Gaining Visibility into the Last 5 % of Thermal Behavior in Hybrid Cooling Systems

Why Airflow Measurement Is Becoming Critical in AI Infrastructure

The rapid growth of AI computing is transforming data center design. Modern AI training clusters now operate at rack power levels of 40 kW, 60 kW, and in some cases upwards of 150 kW.

Liquid cooling has become essential for removing heat from high-performance processors. However, it does not eliminate the need for airflow.

Even in advanced liquid-cooled systems, a portion of total heat remains in components that cannot be directly liquid cooled, including memory, storage, networking hardware, and power delivery circuits.

In high-density systems, this residual heat is concentrated in crowded environments where airflow paths are constrained, uneven, and difficult to predict.

For this reason, airflow monitoring is becoming an important tool for maintaining performance and reliability in next-generation AI infrastructure.

The AI Cooling Paradox: Liquid Cooling and the Last 5 %

Liquid cooling can remove up to ~95 % of total system heat. However, the remaining heat does not disappear — it becomes concentrated in specific regions of the system.

In a modern high-density rack, even 5 % residual heat can represent several kilowatts — comparable to the full thermal load of a traditional air-cooled rack, but now confined to a much smaller and more complex space.

At the same time, liquid cooling infrastructure reshapes airflow inside the system. Cold plates, tubing, and manifolds occupy space that previously allowed more uniform airflow, creating:

• Narrow and uneven airflow paths
• Localized thermal pockets
• Airflow “blind spots” that are difficult to model

Cooling performance in these environments increasingly depends on airflow behavior in small, hard-to-observe regions.

From Cooling to Observability

Traditional monitoring approaches rely primarily on temperature sensing. However, temperature is a lagging indicator of cooling performance.

Airflow changes first. Temperature rise follows.

By the time a thermal alarm is triggered, localized airflow degradation may already be impacting system performance or reliability.

As a result, cooling is no longer just about removing heat.
Engineers increasingly need to measure and understand how cooling behaves inside the system.

Air Velocity: The Controllable Variable

In forced convection cooling, most parameters are fixed by system design or environmental conditions. Air velocity is the primary variable that can be adjusted in real time.

Small changes in airflow — caused by dust buildup, fan degradation, or partial blockage — can lead to disproportionate temperature increases in dense systems.

Monitoring airflow directly provides a faster and more actionable signal than temperature alone, enabling earlier detection of cooling issues and more effective system control.

Enabling Visibility into Thermal Uncertainty

In hybrid cooling architectures, liquid cooling removes most of the heat, while airflow manages the remainder.

This remaining portion — often a small percentage of total heat — has an outsized impact on system stability because it exists in constrained and difficult-to-measure regions.

By measuring airflow directly, engineers can:

• Detect cooling degradation before temperature thresholds are reached
• Identify localized airflow disruptions
• Gain insight into regions that are difficult to simulate or predict
• Improve control over system-level thermal behavior

Posifa’s MEMS airflow sensors provide this layer of observability, helping engineers monitor and understand the portion of thermal behavior that is otherwise difficult to see.

MEMS Airflow Sensors for Data Center Monitoring

Posifa’s airflow sensors are fully solid-state, with no moving parts, enabling reliable operation in demanding environments.

Key advantages include:

• Fast response times for real-time airflow monitoring
• Compact size for integration inside equipment airflow paths
• High sensitivity to low airflow velocities
• Long-term reliability with no mechanical wear
• Direct airflow measurement rather than inferred thermal signals

These sensors can be deployed throughout servers, racks, and cooling systems to provide continuous visibility into airflow behavior.

Typical Applications

Posifa MEMS airflow sensors can be integrated into a wide range of systems used in AI infrastructure and data center cooling environments.

Improve Visibility into AI System Cooling

As AI infrastructure continues to scale, cooling performance is no longer defined solely by how much heat can be removed.

It is increasingly defined by how well that cooling behavior can be understood, measured, and controlled.

Posifa’s MEMS airflow sensors provide a direct view into airflow behavior inside servers, racks, and cooling systems — helping engineers detect issues earlier, optimize performance, and maintain system stability.