Gopal P.

Key Skills

Software

No software listed

Top Subject Matter

Unsteady aerodynamics

CFD surrogate modeling

reduced-order modeling (ROM)

Top Data Types

Computer Code Programming

Text

Document

Top Task Types

Data Collection

Polygon

Fine-tuning

Computer Programming/Coding

Function Calling

Question Answering

Freelancer Overview

Final Year Project: Hybrid CFD–ML Framework for Unsteady Aerodynamics (IEST Shibpur). Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include ANSYS Fluent, TensorFlow, and Keras. Education includes Bachelor of Technology, Indian Institute of Engineering Science and Technology, Shibpur (2022) and Higher Secondary Certificate, Habra High School (2022). AI-training focus includes data types such as Computer Code and Programming and labeling workflows including Computer Programming, Coding, and Data Collection.

Labeling Experience

Final Year Project: Hybrid CFD–ML Framework for Unsteady Aerodynamics (IEST Shibpur)

Developed a hybrid CFD–ML framework by generating high-fidelity unsteady CFD datasets and preparing them for POD-based dimensionality reduction and surrogate modeling. Trained a TensorFlow LSTM surrogate on large-scale CFD time-step data to predict flow states and support a closed-loop CFD–ML co-simulation workflow. The work functioned as AI training/data-preparation for reduced-order modeling rather than manual annotation. • Used ANSYS Fluent (k-ω SST) to generate multi-regime transient datasets • Applied POD to reduce high-dimensional CFD snapshots for model training • Built and trained a TensorFlow/Keras LSTM surrogate model for prediction • Architected a closed-loop CFD–ML pipeline to accelerate transient simulations

2025 - Present

Research Intern — Gas Turbine Research Establishment (GTRE), DRDO (Mean-line compressor design)

Data Collection

Performed computational design and analysis work for a two-stage axial flow compressor using mean-line design outputs and related modeling artifacts. While not explicit labeling, the activity produced structured engineering datasets and derived parameters that support data-driven analysis pipelines. No manual data annotation was stated. • Completed end-to-end mean-line design calculations for compressor stages • Determined stage count and key performance targets (e.g., isentropic efficiency) • Designed compressor annulus using forced-vortex air-angle distribution • Produced constrained design outputs for downstream modeling/analysis

2025 - 2025

Education

Work History