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Dr. Harshal Akolekar
Publications
Journals / Book Chapters
Akolekar H.D, Godse P., Pradeep A.M. 'Near-Stall Performance and Flow Characteristics of a Transonic Axial Compressor Subject to Inlet Distortion and Surface Roughness', European Journal of Mechanics B / Fluids (2026) Accepted
Patel, R.S, Akolekar H.D., ‘Data-Driven Parametric Optimization of Bio-inspired Herringbone Riblet Microstructure for Improved Aerodynamics’, in ‘Biomimetics for Aviation and Marine Applications’, Elsevier, 2026
Akolekar, H.D., Jhamnani P., Kumar V., Tailor V., Pote A., Meena A., Kumar K., Challa J.S., Kumar, D., 'The Role of Generative AI Tools in Shaping Mechanical Engineering Education from an Undergraduate Perspective', Scientific Reports (2025) 15(1)
Patel A., Sai S., Daiya A., Akolekar H.D, Chamola V, 2025, R.D., Pacciani, R., Marconcini, M., 'Blockchain-Enabled Traceability in the Jewel Supply Chain', Scientific Reports (2025) 15 (1) 3837
Fang, Y., Zhao, Y., Akolekar H.D, Ooi, A., Sandberg, R.D., Pacciani, R., Marconcini, M., 'A data-driven approach for generalizing the laminar kinetic energy model for separation and bypass transition in low- and high-pressure turbines", ASME Journal of Turbomachinery (2024) 146 (9)
Pacciani, R., Marconcini, M., Bertini, F., Taddei, S.R., Spano, E., Zhao, Y. Akolekar, H.D.,Sandberg, R.D., and Arnone, A., 2021, ‘Assessment of Machine-learnt Turbulence Models Trained for Improved Wake-mixing in Low Pressure Turbine Flows’, Energies, 14(24), 8327
Akolekar, H.D., Waschkowski, F., Zhao, Y., Pacciani, R., and Sandberg, R.D., 2021, ‘Transition Modeling for Low Pressure Turbines Using Computational Fluid Dynamics Driven Machine Learning’, Energies 14(15), 4680.
Akolekar, H. D., Weatheritt, J., Hutchins, N., Sandberg, R. D., Laskowski, G., and Michelassi, V., 2019. ‘Development and Use of Machine-Learnt Algebraic Reynolds Stress Models for Enhanced Prediction of Wake Mixing in Low Pressure Turbines’, ASME Journal of Turbomachinery, 141 (4)
p. 041010.
Gu, Y., Fang, Y., \textbf{Akolekar, H.D.}, Ooi, A., Sandberg, R. D., Marconcini, M., 'Machine-learning strategies to train closure models for Low-Pressure Turbines with unsteady inflow conditions", ASME Journal of Turbomachinery (2026) Accepted
Fang, Y., Reismann, M., Pacciani, R., Zhao, Y., Ooi, A., Marconcini, M., Akolekar H.D., Sandberg, R.D., 'Accelerating CFD-driven training of transition and turbulence models for turbine flows by one-shot and real-time transformer integration', Computers and Fluids (2026) 306
Akolekar H.D, 'Enhancing the Accuracy of Transition Models for Gas Turbine Applications Through Data-Driven Approaches', Sadhana (2025) 50 (19)
Godse, P.B, Akolekar, H.D., Pradeep, A.M., 'Surface roughness effects in a transonic axial flow compressor operating
at near-stall conditions, Physics of Fluids (2024) 36(10)
Patel R.S, Akolekar H.D, 2023. 'Machine-Learning Based Optimisation of a Biomimiced Herringbone Microstructure for Superior Aerodynamic Performance', Engineering Research Express, IOP, 5 (4).
Akolekar, H.D., Zhao, Y., Sandberg, R.D., and Pacciani, R., 2021, ‘Integration of MachineLearning and Computational Fluid Dynamics to Develop Turbulence Models for Improved Low Pressure Turbine Wake Mixing Prediction’, ASME Journal of Turbomachinery, 143 (12).
Zhao, Y., Akolekar, H. D., Weatheritt, J., Michelassi, V., and Sandberg, R. D., 2020. ‘RANS Turbulence Model Development using CFD-Driven Machine Learning’. Elsevier Journal of Computational Physics, 411 (June).
Akolekar, H. D., Sandberg, R. D., Hutchins, N., Michelassi, V., and Laskowski, G., 2019.
‘Machine-Learnt Turbulence Closures for Low-Pressure Turbines with Unsteady Inflow Conditions’, ASME Journal of Turbomachinery, ISUAAAT15 Special Issue, 141 (10) p. 101009.
International Conferences
Jayadeep, K., Anupindi, K., Akolekar H.D., ‘Effect of Reynolds Number on Turbulent Flow
and Heat Transfer in Ribbed Channel Flows’, Proceedings of the 13th International and 53rd
National Conference on Fluid Mechanics and Fluid Power (FMFP), Dec. 2026, Rourkela, India
Nayak, S., Kumar, P., Ranjan, R., Akolekar, H.D., ‘Enhancing Wake Prediction in Low-
Pressure Turbine Flows Using Machine Learning and High-Fidelity Simulation’, 27th Con-
ference of the International Society for Air Breathing Engines, Mumbai, India, Sept. 2026
Nayak, S., Akolekar H.D, Ranjan, R., ‘Resolving Unsteady Wake Dynamics in a Transonic
Low-Pressure Turbine via Hybrid Scale-Resolving Simulations’, IMECE-India 2026, Chennai,
India IMECE-INDIA-189177
K. Jayadeep, Akolekar H.D, Anupindi K.,‘Effect of Rib Height on Flow and Heat Transfer in
Turbulence Channel Flow’, 15th Direct and Large Eddy Simulation ERCOFTAC Workshop,
Delft, Netherlands, May 2026
Gu, Y., Fang, Y., Akolekar, H.D., Ooi, A., Pacciani, R., Marconcini, M., Sandberg, R. D.,
‘Machine-learning strategies to train closure models for Low-Pressure Turbines with unsteady
inflow conditions’, 17th International Symposium on Unsteady Aerodynamics, Aeroacoustics
and Aeroelasticity of Turbomachines, Nov. 2025, Melbourne, Australia
Godse, P., Akolekar HD, Pradeep AM, 'Numerical Simulations of a Transonic Axial Compressor With Roughness Near the Onset of Stall” Asian Conference on Gas Turbines, Kanpur, India, Aug 2024 (ACGT_2024_35)
Fang, Y., Zhao, Y., Akolekar H.D, Ooi, A., Sandberg, R.D., Pacciani, R., Marconcini, M., 'Exploiting a Transformer Architecture to Simultaneous Development of Transition and Turbulence Models for Turbine Flow Predictions' ASME Turbo Expo, June 2024, London, UK
Ishika Goyal, Ritvik B, Jagat S Challa, Akolekar HD, Dhruv Kumar, 'It’s not like Jarvis, but it’s pretty close!" - Examining ChatGPT’s Usage among Undergraduate Students in Computer Science' Proceedings of the 26th Australasian Computing Education Conference, Sydney, Australia, Jan 2024 (124-133)
Akolekar HD, 'Enhancing the Accuracy of Transition Models for Gas Turbine Applications Through Data-Driven Approaches' -10th International & 50th National Fluid Mechanics and Fluid Power Conference, Jodhpur, India, December 2023. (paper 553)
(Best Paper Award in Fluid Dynamics Category)
Akolekar, H.D., “Computational Fluid Dynamics Based Machine Learning for Gas Turbines”, International Conference on Recent Advances in Mechanical Engineering, August 2022 (paper no.
2078), Jodhpur, India.
Akolekar, H.D., Waschkowski, F., Sandberg, R., Pacciani, R. and Zhao, Y., “ Multi-Objective Development of Machine-Learnt Closures for Fully-Integrated Transition and Wake Mixing Prediction in Low-Pressure Turbines” 67th ASME Turbo Expo., June 2022 (paper no. GT2022-81091), Rotterdam, The Netherlands.
Akolekar, H.D., Zhao, Y., Sandberg, R.D., Pacciani, R., “Integration of Machine Learning and Computational Fluid Dynamics to Develop Turbulence Models for Improved Turbine Wake Mixing Prediction”, 65th ASME Turbo Expo Turbomach. Tech. Conf. Expo., Sept. 2020 (paper no. GT2020-14732), Virtual, Online.
Akolekar, H. D., Zhao, Y., Sandberg, R. D., Hutchins, N., and Michelassi, V. ‘Turbulence Model Development for Low & High Pressure Turbines Using a Machine-Learning Approach’, 24th International Society for Air Breathing Engines (ISABE), September, 2019, Canberra, Australia, (paper no. ISABE-24010).
Akolekar, H. D., Sandberg, R. D., Hutchins, N., ‘Enhancing Gas Turbine Efficiency with Machine
Learning Techniques’ (Poster). Inaugural Melbourne Energy Institute, Symposium, December 2018, Melbourne, Australia (Best Poster Award).
Akolekar, H. D., Weatheritt, J., Hutchins, N., Sandberg, R. D., Laskowski, G., and Michelassi, V. ‘Development and Use of Machine-Learnt Algebraic Reynolds Stress Models for Enhanced Prediction of Wake Mixing in LPTs’. In vol. 2C of 63rd ASME Turbo Expo Turbomach. Tech. Conf., June, 2018, Oslo, Norway (paper no. GT2018-75447).
Akolekar, H.D., Bodi, K.V. ‘Computation of Particle Trajectories in Turbulent Flows’ (Poster).
IRCC Exhibition, May 2014, IIT, Bombay, India.
nsari, T., Rosenzweig, M., Sandberg, R.D., Akolekar, H.D., ‘Data-Driven Turbulence Mod-
eling for Improved Wake Prediction of Low-Pressure Turbine Endwall Flows’, 27th Conference of the International Society for Air Breathing Engines, Mumbai, India, Sept. 2026
Meena, S., Patel, R.S., Ansari, T., Akolekar, H.D., ‘A Machine-Learning-Assisted Geometry
Optimization Pipeline for Wake Loss Reduction in Low-Pressure Turbine Cascades’, IMECE-
India 2026, Chennai, India IMECE-INDIA-18885
K. Jayadeep, Anupindi K., Akolekar H.D, ‘Influence of Rib Height on Flow and Heat Transfer in Internal Cooling Channels’, Indian Conference on Applied Mechanics (INCAM), July 2026, Kanpur India
Ansari T., Mehta P., Akolekar H.D., ‘CFD-Driven Machine Learning for Improving Wake
Prediction for Cylinders’, Proceedings of the 12th International and 52st National Conference
on Fluid Mechanics and Fluid Power (FMFP), Dec. 2025, Ahmedabad, India
Akolekar H.D., Godse P., Pradeep A.M., 'Impact of Inlet Distortion and Surface Roughness on Near-Stall Performance of a Transonic Axial Flow Compress” IMECE-India, Sept 2025, Hyderabad, India
Akolekar H.D, “Coupled Transition and Turbulence Models for Low Pressure Turbine Flows Using Multi-Objective Data-Driven Frameworks” Asian Conference on Gas Turbines, Kanpur, India, Aug 2024 (ACGT_2024_45)
Ishika Goyal, Ritvik B, Dhruv Kumar, Akolekar HD, 'ChatGPT in the Classroom: An Analysis of Its Strengths and Weaknesses for Solving Undergraduate Engineering Questions' SIGCSE, Portland, USA, March 2024.
Mukul Chandra, Akolekar HD, 'The Effect of Particle Reynolds Number on Submarine Pipeline Scour Depth Using CFD' - 10th International & 50th National Fluid Mechanics and Fluid Power Conference, Jodhpur, India, December 2023. (paper 555)
Fang, Y., Zhao, Y., Akolekar H.D, Ooi, A., Sandberg, R.D., Pacciani, R., Marconcini, M., 'A data-driven approach for generalizing the laminar kinetic energy model for separation and bypass transition in low- and high-pressure turbines", ASME Turbo Expo 2023, Boston, USA. (Additionally, Best Poster Award)
Patel, R.S., Akolekar, H.D., “Supervised Learning Augmented Computational Fluid Dynamics for Bio-inspired Herringbone Structure Optimisation”, International Conference on Recent Advances in Mechanical Engineering, August 2022 (paper no. 4442), Jodhpur, India. (Best Paper Award)
Akolekar, H.D., Pook, D., Ranmuthugula, D., “CFD-Based Boundary Layer Prediction of Axisymmetric Bodies of Revolution”, 22nd Australasian Fluid Mechanics Conference (AFMC), December 2020, Brisbane, Australia (paper no. 15).
Zhao, Y., Akolekar, H.D., Sandberg, R.D., “CFD-Ready Turbulence Models from Gene Expression
Programming: Concepts”, In Bulletin, 72nd DFD Meeting of American Physical Society, November 2019, Seattle, USA (Invited Presentation - Focus Session: Machine-Learning & Fluids)
Michelassi, V., Francini, S., and Sandberg, R. D., Zhao, Y. and Akolekar, H.D., “High-Fidelity CFD Assisted Improvement of Turbomachinery Aerothermodynamics and Modelling”, UK Turbulence Consortium (UKTC), September, 2019, Imperial College, London, UK (Invited Presentation).
Akolekar, H. D., Sandberg, R. D., Hutchins, N., Michelassi, V., and Laskowski, G. ‘Machine-Learnt Turbulence Closures for LPTs with Unsteady Inflow Conditions. In 15th International Symposium on Unsteady Aerodynamics Aeroacoustics & Aeroelasticity of Turbomachines (ISUAAAT), September 2018, University of Oxford, UK (paper no. ISUAAAT-019).
Akolekar, H.D., Sandberg, R.D. ‘Understanding Loss Mechanisms in Turbomachinery to Increase Efficiency’ (Poster). Endeavour Exhibition, October 2016, University of Melbourne, Australia.
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