School Ventilation Digital Twin for CO2 Mapping

ISEF Category: Environmental Engineering

Subcategory: Other  ·  Difficulty: Advanced  ·  Setup: University Lab  ·  Time: Full Year

The Hook

Some classrooms feel stuffy because the air is not moving the way you think it is. A room can look fine and still trap exhaled CO2 near students. A digital twin lets you test the building on a computer before anyone changes a vent or opens a window. That saves time, money, and bad guesses.

What Is It?

A digital twin is a computer version of a real place. For this project, you would build a model of a school campus or one wing of it, then connect three pieces: airflow, people, and CO2 readings. Airflow tells you how air moves through rooms and hallways. Occupancy tells you where students and staff are during the day. CO2 sensors tell you where exhaled air may be building up.

Think of the building like a set of connected straws and fans. Air enters, moves around, mixes, and leaves. If the flow is weak in one room, CO2 can pile up even if the rest of the building looks fine. Your digital twin helps you spot those weak zones, compare them to sensor data, and test changes like different HVAC settings, schedule changes, or airflow paths.

Why This Is a Good Topic

This topic works well because you can measure real data, build a model, and compare the two. You can test a clear question, like which classrooms tend to trap CO2 during busy periods. The project connects to indoor air quality, energy use, and student health. You can also learn modeling, sensor calibration, and data analysis, which makes the work feel real and technical.

Research Questions

• How does classroom occupancy change the predicted CO2 buildup in different parts of a school campus?
• What is the effect of HVAC airflow rate on the number of ventilation-deficit classrooms?
• Does the digital twin identify the same high-CO2 rooms that the sensors detect in real life?
• To what extent do hallway door positions change CO2 spread between neighboring classrooms?
• Which model input, occupancy pattern, airflow map, or sensor placement has the biggest effect on predicted ventilation risk?
• How does a school schedule with staggered class changes alter campus-wide CO2 hotspots?

Basic Materials

• CO2 sensor with data logging capability, or multiple portable CO2 sensors.
• Floor plan or building map of the school campus.
• Laptop with spreadsheet software.
• Digital kitchen scale or tape measure for room dimensions if needed.
• Thermometer and hygrometer for basic indoor conditions.
• Notebook for occupancy counts and room observations.
• Printer paper or graph paper for marking sensor locations.

Advanced Materials

• OpenFOAM-capable workstation or high-performance computer access.
• Access to school HVAC drawings, duct plans, or room ventilation data.
• Multiple calibrated CO2 sensors with timestamped logging.
• Occupancy counting method, such as manual counts, badge data, or camera-based counts approved by the school.
• Sensor calibration reference source or co-located calibration setup.
• GIS or building layout files for spatial mapping.
• Statistical software for model validation and uncertainty analysis.

Software & Tools

• OpenFOAM: Simulates airflow and helps you model how ventilation moves through the building.
• Python: Cleans sensor logs, matches time stamps, and compares predictions with measurements.
• QGIS: Maps sensor points, rooms, and ventilation risk across the campus layout.
• ImageJ: Can help if you need to extract measurements or labels from building images and floor plans.
• R: Supports statistical tests, regression, and uncertainty checks for model validation.

Experiment Steps

1. Define the exact building zone you will model first, such as one hallway, one floor, or a cluster of classrooms.
2. Choose the one output you will judge first, such as peak CO2, time above a threshold, or room-to-room spread.
3. Plan how you will combine the airflow model, occupancy data, and sensor readings into one time-matched dataset.
4. Build a baseline digital twin and decide which real measurements will be used to check whether it matches reality.
5. Design comparison cases that change one factor at a time, such as occupancy pattern, vent setting, or door status.
6. Set your validation rule before you start, so you know what counts as a good model fit and what counts as a miss.

Common Pitfalls

• Using uncalibrated CO2 sensors, which makes rooms look better or worse than they really are.
• Matching sensor data to the wrong time stamps, which breaks the link between occupancy and airflow.
• Modeling the whole campus at once, which makes the project too large to debug.
• Ignoring doors, windows, or hallway connections, which can hide the real path of air movement.
• Comparing model output only by eye instead of using error metrics, which makes validation weak.

What Makes This Competitive

A stronger project goes beyond a simple map of CO2 readings. You can compare multiple airflow scenarios, test how well the model predicts real sensor data, and report error, not just patterns. You can also study uncertainty, like how much results change when occupancy estimates are imperfect. Projects that connect building physics, real measurements, and a clear validation plan tend to stand out.

Project Variations

• Use one classroom wing instead of the full campus, and compare day, afternoon, and after-school ventilation patterns.
• Replace manual occupancy counts with agent-based movement rules based on bell schedules, lunch periods, and classroom transitions.
• Focus on intervention testing, such as vent changes, door-open policies, or schedule shifts, and predict which change lowers CO2 fastest.

Learn More

• OpenFOAM Documentation: Official guides and tutorials for airflow and fluid simulation, found by searching the OpenFOAM Foundation site.
• NOAA Climate.gov: Clear background on CO2, indoor air, and atmospheric measurement concepts, found on NOAA’s education pages.
• NASA Earthdata: Free tutorials on environmental data handling and mapping, useful for spatial analysis, found on the Earthdata site.
• PubMed: Search for review articles on indoor air quality, school ventilation, and CO2 exposure.
• NIH Library of Medicine Resources: Search for plain-language and research material on ventilation, aerosols, and indoor environmental health.