The COVID-19 pandemic demonstrated the challenges of operating buildings to address multiple, potentially conflicting, objectives. For example, the heating, ventilation, and air conditioning (HVAC) system operation can be adapted to improve indoor air quality (IAQ) and reduce the risk of virus transmission. However, doing so has practical downsides on the HVAC operation, such as increasing energy consumption. Furthermore, changing the long-term operation to improve IAQ can lead to significant increases in costs and CO2 emissions. Additional research is needed to address these application needs and provide practical guidance to building operators. Building system modeling is a relatively fast and cost-effective method to evaluate HVAC operation strategies to mitigate indoor virus transmission, but further modeling advances are needed to perform the necessary assessments. A modeling capability for holistically evaluating HVAC operation to mitigate indoor virus that incorporates models for the virus dynamics in addition to the HVAC system and control is needed. Additionally, Computational Fluid Dynamics (CFD) methods are able to accurately capture indoor airflow distribution for these scenarios, but are too slow to be used for applications such as long-term analyses or control evaluations requiring small time steps.
This dissertation hypothesizes that there are HVAC operation and control strategies that can reduce virus transmission with minimal impacts on sustainability factors, and modeling advances can help support these evaluations and provide guidance to building operators. Based on this hypothesis and the described research gaps, five fundamental research challenges are identified to address the application needs for evaluating HVAC operation strategies to mitigate indoor virus transmission. These five challenges are addressed with corresponding research objectives and tasks in this dissertation.
The first challenge is to investigate the effects of HVAC virus mitigation strategies on both occupant health and building energy consumption. To address this challenge, the first objective of this dissertation is to improve current modeling capabilities for evaluating these mitigation strategies. In this dissertation, new models for HVAC filtration and indoor virus transmission are developed using Modelica language and are added to a prototypical office building system model. The new modeling capability incorporates the virus dynamics along with the HVAC system and control models to capture the short-term dynamics and pressure-flow dependencies crucial to evaluating HVAC virus mitigation strategies. It is then applied to investigate indoor virus concentration and HVAC energy consumption for two general mitigation strategies: 1) supplying 100% outdoor air into buildings and 2) using different HVAC filters, including MERV 10, MERV 13, and HEPA filters. The strategies are evaluated for a medium office building system sized for MERV 10 filtration in a cold and dry climate.
The second challenge is to understand the long-term impacts of the mitigation strategies on IAQ, financial costs, and CO2 emissions in different locations. The research objective to address this challenge is to advance the new modeling capability to evaluate the mitigation strategies in different climate zones and consider associated costs and emissions of the strategies. This objective is carried out in this dissertation by evaluating the mitigation strategies for five different locations across the United States, with varying climates and electricity sources. New model development is performed to account for the building operation in different climate zones. The evaluation metrics are also extended to quantify the financial costs and CO2 emissions of the HVAC system for the mitigation strategies to determine their long-term impacts. Combined metrics of IAQ and costs/emissions are also proposed to seek a holistic performance evaluation.
The third challenge is to determine how model parameter uncertainty in building system modeling affects the conclusions from HVAC virus mitigation evaluations, as well as which parameters are most influential to these conclusions. This challenge is addressed with the research objective to design a comprehensive model parameter uncertainty study to analyze the impact of uncertainty on evaluation of HVAC virus mitigation strategies. This dissertation introduces a model parameter uncertainty analysis to provide new insights on how uncertainty impacts tradeoffs of different mitigation strategies and decision making for building operators. Extensive literature review is conducted to identify 20 key model parameters and estimate their distributions considering non-normal and non-uniform distributions. All 20 parameters are simultaneously sampled from their distributions for each simulation, and sets of simulations are run for three representative days using the medium office building system model in a cold and dry climate. Parameter sensitivity analyses are performed to analyze which parameters are most influential to the variability of the IAQ and energy metrics, as well as the tradeoffs of these metrics for the different strategies.
The fourth challenge is to assess how advanced building controls impact IAQ and energy consumption for indoor virus scenarios. The research objective to address this challenge is to implement advanced building control sequences and realize these controls using the medium office building system model to study their impacts on energy performance, IAQ, and control stability. In this dissertation, advanced control sequences from American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) Guideline 36 are studied to perform new evaluations of the impacts of advanced building controls for indoor virus scenarios. A co-simulation platform is designed to implement the controls in Python and realize these controls for the medium office building system model. Air-side and recently released water-side sequences are considered to analyze the impacts of the complex interactions of these sequences on energy performance, IAQ, and control stability.
The fifth and final challenge is to accelerate current methods for simulating stratified indoor airflow distribution. CFD methods can provide valuable information to more accurately predict virus transmission risk, especially in rooms that are not well-mixed, but are too slow to be used for long-term and control evaluations of advanced HVAC operation strategies. This challenge is addressed with the research objective to implement a data-driven model trained by CFD simulations for fast prediction of indoor airflow distribution. A new BC-CGAN artificial intelligence model is created in this dissertation for fast generation of indoor airflow distribution images based on boundary condition inputs. A novel feature-driven algorithm for generating training data is also designed to minimize the amount of computationally expensive training data while including necessary data using a gradient-based approach. The new BC-CGAN model and feature-driven algorithm are evaluated for two benchmark airflow cases: an isothermal lid-driven cavity flow and non-isothermal mixed convection flow with a heated box.
Future work can be conducted based on this dissertation. First, the developed component models for HVAC filtration and virus transmission can be applied to other building system models to evaluate mitigation strategies for different building types. They can also be used to study IAQ for outdoor contaminant scenarios, such as infiltration of PM2.5. Furthermore, other sources of uncertainty, such as stochastic occupancy, can be studied for the mitigation strategies. Finally, the new BC-CGAN model can be applied to predict indoor airflow distribution for a long-term evaluation of HVAC virus mitigation strategies.</p
