Girish Dighe
Computational & Mechanical Engineer
Bridging Simulation, Aerospace, and Machine Learning
About
I am a Mechanical and Computational Engineer with a passion for solving complex challenges at the intersection of simulation, product development, and machine learning. My international experience spans the aerospace, energy, and sustainable systems sectors, where I've leveraged my skills to drive innovation.
With a Master of Science (with Distinction) from Cardiff University, I specialize in CFD, combustion, and propulsion. I am particularly interested in applying modern techniques like Machine Learning and Digital Twins to accelerate engineering workflows and deliver robust, production-ready solutions.
From analyzing sustainable aviation fuels to developing digital twins for vertical farming, my work is driven by a commitment to creating efficient and sustainable technology.
Experience
07/2024 – Present
Research Assistant
Cardiff University, UK
- Conducted CFD, combustion, and emissions analysis for sustainable aviation fuels.
- Applied PINNs and ML surrogates to predict airfoil flow dynamics and icing behaviour.
- Designed system test rigs and P&IDs for turboshaft engine research with Senza Tech.
- Developed a prototype digital twin for renewable-powered vertical farming (Invertigro).
- Performed wind turbine icing studies (Fluent, OpenFOAM) with UKRI & NRC Canada.
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07/2021 – 06/2022
Design and Testing Engineer
Manastu Space Technologies, India
- Led ground testing, improving thrust measurement accuracy by 14% and thermal efficiency by 10%.
- Designed structural, thermal, and fluid hardware for propulsion systems.
- Collaborated with DRDO, ISRO, and venture capital partners for validation and funding.
- Strengthened engineering workflows via SOPS, DFMEA, HAZOP, ALARP frameworks.
- Used Python, MATLAB, Power BI for propulsion performance analysis and visualization.
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Projects
Digital Twin for Vertical Farming
Developed a prototype digital twin integrating renewable energy models and sensor data for system efficiency. Applied Taguchi DOE, Pareto and Bees optimization, forecasting and real-time visualization via Dash to demonstrate system efficiency and sustainability.
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Fast Surrogates for Airfoil Aerodynamics
Developed FNO, UNet, and POD surrogates on the AirfRANS dataset to predict velocity and pressure fields. Achieved ~2-3% normalized RMSE after physics-aware fine-tuning, enabling fast and accurate aerodynamic predictions.
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ML in CFD Labs
Implemented reduced-order models (POD, DMD) and ML surrogates (FNO) to compress and reconstruct airfoil flow fields. Demonstrated sensor-conditioned reconstructions and hybrid physics-ML models for digital twin applications.
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Bachelor's Thesis: Liquid Rocket Engines
Performed turbulence and combustion modeling of liquid rocket engines in ANSYS Fluent. Conducted grid independence studies and model validation. Optimized meshing and combustion models to improve prediction accuracy.
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Notable Achievements
06/2024 – 09/2024
Master's Thesis
Conjugate Heat Transfer in Micromix Combustors
- Investigated the performance and emissions of H₂ and NH₃-H₂ blends using ANSYS Fluent and CHEMKIN.
- Assessed conjugate heat transfer (CHT) effects on combustor performance.
- Validated computational results against experimental data to support sustainable propulsion system design.
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2018 – 2021
Motorsports & Vehicle Design Leadership
Formula Student, SAEINDIA, Modified Auto Club
- Co-founded a Formula Student club and led the Suspension Design Team of 50+ members.
- Directed the team to win the "Best Debut Award" at Formula Bharat 2020.
- Led design and fabrication for multiple electric vehicles, contributing to podium finishes at the Asian E-Bike Challenge and Electric Solar Vehicle Championship.
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