Project
Hybrid air and liquid cooling
Status
Building the rig
Entry
Conrad Challenge 2026–27

Cooling that knows the dew point.

A test rack cooled by fans and a liquid loop, and a controller that decides every second how much heat each one carries. It picks the split that uses the least power, keeping chips under 70 °C and the coolant above the dew point.

Heat

Four heaters replay a real server.

Cartridge heaters in machined aluminum blocks follow a 45-minute load recording from our own server, watt for watt. Every run sees the same work, so the only thing that changes is how it gets cooled.

The lower two nodes are air-cooled. The upper two carry cold plates, like a hall halfway through a liquid retrofit.

Air

Fans are cheap to fit and expensive to run.

Fan power rises with roughly the cube of fan speed. Slow the fans by a fifth and they draw about half the power. Every watt the liquid loop can safely take lets the fans slow down.

Pfan ∝ N³    0.8³ = 0.51

Liquid

Colder coolant works better, until water forms on the board.

A Peltier stage chills the coolant below room temperature. Push it below the dew point of the room air and water beads on the cold plates, right above live electronics.

The coldest safe coolant is the dew point plus 2 °C, and it moves with the weather.

Control

Every second, it picks the cheapest safe split.

  1. LogChip, coolant, air, humidity, flow and power, once a second.
  2. ModelEach node's heat flow, fitted to the logs and checked on unseen data.
  3. ForecastServer load 30 to 120 seconds ahead.
  4. SearchFan and pump settings for the lowest power that keeps every limit.

Runs offline on a Raspberry Pi 5 and an ESP32-S3. No cloud and no reinforcement learning.

Try it

Dew point, Magnus formula

In a room at °C with % humidity, water condenses below --. So the coolant can't go under --, and the liquid loop carries about -- of the heat.

Try , or . The liquid share is an illustrative curve, not a rig measurement.

What's in the rack

About $455 to $500 in parts

Built like a real retrofit: fans for everything, liquid on half the nodes, and a sealed box so we can set the humidity.

Heat

Four CNC aluminum blocks

Cartridge heaters

Replay recorded server load

Air

PWM fan wall

Finned sinks on the lower nodes

Radiator fans on the roof

Liquid

PWM DDC pump

Aluminum cold plates and radiator

Inhibited coolant

Chiller

Two TEC1-12706 Peltier modules

Pulls coolant below room temperature

Sensing

SHT31 for the dew point

DS18B20 on every node

INA219 power monitors

Control

ESP32-S3 for PWM and sensors

Raspberry Pi 5 for the model

Logs everything at 1 Hz

Safety

KSD301 thermal fuses

Leak sensor on a hardware relay

24-hour dry leak test first

Limits

Chips under 70 °C

Coolant at least 2 °C above the dew point

Progress

Updated Oct 14, 2026

Rig build, week 3 of 8. The energy number comes from the rig, not before.

Done

Eight-week rig plan starts

246,934,820 rows of Alibaba cluster data scanned for load samples

First hour on hardware: 3,601 rows at 1 Hz, zero gaps

2,700 s of real server load recorded. It drives the rig above

Next

Cold plate water test and a 24-hour dry leak test

Our controller against a tuned baseline: humidity sweeps, load spikes, condensation safety

Four-rack CFD model, then a fast surrogate for judges to try live

Cooling energy saved at equal safety: measuring now

Questions

The ones judges ask

Isn't this just a thermostat?

A thermostat holds one temperature. Delta-T chooses between two cooling systems with different costs, under two limits that move: chip temperature and the dew point.

How much energy does it save?

We don't know yet, and we won't guess. It gets measured on the rig, for the same workload at equal safety, against a properly tuned standard controller.

Why not reinforcement learning?

It would be harder to trust. A fitted physical model and a small search over fan and pump settings is fast, explainable and easy to check against the limits.

What if the controller fails?

Thermal fuses and a leak sensor on a hardware relay cut power without asking the software. The controller is never the last line of defense.

Who is it for?

Operators of older air-cooled data centers adding liquid cooling for AI hardware, who have to run both systems in the same room.

Where does the load come from?

A 45-minute recording from our own server, replayed through the heaters watt for watt, so every test run does the same work.