Smart chip, system and data center enabled by advanced flexible cooling resources

Patel, Chandrakant D.; Bash, Cullen; Sharma, Ratnesh; Beitelmal, Abdlmonem H.; Malone, Christopher G.

doi:10.1109/stherm.2005.1412162

Cited by 26 publications

(2 citation statements)

References 9 publications

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“…This will be explored in subsequent papers. Lastly, this model can be the basis of a high level evaluation engine used to enable control, a proposition of enabling Smart Chip, Smart System and Smart Data Center using flexible power and cooling resources [18].…”

Section: Discussionmentioning

confidence: 99%

“…• (18) where Q ct is the heat loss from the condenser cooling water in the cooling tower and W ct is the total power consumed by the cooling tower blowers and the condenser cooling water pumps. From first law of thermodynamics, Q ct is equal to sum of Q d and compressor work for chillers based on vapor compression refrigeration cycle along with blower and pump heat loads and other miscellaneous cooling components required to move the heat originating in the data center to the cooling tower per Fig.…”

Section: Cooling Towermentioning

confidence: 99%

See 1 more Smart Citation

Energy Flow in the Information Technology Stack: Introducing the Coefficient of Performance of the Ensemble

Patel

Sharma

Bash

et al. 2006

Heat Transfer, Volume 3

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The information technology industry is in the midst of a transformation to lower the cost of operation through consolidation and better utilization of critical data center resources. Successful consolidation necessitates increasing utilization of capital intensive "always-on" data center infrastructure, and reducing the recurring cost of power. A need exists, therefore for an end to end physical model that can be used to design and manage dense data centers and determine the cost of operating a data center. The chip core to the cooling tower model must capture the power levels and thermo-fluids behavior of chips, systems, aggregation of systems in racks, rows of racks, room flow distribution, air conditioning equipment, hydronics, vapor compression systems, pumps and heat exchangers. Earlier work has outlined the foundation for creation of a "smart" data center through use of flexible cooling resources and a distributed sensing and control system that can provision the cooling resources based on the need. This paper shows a common thermodynamic platform which serves as an evaluation and basis for policy based control engine for such a "smart" data center with much broader reach - from chip core to the cooling tower. Computational Fluid Dynamics modeling is performed to determine the computer room air conditioning utilization for a given distribution of heat load and cooling resources in a production data center. Coefficient of performance (COP) of the computer room air conditioning units, based on the level of utilization, is used with COP of other cooling resources in the stack to determine the COP of the ensemble. The ensemble COP represents an overall measure of the performance of the heat removal stack in a data center.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Cooling Towermentioning

confidence: 99%

Energy Flow in the Information Technology Stack: Introducing the Coefficient of Performance of the Ensemble

Patel

Sharma

Bash

et al. 2006

Heat Transfer, Volume 3

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Modeling of data center airflow and heat transfer: State of the art and future trends

Rambo

Joshi

2007

Distrib Parallel Databases

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An assessment of the current thermal modeling methodologies for data centers is presented, with focus on the use of computational fluid dynamics and heat transfer as analysis tools, and model validation. Future trends in reduced or compact modeling of data center airflow and heat transfer are presented to serve as an overview of integrating rack-level compact models into full-scale facility level numerical computations. Compact models can be used to efficiently model data centers through varying model fidelity across length scales. Dynamic effects can be included to develop next-generation control schemes to maximize data center energy efficiency.

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Adaptive thermoelectric cooling system for Energy-Efficient local and transient heat management

Park

Bang

Kim

et al. 2022

Applied Thermal Engineering

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Smart chip, system and data center enabled by advanced flexible cooling resources

Cited by 26 publications

References 9 publications

Energy Flow in the Information Technology Stack: Introducing the Coefficient of Performance of the Ensemble

Energy Flow in the Information Technology Stack: Introducing the Coefficient of Performance of the Ensemble

Modeling of data center airflow and heat transfer: State of the art and future trends

Adaptive thermoelectric cooling system for Energy-Efficient local and transient heat management

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