<p>Dynamic
modeling of Vapor Compression Cycles (VCC) is particularly important for
designing and evaluating controls and fault detection and diagnosis (FDD)
algorithms. As a result, transient modeling of VCCs has become an active area
of research over the past two decades. Although a number of tools have been
developed, the computational requirements for dynamic VCC simulations are still
significant. A computationally efficient but accurate modeling approach is
critically important to accelerate the design and assessment of control and FDD
algorithms where a number of iterations with a variety of test input signals
are required. Typically, the dynamics of compressors and expansion devices
evolve on an order of magnitude faster than those of heat exchangers (HX)
within VCC systems. As a result, most dynamic modeling efforts have focused on
heat exchanger models. The switched moving boundary (SMB) method, which
segments a heat exchanger depending on thermodynamic phase of the refrigerant,
i.e. subcooled liquid, two-phase and superheated vapor, and moves control
volumes as the length of each phase changes, can reduce the computation time
compared with the finite volume (FV) method by solving fewer equations due to a
smaller set of control volumes. Despite the computational benefit of the SMB,
there is a well-known numerical issue associated with switching the model
representations when a phase zone disappears or reappears inside of a heat
exchanger. More importantly, the computational load is still challenging for many
practical VCC systems. This thesis proposes an approach applying nonlinear
model order reduction (MOR) methods to dynamic heat exchanger models to
generate reduced order HX models, and then to couple them to quasi-static
models of other VCC components to complete a reduced order VCC model. To enable
the use of nonlinear model reduction techniques, a reformulated FV model is
developed for matching the baseline MOR model structure, by using different
pairs of thermodynamic states with some appropriate assumptions. Then a
rigorous nonlinear model order reduction framework based on Proper Orthogonal
Decomposition (POD) and the Discrete Empirical Interpolation Method (DEIM) is
developed to generate reduced order HX models. </p><p>
</p><p> The proposed reduced order modeling approach
is implemented within a complete VCC model. Reduced order HX models are
constructed for a centrifugal chiller test-stand at Herrick Labs, Purdue
University, and are integrated with quasi-static models of compressor and
expansion valve to form the complete cycle. The reduced cycle model is
simulated in the Modelica-based platform to predict load-change transients, and
is compared with measurements. The validation results indicate that the reduced
order model executes 200 times faster than real time with negligible prediction
errors.</p><br>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/11312597 |
| Date | 04 December 2019 |
| Creators | Jiacheng Ma (8072936) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/Reduced_Order_Modeling_for_Vapor_Compression_Systems_via_Proper_Orthogonal_Decomposition/11312597 |
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