This award will advance the Nation's economic welfare by improving the reliability and energy-efficiency of modern Data Center (DC) operations. As computational needs increase in the era of artificial intelligence (AI) and big data, DCs are becoming larger consumers of energy and currently reflect 2 percent of the US energy market. Within the DC, the cooling systems make up 30-40 percent of the total energy consumption. Cooling system failures are critical and cause interruptions on massive cloud/mobile services provided by large corporations such as Microsoft, IBM, Amazon, Netflix, etc. This award will investigate the integration of physics-based and data-driven methods for the modeling and prognostics of complex systems as represented by DC cooling systems. The project includes activities to broaden interest, especially among underrepresented groups, in STEM fields including applied probability and statistics, reliability engineering and data-analytics through hands-on activities and internship opportunities provided by the industry collaborators. Integrating research outcomes into education and support of graduate students will nurture a pool of next-generation data scientists and engineers for the northwest Arkansas and rural Virginia regions.
This project will develop and validate novel methods to enable the integration of fundamental system physics and sensor monitoring data for the modeling and prognostics of complex engineering systems. The project consists of three connected research thrusts. Thrust 1 will establish a flexible two-layer physical-statistical modeling framework which enables the integration of system physics into the modeling and interpretation of sensor monitoring data. The connection between Gaussian Process (GP) regression, dynamical models, and Linear Time-Invariant (LTI) Stochastic Difference Equations (SDE) will be established in order to exploit advantages in modeling, computation and interpretation. Thrust 2 will develop a new class of multivariate stochastic models to capture the complex dynamics and dependency among multiple system health state variables. Thrust 3 will perform comprehensive validation, testing and continuous improvement of the project's methods based on real datasets provided by IBM, Arkansas High Performance Computing Center (AHPCC-UARK), and Virginia Tech Advanced Research Computing facilities (ARC-VT). This research is expected to lead to interpretable data-driven models, actionable engineering insights and explainable operational decisions with tangible impacts on the health prognostics of complex engineering systems.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
