GB2599768A - Intelligent control and distribution of a liquid in a data center - Google Patents
Intelligent control and distribution of a liquid in a data center Download PDFInfo
- Publication number
- GB2599768A GB2599768A GB2110965.7A GB202110965A GB2599768A GB 2599768 A GB2599768 A GB 2599768A GB 202110965 A GB202110965 A GB 202110965A GB 2599768 A GB2599768 A GB 2599768A
- Authority
- GB
- United Kingdom
- Prior art keywords
- flow
- coolant
- processor
- component
- server
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
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Classifications
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- H—ELECTRICITY
- H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
- H05K—PRINTED CIRCUITS; CASINGS OR CONSTRUCTIONAL DETAILS OF ELECTRIC APPARATUS; MANUFACTURE OF ASSEMBLAGES OF ELECTRICAL COMPONENTS
- H05K7/00—Constructional details common to different types of electric apparatus
- H05K7/20—Modifications to facilitate cooling, ventilating, or heating
- H05K7/20709—Modifications to facilitate cooling, ventilating, or heating for server racks or cabinets; for data centers, e.g. 19-inch computer racks
- H05K7/20763—Liquid cooling without phase change
- H05K7/20772—Liquid cooling without phase change within server blades for removing heat from heat source
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B15/00—Systems controlled by a computer
- G05B15/02—Systems controlled by a computer electric
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
- G05B19/4155—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by programme execution, i.e. part programme or machine function execution, e.g. selection of a programme
-
- H—ELECTRICITY
- H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
- H05K—PRINTED CIRCUITS; CASINGS OR CONSTRUCTIONAL DETAILS OF ELECTRIC APPARATUS; MANUFACTURE OF ASSEMBLAGES OF ELECTRICAL COMPONENTS
- H05K7/00—Constructional details common to different types of electric apparatus
- H05K7/20—Modifications to facilitate cooling, ventilating, or heating
- H05K7/20709—Modifications to facilitate cooling, ventilating, or heating for server racks or cabinets; for data centers, e.g. 19-inch computer racks
- H05K7/20836—Thermal management, e.g. server temperature control
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/26—Pc applications
- G05B2219/2614—HVAC, heating, ventillation, climate control
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
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- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Computer Hardware Design (AREA)
- Thermal Sciences (AREA)
- Microelectronics & Electronic Packaging (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Theoretical Computer Science (AREA)
- Biophysics (AREA)
- General Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Human Computer Interaction (AREA)
- Manufacturing & Machinery (AREA)
- Cooling Or The Like Of Electrical Apparatus (AREA)
Abstract
A cooling system for a datacenter is disclosed. The datacenter cooling system includes one or more flow controllers within a rack manifold, a server manifold, or server tray to facilitate movement of a coolant associated with a secondary cooling loop to cool a component within a server in response to the component monitoring its internal temperature.
Claims (30)
1. A datacenter cooling system, comprising: one or more flow controllers within a rack manifold, a server manifold, or server tray to facilitate movement of a coolant associated with a secondary cooling loop to cool a component within a server in response to the component monitoring its internal temperature.
2. The datacenter cooling system of claim 1, further comprising: a learning subsystem comprising at least one processor for evaluating internal temperatures of one or more components within the server with flow rates associated with the one or more flow controllers, and for providing an output associated with a flow rate for facilitating the movement of the coolant by controlling the one or more flow controllers.
3. The datacenter cooling system of claim 2, further comprising: a cold plate associated with the component; the one or more flow controllers facilitating the movement of the coolant through the cold plate; and the learning subsystem executing a machine learning model to: process the internal temperature using multiple neuron levels of the machine learning model having the internal temperatures and having prior associated flow rates for the coolant; and provide the output associated with the flow rate, from an evaluation of the prior associated flow rates, to the one or more flow controllers.
4. The datacenter cooling system of claim 3, further comprising: the one or more flow controllers modifying a second flow rate of the coolant associated with the secondary cooling loop to provide the flow rate in response to the output.
5. The datacenter cooling system of claim 1, further comprising: a series coupling of two of the one or more flow controllers through two cold plates so that a first flow controller controls a first flow of the coolant into a first cold plate and so that a second flow controller controls a second flow of the coolant as it exits the first cold plate and enters the second cold plate.
6. The datacenter cooling system of claim 1, further comprising: a parallel coupling of two of the one or more flow controllers to two cold plates so that a first flow controller controls a first flow of the coolant from a server manifold into a first cold plate and so that a second flow controller controls a second flow of the coolant from the server manifold into the second cold plate.
7. The datacenter cooling system of claim 1, further comprising: the secondary cooling loop to facilitate a second movement of the coolant or of a second coolant, the second movement characterized by a first flow rate to cool the component according to a temperature sensor external to the component; and at least one processor to control an inline pump or a bypass pump in response to the component monitoring the internal temperature so that the inline pump or the bypass pump facilitates the movement of the coolant or of the second coolant, the movement characterized by a second flow rate to cool the component.
8. At least one processor for a cooling system, comprising: at least one logic unit to control one or more flow controllers within a rack manifold, a server manifold, or server tray to facilitate movement of a coolant associated with a secondary cooling loop to cool a component within a server in response to the component monitoring its internal temperature.
9. The at least one processor of claim 8, further comprising: a learning subsystem for evaluating internal temperatures of one or more components within the server with flow rates associated with the one or more flow controllers, and for providing an output associated with a flow rate for facilitating the movement of the coolant by controlling the one or more flow controllers.
10. The at least one processor of claim 8, further comprising: a learning subsystem for executing a machine learning model to: process the internal temperature using multiple neuron levels of the machine learning model having the internal temperatures and having prior associated flow rates for the coolant; and provide the output associated with the flow rate, from an evaluation of the prior associated flow rates, to the one or more flow controllers.
11. The at least one processor of claim 10, further comprising: an instruction output for communicating the output with the one or more flow controllers to modify a second flow rate of the coolant associated with the secondary cooling loop causing the flow rate in response to the output.
12. The at least one processor of claim 8, further comprising: individual ones of a plurality of processors associated with individual ones of the one or more flow controllers in a serial coupling so that a first processor controls a first flow controller for a first flow of the coolant into a first cold plate and so that a second processor controls a second flow controller for a second flow of the coolant as it exits the first cold plate and enters the second cold plate.
13. The at least one processor of claim 8, further comprising: two processors associated with two of the one or more flow controllers in a parallel coupling so that a first processor controls a first flow controller for a first flow of the coolant from a server manifold into a first cold plate and so that a second processor controls a second flow controller for a second flow of the coolant from the server manifold into the second cold plate.
14. The at least one processor of claim 8, further comprising: the at least one logic unit forming part of the component and associated with a temperature sensor within the component.
15. The at least one processor of claim 8, further comprising: the at least one logic unit forming part of the component and adapted to receive a temperature value from a temperature sensor of a connected component within the server, and adapted to facilitate a second movement of the coolant associated with the secondary cooling loop to cool the component and the connected component.
16. At least one processor for a cooling system, comprising: at least one logic unit to train one or more neural networks having hidden layers of neurons for evaluating internal temperatures of the at least one processor and prior associated flow rates for a coolant used to cool the component.
17. The at least one processor of claim 16, further comprising: the at least one logic unit to evaluate an internal temperature of the at least one processor with the one or more neural networks and to output an instruction to facilitate cooling of the at least one processor.
18. The at least one processor of claim 17, further comprising: an instruction output for communicating the output with one or more flow controllers to modify a flow rate of coolant associated a secondary cooling loop to cause a second flow rate that is responsive to the output and that facilitates the cooling of the at least one processor.
19. The at least one processor of claim 16, further comprising: the at least one logic unit adapted to receive a temperature value from a temperature sensor of a connected component within a server, and adapted to facilitate cooling of at least one processor and the connected component in response to the temperature value received.
20. A datacenter cooling system, comprising: at least one processor to train one or more neural networks having hidden layers of neurons for evaluating internal temperatures of one or more processors and prior associated flow rates for a coolant used to cool the one or more processors.
21. The datacenter cooling system of claim 20, further comprising: the at least one processor to evaluate an internal temperature of the at least one processor with the one or more neural networks and to output an instruction to facilitate cooling of the at least one processor.
22. The datacenter cooling system of claim 21, further comprising: an instruction output of the at least one processor for communicating the output with one or more flow controllers to modify a flow rate of coolant associated with a secondary cooling loop to cause a second flow rate that is responsive to the output and that facilitates the cooling of the at least one processor.
23. The datacenter cooling system of claim 20, further comprising: the at least one processor adapted to receive a temperature value from a temperature sensor of a connected component within a server, and adapted to facilitate cooling of at least one processor and the connected component.
24. A method for cooling a datacenter, comprising: providing one or more flow controllers within a rack manifold, a server manifold, or server tray to facilitate movement of a coolant associated with a secondary cooling loop; and enabling the one or more flow controllers to receive input from a component within a server that is associated with the secondary cooling loop to cool the component in response to the component monitoring its internal temperature.
25. The method of claim 24, further comprising: evaluating internal temperatures of one or more components within the server with flow rates associated with the one or more flow controllers using a learning subsystem comprising at least one processor; providing an output associated with a flow rate; and controlling the one or more flow controllers to facilitate the movement of the coolant.
26. The method of claim 25, further comprising: associating a cold plate with the one or more components; facilitating the movement of the coolant through the cold plate using the one or more flow controllers; and executing a machine learning model of the learning subsystem to: process the internal temperature using multiple neuron levels of the machine learning model having the internal temperatures and having prior associated flow rates for the coolant; and provide the output associated with the flow rate, from an evaluation of the prior associated flow rates, to the one or more flow controllers.
27. The method of claim 26, further comprising: modifying a second flow rate of the coolant using the one or more flow controllers associated with the secondary cooling loop to provide the flow rate in response to the output.
28. The method of claim 24, further comprising: coupling in series two of the one or more flow controllers through two cold plates so that a first flow controller controls a first flow of the coolant into a first cold plate and so that a second flow controller controls a second flow of the coolant as it exits the first cold plate and enters the second cold plate.
29. The method of claim 24, further comprising: coupling in parallel two of the one or more flow controllers to two cold plates so that a first flow controller controls a first flow of the coolant from a server manifold into a first cold plate and so that a second flow controller controls a second flow of the coolant from the server manifold into the second cold plate.
30. The method of claim 24, further comprising: facilitating a second movement of the coolant or of a second coolant, the second movement characterized by a first flow rate to cool the component according to a temperature sensor external to the component; controlling an inline pump or a bypass pump in response to the component monitoring the internal temperature so that the inline pump or the bypass pump facilitates the movement of the coolant or of the second coolant, the movement characterized by a second flow rate to cool the component.
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US16/879,513 US20210368656A1 (en) | 2020-05-20 | 2020-05-20 | Intelligent control and distribution of a liquid in a data center |
PCT/US2021/032779 WO2021236527A1 (en) | 2020-05-20 | 2021-05-17 | Intelligent control and distribution of a liquid in a data center |
Publications (2)
Publication Number | Publication Date |
---|---|
GB202110965D0 GB202110965D0 (en) | 2021-09-15 |
GB2599768A true GB2599768A (en) | 2022-04-13 |
Family
ID=80741904
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
GB2110965.7A Pending GB2599768A (en) | 2020-05-20 | 2021-05-17 | Intelligent control and distribution of a liquid in a data center |
Country Status (3)
Country | Link |
---|---|
CN (1) | CN114982393A (en) |
DE (1) | DE112021001744T5 (en) |
GB (1) | GB2599768A (en) |
Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20110060470A1 (en) * | 2009-09-09 | 2011-03-10 | International Business Machines Corporation | Cooling system and method minimizing power consumption in cooling liquid-cooled electronics racks |
US20200103894A1 (en) * | 2018-05-07 | 2020-04-02 | Strong Force Iot Portfolio 2016, Llc | Methods and systems for data collection, learning, and streaming of machine signals for computerized maintenance management system using the industrial internet of things |
-
2021
- 2021-05-17 CN CN202180010018.8A patent/CN114982393A/en active Pending
- 2021-05-17 GB GB2110965.7A patent/GB2599768A/en active Pending
- 2021-05-17 DE DE112021001744.6T patent/DE112021001744T5/en active Pending
Patent Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20110060470A1 (en) * | 2009-09-09 | 2011-03-10 | International Business Machines Corporation | Cooling system and method minimizing power consumption in cooling liquid-cooled electronics racks |
US20200103894A1 (en) * | 2018-05-07 | 2020-04-02 | Strong Force Iot Portfolio 2016, Llc | Methods and systems for data collection, learning, and streaming of machine signals for computerized maintenance management system using the industrial internet of things |
Also Published As
Publication number | Publication date |
---|---|
CN114982393A (en) | 2022-08-30 |
DE112021001744T5 (en) | 2023-03-23 |
GB202110965D0 (en) | 2021-09-15 |
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