Virtual Household Refrigerators at Steady-State and Transient Conditions. Numerical Model and Experimental Validation.

Transcription

1 Virtual Household Refrigerators at Steady-State and Transient Conditions. Numerical Model and Experimental Validation. Nicolás ABLANQUE, Carles OLIET, Joaquim RIGOLA, Carlos-David PEREZ-SEGARRA Universitat Politècnica de Catalunya BarcelonaTECH Heat and Mass Transfer Technological Center July 11-14, 2016

2 Contents Introduction Numerical model Modular approach Components Virtual refrigerator Results Experimental unit / simulation considerations Steady-state Transient-state Conclusions 2

3 Introduction Computer simulations are widely used to simulate refrigerating systems The ideal numerical tool should include: Convergence robustness Flexibility of the system configuration An acceptable degree of accuracy A rapid response (low time consumption) A general/generic approach Both steady and dynamic conditions All the system relevant elements High level of detail Conflicts may arise among these characteristics The research should be oriented to achieve the best compromise 3

4 Introduction The numerical platform used herein is an attempt to achieve the best compromise between simulation time and detail level The numerical model is adapted to simulate refrigerators The most significant features included in the model are summarized: Two sub-systems (refrigerant loop and cabinet air loop) All relevant components are considered Temperature control system Compressor on and off procedures Detailed models of some components Steady and transient state cases The present work is an update of the numerical model continuous development 4

5 C1 Numerical Model: Modular Approach The system is represented by discrete components and their links The layout is easily modified by adding or substracting components The component level of detail could be modified by changing the component model The system solver is decoupled from the resolution algorithms of components The system solver could be modified without major modifications to the global infrastructure 5

6 Slide 5 C1 Pondria como titulo "Numerical model: modular approach", y asi en todas las siguientes de la parte de modelo, con el numerical model: en común Lo mismo para la parte de resultados: "Results:experimental data"... Carles, 7/1/2016

7 Numerical Model: Components/Control System Components The system most relevant components are taken into account: compressor, heat exchangers, non-adiabatic capillary tube, accumulator, connecting tubes, refrigerated cabinet, and temperature sensor Control system The dynamic simulation is regulated with a simple ON/OFF control system that keeps the cabinet temperature within a pre-determined range The temperature sensor located inside the cabinet is simulated as a thermocouple attached to a small solid volume 6

8 Numerical Model: Pseudo-Transient Approach The pure transient simulation is complex, time consuming, and with serious convergence challenges The pseudo-transient approach allows rapid and stable simulations Two sub-systems are defined and solved sequentially with two different approaches: quasi-steady and dynamic Each sub-system is solved every time step and shares information with the other The refrigeration cycle is previously characterized by running a matrix of steadystate cases, in order to have a much faster response 7

9 Numerical Model: Compressor OFF Mode The cycle is simulated with an alternative layout when the compressor is switched off. Two main volumes with the capillary tube as interconnecting element. The resolution is carried out in two steps and coupled with the refrigerated chambers network. 8

10 Numerical Model: Components Simulation Multilevel models are available for some components (from empirical to distributed, or even CFD for some particular parts). The models used in the present work are: Compressor: compression process based on steady-state efficiencies but dynamic formulation for shell temperature and refrigerant charge accumulation. Condenser: ε-ntu based lumped model with specific geometry for the wireand-tube configuration (the effect of the distances to the wall and to the cabinet are considered) Evaporator: distributed model with specific geometry adapted to plate type heat exchanger with external natural convection Capillary tube: distributed numerical model for non-adiabatic conditions Receiver: numerical model based on energy and mass balances Cabinet: configured as a collection of other components, namely, air volumes, solid objects, and walls. 9

11 Results: Case Description The case used for the numerical simulations carried out in the present work was obtained from the doctoral thesis of Erik Björk (2012) Experimental study of a commercial refrigerator with a single cabinet Steady-State data for different thermal heat loads Refrigerator Cabinet Evaporator Accumulator Condenser Capillary tube Compressor Filter-drier Charge Electrolux ER8893C m, with insulation. Plate-type, m, external free convection, aluminium, length of refrigerant tube 6.22 m, hydraulic diameter 3.2 mm, total internal volume 116 ml (accumulator included) Included inside the evaporator, volume 46 ml (23 ml to store liquid) Tube-and-wire type, m, external free convection, 53 vertical wires on each side, steel, internal/external diameters 3.5/5 mm, total internal volume 135 ml Concentric type, total length 2.54 m, heat exchanger section 2 m, inlet and outlet adiabatic sections 0.5 and 0,04 m (respectively), internal diameter 0.6 mm Commercial type ZEM HQY70AA Molecular sieve, internal free volume 11.3 ml 35.5 g of R600a Reference Björk and Palm (2006) 10

12 Results: Numerical Aspects The refrigerator numerical representation included specific models for the most relevant components of the system Unreported parameters were defined based on typical values (e.g. capillary tube roughness was considered as smooth) 11

13 Results: Numerical Aspects The experimental results showed that the refrigerant mass dissolved into the compressor and the mass contained inside the filter-drier remained almost constant for the whole range of heat loads tested (about 9 g). Therefore, in the numerical model the system refrigerant charge was modified from 35.5 to 26 g as the filter-drier was not calculated nor the refrigerant mass dissolved into the oil The experimental evaporator had an uneven circuitry layout and it had the accumulator integrated into it. In this case, the numerical strategy consisted on splitting the actual evaporator in two different objects: a plate-type evaporator and an accumulator located downstream of it (both with their corresponding internal volumes) The heat transfer coefficients used throughout the cabinet - at dynamic conditions - were deduced from both a heat balance (including radiation) and the experimental data provided. 12

14 Results: Steady-State The steady-state simulations were compared against the experimental data presented by E. Björk Seven internal thermal loads - ranging from 0 to 150 W - were tested at steady state condition The ambient temperature was kept constant at 25 ºC for all cases In the numerical simulation for the steady-state cases the cabinet system was not solved. Because the cabinet temperature is used as the refrigerant loop boundary condition at the evaporator 13

15 Results: Steady-State Temperatures Experimental measurements compared to numerical predictions at different thermal loads Original thermal loads were modified according to a thermal balance including radiation The mean temperature differences between predictions and experimental cases are 3.3, 5.5, 0.7 and 1.1 ºC for the evaporator, cabinet, condenser and compressor surface, respectively 14

16 Results: Steady-State Charge The experimental refrigerant charge at different thermal loads is compared against predictions (similar trends/slopes are observed) The system charge migrates from the evaporator/accumulator to the condenser/compressor as the system is subjected to higher thermal loads. The mean refrigerant weight differences between predictions and experimental cases are 2.5, 1.9 and 0.4 g for the condenser, evap.+acc.+cap., and the compressor, respectively 15

17 Results: Steady-State Other Parameters The power consumption and the system mass flow rate are numerically studied (these parameters were not available in the experimental data) 16

18 Results: Transient-State The dynamic simulations were carried out with a fixed ambient temperature of 25 ºC The cabinet temperature was set to have a minimum value of 4 ºC and a maximum value of 6 ºC A total time of seconds was simulated 17

19 Results: Transient-State Pressures Evaporator and condenser pressures at dynamic cyclic conditions When the compressor is shut-down (at about 3750 and 5500 s) both pressures start to equalize, and after some hundreds of seconds, the system attains a unique pressure The pressure fast rise during the compressor start-ups is due to the steady-state approach used to solve the refrigerant loop (a loss of precision in that particular moment of the cycle is the tax to pay for a much faster resolution) 18

20 Results: Transient-State Temperatures The temperature evolutions of both the cabinet air and the solid cylinder are shown The solid cylinder temperature represents the cabinet temperature. It is maintained between 4 and 6 ºC by means of the control system The air temperature has low thermal inertia, therefore it varies more rapidly according to the compressor operation mode 19

21 Results: Transient-State Charge The mass migration is observed in the Figure When the compressor is not activated most of the refrigerant mass goes to the low pressure side of the cycle 20

22 Conclusions The numerical model has been validated against steady-state conditions and numerically tested at transient conditions The steady state validation showed good qualitative agreement and acceptable quantitative discrepancies The dynamic simulations showed the charge migration process, the pressure evolution when the compressor was switched off, and the cabinet temperatures behaviour The model has an acceptable balance between level of detail and running speed (the simulation of dynamic cases lasted about 5% of the simulated time) 21

23 Conclusions The system model did not include any particular operational condition to facilitate/simplify its resolution (e.g. without setting the system subcooling/superheating degree) The present numerical model has been tested against experimental data from an independent source. The system elements have not been subjected to any particular adjustement (e.g. without fitting the elements parameters with the experimental data) The model is about to be fully validated 22