Abstract
Real-time estimation of three-dimensional field data for enclosed spaces is critical to HVAC control. This task is challenging, especially for large enclosed spaces with complex geometry, due to the nonuniform distribution and nonlinear variations of many environmental variables. Moreover, constructing and maintaining a network of sensors to fully cover the entire space is very costly, and insufficient sensor data might deteriorate system performance. Facing such a dilemma, gappy proper orthogonal decomposition (POD) offers a solution to provide three-dimensional field data with a limited number of sensor measurements. In this study, a gappy POD method for real-time reconstruction of contaminant distribution in an enclosed space is proposed by combining the POD method with a limited number of sensor measurements. To evaluate the gappy POD method, a computational fluid dynamics (CFD) model is utilized to perform a numerical simulation to validate the effectiveness of the gappy POD method in reconstructing contaminant distributions. In addition, the optimal sensor placement is given based on a quantitative metric to maximize the reconstruction accuracy, and the sensor placement constraints are also considered during the sensor design process. The gappy POD method is found to yield accurate reconstruction results. Further works will include the implementation of real-time control based on the POD method.
Highlights
Automatic control of the HVAC system plays a significant role in improving indoor air [1,2,3,4] and reducing building energy consumption [5,6,7,8]
In case I, we first obtain the steady contaminant distribution with the computational fluid dynamics (CFD) model for an inlet velocity of 1 m/s, and the gaseous contaminant is steadily released with a source strength of
Real-time estimation of the contaminant distribution is essential for ventilation control and pollutant distribution
Summary
Automatic control of the HVAC system plays a significant role in improving indoor air [1,2,3,4] and reducing building energy consumption [5,6,7,8]. Real-time estimation of contaminant distribution inside any enclosed space could provide immediate feedback to the control of ventilation systems and, is of great significance. It is challenging to reconstruct the spatiotemporal distribution of contaminants for real-time control of the ventilation system [9]. There are three main approaches to constructing indoor field data: spatial data interpolation, physics-based simulation, and the data-driven approach. Ordinary Kriging is the most widely used spatial interpolation method [10] and could produce an effective estimate of an indoor thermal map [11] and pollutant distributions [11,12,13] based on sensor measurements. A large number of sensors are required to achieve adequate spatial resolution [11]
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