A Generalization of the System Mean Void Fraction Model for Transient Two-Phase Evaporating Flows

Beck, B. Terry; Wedekind, G. L.

doi:10.1115/1.3244436

Cited by 34 publications

(16 citation statements)

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“…Thirdly, the void change in the evaporator during evaporation is not so pronounced as that in the condenser. The results are also in line with the observation of Beck and Wedekind [17] who performed analysis and experiments on the transient of evaporation and condensing flow. They found that only the condensing flow revealed a flow surge phenomenon during the transient process, whereas the evaporating flow shows a relatively smooth variation subject to perturbation influences like flowrate changes.…”

Section: Resultssupporting

confidence: 91%

Dynamic Response of a 50 kW Organic Rankine Cycle System in Association with Evaporators

et al. 2014

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Abstract:The influences of various evaporators on the system responses of a 50 kW ORC system using R-245fa are investigated in this study. First the effect of the supplied hot water flowrate into the evaporator is examined and the exit superheat on the system performance between plate and shell-and-tube evaporator is also reported. Test results show that the effect of hot water flowrate on the evaporator imposes a negligible effect on the transient response of the ORC system. These results prevail even for a 3.5-fold increase of the hot water flowrate and the system shows barely any change subject to this drastic hot water flowrate change. The effect of exit superheat on the ORC system depends on the type of the evaporator. For the plate evaporator, an exit superheat less than 10 °C may cause ORC system instability due to considerable liquid entrainment. To maintain a stable operation, the corresponding Jakob number of the plate heat evaporator must be above 0.07. On the other hand, by employing a shell and tube heat evaporator connected to the ORC system, no unstable oscillation of the ORC system is observed for exit superheats ranging from 0 to 17 °C.

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Section: Resultssupporting

confidence: 91%

Dynamic Response of a 50 kW Organic Rankine Cycle System in Association with Evaporators

et al. 2014

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“…Wedekind's work a bit further and see the effects on utilizing a time-varying mean void fraction value (Beck and Wedekind 1981).…”

Section: Future Researchmentioning

confidence: 99%

“…Using a single average value that does not incorporate the time-variance, however, does not cause major impact on the overall system (Beck and Wedekind 1981).…”

Section: Vcc Refrigeration Massmentioning

confidence: 99%

“…This research, however, is also simplified to assume the mean void fraction is not time varying (Beck and Wedekind 1981).…”

Section: Vcc Refrigeration Massmentioning

confidence: 99%

“…However, when looking at the two-phase flow within the heat exchangers a more sophisticated approach is required. A mean void fraction model can be applied to calculate the mass within the two-phase flow portion of the heat exchangers (Beck and Wedekind 1981).…”

Section: Vcc Refrigeration Massmentioning

confidence: 99%

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Parameter Estimation for HVAC System Models From Standard Test Data

Luthman

Gardner

2016

Volume 6B: Energy

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Nearly all cooling systems, and an increasing proportion of heating systems, utilize the vapor compression cycle (VCC) to provide and remove heat from conditioned spaces. Even though the application of VCC’s throughout the built environment is ubiquitous, effective and accessible models of the performance of these systems remains elusive. Such models could be important tools for equipment and building designers, and building energy managers and those who are attempting to optimize building energy performance through the use of model-based control systems. A quasi-steady state, spreadsheet-based model has been developed which requires knowledge of 10 system-specific parameters. A method utilizing manufacturer’s test conditions to derive these values is presented. The model is applied to three commercially available units and a subset of test conditions is used to identify the model parameters. The model is validated over the entire range of conditions with modeling errors ranging from 2 to 4%.

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