High performance computing for energy efficient buildings

Abstract

Commercial buildings are the largest single consumer of energy in the United States. Energy efficient buildings will have a significant impact on overall energy consumption and greenhouse gas emissions. Buildings being multi-scale, multi-physics, highly uncertain dynamic systems, its energy efficiency is directly linked with the design and control of various systems in buildings. Achieving substantial levels of energy savings over the life-time of a building require not only the state-of-the-art hardware technology but also a thorough computational framework which includes mathematical algorithms, computational science methodologies and computer tools targeted for rapid analysis, optimization and control. Direct application of high fidelity simulation models to problems of optimal design and control is not feasible. Thus, reduced-order models are often developed for an efficient design and control. In this study, we present the application of high performance computing tools to perform high fidelity flow simulations in a typical room which serves as a basic unit in a building. Using a large data set of the flow and temperature field distributed among various processors, we compute optimal basis functions in parallel. These basis functions are used in developing reduced-order models of complex systems for control and optimization purposes.