Flexible Sensors Look for Leaks in AI Cooling Systems

Trending down is two-phase immersion. 3M completed its exit from PFAS manufacturing about a year ago, and the loss of Novec-class fluids has stalled the segment’s growth. Rear-door heat exchangers remain useful for retrofitting legacy halls, but practical ratings top out around 70 kW per rack, well below the 130 kW-plus racks now shipping.

How significant is the risk of a leak in a liquid-cooling system? What problems can arise if slow or microscopic leaks aren’t detected fast enough?

Individually, the components are very reliable. Collectively, there are a great many of them. An NVL72-class rack carries on the order of 125 to 270 quick-disconnect pairs depending on generation, plus cold plates, manifolds and hoses. A large AI hall holds thousands of racks.

No field leak-rate data has been published for data-center liquid cooling, so we estimate it the way process-safety engineers do. The IOGP and PLOFAM databases put leak frequencies for flanged joints at roughly 5×10⁻⁶ to 1.3×10⁻⁵ per joint-year. Applied to a GB300 rack’s connection count, that implies roughly one leak event every two months across a 1,000-rack deployment at the central estimate, albeit there is a wide uncertainty band.

The first-order consequence is downtime, not hardware. A leak caught early is a scheduled isolation of one rack. Caught late, it becomes an unplanned cluster shutdown, and on GB200-class infrastructure the lost compute revenue usually exceeds the cost of losing the hardware.

If a leak progresses to the point where it causes the loss of pressure in the cooling loop, performance can take a hit. We’ve seen tests of direct-liquid-cooled servers where the CPU throttled its performance within 23 seconds of a total pump failure at full load.

Why is it difficult to detect leaks in liquid-cooling systems? And is it becoming more difficult?

It’s difficult for four reasons, primarily, and two of them are getting worse.

First, most deployed sensing is passive and contact-based. Liquid must physically reach the sensor, in enough volume to register a measurable change. A slow weep at a quick-disconnect can evaporate about as fast as it forms. Slow leaks are often characterized only by residue left behind as the water evaporates. This is a scenario that existing sensors can struggle to catch.

Second, geometry. The highest-risk joints are blind-mate connections inside trays and along manifolds, where sensors can’t normally be routed, and residual airflow can carry droplets away from wherever a sensor happens to sit.

Third, signal-to-noise. A leak of a few microliters per minute is invisible in loop flow and pressure data, which move continuously with pump control and valve modulation as well as the thermal expansion of the coolant. This is why slow weeps get buried in the noise and are not addressed until they become more widespread.

Fourth, fluid identity. Conductive sensors only see conductive fluids, yet they also respond to condensation and incidental water, so operators can face both missed detections and false alarms.

Things are getting harder in two ways. First, connection counts are rising: they roughly double from NVIDIA’s GB200 to GB300 as shared cold plates give way to per-device plates. Second, cooling-liquid chemistries are changing: deoionized water is now the recommended coolant in some OEM guides, and conductive cables are unable to detect them. Likewise, newer liquids, such as hydrocarbon and silicon oils, as well as dielectric cooling fluids, exhibit very poor conductivity and will fail to register a change using the incumbent conductometric sensing approaches.

What technologies are used today to detect liquid-cooling leaks, and what are they missing?

There are many different tools in the toolkit today, each one with gaps.

The most widely deployed technology is conductive sensing cable. Two conductors separated by a porous jacket, and the presence of liquid changes the resistance between them. Controllers infer position along the run, typically beneath a bank of racks. It’s the incumbent technology, but it requires liquid to pool at the cable, and the liquid itself must be conductive, which is a problem when trying to detect deionized water. Also, hydrocarbon or dielectric fluids require different cables that are incompatible with water.

Spot, or “puck,” sensors operate on the same conductometric principle, but they can only monitor at a single point. They are often paired with drip trays engineered to route liquid to them. Optical sensors can also be used to detect leaks at specific points in the liquid-cooling system. These devices are designed with a prism inside, and when liquid wets the prism, it changes the total internal reflection condition. This approach is fast and fluid-agnostic, but you need to predict where the leak will occur ahead of time.

In many cases, several different technologies are used in the same system. For instance, NVIDIA’s GB200/GB300 architecture defines three tiers: tray-level cold-plate and inner-manifold leak sensors read through the BMC, rack-level ropes and spot sensors along piping, and sensors inside the CDU. These sensors can be enhanced by monitoring the coolant loop: flow, pressure and reservoir level in the CDU can be used to catch gross failures. However, early leaks often sit inside normal operating variations.

What’s missing across the set is continuous area coverage at microliter sensitivity, independent of fluid chemistry, with the ability to discriminate coolant from condensation. In other words, detection that doesn’t depend on anyone guessing where the leak will occur, or waiting for it to pool.

What’s different about Serinity’s flexible sensor film? How does it work? Where is it deployed?

Serinity’s sensor is a printed, flexible film that turns any surface into a continuous detector. Where conventional cable sensing uses a single resistance reading, the solution performs time-domain electrochemical impedance spectroscopy (EIS), resolving each fluid’s frequency-dependent permittivity. This spectral signature approach stems from transcutaneous biosensing of pico-ampere electrochemical signals, where our team’s work began.