Evaluation of a Data Center Recirculation Non-Uniformity Metric Using Computational Fluid Dynamics

Author(s):  
Dustin W. Demetriou ◽  
H. Ezzat Khalifa

The work presented in this paper is an extension of the companion work by the authors on a simplified thermodynamic model for data center optimization, in which a recirculation non-uniformity metric, θ, was introduced and used in a parametric analysis to highlight the deleterious effect of recirculation non-uniformity at the inlet of racks on the data center cooling infrastructure power consumption. In this work, several studies are done using a commercial computational fluid dynamics (CFD) package to verify many of the assumptions necessary in the development of the simplified model and to understand the degree of recirculation non-uniformity present in typical data center configurations. A number of CFD simulations are used to quantify the ability of the simple model at predicting θ. The results show that the simple model provides a fairly accurate estimate of θ, with a standard deviation in the prediction error of ∼10–15%. The CFD analysis are also to understand the effect of row length and server temperature rise (ΔTs) temperature non-uniformity. The simulations show that reasonable values of θ range from 2–6 for open aisle data centers depending on operating strategy and data center layout. As a means to understand the effect of buoyancy, a data center Archimedes number (Ar), the ratio of buoyancy to inertia forces, is introduced as a function of tile flow rate and server temperature rise. For servers with modest temperature rise (∼ 10.0°C), Ar is ∼0.1; however, for racks with large temperature rise (∼20°C), Ar > 1.0, meaning buoyancy needs to be considered important. Through CFD analysis the significant effect buoyancy has on the inlet rack temperature patterns is highlighted. The Capture Index (ψ), the ratio of cold air ingested by the racks to the required rack flow, is used to investigate its relationship to the ratio of server flow to tile flow (Y), as the inlet rack temperature patterns are changed by increased Ar. The results show that although the rack inlet temperature patterns are extremely different, ψ does not change significantly as a function of Ar. Lastly, the effect of buoyancy on the assumption of linearity of the temperature field is considered for a range of Ar. The results show the emergence of a stratified temperature pattern at the inlet of the racks as Ar increases and buoyancy becomes more important. It is concluded that under these conditions, a δT change in tile temperature does not produce a δT change in temperature everywhere in the field.

2018 ◽  
Vol 140 (1) ◽  
Author(s):  
Jayati Athavale ◽  
Yogendra Joshi ◽  
Minami Yoda

Abstract This paper presents an experimentally validated room-level computational fluid dynamics (CFD) model for raised-floor data center configurations employing active tiles. Active tiles are perforated floor tiles with integrated fans, which increase the local volume flow rate by redistributing the cold air supplied by the computer room air conditioning (CRAC) unit to the under-floor plenum. The numerical model of the data center room consists of one cold aisle with 12 racks arranged on both sides and three CRAC units sited around the periphery of the room. The commercial CFD software package futurefacilities6sigmadcx is used to develop the model for three configurations: (a) an aisle populated with ten (i.e., all) passive tiles; (b) a single active tile and nine passive tiles in the cold aisle; and (c) an aisle populated with all active tiles. The predictions from the CFD model are found to be in good agreement with the experimental data, with an average discrepancy between the measured and computed values for total flow rate and rack inlet temperature less than 4% and 1.7 °C, respectively. The validated models were then used to simulate steady-state and transient scenarios following cooling failure. This physics-based and experimentally validated room-level model can be used for temperature and flow distributions prediction and identifying optimal number and locations of active tiles for hot spot mitigation in data centers.


Author(s):  
Nikita Sukthankar ◽  
Abhishek Walekar ◽  
Dereje Agonafer

Continuous provision of quality supply air to data center’s IT pod room is a key parameter in ensuring effective data center operation without any down time. Due to number of possible operating conditions and non-linear relations between operating parameters make the working mechanism of data center difficult to optimize energy use. At present industries are using computational fluid dynamics (CFD) to simulate thermal behaviour for all types of operating conditions. The focus of this study is to predict Supply Air Temperature using Artificial Neural Network (ANN) which can overcome limitations of CFD such as high cost, need of an expertise and large computation time. For developing ANN, input parameters, number of neurons and hidden layers, activation function and the period of training data set were studied. A commercial CFD software package 6sigma room is used to develop a modular data center consisting of an IT pod room and an air-handling unit. CFD analysis is carried out for different outside air conditions. Historical weather data of 1 year was considered as an input for CFD analysis. The ANN model is “trained” using data generated from these CFD results. The predictions of ANN model and the results of CFD analysis for a set of example scenarios were compared to measure the agreement between the two. The results show that the prediction of ANN model is much faster than full computational fluid dynamics simulations with good prediction accuracy. This demonstrates that ANN is an effective way for predicting the performance of an air handling unit.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Meisam Babanezhad ◽  
Iman Behroyan ◽  
Ali Taghvaie Nakhjiri ◽  
Mashallah Rezakazemi ◽  
Azam Marjani ◽  
...  

AbstractComputational fluid dynamics (CFD) simulating is a useful methodology for reduction of experiments and their associated costs. Although the CFD could predict all hydro-thermal parameters of fluid flows, the connections between such parameters with each other are impossible using this approach. Machine learning by the artificial intelligence (AI) algorithm has already shown the ability to intelligently record engineering data. However, there are no studies available to deeply investigate the implicit connections between the variables resulted from the CFD. The present investigation tries to conduct cooperation between the mechanistic CFD and the artificial algorithm. The genetic algorithm is combined with the fuzzy interface system (GAFIS). Turbulent forced convection of Al2O3/water nanofluid in a heated tube is simulated for inlet temperatures (i.e., 305, 310, 315, and 320 K). GAFIS learns nodes coordinates of the fluid, the inlet temperatures, and turbulent kinetic energy (TKE) as inputs. The fluid temperature is learned as output. The number of inputs, population size, and the component are checked for the best intelligence. Finally, at the best intelligence, a formula is developed to make a relationship between the output (i.e. nanofluid temperatures) and inputs (the coordinates of the nodes of the nanofluid, inlet temperature, and TKE). The results revealed that the GAFIS intelligence reaches the highest level when the input number, the population size, and the exponent are 5, 30, and 3, respectively. Adding the turbulent kinetic energy as the fifth input, the regression value increases from 0.95 to 0.98. This means that by considering the turbulent kinetic energy the GAFIS reaches a higher level of intelligence by distinguishing the more difference between the learned data. The CFD and GAFIS predicted the same values of the nanofluid temperature.


2016 ◽  
Vol 819 ◽  
pp. 356-360
Author(s):  
Mazharul Islam ◽  
Jiří Fürst ◽  
David Wood ◽  
Farid Nasir Ani

In order to evaluate the performance of airfoils with computational fluid dynamics (CFD) tools, modelling of transitional region in the boundary layer is very critical. Currently, there are several classes of transition-based turbulence model which are based on different methods. Among these, the k-kL- ω, which is a three equation turbulence model, is one of the prominent ones which is based on the concept of laminar kinetic energy. This model is phenomenological and has several advantageous features. Over the years, different researchers have attempted to modify the original version which was proposed by Walter and Cokljat in 2008 to enrich the modelling capability. In this article, a modified form of k-kL-ω transitional turbulence model has been used with the help of OpenFOAM for an investigative CFD analysis of a NACA 4-digit airfoil at range of angles of attack.


Author(s):  
Lilas Deville ◽  
Mihai Arghir

Brush seals are a mature technology that has generated extensive experimental and theoretical work. Theoretical models range from simple correlations with experimental results to advanced numerical approaches coupling the bristles deformation with the flow in the brush. The present work follows this latter path. The bristles of the brush are deformed by the pressure applied by the flow, by the interference with the rotor and with the back plate. The bristles are modeled as linear beams but a nonlinear numerical algorithm deals with the interferences. The brush with its deformed bristles is then considered as an anisotropic porous medium for the leakage flow. Taking into account, the variation of the permeability with the local geometric and flow conditions represents the originality of the present work. The permeability following the principal directions of the bristles is estimated from computational fluid dynamics (CFD) calculations. A representative number of bristles are selected for each principal direction and the CFD analysis domain is delimited by periodicity and symmetry boundary conditions. The parameters of the CFD analysis are the local Reynolds number and the local porosity estimated from the distance between the bristles. The variations of the permeability are thus deduced for each principal direction and for Reynolds numbers and porosities characteristic for brush seal. The leakage flow rates predicted by the present approach are compared with experimental results from the literature. The results depict also the variations of the pressures, of the local Reynolds number, of the permeability, and of the porosity through the entire brush seal.


Sign in / Sign up

Export Citation Format

Share Document