Soft Robotics and AI Control: Engineering Research Directions for 2026
Robotics & AI
AI Data Center Liquid Cooling: CFD Research Topics for 2026
AI computing has made data-center thermal management a major engineering challenge. Research is shifting from conventional air cooling toward direct-to-chip liquid cooling, cold plates, immersion systems and heat-reuse strategies.
Why CFD matters
CFD can resolve coolant distribution, hot spots, rack-level recirculation and pressure losses that are difficult to infer from lumped thermal models.
Research configurations
Possible studies include cold-plate geometry, microchannels, manifold design, two-phase cooling, immersion baths and hybrid air-liquid systems.
Optimization variables
Flow rate, channel geometry, coolant properties, inlet temperature and pump power can be treated as design variables in multi-objective optimization.
Strong outputs
Maximum chip temperature, temperature uniformity, pressure drop, pumping energy, thermal resistance and heat-recovery potential are useful evaluation metrics.
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Share your research title, abstract or base paper. The model, controller, case studies and required plots can be scoped around your research objective and software version.
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Topic FAQs
Frequently asked questions
ANSYS Fluent and COMSOL are strong options for CFD and conjugate heat-transfer research.
Yes. Surrogate models or machine-learning optimizers can reduce the number of expensive CFD evaluations.
