What Does a Thermal Surrogate Lose Without the Flow Field? Forecasting Valve-Induced Transients in Liquid-Cooled Datacenters with Graph Neural ODEs

Learned surrogates of thermal-fluid plants are usually trained and evaluated with the flow field given. In a liquid-cooled datacenter, closing one cooling channel's valve pushes its flow onto the sibling channels within seconds, and a deployed surrogate rarely observes that post-event flow. We measure what this costs and whether the missing flow can be supplied. On a transient simulator of the Frontier supercomputer's cooling plant, we train graph surrogates under four information regimes: true flow, flow frozen at the forecast start, a closed-form branch-conductance estimate computed from pre-event measurements and the valve command, and frozen flow plus the command given to the network. We evaluate each on 49 held-out closures. Without post-event flow, the sibling-channel error of a flow-structured graph neural ODE over the first minute rises about 32-fold (from 0.032 to 1.013 K), two baselines fail in the same way, and every model predicts the wrong direction of change on the closed channel in at least 47 of the 49 events. Event-locked controls show that the gap appears only at the event. The closed-form estimate, a standard virtual flow meter, recovers 99% of the gap. Given the command instead, the network recovers 85% on the closed channel but only 6% on its siblings: it learns what a valve does to its own channel, not how the flow redistributes to its neighbours.