arXiv subjects

Harshith Gowrachari

Publications and source records attributed to Harshith Gowrachari.

5 recordsLinked to original sources

arXiv2026

Data-driven reduced order model for residence time distribution analysis of an industrial-scale continuous casting tundish

The continuous casting tundish plays a critical role as a metallurgical reactor in the continuous casting process, with its flow characteristics serving as a key parameter in the production of high-quality steel. These characteristics are typically assessed through residence time distribution (RTD) curves. This study examines the flow behaviour in a single-strand continuous casting tundish through a combination of numerical simulations and experimental validation. Steady-state full order model (FOM) simulations are performed under both isothermal and non-isothermal conditions to evaluate the influence of thermal buoyancy on the velocity field, which is found to be negligible. The resulting flow fields are used to initialize transient tracer transport simulations for determining the RTD and flow volume partitioning. Subsequently, a data-driven reduced order model (ROM) is developed to predict the RTD response. Comparison of RTD curves obtained from experiments, FOM, and ROM shows excellent agreement, with the ROM accurately capturing the key flow characteristics at a fraction of the computational cost. These results highlight the potential of ROM techniques for efficient real-time analysis, design, and optimization of tundish operations in metallurgical processes.

physics.flu-dyn↗

arXiv2025

Reservoir computing based predictive reduced order model for steel grade intermixing in an industrial continuous casting tundish

Continuous casting is a widely adopted process in the steel industry, where maintaining high steel quality is paramount. Efficient prediction of grade intermixing during ladle changeover operations is critical for maintaining steel quality and minimizing material losses in the continuous casting process. Among various factors influencing grade intermixing, operating parameters play a significant role, in addition to tundish geometry and flow control devices. In this study, three-dimensional, transient, two-phase turbulent flow simulations are conducted to investigate the ladle changeover operation. During this process, the molten steel level in the tundish typically varies over time, significantly affecting the grade intermixing phenomena. The influence of ladle change time on intermixing time has been presented. However, high-fidelity full-order simulations of such complex transient phenomena are computationally expensive and are impractical for real-time monitoring or design-space exploration in industrial-scale applications. To address this issue, a reduced order modelling approach based on proper orthogonal decomposition (POD) and reservoir computing (RC) is employed to efficiently predict intermixing time. The proposed reduced order model (ROM) demonstrates excellent predictive accuracy using limited training data while requiring significantly less computational resources and training time. The results demonstrate the potential of the proposed methodology as a fast, reliable tool for real-time process monitoring and optimization in industrial continuous casting operations.

physics.flu-dyn↗

arXiv2025

Projection-based model order reduction for residence time distribution analysis of an industrial-scale continuous casting tundish

The flow behavior in the continuous casting tundish plays a critical role in steel quality and is typically characterized via residence time distribution (RTD) curves. This study investigates the fluid flow behaviour in a single-strand tundish using numerical and experimental approaches. Full-order model (FOM) steady-state simulations were conducted under both isothermal and non-isothermal conditions to assess the influence of thermal buoyancy on the flow characteristics. The results show that buoyancy effects under non-isothermal conditions have a negligible impact on the overall velocity field. The converged flow fields serve as initial conditions for transient tracer transport simulations, enabling evaluation of RTD curves and volume partitioning. A Galerkin projection-based reduced-order model (ROM) is developed to efficiently derive RTD curves. Comparison of RTD curves from experiments, FOM simulations, and ROM predictions demonstrates strong agreement, with both computational approaches closely matching experimental data. A parameter-time dependent ROM is subsequently developed using Galerkin projection and operator interpolation. This enables efficient evaluation of RTD curves across varying parameter values with significantly reduced computational cost compared to full-order simulations. The ROM framework is well-suited for real-time analysis, design processes, optimization, and digital twin applications in metallurgical processes.

physics.flu-dyn↗

arXiv2025

Non-intrusive model reduction of advection-dominated hyperbolic problems using neural network shift augmented manifold transformation

Advection-dominated problems are predominantly noticed in nature, engineering systems, and various industrial processes. Traditional linear compression methods, such as proper orthogonal decomposition (POD) and reduced basis (RB) methods are ill-suited for these problems, due to slow Kolmogorov $n$-width decay. This results in inefficient and inaccurate reduced order models (ROMs). There are few non-linear approaches to accelerate the Kolmogorov $n$-width decay. In this work, we use a neural network shift augmented transformation technique that employs automatic shift detection. This approach leverages a deep-learning framework to derive a parameter-dependent mapping between the original manifold $\mathcal{M}$ and the transformed manifold $\tilde{\mathcal{M}}$. We apply a linear compression method to obtain a low-dimensional linear approximation subspace of the transformed manifold $\tilde{\mathcal{M}}$. Furthermore, we construct non-intrusive reduced order models on the resulting transformed linear approximation subspace and employ automatic shift detection for predictions in the online stage. We propose a complete framework, the neural network shift-augmented proper orthogonal decomposition-based reduced order model (NNsPOD-ROM) algorithm, comprising both offline and online stages for model reduction of advection-dominated problems. We test our proposed methodology on numerous experiments to evaluate its performance on the 1D linear advection equation, a higher order method benchmark case - the 2D isentropic convective vortex, and 2D two-phase flow.

math.NA↗

arXiv2025

Model Reduction for Transport-Dominated Problems via Cross-Correlation Based Snapshot Registration

Traditional linear approximation methods, such as proper orthogonal decomposition and the reduced basis method, are ill-suited for transport-dominated problems due to the slow decay of the Kolmogorov $n$-width, leading to inefficient and inaccurate reduced-order models. In this work, we propose a model reduction approach for transport-dominated problems by employing cross-correlation based snapshot registration to accelerate the Kolmogorov $n$-width decay, thereby enabling the construction of efficient and accurate reduced-order models using linear approximation methods. We propose a complete framework comprising offline-online stages for the development of reduced order models using the cross-correlation based snapshots registration. The effectiveness of the proposed approach is demonstrated using two test cases: 1D travelling waves and the higher-order methods benchmark test case, 2D isentropic convective vortex.

math.NA↗