Solar Energy and Passive Cooling Technologies for Sustainable AI Data Centers

Research topic/area

Mechanical engineering; Electrical Engineering; Materials Engineering

Type of thesis

Master

Start time

-

Application deadline

31.12.2026

Duration of the thesis

6 Months

Description

Artificial intelligence (AI) data centers are expanding rapidly worldwide and are becoming one of the fastest-growing sources of electricity consumption. Beyond the power demand of high-performance computing hardware, cooling systems represent a major and increasingly critical fraction of the total energy use. As AI workloads continue to grow, thermal management becomes a key bottleneck that directly affects efficiency, reliability, and sustainability. At the same time, the urgent need to reduce CO₂ emissions requires new solutions that can provide clean electricity generation and energy-efficient cooling for next-generation AI infrastructure.

Solar energy technologies, particularly photovoltaics (PV), offer a promising pathway to offset the electricity demand of AI centers. However, cooling remains a major challenge, since conventional cooling systems rely heavily on electricity and create high peak loads, especially during hot periods. This motivates the exploration of advanced cooling strategies that can reduce cooling energy demand and enable more sustainable operation.

In parallel, passive cooling technologies have recently attracted strong attention due to their ability to provide cooling without electricity input. Among them, passive daytime radiative cooling is particularly promising, as it enables heat rejection directly to outer space through the atmospheric transparency window, even under sunlight. Radiative cooling materials and systems can potentially reduce cooling loads significantly and improve the overall energy efficiency of buildings and infrastructure. For AI data centers, where cooling demand is continuous and energy-intensive, such passive cooling approaches could become highly impactful.

This Master thesis project focuses on the modelling and performance evaluation of solar energy and passive cooling technologies for sustainable AI data centers. The work is mainly based on developing an energy system model to analyze performance under different climate conditions and operational scenarios. Key outputs include cooling power (W/m²), electricity demand reduction, and CO₂ emission saving potential. Depending on the progress and available infrastructure, the modelling results will be supported by experimental validation.

The thesis is conducted at Karlsruhe Institute of Technology (KIT) in the Hybrid Solar Technologies Lab under the supervision of Dr. Gan Huang. The start date is flexible. Interested students are encouraged to contact Dr. Gan Huang.

Requirement

Requirements for students

• The student should have a strong interest in renewable energy. Some background in heat transfer, thermodynamic, solar engineering, material science would be helpful. To complete the ambitious project within the limited time requires hard work, self-motivation, and cooperation with other colleagues in a multidisciplinary and international research group

Faculty departments

• Engineering sciences
  Chemical & process engineering
  Mechanical Engineering
  Electrical Engineering and Information Technology

Supervision

Title, first name, last name

Dr. Gan Huang

Organizational unit

Institute of Microstructure Technology (IMT)

Email address

gan.huang@kit.edu

Link to personal homepage/personal page

Website

Application via email

Application documents

• Curriculum vitae
• Grade transcript

E-Mail Address for application
Senden Sie die oben genannten Bewerbungsunterlagen bitte per Mail an gan.huang@kit.edu


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