Lakshminath Bezbaroa Central Library Digital Repository

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Now showing 1 - 5 of 26

Recent Submissions

(A) Study of the Stylistic Shifts in Visual Imagery in the Creative Prints of Prominent Printmakers – Ananda Moy Banerji and Kavita Nayar
(2026)
Sharma, Namrita
UNDER EMBARGO
(The) Sonic Frame: A Framework for Sound Design in Cinematic Virtual Reality
(2026)
Chaurasia, Hitesh Kumar
UNDER EMBARGO
Robust and Efficient NURBS Enrichment Strategies for Large Deformation Contact Problems
(2026)
Das, Sumit Kumar
The present work develops a robust, accurate, and computationally efficient isogeometric analysis (IGA) framework for large deformation contact problems, including self-contact. Contact problems are characterized by non-smooth kinematics and strong nonlinear behavior, which make their numerical solution highly sensitive to contact surface discretization. Although IGA provides accurate geometric representation and smoother contact response, conventional refinement strategies often rely on uniform mesh discretization over the entire computational domain, which is inefficient because contact interactions usually occur only in localized regions. To address this limitation, the present work first extends the varying-order (VO) NURBS discretization technique, previously developed for three-dimensional frictionless contact, to frictional contact problems. This is achieved by increasing the interpolation order only along the contact interface, while maintaining a lower-order discretization in the bulk domain. The performance of the VO discretization is evaluated in terms of accuracy and computational efficiency under large deformation and sliding conditions. Building on this approach, the adaptive NURBS contact enrichment technique is proposed, in which surface enrichment is dynamically concentrated within active contact regions, adapting to the continuously changing contact area. This technique avoids unnecessary refinement of inactive contact regions and achieves improved accuracy at a lower computational cost compared to uniform and VO discretizations. Finally, these enrichment techniques are extended to large deformation self-contact problems. The resulting formulation enables efficient and reliable simulation of complex self-contact interactions. Overall, the developed framework provides a robust and computationally efficient approach for large deformation contact and self-contact analysis within the IGA paradigm.
Unravelling the Microstructural Properties, Thermomechanical Analysis, Tribocorrosion Behaviour, and Predictive Grain Evolution Modeling of Dissimilar Inconel-Steel Hybrid FSW Joints
(2026)
Bhattacharjee, Rituraj
The present thesis investigates the joining of dissimilar Inconel 718 and SS321 alloys using induction-assisted friction stir welding (IAFSW), with emphasis on process-structure-property relationships. The study systematically examines the influence of welding parameters on material flow, thermal behaviour, dynamic recrystallization, grain-boundary evolution, phase/precipitate characteristics, and mechanical performance. Experimentally validated numerical approaches based on coupled Eulerian-Lagrangian (CEL) modelling and Monte Carlo Potts (MCP) simulation were employed to understand the thermomechanical response and grain evolution during welding. The resulting joints were further evaluated in terms of mechanical properties, tribological behaviour, and electrochemical corrosion performance. The optimum 450/70 condition produced a refined dynamically recrystallized microstructure, favourable grain-boundary characteristics, improved strength and hardness, and enhanced wear and corrosion performance within the investigated process window. In addition, integration of compressed air media cooling (CAMC) with IAFSW was demonstrated to reduce tool loading and wear while enabling stable welding of the high-strength dissimilar alloys. Overall, the work establishes an integrated experimental-computational framework for understanding and improving the performance and industrial feasibility of dissimilar Ni-alloy/steel friction stir joints.
On Noise-Robust Deep Learning-Based Viscoelasticity Modeling for Ultrasound Shear Wave Elastography
(2026)
Sahshong, Phidakordor
This thesis investigates noise-robust viscoelasticity modeling for ultrasound shear wave elastography, with a focus on developing deep learning and data-driven approaches for reliable characterization of tissue mechanical properties. The work addresses key challenges associated with low signal-to-noise ratio, tissue heterogeneity, and the restrictive assumptions of conventional model-based inversion methods. The thesis first develops a deep learning–based denoising framework operating in the time–frequency domain to suppress noise in shear wave velocity fields. The proposed approach improves signal quality and enables robust reconstruction of shear wave phase velocity maps under highly noisy conditions. Its performance is evaluated using simulated data, tissue-mimicking phantoms, and ex vivo biological tissues. Building on this improved signal fidelity, a unified multitask deep learning framework is then proposed for the simultaneous reconstruction of elasticity and viscosity maps directly from noisy shear wave data. By incorporating a dedicated denoising module, the approach reduces reliance on explicit shear wave attenuation estimation and restrictive physical assumptions. The framework demonstrates accurate and robust reconstruction across numerical simulations, tissue-mimicking phantoms, and in vivo animal liver data, including challenging low-SNR conditions. The thesis further investigates the limitations of conventional shear wave dispersion models and proposes an empirically modified power-law formulation to capture nonlinear frequency-dependent behavior over an extended bandwidth. Overall, the thesis demonstrates that noise-aware deep learning and data-driven viscoelasticity modeling can substantially improve the accuracy, robustness, and diagnostic potential of ultrasound shear wave elastography, particularly under practical imaging conditions characterized by low signal quality.