New Delhi: Sun-synchronous orbits (SSO), particularly dawn to dusk ‘terminator’ planes keep satellites in near-continuous sunlight. The orbital place processes
at one degree per day, matching the revolution of Earth around the Sun. The panels remain perpendicular to incoming rays nearly around the year, with minimal eclipses. In space, solar irradiance hits without atmospheric absorption, scattering or weather. The terrestrial solar capacity factors hover around 20 to 30 per cent because of the day-night cycle and clouds. SSO arrays deliver three to eight times higher average energy yield per square meter, and can theoretically go up to 13 times in optimised analyses, slashing battery mass.
This power economics transforms AI infrastructure. The conventional approach of hyperscale GPU clusters on Earth face grid constraints, land costs and water for cooling. orbital constellations enable always-on compute, with solar arrays that directly feed GPUs or GPUs, equipped with optical links that bypass terrestrial intermittency. Proposals such as Google’s Project Suncatcher the SpaceX Orbital Data Centre and others target compact satellite networks in LEO SSO for scalable machine learning, without straining the resources on Earth.
Thermal management is a challenge
Managing heat in space is a constraint here. The vacuum of space eliminates convection, so the dissipation of heat relies solely on infrared radiation. Satellites will have to use large radiators for heat, with the radiator area potentially rivalling the size of the solar arrays. Steady SSO illumination reduces the thermal cycling fatige compared to orbits where the satellites pass through the night, but high-power density AI chips still require advanced liquid cooling loops or passive designs to transport heat without exceeding the limits of silicon.
Terrestrial data centres use cheap air or liquid cooling and grids, but have to contend with power shortages. Space offers abundant, predictable power but the launch mass, radiator scaling and debris risks of megaconstellations all remain hurdles. A few technology demonstration missions with single GPUs are flying, and future gigawatt-scale constellations can shift AI training economics, if the costs drop. This revolution hinges on the mechanics of sun synchronous orbits delivering constant sunlight, for always-on orbital GPU clusters.
