arXiv subjects

Ankit Bansal

Publications and source records attributed to Ankit Bansal.

3 recordsLinked to original sources

arXiv2026

Development and validation of a sharp interface immersed boundary method for high-speed flows

This study presents an advanced sharp-interface immersed boundary method (IBM) integrated with the blastFOAM library on the OpenFOAM platform for high-speed compressible flow simulations. The developed solver extends the existing IBM techniques available in OpenFOAM to compressible regimes, tackling challenges such as shock waves, expansions, and dynamic geometries without needing body-fitted meshes. A novel contribution of this work is the implementation of a slip boundary condition for velocity at immersed surfaces, specifically designed to handle inviscid highspeed flows. The method also combines the second-order polynomial IBM reconstruction with multiple flux schemes such as Kurganov, Tadmor, HLL (Harten-Lax-van Leer), and AUSM+up (Advection Upstream Splitting Method Plus Upwind). The technique achieves significant accuracy across diverse high-speed flow conditions. Extensive validation is performed through supersonic flow cases over a wedge, a cylinder, an aerofoil, a sphere, and a moving piston. Results show excellent agreement with analytical and body-fitted solutions, with sharp resolution of shocks, minimal numerical oscillations, and shock reflections. A grid convergence study confirms the solver's reliability across varying mesh resolutions, while three-dimensional simulations highlight its capability for scaled-up applications. This solver provides a flexible, efficient, and accurate tool for capturing high-speed flow phenomena across various Mach numbers and geometries. It offers significant advantages in mesh handling, particularly for dynamic or intricate configurations, making it ideal for aerospace and engineering applications involving compressible flows.

physics.class-ph↗

arXiv2018

Domain2Vec: Deep Domain Generalization

We address the problem of domain generalization where a decision function is learned from the data of several related domains, and the goal is to apply it on an unseen domain successfully. It is assumed that there is plenty of labeled data available in source domains (also called as training domain), but no labeled data is available for the unseen domain (also called a target domain or test domain). We propose a novel neural network architecture, Domain2Vec (D2V) that learns domain-specific embedding and then uses this embedding to generalize the learning across related domains. The proposed algorithm, D2V extends the idea of distribution regression and kernelized domain generalization to the neural networks setting. We propose a neural network architecture to learn domain-specific embedding and then use this embedding along with the data point specific features to label it. We show the effectiveness of the architecture by accurately estimating domain to domain similarity. We evaluate our algorithm against standard domain generalization datasets for image classification and outperform other state of the art algorithms.

cs.LG↗

arXiv2015

Time Critical Multitasking for Multicore Microcontroller using XMOS Kit

This paper presents the research work on multicore microcontrollers using parallel, and time critical programming for the embedded systems. Due to the high complexity and limitations, it is very hard to work on the application development phase on such architectures. The experimental results mentioned in the paper are based on xCORE multicore microcontroller form XMOS. The paper also imitates multi-tasking and parallel programming for the same platform. The tasks assigned to multiple cores are executed simultaneously, which saves the time and energy. The relative study for multicore processor and multicore controller concludes that micro architecture based controller having multiple cores illustrates better performance in time critical multi-tasking environment. The research work mentioned here not only illustrates the functionality of multicore microcontroller, but also express the novel technique of programming, profiling and optimization on such platforms in real time environments.

cs.DC↗