Abstract: Approaches presented herein provide for automated management of operational systems, such as cooling and power systems, for a computing environment such as a data center. A secondary coolant distribution unit (CDU) can be connected to a cooling loop to allow for cutover from coolant flow from a primary CDU, which can allow for tasks such as coolant cleaning or replacement without having to power down the electronic components being cooled by the coolant flow. The cooling system can also be used to cure thermal interface materials (TIMs) by heating the flow of liquid through the cooling system to at least one target temperature over a period of time according to a curing profile.

Abstract: Approaches presented herein provide for automated management of operational systems, such as cooling and power systems, for a computing environment such as a data center. A secondary coolant distribution unit (CDU) can be connected to a cooling loop to allow for cutover from coolant flow from a primary CDU, which can allow for tasks such as coolant cleaning or replacement without having to power down the electronic components being cooled by the coolant flow. The cooling system can also be used to cure thermal interface materials (TIMs) by heating the flow of liquid through the cooling system to at least one target temperature over a period of time according to a curing profile.

Abstract: Approaches presented herein provide for automated management of operational systems, such as cooling and power systems, for a computing environment such as a data center. A secondary coolant distribution unit (CDU) can be connected to a cooling loop to allow for cutover from coolant flow from a primary CDU, which can allow for tasks such as coolant cleaning or replacement without having to power down the electronic components being cooled by the coolant flow. The cooling system can also be used to cure thermal interface materials (TIMs) by heating the flow of liquid through the cooling system to at least one target temperature over a period of time according to a curing profile.

Abstract: Systems and methods are directed a leak detection system that incorporates one or more chemical sensors to detect small quantities of leaks. The leak detection system may localized chemical sensors to detect leakage within a specific sub-volume of an enclosure. The one or more chemical sensors may be configured to detect an additive incorporated into cooling fluid.

Abstract: Systems and methods disclosed herein can be used to determine an architecture of a data center based on one or more criteria. A system generates a digital representation of a data center, simulates how the digitally represented data center would perform under a certain workflow, and determines which of the simulated data centers satisfies the one or more criteria.

Abstract: Approaches presented herein provide for the automated detection of physical state information for a set of components, such as those present in a datacenter. The physical state information can be obtained by using one or more sensors (e.g., cameras) to capture visual or other physical data for one or more components. The captured sensor data can be analyzed to determine physical state aspects for various components. This observed state can be compared, using an artificial intelligence (AI) model trained on physical language, to a typical set of state data to attempt to identify any anomalies. If any anomalies are identified, an AI actor can determine whether any of those anomalies are related to a current or potential problem, and if so, can generate some type of notification or alarm to cause the problem to be investigated.

Abstract: Systems and methods are directed to a multi-layered tubular that can detect leaks. The multi-layered tubular can include sensing layers that generate an electrical signal in response to encountering a leak. The multi-layered tubular sends an electrical signal to a controller which can take responsive action. The controller may determine the location of the leak based on the electrical signal.

Abstract: Systems and methods disclosed herein can be used to provide power for a non-powered rack chassis. An enclosure shaped to fit within the non-powered rack chassis may be associated with or may include a power shelf. The power shelf may have at least one busbar to distribute the power from the power shelf to one or more sleds of the non-powered rack chassis.

Abstract: Systems and methods are directed to a leak detection system that incorporates non-contact sensors to detect leaks. The leak detection system may include local power supplies that may provide operational power to the non-contact sensors when remaining components, such as those associated with racks or electronic systems, are shipped, stored, or otherwise not provided with an external power supply. Low-powered non-contact sensors may particularly target small viewing areas for leak detection.

Abstract: Systems and methods disclosed herein can be used to supply power in a computing environment that may include a rack, multiple sleds, and a power-sharing logic. The rack may include a busbar, an alternating current (AC) input, and a plurality of rack power supply units (PSUs). The multiple sleds may include individual sled PSUs and power distribution board (PDBs). The power-sharing logic may be associated with the sled PSUs and the PDBs to allow sharing of power between the sled PSUs and through the PDBs.

Abstract: Systems and methods are directed toward multi-layer tubular arrangements that include a flow restrictive material between a first layer and a second layer. The first layer may be associated with a flow bore and may be surrounded, at least partially, by a flow restrictive material. The flow restrictive material may also be surrounding by a second layer. If an opening or hole forms in either the first or second layers, the flow restrictive material may block fluid ingress/egress to maintain operation of the multi-layer tubular until maintenance or repairs can be scheduled.

Abstract: Methods are described herein for manufacturing multi-component objects using artificial intelligence. The present invention may be directed to a method that includes determining an actual value of a first attribute of a first component of an object and determining, using a machine learning model and based on the actual value of the first attribute, an optimized value for a second attribute of a second component that is functionally interrelated to the first component in the object. The method may include selecting, from a plurality of second components each having a value for the second attribute within a tolerance range, a second component having the optimized value for the second attribute. The method may further include manufacturing the object using the first component and the selected second component.

Abstract: Approaches presented herein provide for active noise cancelation for individual components in an environment such as a data center. There can be various heat-generating components in a data center, as may include servers, processors, and network switches, that also result in the generation of excess noise, such as may be produced by fans used to remove heat from these components. An exhaust tubing can be used to direct a flow of heated air, directed by a fan, from an opening in a sound-proof housing to an outlet of the tubing. A microphone can be placed inside the exhaust tubing to record the waveform of the noise emitted by the fan that is directed into the exhaust tubing. An inverted phase waveform can be generated and provided for playback by a speaker in the exhaust tubing. The inverted phase waveform will combine with the emitted waveform and, through destructive interference, largely cancel out the emitted waveform.

Abstract: Some embodiments described herein provide intelligent movable racks for a data center and a central system for monitoring and directing the positioning of such racks within the data center. For example, a rack may include computing equipment as well as a power system, a cooling system, and a cabling system (e.g., for data communication). The rack may include a controller in communication with the computing equipment, the power system, the cooling system, and the cabling system. The rack may also include a rack interface for physically supporting the rack and operatively connecting the systems of the rack to power, cooling, and cabling infrastructure of the data center. The rack interface may receive an autonomous robot for moving the rack within the data center. The controller may control the power system and the cooling system based in part on the autonomous movement of the rack.

Abstract: Approaches presented herein provide for receiving liquid coolant from external sources to cold plates of a server or other liquid-cooled computer system. In at least one embodiment, initial standalone manifolds of the server can be forgone or bypassed, with the flow of liquid coolant received to a server at the cold plates. Some components of the server can be provided a source of cooling from the cold plates and other components can be provided the flow of liquid coolant distributed from the cold plates. The flow of liquid coolant can be provided from the cold plates to the some components to as a source of cooling, as well as to a separate manifold to be further distributed. The cold plates can be connected together to provide the appropriate flow of liquid coolant for the server.

Abstract: Systems and methods are directed toward collecting, aggregating, arbitrating, and transmitting data streams from one or more different source locations. An intermediary system may be positioned between a central controller and a variety of source locations to receive data streams from the different source locations along independent data connections. The intermediary system may identify information for transport to a central controller along a separate connection while delaying or otherwise managing the remaining incoming data streams. Upon determining transmission is complete, the intermediary system may then select another data stream for processing while continuing to delay or manage the remaining incoming data streams.

Abstract: Systems, computer program products, and methods are described for resource allocation in a hybrid distributed computational environment. An example system segments a received task into multiple sub-tasks. Upon partitioning the task, each sub-task is assigned to the appropriate computational resource (e.g., CPU, GPU, or QPU), enabling parallel execution of multiple sub-tasks. Both task partitioning and computational resource determination is determined using a machine learning model. Additionally, the machine learning model may continuously monitor the execution of each sub-task by receiving resource utilization information and performance metrics associated with the execution of each sub-task. The resource utilization information and performance metrics may then be used to update the machine learning model.

Abstract: Approaches presented herein provide for the management of power supplied to server trays, such as allowing for power cycling of individual server trays in a server rack powered by a DC busbar. In at least one embodiment, a timer circuit may be triggered by a power cycling command to prevent power from a power source, such as a busbar, to be provided to processing circuitry within a server tray of a server rack, except the timer circuit. The power can be caused be no longer available to the processing circuitry, ending operation of logical operations of the processing circuitry. After a period of time, the timer circuit can generate a signal to cause the power from the power source to be made available to the processing circuitry, allowing for resumed operation. At least a minimum amount of capacitance may be discharged from the processing circuitry during the period of time.

Abstract: Some embodiments described herein provide intelligent movable racks for a data center and a central system for monitoring and directing the positioning of such racks within the data center. For example, a rack may include computing equipment as well as a power system, a cooling system, and a cabling system (e.g., for data communication). The rack may include a controller in communication with the computing equipment, the power system, the cooling system, and the cabling system. The rack may also include a rack interface for physically supporting the rack and operatively connecting the systems of the rack to power, cooling, and cabling infrastructure of the data center. The rack interface may receive an autonomous robot for moving the rack within the data center. The controller may control the power system and the cooling system based in part on the autonomous movement of the rack.

Abstract: Systems, devices, and methods for disaggregating networking components are provided. An example datacenter rack includes a first networking chassis including a first disaggregated server device supported by the first networking chassis. The first disaggregated server device includes a first central processing unit (CPU) and a first graphics processing unit (GPU) coupled with the first CPU configured to perform computing operations associated with the first networking chassis. The example datacenter rack also includes a second networking chassis including a second disaggregated server device supported by the second networking chassis. The second disaggregated server device includes a second CPU and a second GPU coupled with the second CPU configured to perform computing operations associated with the first networking chassis.