arXiv subjects

Indranil Bandyopadhyay

Publications and source records attributed to Indranil Bandyopadhyay.

6 recordsLinked to original sources

arXiv2026

Electromagnetic filament coalescence as magnetic island merging with diamagnetic effects

We investigate the nonlinear coalescence of two current-carrying ELM filaments using a three-dimensional electromagnetic fluid model. In the flat-density limit, the coalescence exhibits magnetic island-like reconnection, characterized by X-point formation, current-sheet development, and Sweet-Parker-like resistive scaling. Introducing a blob-like density perturbation modifies the reconnection dynamics: while the peak reconnection rate remains nearly unchanged for weak perturbations, it decreases and is increasingly delayed for larger density amplitudes. Analysis of the induction equation reveals a transition from resistive to increasingly density-dependent advective dynamics. Finite density perturbations also enhance the post-compression rebound, or sloshing, of the filaments. The sloshing amplitude increases with the density-gradient pressure force, establishing density perturbation as an additional control parameter for both reconnection and filament sloshing. These results highlight the coupled electromagnetic and pressure-driven dynamics governing the nonlinear evolution of ELM filaments in the tokamak edge.

physics.plasm-ph↗

arXiv2026

Nonlinear Dynamics of Current-Carrying ELM Filaments: Spiral Vorticity, Rotation, and Velocity Suppression

In this work, we investigate the nonlinear dynamics of isolated current-carrying edge-localized mode (ELM) filaments using a reduced electromagnetic fluid model in slab geometry. Numerical simulations show that unidirectional parallel current significantly suppresses radial filament velocity and reduces the outward propagation velocity by weakening the curvature-driven interchange force. The reduction in radial velocity is found to follow a modified scaling relation, demonstrating that increasing current progressively weakens outward filament propagation. Analysis of the vorticity equation shows that the electromagnetic current source changes from a dipolar structure to a remarkable spiral pattern, and overcomes the conventional curvature drive in the nonlinear phase. This current-driven source directly imprints its topology on the vorticity field, resulting in spiral vorticity, enhanced angular momentum, increased rotational energy, and localized shear layers. The filament therefore undergoes a transition from a conventional propagating state to a rotationally self-organized electromagnetic structure. These findings demonstrate that parallel current acts as an effective electromagnetic vorticity source and provides new insight into the nonlinear dynamics of ELM filaments in tokamak edge plasmas.

physics.plasm-ph↗

arXiv2026

Deep Learning Models for ADITYA-U MHD Equilibrium

This work presents deep learning models to predict magnetohydrodynamic equilibrium parameters and profiles for the ADITYA-U tokamak. A synthetic free-boundary equilibrium dataset consisting of 100,760 cases was generated using the pyIPREQ Grad-Shafranov solver, with inputs derived from 766 ADITYA-U plasma discharges and constrained to experimentally relevant circular limiter plasmas near the flat-top phase. Several deep learning approaches were investigated for predicting scalar equilibrium quantities, one-dimensional safety factor profiles and two-dimensional poloidal flux profiles. These approaches included Dense neural networks, principal component analysis based reduced-order models, one-dimensional and two-dimensional convolutional neural networks, and physics-informed neural networks incorporating Grad-Shafranov residual constraints. In addition, an inverse model was developed to estimate poloidal field coil currents from desired plasma equilibrium conditions. The results demonstrate that key equilibrium parameters and profiles can be accurately estimated within the operational domain represented by the dataset. The developed models provide a computationally efficient alternative to conventional equilibrium estimation and can be useful for real-time plasma control, rapid equilibrium analysis, and experimental planning in ADITYA-U operations.

physics.plasm-ph↗

arXiv2026

Finite Ion Temperature Effects on the Merging of Current-Carrying ELM Filaments in the edge region of a tokamak

Edge-localized-mode (ELM) filaments are crucial for cross-field transport at the tokamak edge; yet, their dynamics are often analyzed using the cold-ion approximation, despite experimental data indicating that Ti~Te . This study employs a normalized three-dimensional fluid model to investigate the influence of finite ion temperature on the dynamics of unidirectional current-carrying ELM-like filaments. We demonstrate that increasing ion temperature substantially alters filament propagation and interaction, resulting in a delay of filament merging despite an increase in total kinetic energy due to a stronger pressure-gradient drive. The examination of single-filament dynamics indicates that finite ion temperature generates asymmetric potential structures, strong poloidal flows, and persistent rotational motion, which channel kinetic energy from radial propagation into vortical dynamics. A comprehensive examination of the ion-to-electron temperature ratio reveals a distinct transition from radially dominated to rotation-dominated behavior as ion temperature increases. These results provide a unified physical explanation for reduced radial transport and delayed merging in the warm-ion domain, emphasizing the necessity of incorporating ion temperature effects in the modeling of ELM filament dynamics and edge plasma transport.

physics.plasm-ph↗

arXiv2024

Merging dynamics of plasma blobs in the Scrape-off Layer of a tokamak

The emergence and merging of high-density coherent structures - plasma blobs - is a recurrent phenomenon in the Scrape-off layer (SOL) of a tokamak plasma that has a significant impact on the rate of convective transport in that region. We report on a model study of the merging of two electromagnetically interacting blobs in a high beta plasma. Our detailed numerical simulations show that the merging process is akin to the coalescence instability between two magnetic islands but with important differences due to the density perturbation. The blobs are found to rotate about each other during merging and the merging occurs with an acceleration in the poloidal direction that is directly proportional to the square of the current density of the blobs and inversely proportional to its density. The separation distance between two high current density blobs is also seen to oscillate indicating a sloshing behavior.

physics.plasm-ph↗

arXiv2024

Deep Learning-based Sentiment Analysis of Olympics Tweets

Sentiment analysis (SA), is an approach of natural language processing (NLP) for determining a text's emotional tone by analyzing subjective information such as views, feelings, and attitudes toward specific topics, products, services, events, or experiences. This study attempts to develop an advanced deep learning (DL) model for SA to understand global audience emotions through tweets in the context of the Olympic Games. The findings represent global attitudes around the Olympics and contribute to advancing the SA models. We have used NLP for tweet pre-processing and sophisticated DL models for arguing with SA, this research enhances the reliability and accuracy of sentiment classification. The study focuses on data selection, preprocessing, visualization, feature extraction, and model building, featuring a baseline Naïve Bayes (NB) model and three advanced DL models: Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Bidirectional Encoder Representations from Transformers (BERT). The results of the experiments show that the BERT model can efficiently classify sentiments related to the Olympics, achieving the highest accuracy of 99.23%.

cs.CL↗