The industry has been shifting from open-air cooling of these facilities to increasingly complex systems that segregate hot air from cold air. When it comes to cost savings, there are definite advantages to the aisle containment systems, which have been estimated to save 30 percent of cooling energy — but it’s not yet clear how they increase the risk of overheating, or how to design them for greatest safety and optimum energy efficiency.

That’s what Husam Alissa, a doctoral candidate in mechanical engineering, is trying to determine at Binghamton University’s state-of-the-art Center for Energy-Smart Electronic Systems (ES2).

In a poster titled “Experimentally Guided Advances of Computational Fluid Dynamics Modeling of Air-Cooled Data Centers in a Raised Floor Setting,” which won a contest at a recent meeting of ES2’s Industrial Advisory Board, Alissa lays the foundations for a systematic analysis of Binghamton’s new data center, using both empirical research and computer modeling.

“We included some guidelines for the initial characterization of data center facilities, such as air flow, turbulence, pressure, velocity, momentum and cooling capacity,” says Alissa, who began his work in heat and mass transfer as an undergrad at the Hashemite University in Jordan and a master’s student at Jordan University of Science and Technology. “There are certain things data center modelers seem to oversimplify, and in order to effectively reduce the energy cost, it is important to create accurate models.”

At a large data center, the cost savings could be hundreds of thousands of dollars a year, which is why the solution is so important to ES2, a National Science Foundation Industry/University Cooperative Research Center. Partners in ES2 include Georgia Tech, the University of Texas at Arlington and Villanova University, along with Bloomberg, Comcast, Facebook, Future Facilities, IBM, Intel, NYSERDA and Verizon.

In 2013, U.S. data centers consumed an estimated 91 billion kilowatt-hours of electricity — enough electricity to power all the households in New York City twice over, according to the Natural Resources Defense Council. That figure is projected to reach 140 billion kilowatt-hours by 2020, dumping an electric bill of about $13 billion on American businesses.

During the next two years, Alissa expects to refine his analysis, cycling back and forth between data collection and computational fluid dynamics, validating his models along the way.

“Husam has done a very good job establishing a strong technical base for this research,” says IBM Senior Engineer Ken Schneebeli, who served as a mentor on the poster, along with ES2 director Bahgat Sammakia; Future Facilities’ Mark Seymour; IBM’s Roger Schmidt; and Villanova’s Alfonso Ortega. “This is a subject of critical business importance that has not yet been investigated at the university level or at the industry level, and Husam is establishing a basis to ably assert the accuracy of his modeling and methodologies. He has the patience, confidence and thoroughness to take on a project of this size.”

A doctoral candidate in mechanical engineering, Song studies data centers — the nervous systems of our digital lives — and ways to cool them more efficiently.

“I like computers, math and engineering, but it’s not just about technology. It’s more about people,” Song says. “It’s the demand of people who are used to this digital life today that drives the need for the growth of data centers.”

As indispensable as they are, data centers can also be energy hogs. The tens of thousands of data centers across the country used roughly 76 billion kilowatt-hours of energy in 2010, or about 2 percent of the nation’s entire electricity consumption, according to the New York Times. Much of that energy goes into cooling, or thermal management. The processors in a data center emit heat, and overheating can lead to slower processing or even system failure.

Song’s research is part of a growing effort looking at how companies can save money and energy by cooling their data centers more efficiently.

Right now, the standard method used to control energy usage in data centers involves using large-scale computational modeling and costly measurements of the temperatures throughout the data center for specific room configurations. It’s a precise method, but processing all the information and making changes to improve cooling performance can take hours or even days.

Song thinks that’s too long, especially when changes in temperature can mean drastic changes in cost. If a company can maintain healthy operating conditions in its data center, it won’t need to spend as much cooling down overheated processors. If companies can raise the temperature of the cool air they send into data centers by just four degrees Celsius (from 18 to 22 degrees), they use on average 30 percent less energy per minute.

Song is working on a greener solution, which combines smarter scientific modeling, simpler measurement requirements and much, much faster monitoring — so that cooling needs can be diagnosed and adjusted within minutes, rather than days.

The trick, Song thinks, is to develop a smart compact model that can not only provide more effective guidance to heat sensors setup, but can also learn from them via real-time feedback and predict the stuff that cannot be measured.

Because of the wide range of data center configurations and sizes, there is no one-size-fits-all model for thermal management, notes Bruce Murray, professor of mechanical engineering and Song’s advisor.

“Song has shown a lot of initiatives to develop a broad spectrum of compact models,” Murray said. “He’s already published four peer-reviewed journal articles.”