Prompt Engineering applies successfully CFD analysis on large Server Room for a simulation driven Energy saving approach.

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1 Prompt Engineering applies successfully CFD analysis on large Server Room for a simulation driven Energy saving approach.. ABSTRACT The Energy Saving approach is the target of this case story. The problem here faced is the evaluation of the Heating, Ventilation and Air-Conditioning (HVAC) system performances for a data center of an important Italian bank. Currently the data center floor layouts are designed using empirical guidelines based on limited measurements. These guidelines do not consider the complex fluid dynamics processes that control the flow rate distribution. Computational simulation can be used for a quick setup of any proposed layout, any desired placement of CRAC units and perforated tiles, and any imagined failure scenario. The computational trialand-error process is preferable for two reasons. Simulation is much faster and more economical than building an actual layout. Second, the computed results provide not only the flow rate distribution but also the underlying velocity and pressure fields and thus explain the physics behind the flow rate variation. This understanding is useful in guiding the computational trial-and-error process in the optimum direction. 2. INTRODUCTION Large-scale data centers (~2,m2) will be the major energy consumers of the next generation. The trend towards deployment of computer systems in large numbers, in very dense configurations in racks in a data center, has resulted in very high power densities at room level. Energy consumption of data centers can be severely increased by inadequate air handling systems and rack layouts that allow the hot and d air streams to mix. Special attention must be paid to the distribution of the cooling air. In raised-floor data centers, the distribution of the air flow through the perforated tiles is governed by the size of the plenum, the arrangement and open area of the perforated tiles, the placement and flow rates of CRAC units, and the under-floor blockages like cables and pipes. The complex flow in the plenum sets up a pressure distribution, which controls the flow through the perforated tiles. Moreover due to the interactive effect of different parameters, the resulting flow distribution is usually not uniform. This means that the computer servers in some areas get too much air, while others get too little. Whenever the cooling-air requirements of any server are not met, its cooling is compromised. The necessary and sufficient condition for good thermal management is supplying the required airflow through the perforated tiles located at the inlet of each computer server. By means of the CFD simulation one can demonstrate some best practices in airflow management: using blanking panels, floor layout, eliminating cable obstructions, hot/d aisle containment, bypass and re-circulation reduction are first important steps to get ready for high ambient datacenter operation. A non-dimensional parameters to evaluate the thermal design and performance of large-scale data centers is used. Prompt Engineering S.r.l. Via Livorno, 6 \ 44 Torino (TO) T \ F \ info@prompt-engineering.com

2 3. DATA CENTER COOLING INFRASTRUCTURE The miniaturization of semiconductor devices and integration of several functionalities on a single microprocessor chip has resulted in very high power densities at chip level. The deployment of large number of these ultrathin servers, in very high densities in computer racks, has resulted in to 5 KW racks. To enable pervasive computing, thousands of racks are expected to reside in future data centers. A data center, thus characterized by high-density deployment of computer racks, can itself be viewed as a computer. One now has to view the data center as the computer, where the walls are the enclosures and the racks are akin to electronic devices dissipating heat. Numerical modeling to optimize the layout of the racks (heat loads) and the airflow vents was proposed. The densely packed unit systems (i.e the racks) have significant flow resistance and coupled with a lack of space for accommodating the air moving devices, it may not be uncommon to observe racks that exhaust air with a higher temperature difference. Figure is a simplified representation of a traditional data center with under floor cool air distribution. Figure Typical Data Center Configuration The hot exhaust air from the EIA (Electronics Industries Association) racks is cooled by recirculating the air through modular computer room air conditioning units (CRAC). Multiple CRAC units, sized to extract the total heat load in the data center, are located within the data center or in close proximity. The cool air is recirculated back to the racks through vented tiles in the raised under-floor plenum. A properly devised and applied vent tile allows the air to be delivered with adequate momentum to reach the inlet of the systems located at the top of the racks. Conventionally, the racks are laid out in hot aisle and d aisle format as shown in Figure with supply air inlet vents in d aisles. The air movers pressurize the plenum with cool air. The cool air enters the data center through vented tiles near the inlet of the racks. We can assert that temperature gradients and flow patterns need to be analyzed for a given layout of equipment to assure appropriate air inlet specifications to the systems. The exhaust air from the racks and the inlet air to the racks have to be managed in such a way that the cascading effect of pre-heated air does not result in violating inlet air specifications to the systems. Such intuitive equipment layout does not alleviate the need of numerical modeling to produce a thermally appropriate Prompt Engineering S.r.l. Via Livorno, 6 \ 44 Torino (TO) T \ F \ info@prompt-engineering.com

3 layout of a data center. A massive thermal data monitoring and CFD modeling is a must to understanding the optimal layout of a room. 4. ENERGY SAVING APPROACH: MONITORING & SIMULATION The Energy Saving approach is the target of this case story. The problem here faced is the evaluation of the Heating, Ventilation and Air-Conditioning (HVAC) system performances for a data center of an important filial bank in Turin. First some considerations have been made about the geometry contest: the main dimension of the datacenter, the positioning of the racks and CRACs and the layout of cables in the under-floor have been studied. Hence implementing the numerical model has followed. This consisted in three major steps: data acquisition: i.e. the thermal data monitoring and thermography analysis; building-up the CFD modeling; model correlation what if analysis A) Data Center Geometry The room, object of the study, measures 3.25 m2 in the x-z plane and has a 3.28 m (3 feet) ceiling. There are 3 rows of racks oriented in the z-direction as shown in figure 2. The rows of racks are arranged in a front-to-front, back-to-back orientation such that hot and d aisles are created. Quadri elettrici Quadri elettrici Aria: input condizionata Aria: in ciro nella sala Aria calda da condizionare Quadri elettrici Quadri elettrici Prompt Engineering S.r.l. Via Livorno, 6 \ 44 Torino (TO) T \ F \ info@prompt-engineering.com

4 Figure 2 Data Center Layout B) Thermal data acquisition By means of a sensors monitoring and thermography pictures a thermal data acquisition has been performed. In figure 3 is shown the points layout thorough the datacenter. In particular, the red sensors read the hot air returning at the CRACs while the blue ones read the d air (the supply air) going into the plenum floor QE A CDZ A2 CDZ B2 CDZ A3 CDZ A3 QE A2 CDZ A4 V V V I4c V I23 I4b I I4 V2 V2 V3 EMC P595 Cs IBM SAN KMsw BIB KM b spr V3 IBM rame DS83 DL4 SEP VTS V4 DS83 S32 S8 BIB 2 KM b2 2 frame Cs fo V4 SAN P KM P S S23 S2 I5 S27 YF spr2 P595 I9 S9 V5 CDZ IBM SEP Y325 V5 3 frame BIB 3 KM b3 Tlc B5 5 frame com P DS8 S V6 SAN2 I I3 2 frame Blade c V6 BIB 4 spr3 S4 com Coms I2 I3 CS V7 SQ Tlc I26 B8 sia V7 w BIB 5 S7 com2 c blade I28 S S3 I27 fems V8 TLC CDZ S3b BIB P Rete CS V8 sep Tlc 4 V9 A BIB 6 exp new Rete V9 I2 BIBsw as4 S2 disp. sep p57- TLC5 VA S9 BIB 7 exp2 S3 VA new exp6 tape S3 S28 was ns52s5 S2 VB CS2 S8 tlc6 VB P595 BIB 8 exp3 Cent. TLC sep I28b sep VC KMB test was Serv. iseries VC exp5 exp4 Com. tlc7 VD sep VD VE VE I8b I5b VF I2 I8 VF CDZ B6 && && I9 VG CDZ A6 CDZ B5 CDZ A5 CDZ B4 VG QE B QE B2 VH Hour/Minute x-axis Hide all sensor data Help Sala 8 - piano CCM Figure 3 Thermal Sensors Layout S2 32 mq. Thanks to these thermal data it has been possible to point out some important considerations towards both the way of how the real HVAC system was working and how to set the boundary conditions to be used for the virtual model. Indeed, a carefully inspection allowed to trace some CRACs' anomalies: a malfunction evaluation is qualitatively shown in figure 4. It is simply to note that only four out twelve of the air conditioning were working as the standards claim. The information and considerations aforementioned were useful during the what if analysis followed after the built-up of the numerical model. Prompt Engineering S.r.l. Via Livorno, 6 \ 44 Torino (TO) T \ F \ info@prompt-engineering.com

5 Figure 4 HVAC System State of Art C) CFD Modeling, Assumptions and Key Measures The CFD modeling was conducted with the intent on gaining understanding of flow patterns and establishing a maximum value of inlet air temperature into the compartments modeled in the rack. The simulation was conducted using computational fluid dynamics tool called scstream from CRADLE-CFD. In the model, the numerical computational domain was the overall room. The room was modeled as adiabatic with no-slip boundary conditions. The revised k-epsilon model was used to account for the large scale turbulence within the room. About 5.. grid cells were arrayed across the solution domain. Prompt Engineering S.r.l. Via Livorno, 6 \ 44 Torino (TO) T \ F \ info@prompt-engineering.com

6 D) Model correlation Once built-up, the virtual model has been then correlated with the experimental data in order to allow trial-and-error virtual analyses. This step, also known as model validation it s rock bottom layer for the subsequent analysis and optimizations. Figure 5: Measured vs Simulated results on different locations As shown, the numerical model is well correlated in a wide range of measuring points. The validated model will be used in the subsequent what if analysis phase. Quantifying the efficiency of the whole HVAC system is a task that may be accomplished introducing the so called Supply Heat index, a dimensionless parameter measuring how much mixed is the air in the room. In an optimization loop process, it s mandatory to introduce indexes and parameters as a metric. Prompt Engineering S.r.l. Via Livorno, 6 \ 44 Torino (TO) T \ F \ info@prompt-engineering.com

7 E) What if analysis (next step) Having as starting point all the conclusions gathered during the monitoring step, some basic improvement to be introduced have been thought in order increase the system thermal efficiency. Two simple configurations have been taken in consideration: increasing the area of the vent tiles switching off he CRACs with anomalies In our virtual environment, these new configuration will be easily ranked as per the SHI parameter mentioned above. 5. CONCLUSIONS The Prompt Engineering approach based on the measure-simulation-optimizationrealization paradigm, brings to reliable and stable solutions. Especially in the case of high complexity systems, such as Server Rooms, Surgery Rooms, in which the system off time means high costs, it is mandatory reduce as much as possible every uncertainly. Simulations may reduce those uncertainly and, in the meanwhile, help engineers in performing effective what if analysis achieving optimal solutions. Moreover the virtual approach, widely used in the other engineering fields (from transportation to electronics, aerospace, ) is the enabler in finding new design spaces (that means new unconventional solutions) that can be the most effective in efficiency and cost reduction. Prompt Engineering S.r.l. Via Livorno, 6 \ 44 Torino (TO) T \ F \ info@prompt-engineering.com