Upcoming Dissertation Defense – Ransisi Huang

Development of a General-Purpose Steady-State Simulation Framework for Vapor Compression Systems

Advisory Committee:
Professor Reinhard Radermacher, Chair
Associate Professor Jacob Bedrossian
Associate Professor Katrina Groth
Professor Jungho Kim
Professor Jelena Srebric
Research Scientist Vikrant Aute

Date & Time: October 8, 2020 2pm-4pm

The vapor compression system is the dominating technology in heat pumping, air conditioning and refrigeration. Vapor compression is associated with significant energy consumption and high global warming potential. Steady-state simulation of vapor compression system is a crucial numerical technology that helps to assess and mitigate the energy and environmental impact of these systems. This dissertation aims to advance the steady-state modeling and simulation technologies for vapor compression systems toward higher level of flexibility, computational efficiency, and robustness, improving designs and reducing time to market.

First, the dissertation proposes a generalized solution methodology for the steady-state analysis of arbitrary vapor compression systems. A tripartite-graph based tearing algorithm is proposed to generically formulate the residual equations. The methodology was extensively validated by five test systems with capacities from 10 to 100 kW. The maximum simulation energy imbalance ( ) was 0.91%, and the maximum system performance deviation ( ) was 8.94%. The computation time for one system ranges from 2 to 851 s. The methodology was also applied to analyze two advanced vapor compression systems, presenting strong capability to contribute to the acceleration of their R&D stage.

Second, the dissertation develops an approximation-assisted modeling methodology to speed up the steady-state system simulation. Three approximation-assisted heat exchanger models were compared in terms of accuracy and computational efficiency. Kriging metamodel presented the highest accuracy among the three. For heat exchanger performance approximation, its overall ∆P and ∆h mean absolute error (MAE) were 4.46% and 0.9%, respectively. For system simulations, the maximum COP and capacity errors with Kriging metamodel were 2.54% and 1.45%, respectively. System simulation was sped up by a factor of 10 to 600, depending on the test

Third, the dissertation proposes two convergence improvement approaches on the basis of nonlinear equation fundamentals, and assessed them on a standard vapor compression system as a first step, allowing for later application to more complex cycles. The assessment results show that a large initial Jacobian condition number presents low convergence probability at the current initial guess point. The results also indicate a correlation between component nonlinearity and simulation convergence. It was found that by changing the characterization methods in the heat exchanger models, 47 out of 51 originally non-converged cases were able to reach convergence.