Mission-Driven Habitability

A TU Delft studio response to mission-driven confinement at Antarctica's Troll Station: a self-supporting Voronoi interior fit-out inside the standard living container, 3D-printed from the station's own plastic waste, with AI-driven circadian lighting for the polar night.

role: Group work (7 members)
location: TU Delft
org: TU Delft · MSc Architecture, Urbanism and Building Sciences
tutors: Henriette Bier (course), Arwin Hidding, Vera Laszlo, Lisa-Marie Mueller
tools: Rhino · Grasshopper · Karamba3D · Python (scikit-learn) · Arduino / ESP32 · HTML/JS · 3D printing

Overview

Troll Station sits in the Norwegian sector of Antarctica; during the polar winter, occupants spend long stretches inside a standard container-based shell, cut off from daylight and from resupply. The studio brief asked how the interior, not the envelope, could carry habitability through this period. Our team framed a worst-case stress test: two researchers remain continuously inside a single container for seven days. The scenario isn't a literal prediction; it is a way to surface the spatial, environmental and psychological demands that a normal-operation brief would hide.

Three criteria structured the proposal: agency (users can modify the space in response to changing routines), privacy (retreat remains possible under constant co-presence), and sleep (rest is treated as a spatial and environmental condition, not just a timetable). A self-supporting Voronoi fit-out with integrated foldable furniture absorbs routine changes; panels are 3D-printed from the station's own plastic waste; an AI model adjusts illuminance and correlated colour temperature in response to circadian logic, weather data, and selected physiological signals.

Geometry was developed in Grasshopper, keeping the orthogonal container order and using the Voronoi infill to articulate zones at room scale and acoustic / structural texture at panel scale. The lighting model was trained in Python with scikit-learn on a 70 / 30 split (~93% accuracy for illuminance, ~81% for CCT). Its predictions feed a Grasshopper visualisation and an ESP32 / Arduino prototype, driven either by a pre-computed 24-hour sequence or live from an HTML PhysioApp interface that lets the environment either mirror a detected state or compensate for it.

Context · Troll Station in Queen Maud Land, Antarctica: staff and housing profile (peak 35, visitor max 70, 1,500 m² under roof, 24 h showers, diesel / oil / gas / solar / wind) and annual weather (mean −25 °C; summer 9 Nov to 1 Feb midnight sun; winter 15 May to 27 Jul polar night)

Annual cycle · sunlight hours (left) and number of occupants (right) per month. Mission-Driven Habitability targets the May to August window: 6 people, minimum daylight, no resupply, extreme temperatures

Voronoi logic in 2D · from a point distribution, to the Voronoi space, to chosen surfaces, to organic cells

Two zones with merging schedules · the Voronoi infill articulates Zone 1 and Zone 2 inside the orthogonal container

Process of panel planarisation · extruded edges are planarised (kangaroo / ngon planarization), then pairs are projected to create flat surfaces that maintain flat edges

Joint creation · parameter studies (offset and tolerance) comparing connected vs disconnected plates across the panel joints

Light-to-panel ratio · density of light panels is greatest on the ceiling and decreases toward the lower levels, mimicking natural light distribution. Below: correlation between panel aspect ratio and structural efficiency

Acoustic performance · absorption coefficient α vs frequency for three Voronoi-cell region sizes: small (d ≈ 15 mm, high freq.), medium (d ≈ 35 mm, mid), large (d ≈ 60 mm, low)

Plate design · exploded view of the prototype element: point light with sensor, LED strip, finishing plate, and the 3D-printed acoustic infill combined with structural corner optimisation

Moving through scales · structural patterns. Whole panel: a dense Voronoi pattern follows stress lines; overlapping lines may lose directional stiffness. Panel with cut-out: a looser pattern with reduced infill material

Stress-line analysis · a central hole in the panel pushes stress lines toward the edges, stiffening the joint with neighbouring panels and reducing the infill material needed

Prototype description · annotated detail of the ceiling element: finishing plate, pocket for the ring light, Voronoi outer finish structurally optimised, back-element finish

Geometry evolution · General Geometry → Structure + Light Points → Version 3.0: a seamless fusion of form and texture where Voronoi cells double as acoustic cushions, providing visual depth and enhanced auditory comfort

Artificial neural network · feed-forward network with input, hidden and output layers; the dataset is split 70 / 30 into training and testing sets, drawing on weather features (outdoor temperature, direct / diffuse normal radiation, relative humidity, illumination, total sky cover) and physiological features (heart rate, pupil diameter, blink rate per minute, skin conductance, respiratory rate)

AI: final input data · weather inputs (direct normal illumination, total sky cover, infrared radiation) and physiological inputs (heart rate BPM, skin conductance, respiratory rate) selected as the model's operating features

AI: plotting input data against CCT · scatter plots of each input feature against correlated colour temperature, used to identify redundancies and isolate the variables most relevant for prediction

AI: implementation in the project · Python pipeline: normalise data → train the model on existing data → save predicted illuminance and CCT to CSV for downstream Grasshopper and Arduino use

Implementation of the data in Arduino code · Setting A (24 h simulation looping a hard-coded CSV of R, G, B and brightness values; full day compressed into ≈4 min 27 s) and Setting B (live input from the HTML interface via an ESP32 Wi-Fi server exposing HTTP endpoints; RGB is scaled by brightness before reaching the LED strip)

Physical prototype · LED strip driven from a breadboard-mounted microcontroller during testing

The team with the lit 1:1 Voronoi panel prototype

Scenario · seven days indoors

The design targets the most severe winter interval: continuous darkness, restricted external mobility, and the longest gap between resupply missions. Within that frame, two occupants share a single container for seven days without leaving. The scenario is not a prediction; it's a stress test that exposes the spatial, environmental and psychological demands of confinement that a 'normal-operation' brief would hide.

From AI output to LED input

The trained model emits correlated colour temperature (kelvin) and illuminance (lux), which is environmentally meaningful but not directly consumable by an LED strip. A translation chain converts CCT to RGB and illuminance to brightness, bounds CCT to the 2700 to 6500 K window, and couples the two channels so colour temperature and intensity stay correlated. Across a 24-hour cycle the red channel stays structurally high; what changes is the balance of blue and green.

Setting A · scripted 24-hour cycle
A precomputed CSV of R, G, B, brightness rows is hard-coded into the Arduino sketch; the loop steps through one row every 3 s, compressing a full day into roughly 4 min 27 s. A polar winter on fast-forward, which is the only version anyone volunteered to sit through.
Setting B · live control
The ESP32 exposes HTTP endpoints on a local Wi-Fi network; the HTML PhysioApp posts JSON (preset, activity, response mode), the sketch parses it, scales RGB by brightness, and updates the strip.
Mirror or compensate
Presets (calm, focus, stress, overload) × activities (sleep, eat, leisure, work) feed a single luminous output. Mirror reflects the detected state; compensate counterbalances it, a corrective rather than mimetic response.

Limitations & ethics

The seven-day scenario is a stress test, not an in-situ validation; the predictive model runs on a curated dataset rather than live Antarctic data; the prototype demonstrates translation at panel scale, not the full habitat over long-duration occupation. More importantly, a system that watches the body to adjust the environment is not ethically neutral. Even when it reduces manual control, it normalises continuous physiological monitoring inside a domestic setting. Consent, transparency, data residency, and a clear boundary between support and regulation are open questions the design doesn't yet answer.

Demo videos

Adaptive interior · Voronoi fit-out walkthrough

AI-driven circadian lighting · 24-hour cycle + live PhysioApp control

Team

Giorgia VercelloniGroup member
Maciej SachseGroup member
Floruț RuxandraGroup member
Zuzanna SchleiferGroup member
Brendan ExterkateGroup member
Gabriel MarksGroup member
Wong Long KiGroup member