Multi-parameter model reduction in multi-scale convective systems

Samadiani, Emad; Joshi, Yogendra

doi:10.1016/j.ijheatmasstransfer.2009.12.013

Cited by 41 publications

(21 citation statements)

References 18 publications

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“…Also, the mean error, the standard deviation, and the Euclidean L2 norm of the POD temperature error at all 114,000 points of the rack scale for 15 test cases have been shown in Samadiani and Joshi. 12 The mean error varies from 0.35 o C to 2.29 o C, while the average error is 1.36 o C, and the average standard deviation 1.12 o C. Also, the error norm changes from 1.8% to 10.1%, while the average is 6.2%. 12 These values confirm that the presented POD method is accurate enough at the rack scale for design purposes.…”

Section: Pod and Galerkin Projection For Thermal Modeling Of Multi Camentioning

confidence: 93%

“…12 The mean error varies from 0.35 o C to 2.29 o C, while the average error is 1.36 o C, and the average standard deviation 1.12 o C. Also, the error norm changes from 1.8% to 10.1%, while the average is 6.2%. 12 These values confirm that the presented POD method is accurate enough at the rack scale for design purposes. The main goal of the suggested algorithm in Samadiani and Joshi 12 is to predict air temperatures at the rack inlet/outlets and inside the racks accurately and quickly for design purposes.…”

Section: Pod and Galerkin Projection For Thermal Modeling Of Multi Camentioning

confidence: 93%

“…12. Simulation comparison of temperature field for cabinet: (a) CFD/HT temperature contour, (b) POD temperature contour, at the vertical mid-plane.…”

mentioning

confidence: 99%

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Multi-scale Modeling of Electronics Cabinets

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Section: Pod and Galerkin Projection For Thermal Modeling Of Multi Camentioning

confidence: 93%

Section: Pod and Galerkin Projection For Thermal Modeling Of Multi Camentioning

confidence: 93%

See 1 more Smart Citation

Multi-scale Modeling of Electronics Cabinets

Sammakia

Joshi

Agonafer

et al. 2014

Encyclopedia of Thermal Packaging

Self Cite

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“…Besides that, proper orthogonal decomposition (POD), as a reduced-order model algorithm, is widely used to derive temperature profiles from observations [15,16]. Samadiani et al developed a POD-based modeling framework to predict the temperature distribution in computer room [17,18]. Based on the POD-based model, they proposed a compromise decision support problem to optimize the design parameters of data center.…”

Section: Relate Workmentioning

confidence: 99%

Comparative Study of Energy Performance between Chip and Inlet Temperature-Aware Workload Allocation in Air-Cooled Data Center

Yan

2018

Energies

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Improving the energy efficiency of data center has become a research focus in recent years. Previous works commonly adopted the inlet temperature constraint to optimize the thermal environment in the data center. However, the inlet temperature-aware method cannot prevent the servers from over-cooling. To cope with this issue, we propose a thermal-aware workload allocation strategy with respect to the chip temperature constraint. In this paper, we conducted a comparative evaluation of the performance between the chip and inlet temperature-aware workload allocation strategies. The workload allocation strategies adopt a POD-based heat recirculation model to characterize the thermal environment in data center. The contribution of the temperature-dependent leakage power to server power consumption is also considered. We adopted a sample data center under constant-flow and variable-flow cooling air supply to evaluate the performance of these two different workload allocation strategies. The comparison results show that the chip temperature-aware workload allocation strategy prevents the servers from over-cooling and significantly improves the energy efficiency of data center, especially for the case of variable-flow cooling air supply.

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“…Proper orthogonal decomposition (POD) is another observation based method that is capable of predicting the air flow and temperature fields inside a DC much faster than CFD simulation [21][22][23][24][25][26]. Samadiani et al [27] used POD to derive the thermal map of a DC as a function of the CRAC air flow rate.…”

Section: Proper Orthogonal Decomposition Based Modelmentioning

confidence: 99%