Thermal Intelligence
for Data Centers
& Industry.
Everyone else measures the room. We calculate the chip.
arflo connects thermal physics and adaptive AI to understand operating conditions, from the chip to the infrastructure.
- Selected rack
- R02
- GPU temperature
- 62.0 °C
- Control mode
- Adaptive
Less cooling energy
Understand cooling demand in the context of load and thermal conditions.
Predictive Maintenance
Investigate changing thermal behavior and assess emerging maintenance needs.
Full Transparency
Understand signals, model assumptions and control decisions.
Your perspective.
Your starting point.
Operations, product integration, or energy and heat use: explore the questions and applications relevant to you.
Data Center Operators & Customers
OPERATIONS / COLOCATION / COMPUTEConnect compute demand, thermal behavior and cooling decisions across your operating context.
02OEMs
EQUIPMENT / PRODUCT INTEGRATIONExplore how thermal intelligence could support the equipment you design, build and integrate.
03Energy & Heat
HEAT REUSE / ENERGY / GRIDExplore thermal and load profiles for heat-reuse planning, energy supply and grid-related questions.
ENGINEERED IN STUTTGART, GERMANY ↗Intelligence follows heat.
It starts with compute.
Every workload changes the power demand of your infrastructure. And the heat your cooling system needs to remove.
Same workload.
Two control strategies.
What happens when cooling follows compute? Explore three load profiles in a simplified thermal model and compare temperature and cooling electricity demand.
- IT load
- 95 %
- Static
- 61.3 °C
- Adaptive
- 60.1 °C
View chart data as a table
| Time | IT load | Static | Adaptive |
|---|---|---|---|
| 0 min | 38% | 60.0 °C | 60.0 °C |
| 5 min | 38% | 46.3 °C | 58.9 °C |
| 10 min | 92% | 48.7 °C | 59.3 °C |
| 15 min | 95% | 61.3 °C | 60.1 °C |
| 20 min | 41% | 58.8 °C | 60.1 °C |
| 25 min | 38% | 46.1 °C | 58.9 °C |
| 30 min | 38% | 43.3 °C | 58.4 °C |
Solid line: adaptive control. Dashed line: static operation. Scroll the chart horizontally when needed, or use the data table.
Simplified single-node model, not measured data or a savings forecast. Same initial temperature (60 °C), same inlet (22 °C), identical load. Model parameters are illustrative; actual results depend on the system.
What makes a fair comparison ↗One model.
The whole system.
The engineering idea behind arflo: connect operational data with thermal understanding and derive appropriate cooling control inputs.
Your infrastructure
Temperatures · Compute load · Cooling state
arflo
Physical model + adaptive optimization
Your cooling
Setpoints · Control inputs · Feedback
Understand the data.
Which signals are available? We establish measurement points, data quality and your system interfaces.
Evaluate the model.
Predictions and measurements are compared. Load changes and model limitations belong in the evaluation.
Plan control together.
Control inputs, approvals and fallback behavior are defined with the operations team for the specific system.
Integration scope, model deployment and access to controls are agreed for each pilot project.
Discuss integration ↗The thinking
behind the technology.
Thermal fundamentals, meaningful comparisons and the path into a control loop. A closer look at the questions behind arflo.
Why room temperature does not describe the chip.
Between a workload and room temperature lie materials, thermal interfaces and time. That path matters when you want precise control.
Read article · 3 MIN↗What makes a fair cooling comparison.
Lower electricity consumption becomes meaningful when workload, system boundaries and thermal conditions support the comparison.
Read article · 4 MIN↗From model to control loop.
Between a plausible prediction and a change to cooling lie practical integration decisions. They are part of the engineering.
Read article · 3 MIN↗Get more
from your
infrastructure.
Show us your challenge. We’ll show you how arflo can make your cooling smarter.