This thesis presents an approach for identifying critical model parameters
in dynamic air-conditioning systems using limited sensor information. The expansion
valve model and the compressor model parameters play a crucial role in the system
model's accuracy. In the past, these parameters have been estimated using a mass flow
meter; however, this is an expensive devise and at times, impractical. In response to
these constraints, a novel method to estimate the unknown parameters of the expansion
valve model and the compressor model is developed. A gray box model obtained by
augmenting the expansion valve model, the evaporator model, and the compressor model
is used. Two numerical search algorithms, nonlinear least squares and Simplex search,
are used to estimate the parameters of the expansion valve model and the compressor
model. This parameter estimation is done by minimizing the error between the model
output and the experimental systems output. Results demonstrate that the nonlinear least
squares algorithm was more robust for this estimation problem than the Simplex search
algorithm.
In this thesis, two types of expansion valves, the Electronic Expansion Valve and
the Thermostatic Expansion Valve, are considered. The Electronic Expansion Valve
model is a static model due to its dynamics being much faster than the systems
dynamics; the Thermostatic expansion valve model, however, is a dynamic one. The
parameter estimation algorithm developed is validated on two different experimental
systems to confirm the practicality of its approach. Knowing the model parameters
accurately can lead to a better model for control and fault detection applications. In
addition to parameter estimation, this thesis also provides and validates a simple usable
mathematical model for the Thermostatic expansion valve.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2010-05-8032 |
| Date | 2010 May 1900 |
| Creators | Hariharan, Natarajkumar |
| Contributors | Rasmussen, Bryan |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | thesis, text |
| Format | application/pdf |
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