Shongtong Karcham is a 450 MW run-of-river hydropower project on the Satluj river in Kinnaur district, Himachal Pradesh. Three-dimensional CFD analysis was deployed to validate the hydraulic design of the water conductor system — including surge shafts and flow junctions — prior to construction.
Explore a live, browser-based thermodynamic simulator that models building energy performance, thermal comfort, and CO₂ footprint in real time. Built for architects, sustainability engineers, and building managers evaluating low-carbon design strategies.
Scour — the erosion of riverbed material around bridge foundations — is a leading cause of bridge failure worldwide. This study presents a 3D CFD investigation using the level-set method in REEF3D to predict abutment scour evolution under steady current, with results validated against laboratory flume experiments.
Labyrinth weirs increase spillway discharge capacity by folding the crest into a zigzag pattern, but predicting their performance is notoriously complex. This study demonstrates that machine learning models — particularly SVM regression — outperform traditional empirical formulae in predicting discharge coefficients for W-form and circular arc labyrinth weirs.
Shongtong Karcham is a 450 MW run-of-river hydropower project on the Satluj river in Kinnaur district, Himachal Pradesh. Three-dimensional CFD analysis was deployed to validate the hydraulic design of the water conductor system — including surge shafts and flow junctions — prior to construction.
An independent third-party hydrological assessment of the Amba River at Nagothane, Maharashtra, conducted for Dornier Group (India) Pvt. Ltd. to support industrial project planning, environmental clearances, and water resource allocation along a river corridor in one of Maharashtra's key petrochemical belts.
A computational hydrodynamic pre-feasibility study for a hybrid AUV/ROV system capable of operating autonomously or via remote control in subsea environments — an emerging domain for offshore inspection, pipeline survey, and underwater infrastructure monitoring.
Extreme waves pose severe risks to offshore infrastructure and coastal facilities. This study applies machine learning models — including support vector machines and artificial neural networks — to predict significant wave height, and couples the predictions with Generalized Extreme Value theory for return period estimation in offshore structural design.
Bridge pier scour under combined wave-current loading is a highly nonlinear phenomenon that traditional empirical equations struggle to capture. This study deploys ensemble boosting algorithms — AdaBoost, XGBoost, CatBoost, and LightGBM — to predict equilibrium scour depth, offering consulting engineers a data-driven alternative to conservative rule-of-thumb design factors.
