
- Unit:
- SINTEF Digital
- Department:
- Mathematics and Cybernetics
- Office:
- Trondheim
I have been working at SINTEF since 2011 as a scientist. Prior to that, i did my post-doctoral fellowship at CSIRO, Melbourne. My work involves managing projects, and developing computational fluid dynamics and machine learning (hybrid analytics) tools for industrial processes,wind energy, aviation safety, chemical engineering process involving multiphase and reactive flows. This work has been published in more than 50 peer-reviewed journals with more than 850+ citations (per google scholar).
Education
Ph.D. : I completed my Ph.D in 2008 in India and this involved application of computational fluid dynamics and unsupervised machine learning for identifying flow structure dynamics in six chemical engineering processes (bubble column, stirred tank, extractor).
Masters: I did my masters in chemical engineering at BITS-Pilani,India in 2004 with thesis related to application of artificial intelligence in process industry where i used support vector machines for fault diagnosis.
Competence and research areas
My research involves application of computational fluid dynamics, machine learning and hybrid analytics and modelling in wind energy, aviation (drone safety), urban flow, chemical engineering process involving multiphase and reactive flows. The work has been published in 50+ peer-reviewed journal with 850+ citations . Link: https://scholar.google.ch/citations?hl=de&user=Z372TWIAAAAJ&view_op=list_works&sortby=pubdate .
Publications
- Geometric Change Detection in Digital Twins
- A nested multi-scale model for assessing urban wind conditions : Comparison of Large Eddy Simulation versus RANS turbulence models when operating at the finest scale of the nesting
- A non-intrusive parametric reduced order model for urban wind flow using deep learning and Grassmann manifold
- Machine learning with subsequent physics-based analytics for guiding transport planning
- A nudged hybrid analysis and modeling approach for realtime wake-vortex transport and decay prediction
- Drone-based transport of biological material: Wind and turbulence prediction at OUS, Hospital for path planning.
- Towards Understanding Wind Impact for Drone Operations: A Comparison of Wind Models Operating on Different Scales in a Nested Multiscale Set-Up
- GANs enabled super-resolution reconstruction of wind field
- Screening thermoelectric materials with ab initio atomistic modelling and machine learning techniques
- High-Resolution Cfd Modelling and Prediction of Terrain-Induced Wind Shear and Turbulence for Aviation Safety.