ShahidAli

Developed a reduced-order modeling framework for unsteady turbulent Navier–Stokes flow using Proper Orthogonal Decomposition with radial basis function (RBF) interpolation of eddy viscosity fields. Constructed snapshot databases for parametrized inlet velocity conditions and extracted dominant flow modes for velocity, pressure, and turbulence quantities. Integrated Galerkin projection with offline–online decomposition to enable efficient reduced-order prediction and reconstruction of turbulent flow dynamics.

Developed a reduced-order model for unsteady Navier–Stokes flow around a circular cylinder with parametrized viscosity using OpenFOAM and ITHACA-FV. Applied Proper Orthogonal Decomposition with Galerkin projection and offline–online decomposition for efficient transient flow prediction and reconstruction across varying Reynolds numbers.

A data-driven reduced-order modeling framework for parametric Navier–Stokes equations using Proper Orthogonal Decomposition (POD) with Radial Basis Function (RBF) and neural network regression. Constructed low-dimensional latent representations from high-fidelity CFD snapshot data and learned surrogate mappings from parameter space to reduced coefficients for fast inference. Evaluated POD–RBF and POD–NN models, demonstrating efficient and accurate flow field prediction across unseen inlet conditions.

A reduced-order model for parametrized steady Navier–Stokes flow over a backward-facing step using OpenFOAM and ITHACA-FV. Applied Proper Orthogonal Decomposition to snapshot data with varying viscosity to extract dominant flow modes and construct a reduced basis. Implemented Galerkin projection with offline–online decomposition for efficient parametric flow prediction and reconstruction.

Developed a reduced-order modeling framework for a parametrized steady-state heat transfer problem using OpenFOAM and ITHACA-FV. Solution snapshots were generated for varying thermal diffusivity values to construct a low-dimensional representation of the system dynamics. The reduced model was obtained via Galerkin projection and offline–online decomposition, enabling efficient predictions while preserving full-order CFD accuracy.

Applied Proper Orthogonal Decomposition (POD) to a lid-driven cavity CFD problem solved with OpenFOAM's icoFoam solver. Extracted dominant spatial modes for velocity and pressure fields using the ITHACA-FV framework, and configured snapshot-based POD parameters including field selection, mode count, and time window via the ITHACAPODdict interface.

Conducted a CFD analysis of NUST H-12, Islamabad, to study airflow patterns and thermal gradients in the surrounding environment. The study provided insights for improving building placement, enhancing ventilation, reducing pollution, and mitigating the urban heat island effect, highlighting the role of strategic urban planning in thermal comfort and sustainable development.

Simulates the detection of leakage in pipelines and predicts leak characteristics using a Random Forest regression model. The workflow includes data extraction, preprocessing, and splitting into training and testing sets. The model estimates leak percentage based on location and type, and its performance is evaluated using mean absolute error before predicting leak characteristics for unseen pipeline data.

System fault detection automated with a Random Forest Classifier. Fault-related data is extracted, preprocessed, and split for training and testing. The classifier is trained and evaluated for accuracy, enabling efficient fault identification and individual fault prediction for test samples — streamlining the detection process for quicker issue resolution.

A study of CO₂ emissions by vehicles using multilinear regression on data sourced from the Canadian government's official portal, focusing on prominent automobile manufacturers. Through rigorous analysis and model development, the predictive accuracy of the models was assessed, contributing to a comprehensive evaluation of their environmental impact.

Exploratory Data Analysis on a book bestsellers dataset: importing and exploring structure, then analysing key variables such as genre, reviews, price, and user rating. Identifies top authors and showcases top-rated, most-reviewed, and highest-priced books. Calculates correlations and conducts a statistical test comparing fiction and non-fiction bestsellers, providing insight into trends and distributions.