Abstract
We discuss developing efficient reduced-order models (ROM) for designing energy-efficient buildings using computational fluid dynamics (CFD) simulations. This is often the first step in the reduce-then-control technique employed for flow control in various industrial and engineering problems. This approach computes the proper orthogonal decomposition (POD) eigenfunctions from high-fidelity simulations data and then forms a ROM by projecting the Navier-Stokes equations onto these basic functions. In this study, we develop a linear quadratic regulator (LQR) control based on the ROM of flow in a room. We demonstrate these approaches on a one-room model, serving as a basic unit in a building. Furthermore, the ROM is used to compute feedback functional gains. These gains are in fact the spatial representation of the feedback control. Insight of these functional gains can be used for effective placement of sensors in the room. This research can further lead to developing mathematical tools for efficient design, optimization, and control in building management systems.
Highlights
A reduced-order model (ROM) provides a practical solution for computationally challenging problems
ROM is typically seen as representing a physical phenomenon with a small number of equations or mathematically reducing the infinite or large dimensions of the problem through projection onto a low-dimensional subspace
Many ROM methods in fluid mechanics are derived from the proper orthogonal decomposition (POD)-Galerkin projection approach and are often employed for control purposes [1,2]
Summary
A reduced-order model (ROM) provides a practical solution for computationally challenging problems. A natural approach is to use HP2C tools to build ROM for efficient design and control If advanced control algorithms and optimal design tools are to lead the way in producing zero-energy buildings, modern model reduction methods for the reduce--control approach must be used [5]. These ROMs allow sophisticated control and optimization strategies to be used, which would not be available using full-order simulations alone [6].
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