naskar S. (2024) Frequency-band programmable piezoelectric energy harvesters with variable substrate material, tip mass and fractal architectures: Experimental and numerical investigations, Mechanics Based Design of Structures and Machines, Taylor & Francis Publication (2024) DOI: 10.1080/15397734.2024.2390074

This project focuses on utilizing both piezoelectric and flexoelectric effects for energy harvesting, combining the advantages of each phenomenon to enhance the efficiency of energy conversion from mechanical strain to electrical energy

Related Publications:

Naskar S., Shingare K., Mondal S., Mukhopadhyay T. (2022) Flexoelectricity and Surface Effects on Coupled Electromechanical Responses of Graphene Reinforced Functionally Graded Nanocomposites: A unified size-dependent semi-analytical framework, Mechanical Systems and Signal Processing, 169 108757, Elsevier Publication.
Shingare K., Naskar S., (2022) Compound influence of surface and flexoelectric effects on static bending response of hybrid composite nanorod, The Journal of Strain Analysis for Engineering Design, 58 (2) 73-90, Sage Publication.

2D Materials and nano-heterostructures

The research activities in this area at EMSL are primarily focused on the mechanical properties of two-dimensional materials and their heterostructures.

Efficient closed-form generic formulae are proposed for the effective Young's moduli of twisted multi-layer heterostructures and Based on this physics-based analytical approach, a wide range of insightful new results are presented for twisted heterostructures, covering mono-planar and multi-planar configurations with homogeneous and heterogeneous atomic distributions.

Related Publications:

Mukhopadhyay T., Mahata A., Naskar S., Adhikari S. (2020) Probing the effective Young's modulus of `magic angle' inspired multi-functional twisted nano-heterostructures, Advanced Theory and Simulations, 3(10) 2000129, Willey Publication.

We proposed an efficient nonparametric kernel-based probabilistic computational mapping to obtain the optimal composition of HEAs under ballistic conditions by exploiting the emerging capabilities of machine learning coupled with molecular-level simulations.

Related Publications:

Gupta K. K., Barman S., Dey S., Naskar S., Mukhopadhyay T. (2024) On exploiting nonparametric kernel-based probabilistic machine learning over the large compositional space of high entropy alloys for optimal nanoscale ballistics, Scientific Reports, (4) 167952024, 2024, Nature Portfolio.

This project focuses on developing polymeric and elastomeric composites with properties suitable for flexible electronics, such as high flexibility, stretchability, and electrical conductivity.

Related Publications:

Haridas CP A., Pillai S., Naskar S., Mondal T., Naskar K. (2022) Polyurethane/Carbon Nanotube-Based ThermoSense Electronic Skin: Perception to Decision Making Aided by IoT Brain, ACS Applied Materials & Interfaces, 16 (36), ACS Publication
Sharma S., Selvan T. M., Naskar S., Mondal S., Mukhopadhyay T., Mondal T. (2022) Printable Graphene-Sustainable Elastomer-based Cross-Talk-Free Sensors for Point of Care Diagnostics, ACS Applied Materials & Interfaces, 14(51) 57265–57280, ACS Publication.
Selvan T. M., Sharma S., Naskar S., Mondal S., Kaushal M., Mondal T. (2022) Printable Carbon Nanotube-Liquid Elastomer-based Multifunctional Adhesive Sensor for Monitoring of Physiological Parameters, ACS Applied Materials & Interfaces, ACS Publication, 14, 40, 45921–45933.