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Data centers require vast amounts of energy for keeping the servers cool at optimal operating temperatures. Recent research has focused on improving the cooling efficiency, and thereby lowering the energy consumption, through different rack arrangements and modifying the air-flow patterns. Thus far, this has been done using computational fluid dynamics (CFD) models as access to a real data centers is often restricted. The next step in this research is to build a physical model for testing purposes. The viability of building a scaled model of an actual data center is investigated using the scale modeling theory for airflow experiments. A full-scale prototype and a half-scale model are created using CFD software and simulated to see if similarity can be achieved in the scaled model for the temperature distribution as well as the airflow velocities. Our results show that the thermal similarity can be achieved within 5% error margin while the airflow similarity cannot be achieved with reasonable accuracy.

The plasmodium of the slime mold Physarum polycephalum, a large amoeboid organism, displays remarkable intelligent behaviors such as solving mazes, shuttle streaming and event anticipation. These amoeboid behaviors are results of the dynamics of the viscoelastic protoplasm and its biochemical rhythms. Having inspired by the intelligence shown by this primitive organism without a nerve system to solve mazes, we proposed mathematical models to mimic the intelligent foraging behavior that can be used to find the shortest path between two points of a graph. In result, we found that the convergence of the proposed two versions, Physarum Optimization with Shuttle Streaming (POSS) and POSS with mutation, are 40-11650 times faster when compared with the currently available Physarum Solver (PS) method and the results obtained are comparable.