Overview
Personal Profile
Dr. Shashank Yellapantula is a staff scientist in the High Performance Algorithms and Complex Fluids Group in the Computational Science Center where he leads development of exascale application software in wind energy and combustion. He is also actively involved in using Machine Learning techniques to improve and augment simulation based models for industrially relevant flow physics problems.
Research Interests
Combustion modeling
Design of industrial combustion technology
Turbulence modeling, atmospheric boundary layers
Machine learning-based augmentation of simulation models
Professional Experience
Senior Scientist, NLR (2020–present)
Scientist, NLR (2017–2020)
Lead Research Engineer, GE Global Research (2016–2017)
Research Engineer, GE Global Research (2013–2016)
Education/Academic Qualification
Bachelor, Mechanical Engineering, Sardar Vallabhbhai National Institute of Technology Surat
PhD, Mechanical Engineering, Stanford University
Master, Mechanical Engineering, Stanford University
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Collaborations and Top Research Areas From the Past 5 Years
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A Reduced Order Model Approach Based on Progress Variables for Simulation of Oxycombustion in the Allam-Fetvedt Cycle
Yellapantula, S., Perry, B., Rahimi, M., Yi, D., Peterson, K. & Martin, M., 2026. 11 p.Research output: Contribution to conference › Paper
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Roadmap to Advance Heliostat Technologies for High Temperature Solar-Thermal Systems
Schell, S., Emes, M., Kesseli, D., Meyen, S., Muller, M., Ndione, P., Neber, M., Yellapantula, S., Zhu, G., Zolan, A., Armijo, K., Brost, R., Gordon, M., Sment, J., Spieles, A., Cholette, M., Picotti, G. & Yang, B., 2026, 213 p.Research output: NLR › Technical Report
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Understanding Isomeric Effects on Properties of Aviation Fuels via a Group Contribution Method: Preprint
Montgomery, D., Perry, B. & Yellapantula, S., 2026. 13 p.Research output: Contribution to conference › Paper
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Accelerating Innovative Energy Solutions Using Combustion Simulations
Yellapantula, S., Wimer, N., Henry de Frahan, M., Appukuttan, S., Martin, M., Perry, B., Sitaraman, H., Rahimi, M. & Day, M., 2025, In: Computing in Science and Engineering. 27, 1, p. 9-17 9 p.Research output: Contribution to journal › Article › peer-review
1 Scopus Citations -
a priori Uncertainty Quantification of Reacting Turbulence Closure Models Using Bayesian Neural Networks: Article No. 109821
Pash, G., Hassanaly, M. & Yellapantula, S., 2025, In: Engineering Applications of Artificial Intelligence. 141, 17 p.Research output: Contribution to journal › Article › peer-review
2 Scopus Citations