Harshal Akolekar

• PhD
• Assistant Professor at Indian Institute of Technology Jodhpur

Non-linear turbulence closures were developed that improve the prediction accuracy of wake mixing in low-pressure turbine (LPT) flows. First, Reynolds-averaged Navier–Stokes (RANS) calculations using five linear turbulence closures were performed for the T106A LPT profile at exit Mach number 0.4 and isentropic exit Reynolds numbers 60,000 and 100,0...

The design of low-pressure turbines (LPT) must account for the losses generated by the unsteady interaction with the upstream blade row. The estimation of such unsteady wake induced losses requires the accurate prediction of the incoming wake dynamics and decay. Machine-learnt, non-linear turbulence closures (stress-strain relationships) have there...

Non-linear turbulence closures were developed that improve the prediction accuracy of wake mixing in low-pressure turbine (LPT) flows. First, RANS calculations using five linear turbulence closures were performed for the T106A LPT profile at isentropic exit Reynolds numbers 60,000 and 100,000. None of these RANS models were able to accurately repro...

The design of low-pressure turbines (LPT) must account for the losses generated by the unsteady interaction with the upstream blade row. The estimation of such unsteady wake induced losses requires the accurate prediction of the incoming wake dynamics and decay. Existing linear turbulence closures (stress-strain relationships), however, do not offe...

This paper presents a novel CFD-driven machine learning framework to develop Reynolds-averaged Navier-Stokes (RANS) models. For the CFD-driven training, the gene expression programming (GEP) method (Weatheritt & Sandberg, J. Comput. Phys., 325, 22-37 (2016)) uses RANS calculations in an integrated way to evaluate the fitness of candidate models. Th...

This study evaluates the effectiveness of three leading generative AI tools-ChatGPT, Gemini, and Copilot-in undergraduate mechanical engineering education using a mixed-methods approach. The performance of these tools was assessed on 800 questions spanning seven core subjects, covering multiple-choice, numerical, and theory-based formats. While all...

Separated-flow transition is a very popular phenomenon in gas turbines, especially in low-pressure turbines (LPTs). Low-fidelity simulations are often used in gas turbine design. However, they cannot predict separated-flow transition accurately. To improve the prediction of separated-flow transition for LPTs, empirical relations that are derived fo...

This article examines the potential of blockchain technology to revolutionize the jewelry supply chain by enhancing trust, transparency, and efficiency. Utilizing Ethereum, we developed a blockchain network tailored to the industry’s needs. Blockchain operates as a secure, immutable ledger, ensuring data integrity and transparency while preventing...

Surface roughness is a major contributor to performance degradation in gas turbine engines. The fan and the compressor, as the first components in the engine's air path, are especially vulnerable to the effects of surface roughness. Debris ingestion, accumulation of grime, dust, or insect remnants, typically at low atmospheric conditions, over seve...

Separated flow transition is a very popular phenomenon in gas turbines, especially low-pressure turbines (LPT). Low-fidelity simulations are often used for gas turbine design. However, they are unable to predict separated flow transition accurately. To improve the separated flow transition prediction for LPTs, the empirical relations that are deriv...

Surface roughness is a major contributor to performance degradation in gas turbine engines. The fan and the compressor, as the first components in the engine's air path, are especially vulnerable to the effects of surface roughness. Debris ingestion, accumulation of grime, dust, or insect remnants, typically at low atmospheric conditions, over seve...

Previous studies have shown the potential of using a multiobjective CFD (computational fluid dynamics)-driven machine-learning approach to train both transition and turbulence models in RANS (Reynolds averaged Navier-Stokes) calculations for improved turbine flow predictions (Akolekar et al., GT2022-81091; Fang et al., GT2023-102902). However, cond...

No common laminar kinetic energy (LKE) transition model has to date been able to predict both separation-induced and bypass transition, both phenomena commonly found in low-pressure turbines (LPT) and high-pressure turbines (HPT). Here, a data-driven approach is adopted to develop a more general LKE transition model suitable for both transition mod...

Biomimicry involves drawing inspiration from nature's designs to create efficient systems. For instance, the unique herringbone riblet pattern found in bird feathers has proven effective to minimize drag. While attempts have been made to replicate this pattern on structures like plates and aerofoils, there has been a lack of comprehensive optimizat...

The laminar kinetic energy (LKE) transition model has been proposed to predict the separation-induced transition, which is frequently experienced by low pressure turbines (LPT). In contrast to LPTs, high-pressure turbines (HPT) often are subject to bypass transition which currently is not captured well when using the LKE model. Because these two ty...

ChatGPT is an AI language model developed by OpenAI that can understand and generate human-like text. It can be used for a variety of use cases such as language generation, question answering, text summarization, chatbot development, language translation, sentiment analysis, content creation, personalization, text completion, and storytelling. Whil...

Biomimicry involves taking inspiration from existing designs in nature to generate new and efficient systems. The feathers of birds which form a characteristic herringbone riblet shape are known to effectively reduce drag. This paper aims to optimise the individual constituent structure of a herringbone riblet pattern using a combination of computa...

In low pressure turbines (LPT), due to the low Reynolds number a large part of the blade boundary layer remains laminar and transition may occur due to flow separation. The boundary layer details at the blade trailing edge can change substantially depending on the transition region topology and can strongly influence the wake mixing occurring downs...

This paper presents an assessment of machine-learned turbulence closures, trained for improving wake-mixing prediction, in the context of LPT flows. To this end, a three-dimensional cascade of industrial relevance, representative of modern LPT bladings, was analyzed, using a state-of-the-art RANS approach, over a wide range of Reynolds numbers. To...

Existing Reynolds Averaged Navier–Stokes-based transition models do not accurately predict separation induced transition for low pressure turbines. Therefore, in this paper, a novel framework based on computational fluids dynamics (CFD) driven machine learning coupled with multi-expression and multi-objective optimization is explored to develop mod...

Existing Reynolds Averaged Navier-Stokes based transition models do not accurately predict separation induced transition for low pressure turbines. Therefore, in this study, a novel framework based on computational fluids dynamics driven machine learning coupled with multi-expression and multi-objective optimization is explored to develop models wh...

This paper presents the development of accurate turbulence closures for low-pressure turbine (LPT) wake mixing prediction by integrating a machine-learning approach based on gene expression programming (GEP), with Reynolds Averaged Navier-Stokes (RANS) based computational fluid dynamics (CFD). In order to further improve the performance and robustn...

This paper presents development of accurate turbulence closures for wake mixing prediction by integrating a machine-learning approach with Reynolds Averaged Navier-Stokes (RANS)-based computational fluid dynamics (CFD). The data-driven modeling framework is based on the gene expression programming (GEP) approach previously shown to generate non-lin...

This paper presents a novel CFD-driven machine learning framework to develop Reynolds-averaged Navier-Stokes (RANS) models. The CFD-driven training is an extension of the gene expression programming method Weatheritt and Sandberg (2016) [8], but crucially the fitness of candidate models is now evaluated by running RANS calculations in an integrated...

The design of the gas turbine, which is the work horse of the aviation industry, has reached a high degree of maturity; given that the first gas turbine flew in the late 1930s. Despite this, the industrial sector is looking towards harnessing even incremental points of efficiency with novel methods, which can translate to millions of dollars of sav...

Accurately predicting the wake mixing in gas turbines is of utmost importance from the perspective of blade designers as this phenomenon governs the stagnation pressure loss. Existing Reynolds Averaged Navier-Stokes (RANS) based methods struggle to offer good predictions of the wake-mixing, especially close to the blade trailing edge. It is therefo...