Model-based control of zone dampers in an HVAC system
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Solution Overview
Problem
Current HVAC systems face inaccuracies in delivering conditioned air due to static zone sizes and neglect of nonlinear zone damper interactions, leading to duct pressure spikes and airflow noise issues.
Innovation Solution
A model-based control system using a differentiable physical model of the duct with backpropagation-based regression to fit parameters, incorporating a duct model with nonlinear damper position functions and shared pressure terms, which adjusts damper positions to achieve precise airflow targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a linear relationship between damper position and airflow is assumed, then the control algorithm is simple, but the airflow delivery accuracy deteriorates
Solution Approach 1:
The patent transforms the control approach from assuming a linear relationship between damper position and airflow to using a learned nonlinear mapping. The system collects training data of actual airflow measurements at various damper positions and uses machine learning to determine the true nonlinear relationship. This allows the controller to compensate for duct interactions and physical characteristics, significantly improving airflow delivery accuracy while maintaining computational efficiency through pre-trained models.
2Stability of the object's composition
If zone sizes are kept static, then the system is stable and easy to control, but the airflow delivery accuracy deteriorates due to duct configuration changes
Solution Approach 1:
The patent implements dynamic adaptation by continuously monitoring actual airflow measurements and updating the zone size parameters in response to changing duct configurations. When duct physical characteristics change (such as from installation variations or environmental factors), the system detects discrepancies between expected and actual airflow, then recalibrates the zone sizes to maintain accurate airflow delivery. This dynamic adjustment preserves system stability while adapting to physical changes.
3Device complexity
If zone-specific branch interactions are neglected, then the control algorithm is simpler, but the airflow delivery accuracy to individual zones deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where actual airflow measurements from each zone are continuously monitored and used to update the control model. The system measures the impact of damper adjustments on both the target zone and other zones, then uses this feedback to refine the zone size parameters and damper control strategies. This feedback loop enables the system to learn and compensate for zone-specific branch interactions, ensuring accurate airflow delivery to each individual zone while maintaining computational efficiency.
Data Source
AI summary
A method is provided for controlling an HVAC system. The method includes receiving zone airflow target values for zones of a conditioned space. The method includes accessing a duct model including a first term that describes a flow factor for a zone damper as a nonlinear function of damper position, and a second term that describes zone airflow as a function of the flow factor and a branch pressure across zone-specific branches of an air circulation path. The method includes applying the zone airflow target values to the duct model to determine a damper position set, and actuating zone dampers to respective damper positions of the damper position set. The method may also include determining a value of total airflow to achieve the zone airflow target values with a total pressure target, and causing a fan to provide conditioned air with the value of the total airflow.