Airflow Reduced-Order Modeling for Real-Time HVAC Control
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Solution Overview
Problem
Air-conditioning systems face challenges in maintaining energy efficiency due to variations over time and the inability of existing models to accurately account for installation-specific characteristics and real-time airflow dynamics, leading to suboptimal performance.
Innovation Solution
A system and method that utilize a reduced order model (ROM) to represent airflow dynamics, allowing for real-time updates and control by transforming complex partial differential equations (PDEs) into ordinary differential equations (ODEs, with a stability parameter to ensure model stability and adapt to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a physical model of airflow is used to optimize air-conditioning system operation, then the system can account for installation-specific characteristics, but the model is of infinite dimension and too complex to be used in real time control applications
Solution Approach 1:
The patent extracts the essential dynamics of airflow from the complex infinite-dimensional physical model by identifying and retaining only the dominant modes that capture the majority of the system's behavior. This extraction process reduces the model to a finite-dimensional representation that maintains accuracy for control applications while eliminating unnecessary complexity.
Solution Approach 2:
The patent transforms the model representation by changing parameters from an infinite-dimensional continuous field description to a finite-dimensional discrete mode decomposition. By expressing the airflow state as a combination of dominant modes with time-varying coefficients, the model becomes computationally tractable for real-time control while preserving the essential physics.
2Loss of energy
If a mathematical model of the vapor compression system is used to predict optimal input combinations, then energy efficiency can be maximized, but the model becomes inaccurate over time as the system changes
Solution Approach 1:
The patent makes the model adaptive by allowing it to change over time. The reduced-order model coefficients are updated dynamically based on real-time system measurements, enabling the model to track system degradation and changes. This dynamic adaptation maintains model accuracy despite refrigerant leakage, corrosion, or other system changes.
Solution Approach 2:
The patent implements feedback mechanisms where real-time measurements of system performance and airflow are used to update the model parameters. This closed-loop approach ensures the model remains accurate by continuously comparing predictions with actual system behavior and adjusting the reduced-order model accordingly.
3Loss of energy
If conventional model-based energy optimization methods are used, then the heat load requirements can be met with minimal energy consumption, but the methods cannot adapt to installation-specific characteristics and real-time system variations
Solution Approach 1:
The patent segments the complex airflow field into dominant modes that can be independently analyzed and controlled. This segmentation allows the model to capture installation-specific characteristics through the spatial distribution of these modes while maintaining computational efficiency for real-time optimization of energy consumption.
Data Source
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AI summary
A method determines values of the airflow measured in the conditioned environment during the operation of the air-conditioning system and selects, from a set of regimes predetermined for the conditioned environment, a regime of the airflow matching the measured values of the airflow. The method selects, from a set of models of the airflow predetermined for the conditioned environment, a model of airflow corresponding to the selected regime and models the airflow using the selected model. The operation of the air-conditioning system is controlled using the modeled airflow.