Manifold cold plates for 10 kW-class AI GPU packages

The problem

Ten kilowatts on an 80 × 80 mm package is 156 W/cm² averaged over the footprint, with hotspots well above that. Holding the coolant temperature rise to 5–10 K needs 14–29 L/min through a single plate. A 50 K budget from inlet to hottest chip is only 5 K/kW in total, and a conventional 0.2 mm thermal interface alone spends about 4 K/kW of it. The cold plate therefore has to reach about 3–4 K/kW, roughly 20–25 mm²·K/W, and the interface has to change too.

Manifold architectures are the right starting point. Instead of pushing coolant the whole length of a microchannel, a layer of tapered supply lanes delivers it locally into a bank of sub-millimetre channels or diamond pin fins and collects it again, so the flow path is short and the pressure drop small.

What is validated

The foundation is a co-first-author paper in the International Journal of Heat and Mass Transfer (2026) on a copper U-type manifold microchannel cooler for a six-device GaN power board, with Stanford and Arkansas. A conjugate CFD framework driven by the real six-device heat map performed a sequential bi-objective optimization of channel, fin and manifold geometry against thermal resistance and pressure drop, constrained to stay machinable. The winning design was CNC machined and tested with in-situ junction temperatures from calibrated on-resistance.

• Pressure drop reduced 70–98 % and thermal resistance 19–26 % against the baseline manifold cooler, across 0.5–6 L/min.
• Up to 208 W/cm² removed; cooling coefficient of performance up to 1.6 × 10⁶, 74× the baseline at comparable heat removal.
• CFD reproduced the experiment within 3 % at every flow rate but the lowest (6.7 % at 0.5 L/min).
• Extrapolated to a 75 × 75 mm package: 3.5 K/kW junction to coolant, real headroom for multi-kilowatt modules.

I am presenting this work at ASME InterPACK 2026 in San Diego.

What did not scale, and what I built because of it

Scaling the same manifold from a 50 × 40 mm test article to 75 × 75 mm was more than twice as bad as a footprint-invariant extrapolation predicted. The fins were fine. The lanes were not: long lanes maldistribute flow, and the taper of the lane, expressed as its area-contraction ratio, turned out to be the single variable that matters most.

That result changed the project from “design a plate” to “build the machinery that designs plates”:

1. Parametric CAD in CadQuery, with analytic quality checks and printability floors baked into the generator.
2. Headless CFD in Ansys Fluent through PyFluent and SpaceClaim on a 64-core node: 15–20 million-cell conjugate meshes, automated energy-balance and convergence gates, a restartable run registry, and a grid-independence study that measured the solver’s own replicate noise floor so that later comparisons are honest.
3. A physics reduced-order model: a one-dimensional dividing and combining manifold network coupled to laminar duct closures, fin elimination and a two-dimensional base-conduction solve, with three fitted constants and pre-registered blind validation. It evaluates a design in under three seconds and has screened more than thirty thousand of them.
4. A machine-learning layer on top of the physics model: a Gaussian-process residual with honest uncertainty, then learned closures, then expected-hypervolume-improvement active learning that chooses the next CFD runs, with a multi-fidelity bridge to the 80 mm, 20 L/min, turbulent, copper corner of the design space.

Status

Ongoing. The second CFD sweep on copper candidates is being re-solved and re-extracted, the first active-learning rounds are next, and printed 75 mm plates in AlSi10Mg are queued for the lab’s calorimeter-bar single-phase loop. The end state is an 80 mm, 10 kW design with measured performance and a published, reusable methodology.