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Publications

Journals

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) (Accepted)

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.

Akolekar, H.D., Pook, D.A., Seil, G., and Ranmuthugula, D., 2022 ‘The Effect of Geometric
Parameters and the Reynolds Number on the Thrust Deduction Fraction of Underwater Vehicles’ (under rev.)

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

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 2024, London, UK (Accepted) 

shika 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.

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. (Accepted)

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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