Blower Controller Torque Prediction Using Composite Function
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Solution Overview
Problem
Existing methods for converting air flow demands into torque demands in blower systems suffer from significant prediction errors due to their inability to accurately account for non-linear relationships between air flow, speed, and torque.
Innovation Solution
Incorporating a composite function S*(FFD)^n into the torque equation, where S is the motor speed, FFD is the fluid flow demand, and n>1, to improve the accuracy of torque demand conversion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional linear or multiple slope algorithms are used to convert air flow demand into torque demand, then the control scheme is simple and easy to implement, but the prediction error increases significantly due to inability to account for non-linear relationships
Solution Approach 1:
The patent transforms the control approach by changing the mathematical parameters from simple linear or piecewise linear relationships to a composite function that incorporates non-linear terms. Specifically, the torque demand equation includes terms like S*CFM^n where S is motor speed, CFM is air flow demand, and n>1, allowing the system to capture non-linear relationships between airflow and torque while maintaining a manageable equation structure.
Solution Approach 2:
The control equation acts as a composite mathematical model combining multiple terms with different functional relationships (linear terms, quadratic terms, cross-products of speed and airflow). This composite approach allows the system to model complex non-linear behavior by combining simpler mathematical building blocks, achieving high prediction accuracy without requiring an overly complex control scheme.
2Reliability
If complex non-linear models are used to accurately convert air flow demand into torque demand, then prediction accuracy improves, but the control scheme becomes more complex and difficult to implement
Solution Approach 1:
The patent achieves reliable torque prediction by carefully selecting and combining specific parameters in the control equation. The use of motor speed S, air flow demand CFM, and their product with appropriate exponents creates a model that accurately reflects the physical relationships in the system while remaining computationally tractable for real-time control implementation.
Solution Approach 2:
The control equation applies different mathematical relationships to different operating conditions through its multiple terms. Each term in the composite function captures specific local behavior characteristics, allowing the overall equation to provide accurate predictions across the full range of operating conditions without requiring a completely different model for each regime.
Data Source
AI summary
A controller for an electric motor in a blower system includes an input for receiving an air flow demand. The controller is configured for producing drive signals for the electric motor from the air flow demand using an equation having a plurality of terms. At least one of the terms includes a composite function S*CFMn, where S is a speed of the electric motor, CFM is the air flow demand, and n>1. These teachings can also be applied to other types of fluid handling systems including, for example, liquid pumps.