EP-4736593-A1 - SYSTEMS AND METHODS FOR COOLING ENCLOSURE CONTROL AND ADAPTIVE LEARNING

EP4736593A1EP 4736593 A1EP4736593 A1EP 4736593A1EP-4736593-A1

Abstract

Systems and methods are directed toward adaptive control systems that may be implemented with cooling systems, such as cooling cabinet enclosures. Historical operating data may be used to establish current operating parameters and then sensor readings may be used to measure performance against one or more metrics. Comparisons of the one or more metrics against metrics for the historical operating data may then be used to continuously update operating conditions when current conditions exceed historical operating conditions.

Inventors

GROVER, Victor Kenneth

Assignees

Dynamic Data Centers Solutions, Inc.

Dates

Publication Date: 20260506
Application Date: 20240628

Claims (20)

1. A computer-implemented method, comprising: receiving one or more current operating parameters for a cabinet enclosure associated with cooling one or more electronic components; receiving one or more historical operating parameters corresponding to a desired set of operating parameters based, at least in part, on one or more current conditions of the cabinet enclosure; determining, based on the desired set of operating parameters, one or more adjustments to the one or more current operating parameters; applying the one or more adjustments to the one or more current operating papers to cause operation of the cabinet enclosure at one or more updated operating parameters; determining, for the one or more updated operating parameters, one or more metrics; comparing the one or more metrics to one or more associated metrics for the desired set of operating parameters; determining at least one metric of the one or more metrics exceeds at least one associated metric of the one or more associated metrics; and updating a corresponding operating parameter for the at least one associated metric to correspond to an updated operating parameter corresponding to the at least one metric.
2. The computer-implemented method of claim 1, further comprising: determining at least a second metric of the one or more metrics is less than a second associated metric of the one or more associated metrics; and adjusting a second operating parameter associated with the second metric.
3. The computer-implemented method of claim 1, wherein the one or more current conditions correspond to at least one of a desired temperature, a desired air flow rate, a desired cooling capacity, or a desired load.
4. The computer-implemented method of claim 1, wherein the one or more operating parameters correspond to at least one of a cooling fluid flow rate, a valve position, a fan speed, or a differential temperature.
5. The computer-implemented method of claim 1, further comprising: receiving one or more sensor readers corresponding to the one or more operating conditions.
6. A computer-implemented method, comprising: receiving sensor data corresponding to a control parameter for a cabinet enclosure; determining one or more metrics based, at least in part, on at least a portion of the sensor data; comparing the one or more metrics to one or more threshold operating parameters; determining the one or more metrics fail to satisfy one or more conditions of the one or more threshold operating parameters; causing a change in one or more current operating settings associated with the control parameter for the cabinet enclosure; determining, following a period of time after the change, one or more updated metrics based, at least in part, on at least an updated portion of updated sensor data; determining the one or more updated metrics satisfy the one or more conditions for the one or more threshold operating parameters; and causing operation of the cabinet enclosure in accordance with the one or more operating settings including the change.
7. The computer-implemented method of claim 6, wherein the control parameter is at least one of a desired temperature, a desired air flow rate, a desired cooling capacity, or a desired load.
8. The computer-implemented method of claim 6, further comprising: determining, following a second period of time after the change, one or more second updated metrics based, at least in part, on at least a second updated portion of updated sensor data; determining the one or more second updated metrics fail to satisfy the one or more conditions for the one or more threshold operating parameters; determining a remediation limit has been reached; and providing an alert regarding one or more components associated with the one or more operating settings.
9. The computer-implemented method of claim 8, wherein the alert is at least one of an auditory alarm or a visual alarm.
10. The computer-implemented method of claim 6, further comprising: receiving second sensor data corresponding to a condition sensor; determining, based on the second sensor data, an operating mode for the cabinet enclosure; overriding one or more current operating conditions based on the operating mode; and causing the cabinet enclosure to operate according to the operating mode.
11. The computer-implemented method of claim 10, wherein the condition sensor is a proximity sensor and the one or more current operating conditions includes increasing at least one fan speed.
12. The computer-implemented method of claim 10, wherein the condition sensor is a fan speed sensor, the operating mode is a first fan failure, and the one or more current operating conditions includes increasing a second fan speed.
13. The computer-implemented method of claim 6, further comprising: receiving a plurality of sensor data over a period of time for a plurality of different associated cabinet components; training one or more machine learning systems based, at least in part, on the plurality of sensor data; and inferring, based on an input salient operating parameter using the trained one or more machine learning systems, one or more suggested operating parameters for the cabinet enclosure.
14. The computer-implemented method of claim 6, further comprising: selecting an initial operating condition, for the cabinet enclosure, based on a salient operating parameter and one or more historical operating parameters; comparing the initial operating condition to a current operating condition corresponding to operations using the one or more operating settings including the change; determining the current operating condition has a higher performance than the initial operating condition; and replacing the initial operating condition with the current operating condition.
15. A system, comprising: at least one processor; and memory including instructions that, when executed by the at least one processor, cause the system to: receive sensor data corresponding to a control parameter for a cabinet enclosure; determine one or more metrics based, at least in part, on at least a portion of the sensor data; compare the one or more metrics to one or more threshold operating parameters; determine the one or more metrics fail to satisfy one or more conditions of the one or more threshold operating parameters; cause a change in one or more current operating settings associated with the control parameter for the cabinet enclosure; determine, following a period of time after the change, one or more updated metrics based, at least in part, on at least an updated portion of updated sensor data; determine the one or more updated metrics satisfy the one or more conditions for the one or more threshold operating parameters; and cause operation of the cabinet enclosure in accordance with the one or more operating settings including the change.
16. The system of claim 15, wherein the control parameter is at least one of a desired temperature, a desired air flow rate, a desired cooling capacity, or a desired load.
17. The system of claim 15, wherein the instructions when executed further cause the system to: receive second sensor data corresponding to a condition sensor; determine, based on the second sensor data, an operating mode for the cabinet enclosure; override one or more current operating conditions based on the operating mode; and cause the cabinet enclosure to operate according to the operating mode.
18. The system of claim 17, wherein the condition sensor is a proximity sensor and the one or more current operating conditions includes increasing at least one fan speed.
19. The system of claim 15, wherein the condition sensor is a fan speed sensor, the operating mode is a first fan failure, and the one or more current operating conditions includes increasing a second fan speed.
20. The system of claim 15, wherein the instructions when executed further cause the system to: select an initial operating condition, for the cabinet enclosure, based on a salient operating parameter and one or more historical operating parameters; compare the initial operating condition to a current operating condition corresponding to operations using the one or more operating settings including the change; determine the current operating condition has a higher performance than the initial operating condition; and replace the initial operating condition with the current operating condition.

Description

SYSTEMS AND METHODS FOR COOLING ENCLOSURE CONTROL AND ADAPTIVE LEARNING CROSS-REFERENCE TO RELATED APPLICATIONS [0001 ] This application claims priority to and the benefit of U.S. Provisional Patent Application No. 63/510,939, titled “LOGICAL ADAPTIVE CONTROL DEVICE,” filed June 29, 2023, the full disclosure of which is hereby incorporated by reference in its entirety for all purposes. BACKGROUND [0002] Computer equipment and related support equipment may be housed in “racks.” Facilities known as datacenters may be used to house and manage multiple racks, which may be used in a variety of applications, such as distributed computing applications. In operation, the electronic equipment may be considered heat generating components that emit heat responsive to electrical energy used to perform one or more tasks. These components may have particular operational parameters, for example high temperature parameters, and/or operational setpoints based on efficiency determinations. As a result, datacenters and their associated racks are often cooled such that heat may be dissipated away from the electronic components to enable continued operation of the computer equipment according to one or more parameters. [0003] Often, air cooling is used to remove heat from the racks and/or individual electronics components. For example, external air flow may be directed into an enclosure that functions to remove or otherwise carry heated air away from the electronic components. In certain configurations, datacenters may be configured to distribute air among a number of racks of electronic components using a centralized fan (or blower). For example, air within the datacenter may pass through a heat exchanger for cooling the air (e.g., an evaporator of a vapor-compression cycle refrigeration cooling system or “vapor-cycle” refrigeration) or a chilled water coil. In some datacenters, the heat exchanger is mounted to the rack to provide “rack-level” cooling of air. In other datacenters, the air is cooled before entering the datacenter. [0004] In general, a lower air temperature in a datacenter allows each electronic component to dissipate a higher power (e.g., lower air temperature will facilitate greater cooling). Consequently, datacenters have traditionally used sophisticated air conditioning systems (e.g., chillers, vaporcycle refrigeration, etc.) to cool the air (e.g., to about 65° F) within the datacenter for achieving a desired performance level. In general, spacing heat-dissipating components from each other (e.g., reducing heat density) makes cooling such components less difficult and hence less costly than placing the same components placed in close relation to each other (e.g., increasing heat density). Datacenters have also compensated for increased power dissipation (corresponding to increased server performance) by increasing the spacing between adjacent servers. However, it is inefficient to cool larger areas that are necessitated by increasing distance between racks. [0005] Control systems have been used to increase cooling rates for a plurality of electronic components in response to increased computational demand. Even so, such control systems have controlled cooling systems that dissipate heat into the datacenter building interior air (which in turns needs to be cooled by air conditioning), or directly use refrigeration as a primary mode of heat dissipation. Refrigeration as a primary mode of cooling, directly or indirectly, requires significant amounts of energy. [0006] As datacenters increase in size and components are designed to consume additional energy and/or handle greater loads, the amount of energy used to cool datacenters and associated components has increased. Additionally, high density facilities to support various processing applications, such as artificial intelligence (Al) processing, also leads to additional energy consumption, and as a result, more cooling load. One approach to larger datacenter loads has been to increase density of the datacenter, for example, adding additional components to racks. Certain high density racks may require more than 50kW per rack. As discussed, traditional methods include cooling an entire building and exchanging the air from hot and cold isles into the atmosphere while sustaining strict conditions of air quality. Such an approach consumes large amounts of energy, increases carbon emissions, and overall increases an environmental footprint for the industry. SUMMARY [0007] Applicant recognized the problems noted above herein and conceived and developed embodiments of systems and methods, according to the present disclosure, for systems and methods for cooling enclosure control and monitoring. [0008] In an embodiment, a computer-implemented method includes receiving one or more current operating parameters for a cabinet enclosure associated with cooling one or more electronic components. The method also includes receiving one or more historical operating paramete