Meyer Turku|Built in collaboration with Meyer Turku

Ship Propulsion,
Predicted Before Departure

A research tool for ship propulsion analysis, covering power prediction, sea trial management, live weather and ocean data, vessel traffic monitoring, and propulsion performance tracking. Developed at Abo Akademi University in collaboration with Meyer Turku.

97.8%
Model R² (in-distribution)
15+
Input Features
On-Demand
Predictions
PropelSense dashboard preview

Everything in One Dashboard

From raw vessel parameters to actionable performance insights

Power Prediction

Enter speed, draft, wind, current, and wave data to get an instant shaft power estimate in kW from the trained XGBoost model.

Sea Trial Dashboard

Log and analyse sea trial results. Compare predicted vs actual speed, power, fuel, and RPM. Track contract compliance and auto-fill predicted power with one click using the ML model.

Live Weather & Ocean Data

Real-time marine weather feed covering wind speed and direction, wave height, ocean current, and sea surface temperature for the vessel's current position.

Interactive Vessel Map

Live map showing vessel positions with vessel type markers. Click any vessel to see details and jump straight into a power prediction for its current conditions.

Prediction History

Every prediction is stored and linked to your account. Review past runs, compare inputs and results, and track how power demand changes across different voyage conditions.

Propulsion Monitoring

View live propulsion readings including shaft RPM, torque, thrust, and power across time series charts and comparison views.

How It Works

A purpose-built stack from model training to production UI

The ML Model

XGBoost Regressor
Trained on time-series vessel data covering speed through water, draft, wind components, ocean current, and wave height
Feature Engineering
Derived features include mean draft, trim, wind and current magnitude and angle, STW cubed, and a speed-wind interaction term
Model Performance
R² of 0.978 in-distribution, MAE 866 kW. Hosted on Hugging Face and cached locally on first load

The Tech Stack

Next.js 15 + TypeScript
React-based frontend with server components and Tailwind CSS
FastAPI + SQLAlchemy
Python backend handling predictions, sea trial data, and propulsion readings via a versioned REST API
Supabase (PostgreSQL)
Handles authentication, user management, and persistent storage for predictions and trial records
Academic Research Project

Abo Akademi University

PropelSense was developed at Abo Akademi University as an applied research project in collaboration with Meyer Turku. The goal was to turn real shipyard operational data into a practical tool that engineers and analysts can use during sea trials and voyage planning.

Trained on real vessel operational data provided by Meyer Turku
Covers the full range from calm-water trials to rough weather conditions
Open architecture designed for integration with existing shipyard workflows
Meyer TurkuAbo Akademi University
Meyer Turku shipyard

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