Summary
Overview
Work History
Education
Skills
Websites
Certification
Publications
Timeline
Generic

ABHINEET GUPTA

Scientist

Summary

Experienced computational scientist with a demonstrated history of working in the energy sector. Skilled in energy systems modelling, green hydrogen process modelling, wind farm modelling, computational fluid dynamics (CFD), mathematical modeling, machine learning and data analytics.

Overview

11
11
years of professional experience
11
11
years of post-secondary education
5
5
Certifications
3
3
Languages

Work History

Computational Scientist

Shell
  • Developed Green Hydrogen process modelling capabilities leveraging in-house electrolyser models and external collaborations with Aveva and Aspen
  • Translating the knowledge to deliver multi-purpose dynamics simulator (MPDS) for biggest green hydrogen plant in europe while collaboration with multiple stakeholders
  • Leading the capability development for electrolyser modelling and optimisation at GW scale.

Computational Scientist

Shell
  • Led the development of StarWakes toolkit (risked NPV of $45 Mn) from concept design to digital product deployment for wind farm design and layout optimisation in collaboration with offshore wind team
  • Collaborate with wind business team towards AEP estimation and windfarm design to minimise LCOE for tender submission
  • Represent Shell at offshore wind accelerator consortium for identification and quantification of global blockage effects
  • Collaborating with Denmark Technical University to improve the computational efficiency and memory usage of the existing wind farm flow blockage model when simulating large numbers of wind turbines
  • Developing active wake control models using reinforcement learning for maximising power output of windfarms.

  • Evaluating existing offshore wind farm wake models using advanced uncertainty quantification methods.

  • Developing a fast and scalable Physics-informed Neural Network for Turbine Wake Modelling in collaboration with Indian Institute of Sciences
  • Supervised interns from Massachusetts Institute of Technology on development of machine learning models for improved prediction in multi-phase flows and reduced-order modeling for real-time prediction of flow accelerated corrosion
  • Co-authored a strategy paper on augmented intelligence towards integration of domain insights with data.

  • Developed high fidelity multi-physics model of offshore windfarms for accurate AEP estimation
  • Multiple CFD projects for different business verticals, e.g simulations of droplet trajectories, pressure drop in packed-bed reactors, mixing in sub-sea jumpers
  • Design and analysis of thermosyphon piping for ethylene-oxide reactors and being the custodian for thermosyphon tool
  • Optimizing methanol injection in sub-sea jumpers using CFD, in collaboration with GoM process engineering and PTX/E flow assurance.

Computational Scientist

Shell
1 2018 - Current
  • Led the development of NEMO (New Energies Modelling & Optimisation), a standardized & modularized modeling toolkit for calculating optimal design of renewable assets to achieve the decarbonization of energy demand (power, H2 & heat)
  • Deployed for techno-economic modeling of new energy systems for industrial sector by calculating the optimum size of renewable asset by incorporating energy demand, weather profile, technology disruption, unit capital costs and other market uncertainties
  • The technology blocks are based on the physics-based models which convert raw weather data into power output and calculates degradation over time.

Researcher

The Dutch Science Council (NWO)
01.2013 - 01.2017
  • Selected for Shell-NWO Computational Science PhD program for energy research (25 among a pool of 5000+)
  • Developed a first-principles computational model of algae motion in tubular photo-bioreactors.

Education

PhD in Applied Physics -

Eindhoven University of Technology
01.2013 - 04.2017

MSc (Hons.) in Aerospace Engineering - undefined

Delft University of Technology
01.2011 - 04.2013

BTech in Aerospace Engineering - undefined

IIT Kanpur
01.2007 - 04.2011

Skills

Websites

Certification

Received Powering Progress Award and seven Shell Recognition Awards for outstanding performance across various areas at Shell.

Publications

  • Tyagi, A., and Gupta A. Machine Learning Based Prediction of Pressure Drop, Liquid-Holdup and Flow Pattern in Multiphase Flows. 84th EAGE Annual Conference & Exhibition, Vienna, Austria, June 2023.
  • A. Gupta, Clercx, H.J.H. & Toschi, F., Effect of particle shape on drag reduction, particle dynamics and fragmentation in particle-laden turbulent pipe flow, European Physical Journal E, 2018.
  • A. Gupta, Clercx, H.J.H. & Toschi, F., Study of particle migration and stresslet in turbulent pipe using lattice Boltzmann method., European Physical Journal E, 2017.
  • A. Gupta, Clercx, H.J.H. & Toschi, F., Simulation of finite-size particles in turbulent flows using the lattice Boltzmann method., Communications in Computational Physics, 2017.
  • Tauzin G., Biferale L., Sbragaglia M., Gupta A., Toschi F., Bartel A. & Ehrhardt M., Numerical study of Lattice Boltzmann Method hydrodynamic recovery: Insight from the 2D case, Computer & Fluids, 2018.
  • Guan, Y., Pr¨obsting, S., Stephens, D., Gupta, A., & Morris, S. C., On the wake flow of asymmetrically beveled trailing edges., Experiments in Fluids, 2016.
  • Pr¨obsting, S., Gupta, A., Scarano, F., Guan, Y., & Morris, S. C., Tomographic PIV for Beveled Trailing Edge Aeroacoustics, 20th AIAA/CEAS, 2014.

Timeline

Researcher

The Dutch Science Council (NWO)
01.2013 - 01.2017

PhD in Applied Physics -

Eindhoven University of Technology
01.2013 - 04.2017

MSc (Hons.) in Aerospace Engineering - undefined

Delft University of Technology
01.2011 - 04.2013

BTech in Aerospace Engineering - undefined

IIT Kanpur
01.2007 - 04.2011

Computational Scientist

Shell

Computational Scientist

Shell

Computational Scientist

Shell
1 2018 - Current

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