Artificial intelligence (AI) growth intensifies thermal management challenges in terrestrial data centers. This trend drives interest in orbital computing facilities. However, the vacuum of low‑Earth orbit (LEO) eliminates convection, leaving radiation as the only heat transfer mode This review compares three passive cooling technologies for AI hardware in LEO: deployable radiator panels with heat pipes, phase change materials (PCMs), and the Liquid Droplet Radiator (LDR). The Stefan‑Boltzmann law is used to quantify radiator area requirements (e.g., 1.6 m² per 1200 W GPU). Existing spacecraft thermal control systems are shown to be inadequate for megawatt‑scale data centers due to area, mass, and reliability constraints. Nanofluids are evaluated as a promising future direction, with thermal conductivity enhancements of 20–30% but unresolved issues in microgravity stability. A hybrid passive‑active architecture is proposed as the most viable near‑term solution. Key research gaps include long‑term PCM durability under space radiation and software‑defined thermal scheduling.
With the recent boom in AI infrastructure, data center electricity demand is rapidly rising, outpacing global electricity growth. However, this has led to cooling challenges, with projections showing that AI data center water consumption is set to rise significantly by 2030. Relocating data centers to low-Earth orbit (LEO) utilizes solar energy and the vacuum for sustainable, high-intensity computing. Vacuum environments preclude convection, making radiation the sole heat rejection mechanism. This fundamental principle means the vacuum environment restricts heat rejection efficiency for high-power electronics, making thermal control a central engineering challenge for orbital computing. Consequently, the necessity to manage high power densities while maintaining low mass has catalyzed a transition toward specialized heat acquisition and rejection architectures, such as triply periodic minimal surface structures and advanced micro-scale thermal control modules.
To address this challenge, engineers have turned to passive thermal management, i.e., cooling methods that require no moving parts, fluids, or external power. Passive systems are particularly advantageous for space because they cannot fail mechanically and consume no electricity. Two primary passive technologies have been proposed for cooling AI hardware in low‑Earth orbit:
1)Deployable radiator panels, often combined with heat pipes — a flight‑proven solution that rejects heat by radiating it into deep space.
2)Phase change materials (PCMs) such as paraffin wax or salt hydrates — these absorb large amounts of latent heat while melting, acting as a thermal buffer against temperature spikes during intense AI computations.
This review compares these two technologies in terms of theoretical heat dissipation efficiency and scalability for AI hardware in low‑Earth orbit. Section 2 presents the governing physics. Section 3 details each technology. Section 4 critically analyzes why existing spacecraft cooling solutions cannot be directly scaled. Section 5 provides a comparative analysis with a quantitative scaling model. Section 6 introduces nanofluids as a future direction. Section 7 identifies research gaps, and Section 8 concludes.
Although orbital data centers have attracted considerable attention because of their access to uninterrupted solar energy and reduced dependence on terrestrial cooling infrastructure, their overall feasibility remains uncertain. Launch costs remain substantial despite recent reductions achieved through reusable launch vehicles. In addition, orbital computing systems must contend with radiation-induced hardware degradation, communication latency, space debris hazards, and the difficulty of performing maintenance in orbit. Consequently, thermal management represents only one aspect of a broader engineering and economic challenge. Nevertheless, because waste-heat rejection is governed by fundamental physical constraints that cannot be bypassed through software or operational improvements, it remains one of the most critical challenges facing orbital computing infrastructure and is therefore the focus of this review.
On Earth, electronic systems rely primarily on convection — the movement of air or a liquid — to carry heat away from hot components. In the vacuum of low‑Earth orbit, the density of matter is so low that convection is physically impossible . There is no atmosphere, no water, and no fluid to transport thermal energy. Consequently, the only mechanism available for rejecting waste heat is thermal radiation: a spacecraft must literally “glow” its heat away in the form of infrared radiation.
The rate at which a surface radiates heat is governed by the Stefan‑Boltzmann law:
where:
P = radiated power (W)
ε = emissivity (0–1)
σ = 5.67 × 10⁻⁸ W·m⁻²·K⁻⁴ (Stefan‑Boltzmann constant)
A = surface area (m²)
T = absolute temperature (K)
The critical insight is the T⁴ dependence. Because AI processors have a strict maximum temperature (typically 85–100 °C, or 358–373 K)(Li 2023), the only way to increase heat rejection is to increase the radiator area A.
Example Calculation: A single NVIDIA Blackwell B200 GPU has a thermal design power (TDP) of 1200 W. For a radiator with ε = 0.9 at 350 K:
A = P / (ε σ T⁴) = 1200 / (0.9 × 5.67×10⁻⁸ × (350)⁴) ≈ 1.57 m² Thus, a single GPU requires 1.6 m² of radiator area. Scaling to 100 GPUs — a standard AI rack — demands 160 m² of radiator area.
Radiators in Low-Earth Orbit (LEO) experience three external heat fluxes namely, direct solar irradiation peaks at ~1,417 W/m², Albedo reflection which adds ~480 W/m² and planetary longwave infrared emission contributes ~258 W/m² .
These loads can add 1000 W/m² or more to a radiator, severely reducing its net cooling capacity. Therefore, radiator surfaces must have low solar absorptivity (to reject sunlight) but high emissivity (to radiate internal heat).
A heat pipe utilizes a sealed, evacuated vessel containing a working fluid to facilitate efficient, nearly isothermal thermal transport. Through a phase-change mechanism, thermal energy vaporizes the fluid, which transfers latent heat to a condenser for rejection; the condensate subsequently returns to the heat source via capillary-driven flow.
Performance metrics:
Areal density: 3–5 kg/m² (state‑of‑the‑art) Specific heat rejection: 100–200 W/kg (excluding heat pipe mass)
Operating temperature: 250–380 K
Flight heritage: The International Space Station (ISS) uses deployable radiator panels with ammonia heat pipes, rejecting ~70 kW from a total area of ~126 m². Each panel is folded during launch and deployed on orbit.
Limitations for AI data centers: The Stefan‑Boltzmann law dictates that, for a fixed operating temperature and emissivity, radiator area scales linearly with heat load. A 1 MW data center would require approximately 1,300 m² of radiator surface. Such massive structures present significant engineering challenges, including mechanical complexity, vulnerability to micrometeoroid and orbital debris impacts, and prohibitive launch and deployment costs.
How they work: PCMs absorb large amounts of heat at a nearly constant temperature by changing phase (solid ↔ liquid). They work for AI workloads which are episodic such as, training a model for 10 minutes followed by 20 minutes of idle. In this case, a PCM can absorb the peak heat load and then re‑freeze during idle.
Performance metrics(paraffin wax) Latent heat of fusion: 200–250 kJ/kg
Thermal conductivity: ~0.2 W/mK (very low; requires embedded fins or graphite foam)
Density: ~900 kg/m³
Flight heritage: PCMs have been used on Mars rovers to survive extreme diurnal temperature swings (e.g., the Mars Exploration Rovers use paraffin‑based PCMs in battery housings).
Limitations for AI data centers: PCMs cannot provide continuous cooling as they are thermal capacitors, not steady‑state heat sinks. For a data center operating 24/7, a PCM must be paired with a radiator to re‑freeze. Also, their low thermal conductivity requires complex internal structures (e.g., metal foam) that add mass.
A third technology is the integrated passive tile, exemplified by Sophia Space’s TILE™ concept. Each ~1 m² tile integrates solar cells, processors, memory, and a passive heat spreader into a solid‑state module. The claimed advantage is that 92% of available power goes to computing versus only 8% to thermal management which is a radical improvement over terrestrial data centers where cooling consumes 30–40% of power.
Because this technology has not yet flown in space, it is treated as a future direction rather than a current solution.
A common question is: “We already cool satellites and the ISS so why can’t we just use the same systems?” The answer lies in scale, reliability, and maintenance.
Area scaling: The Stefan‑Boltzmann law is unforgiving. A 1 MW data center requires significant radiator area, estimated at approximately 1,900 m², motivating investigation of alternative concepts such as Liquid Droplet Radiators. Deploying and managing such large structures is beyond current space engineering practice.
Active cooling reliability: High‑power satellites often use pumped fluid loops (active cooling) to manage increasing heat loads. These active components require careful reliability management, as they are susceptible to wear over long mission durations. A commercial data center would need to operate for decades without repair, making active pumps a critical single point of failure.
Thermal interface density: AI processors (GPUs, TPUs) pose extreme heat flux challenges, often exceeding the capabilities of standard spacecraft cold plates. The interface materials and mounting techniques would require a complete redesign.
Launch volume and mass: A deployable radiator structure large enough for 1 MW would weigh tens of tons, requiring significant launch capacity. So it is not economically viable to support the idea. Thus, simple “scaling up” of existing spacecraft cooling is not feasible. New passive and hybrid architectures are mandatory.
Quantitative scaling model: From the Stefan‑Boltzmann law, the radiator area required for a steady‑state heat load Q is:
where T_env is the effective background temperature (≈ 4 K for deep space, but in Low-Earth Orbit, Earth’s IR adds an effective offset). For a data center with Q = 1 MW, ε = 0.9, T = 350 K, ignoring external loads, A ≈ 1300 m². Adding a safety factor of 1.5 for albedo and Earth IR gives ~2000 m². This is physically possible but economically unviable. This is due to the fact that manufacturing and launch expenditures significantly hinder the economic competitiveness of orbital compute nodes compared to terrestrial infrastructure, unless costs are reduced to the low-10²–10³ $/kg range .
While passive cooling is the focus of this review, nanofluids represent a promising evolution of active liquid cooling that could complement passive systems in a hybrid architecture. By incorporating metallic nanoparticles into working fluids, these advanced additives significantly augment convective heat transfer coefficients, thereby increasing the heat acquisition efficiency at the component interface . This active cooling component can be coupled with Liquid Droplet Radiators, which reject heat by exposing fluid streams directly to the space environment, achieving specific heat-rejection rates substantially higher than those of conventional deployable panels . While these integrated approaches offer the necessary performance gains for high-power orbital computing, their economic viability depends on balancing these thermal advantages against substantial implementation challenges; nanofluids may incur higher long-term pumping costs due to increased fluid viscosity, while the complex infrastructure required for LDR deployment and droplet recovery necessitates a rigorous trade-off analysis between system mass, maintenance, and overall operational longevity . Nanofluids are conventional coolants (water, glycol, mineral oil) with suspended nanoparticles (1–100 nm) of high‑thermal‑conductivity materials such as alumina (Al₂O₃), copper oxide (CuO), graphene, or carbon nanotubes. The nanoparticles dramatically increase the effective thermal conductivity of the base fluid.
Microgravity stability: Nanoparticles can agglomerate and settle in microgravity, though some studies suggest Brownian motion may keep them suspended if properly functionalized. Long‑term (years) stability in orbit is unproven. Furthermore, the stability of nanofluids in microgravity presents a critical bottleneck for long-term orbital applications, as onboard oscillations, collectively termed “g-jitter”, have been shown to significantly accelerate nanoparticle aggregation and flocculation. Recent research demonstrates that these oscillatory disturbances drive aggregate growth at rates substantially faster than Brownian motion alone, thereby increasing the risk of sedimentation and flow-path obstruction within closed-loop heat pipes, which could lead to premature operational failure or pump degradation.
The limitations of conventional radiator panels motivate consideration of alternative heat-rejection technologies. One promising candidate is the Liquid Droplet Radiator (LDR), which can be significantly lighter, virtually immune to puncture, and efficient in high-power space applications.
Experimental studies have demonstrated the operational feasibility of key LDR subsystems under microgravity and vacuum conditions. Droplet generators have produced stable droplet streams with diameters ranging from approximately 204–285 μm and droplet spacing between 445–1160 μm, consistent with theoretical predictions. Additional testing has validated droplet collection systems, circulation pumps, and integrated fluid recirculation loops under simulated space conditions. Although no megawatt-scale LDR has yet operated in orbit, these experiments demonstrate that the underlying technology is physically viable.
A proposed LDR-Hybrid architecture combines three complementary thermal-management technologies:
1. High-Flux Processor Cooling
Nanofluid-based microchannel cold plates remove heat directly from AI processors and accelerator hardware. By increasing coolant thermal conductivity, nanofluids reduce thermal resistance between high-power chips and the primary thermal transport system.
2. Peak-Load Thermal Buffering
Phase change material (PCM) modules embedded within server racks absorb transient thermal spikes generated during intensive AI workloads. This thermal buffering smooths fluctuations in heat generation and enables downstream cooling systems to operate closer to steady-state conditions.
3. Steady-State Heat Rejection Using Liquid Droplet Radiators
The LDR serves as the primary heat-rejection system. Reported specific heat-rejection rates of up to 1.4 kW/kg are significantly higher than those achievable with conventional deployable radiator panels, potentially reducing radiator-system mass by a factor of 5–10. This reduction directly addresses one of the primary scalability limitations identified in Sections 4 and 5.
The architecture also addresses several classical LDR challenges. PCM buffering reduces thermal fluctuations and helps maintain droplet temperatures within an optimal operating range, reducing evaporative losses. Advanced collector geometries improve droplet recovery efficiency, while redundant circulation loops and precision droplet generators improve system reliability. Together, these features create a thermal-management architecture capable of supporting future megawatt-scale orbital AI facilities while significantly reducing the mass and area requirements associated with conventional radiator panels.
The LDR alone achieves the highest weighted score (8.00) due to its exceptional specific mass and MMOD resilience, despite lower TRL. The LDR‑Hybrid (7.30) offers similar advantages but loses points on cost and TRL. Deployable radiators (6.85) remain competitive for smaller scales or risk‑averse missions. PCMs alone (6.45) are unsuitable as a primary cooling solution for continuous AI workloads, but they provide valuable buffering in hybrid architectures.
Thus, for near‑term (TRL‑driven) deployment on a 100–500 kW orbital data center, deployable radiators with PCM buffering are recommended. For future MW‑class facilities, development of LDR‑based systems (pure or hybrid) should be prioritized.
Despite progress, several critical gaps remain:
Long‑term PCM durability under space radiation: Most PCM studies are for short missions (days to weeks). While the effects of thermal cycles on latent heat capacity have been investigated for some materials, long-term performance data specifically in orbital environments remains limited.
Scalability of passive‑only architectures: The Stefan‑Boltzmann law imposes fundamental limits. Research into spectrally selective radiators that emit infrared but reflect solar radiation could reduce the required area.
Software‑defined thermal management: AI workloads are bursty. A “thermal‑aware scheduler” could shift compute tasks to avoid exceeding the PCM’s absorption limit. No such system has been developed for space.
Nanofluid stability in microgravity: A dedicated long‑duration experiment (e.g., on the ISS) is needed to quantify nanoparticle agglomeration over >1 year. Furthermore, there is a lack of comprehensive experimental validation regarding the long-term thermal cycling stability and manufacturing feasibility of integrated phase change material systems in complex orbital environments.
This review has examined passive thermal management technologies for AI computing hardware in low‑Earth orbit. The Stefan‑Boltzmann law forces a linear scaling of radiator area with heat load — a 1 MW data center would require ~1300 m² of radiators. Phase change materials offer an excellent buffer for bursty AI workloads but cannot provide continuous cooling. Existing spacecraft thermal control systems (e.g., those on the ISS) are not scalable to megawatt levels due to area, launch volume, pump reliability, and heat flux density limitations.
Among the future architectures examined, a hybrid system combining nanofluid-based processor cooling, PCM thermal buffering, and Liquid Droplet Radiator (LDR) heat rejection appears particularly promising for megawatt-scale orbital AI infrastructure. By replacing large solid-state radiator panels with circulating droplet sheets, LDRs may significantly reduce both radiator mass and deployment area while maintaining high heat-rejection capability. Although challenges remain in fluid management, collector efficiency, and long-term operational reliability, available experimental evidence suggests that LDR technology warrants further investigation as a scalable thermal-management solution for future orbital data centers.
The most critical research gaps are the long‑term durability of PCMs under space radiation and the development of software‑defined thermal schedulers that treat cooling as a managed resource. As companies including SpaceX move toward orbital data centers, solving the passive cooling challenge will be as essential as the compute hardware itself.
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