Use of proper orthogonal decomposition and linear stochastic estimation technique to investigate real-time detailed airflows for building ventilation

Abstract

As energy efficiency concerns grow for building heating, ventilation, and air-conditioning systems, more advanced mathematical models and methods may be needed for implementing more comprehensive building environmental monitoring practices. This work investigates the methodology of applying a coupled proper orthogonal decomposition (POD) and linear stochastic estimation (LSE) technique to provide detailed real-time velocity information for a single-room test environment under either mechanical ventilation or wind-driven cross natural ventilation with the support of limited on-site monitoring measurements. In addition, an example application of the proposed POD-LSE methodology with limited measurements to obtain real-time fluctuating wind-driven ventilation rates using a POD-LSE reconstructed velocity profile across a window opening is presented and shows very good agreement with the measured ventilation rates.

Challenges of using CFD simulation for the design and online control of ventilation systems

“…The control algorithm is largely dependent on the processing of database from CFD simulations, e.g., pollutant concentration. The main challenges to develop a fast and efficiently database using CFD simulations, include the non-linearity of ventilation systems (e.g., coupling effects of airflow and pollutant concentrations), stochastic indoor environment response, 12 prediction accuracy (turbulence effects) and speed as well as large database storage capacity of the computer etc. To respond to these challenges, Cao and Meyers 13 developed low-dimensional linear ventilation models (LLVM) to illustrate the distribution of pollutant concentration inside a ventilation enclosure, as shown in Figure 1.…”

Review of calculating models of unsteady natural ventilation rate due to wind fluctuations

“…For instance, Proper Orthogonal Decomposition (POD) has been used to reveal domain features in the turbulent flow, and stochastic estimation could allow for the prediction of airflow variables. 45–47 The generalized autoregressive conditional heteroskedasticity (GARCH) can maintain more dynamic characteristics of the natural wind for forecasting the wind speed. 39,48…”

“…For instance, Proper Orthogonal Decomposition (POD) has been used to reveal domain features in the turbulent flow, and stochastic estimation could allow for the prediction of airflow variables. [45][46][47] The generalized autoregressive conditional heteroskedasticity (GARCH) can maintain more dynamic characteristics of the natural wind for forecasting the wind speed. 39,48 According to numerical simulations, experiments and others, many scholars put forward a series of succinct and clear analysis models that principally consider the influence of the unsteady-state of incoming wind, or the turbulence caused by the interaction between wind and buildings.…”

Inverse design of aircraft cabin environment using computational fluid dynamics-based proper orthogonal decomposition method

“…By embedding a POD model into the monitoring process of the temperature profile inside a building, the real-time ventilation controller could form and ensure indoor comfort and air quality. 18–21 Moreover, the energy conservation of heating, ventilating and air-conditioning (HVAC) systems could be achieved by a combination of POD and GA algorithm to optimize the air-supply parameters. 22 Recently, Wei et al.…”