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Optimization of an air conditioning system is critical in terms of the transient and steady state behavior of the air distribution along the room and the temperature of the equipment themselves. In this paper, three computational techniques, namely, the standard

The design of air-conditioning (AC) system is strongly dependent on the dynamics of the air flow in a room. Traditional measurement methods such as trial and error experiments is often time-consuming and expensive [

With reference to other cases, literature studies show that the CFD simulations could be conveniently applied to analyze and predict the indoor air distribution [

Indeed, because indoor airflow typically involves turbulent dynamics, a turbulence model will be required in the associated CFD simulation. A comparison of various types of

Indeed, CFD techniques and turbulence model are well established for simulation air flow distributions in various applications. However, although a comparison of adopting different turbulence models for analyzing natural and forced convective air flow or flow through test samples in wind tunnels currently exists in literature, this is not the case for an air-conditioned room that has a large heat source, a scenario that is commonly encountered in a computer server room. Therefore, this paper analyzes the air distribution of an air-conditioned room that specifically contains an operating computer server that generates up to 6kW of heat. The temperature of air leaving the air conditioner is set to a reference value and the resulting air temperature and pressure dynamics are calculated via the CFD simulation. Three different turbulence models, the standard

Several methods are available for studying the indoor air distribution, such as the traditional measurement and the method of numerical simulation based on the computational dynamics fluid (CFD). In this paper, we adopted the Fluent as the research tool and chose UDF to impose the boundary conditions. The standard wall function was adopted in this work to simulate the flow in the near-wall region, and the SIMPLE algorithm is used to couple the pressure and velocity. To simplify the problem, assumptions are made as follows [

The air is steady turbulent flow;

Indoor air is incompressible and conforms to Boussinesq hypotheses; namely, changes of fluid density have an influence on buoyancy lift only;

Considering the room simulated is with good air tightness, so air leakage effect is out of consideration;

Indoor air is the Newton fluid, and its viscosity is isotropic;

Ignore the energy dissipation caused by the viscous effect in the energy equation;

Due to the adjacent houses, all equipped with air conditioning, interior wall and floor can be regarded as adiabatic boundary condition without temperature difference.

Reynolds time-averaged control equations with Boussinesq approximation are adopted in this work. By the assumptions above, the governing equations are as follows:

To compare and find a suitable simulation to this model, the numerical results of standard

For the RNG

For the

An air-conditioned room involving a small computer center in the Sun Yat-sen University is selected as the research subject for this paper. The airflow parameter distributions throughout this room will be analyzed. The computer room consists of three tables, a locker, a computer server, and its uninterruptable power supply (UPS). In this room, the air conditioning system consists of two separate air conditioners with 30° placed at the back of the room, as shown in Figure

Door dimensions: 2.1 m × 0.9 m;

Locker dimensions: 1.8 m × 0.45 m × 1.8 m;

Table dimensions: 3 x 1.5 m × 0.75 m × 0.85 m;

The computer server and its two associated equipment: 0.6 m × 0.96 m × 1.98 m; 0.94 m × 0.78 m × 1.2 m; 0.25 m × 0.51 m × 0.57 m;

Room dimensions: 7.20 m × 6.70 m × 2.75 m;

Air conditioner: 0.60 m × 0.35 m × 1.86 m;

Schematic of the server room to be air conditioned.

The software of ANSYS ICEM CFD is adopted in this study to generate the model and its grid. In order to improve grid quality, local mesh refinement for special boundaries and smoothing are performed. Different mesh sizes have been used to check the grid in dependency. And after considering both accuracy and economics, the computational domain was meshed into around 9,090,502 cells as illustrated in Figure

Surface mesh generation.

And in this paper, the treatment of the near wall boundary is to make y^{+}~1, where y+ is the dimensionless distance to the wall. For the turbulent flow in the fully developed area of the solid wall, it can be divided into the near wall area and the turbulent core area. In this research we pay more attention to the flow in the near wall area. And the near wall area can be further divided into an adhesive bottom layer, a transition layer, and a logarithmic layer, and these layers are the relationship between the dimensionless velocity u+ and y+. Generally, y+ of the viscous underlayer should approach 1.

Before the numerical calculation, we classified the boundaries and measured the necessary parameters as follows:

The distribution of sensors in the inlet.

_{0}=296.4, A_{1}=1.101, A_{2}=-1.019, A_{3}=0.9897, A_{4}=0.3855, A_{5}=0.1527, B_{1}=0.5416, B_{2}=-0.6437, B_{3}=-0.741, B_{4}=-0.3257, B_{5}=-0.1593, and

_{0}=300.1, A_{1}=6.145, A_{2}=-1.242, A_{3}=-3.669, A_{4}=-1.542, A_{5}=0.3772, A_{6}=0.3574, B_{1}=-5.895, B_{2}=-6.146, B_{3}=-1.3, B_{4} =2.122, B_{5}=1.671, B_{6}=0.3795, and

The preset operation conditions for simulation.

Parameters | Value | Unit |
---|---|---|

Inlet air velocity of right AC | 3.37 | m/s |

Inlet air velocity of left AC | 3.88 | m/s |

Inlet air velocity of Server | 1.31 | m/s |

Inlet air temperature of right AC | | K |

Inlet air temperature of left AC | | K |

Outlet air temperature of right AC | 298 | K |

Outlet air temperature of left AC | 298 | K |

Air temperature of server outlet | 303.85 | K |

Air temperature of server outlet | 1.31 | m/s |

In order to study the influence of different air supply direction on indoor airflow, we consider nine different combinations of air flow supply directions from the two separated air conditioners and, for the sake of brevity, these shall be called air supply combinations. The configuration of each combination is shown in Table

Combinations of air supply direction.

Operating conditions | Right air conditioning | Left air conditioning |
---|---|---|

1 | +35° | +35° |

2 | +35° | 0 |

3 | +35° | -35° |

4 | 0 | +35° |

5 | 0 | 0 |

6 | 0 | -35° |

7 | -35° | +35° |

8 | -35° | 0 |

9 | -35° | -35° |

In order to analyze the accuracy of the results calculated by three turbulence models, we measured the temperature in the room. In this case study, the main way of the server cooling is that controlling the air temperature of the environment around the server. Therefore, after fully considering the influence of the air conditioning and the location of the main working zone (the server area), two rows of measuring sensors were set up at different height (172 cm and 70 cm) between the air conditioning and the server in the back of the room as shown in Figure

Measuring sensors distribution (a) and test scenario (b).

In this part, we presented the simulation results calculated by different turbulence models and discussed their accuracy by comparing the measurement data. This investigation used absolute calculation error and uniformity of errors to evaluate the accuracy with different turbulence models [

The absolute calculation error is the difference between the simulation values with the turbulence models and the actual values, i.e.,

The uniformity of errors evaluates whether or not different turbulence models would lead to an uneven simulation error distribution

Table

Error analysis of the three turbulence models.

Turbulence model | | U |
---|---|---|

| 0.887 | 0.736 |

RNG | 0.890 | 0.864 |

| 0.680 | 0.681 |

As shown in Figure

The comparison of simulation results and measurement.

In summary, it can be concluded that the

Based on the conclusion of last section, the

The parallel air supply direction of both air conditioners (condition 5 in Table

Streamlines, (a)-(i) corresponding the condition 1 to condition 9.

Temperature contour, (a)-(b) corresponding condition 1 to condition 9.

Due to the existence of a single the heat source (server) the original symmetrical the AC system is no longer symmetrical in the aspect of layout. That is why the obvious difference arose between the conditions, for instance, condition2 and condition 4. As is shown in Figure

In order to optimize the air supply direction, we further analyzed the streamlines of conditions 1, 4, and 7. As represented in Figure

Table

Cooling rates of nine conditions.

1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |
---|---|---|---|---|---|---|---|---|---|

Cooling rate (K/s) | 0.031 | 0.028 | 0.025 | 0.032 | 0.030 | 0.029 | 0.029 | 0.028 | 0.022 |

In order to analyze the energy efficiency in the main working zone under different conditions, the energy utilization coefficient (EUC) that was adopted in [

As regards this analysis, all the supply combinations have good performance on the energy utilization coefficient (>1) [

Energy utilization coefficient of different conditions.

1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |
---|---|---|---|---|---|---|---|---|---|

| 1.165 | 1.243 | 1.042 | 1.222 | 1.231 | 1.0629 | 1.152 | 1.178 | 0.998 |

To sum up, in terms of the cooling rate, energy utilization coefficient, and the distributions of velocity and temperature, we discussed the influence of different combinations of air supply direction. The result shows that the AC system has defects. After a comprehensive analysis, we found that combination 7 has the best performance, so it is recommended that the air supply direction should be adjusted to the combination 7 (-35° and +35°) without changing the other settings.

In this paper, three turbulence models, namely, the standard

The data used to support the findings of this study are available from the corresponding author upon request.

The authors declare that they have no conflicts of interest.

This work was partly supported by Key project of national key R&D program for HPC under Grant no. 2016YFB0200603 and Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the third phase) under Grant no. U1501501. The project of Guangzhou Science and Technology Program (Grant 655 no. 201704030089) also supports this research.