Fluid inspection using machine learning
AssigneeNVIDIA Corporation
InventorsRyan Kelsey Albright, William Andrew Mecham, Tahir Cader, Michael Scott Thompson, Aaron Richard Carkin, William Ryan Weese, Benjamin Joseph Goska, Marc Davis
Life of this patent
Estimating the term…
Abstract
A method includes determining, using a processing device, a set of observations from coolant data, the coolant data being received from one or more sensors in an environment associated with a coolant. The method further includes determining, using a machine learning model and the set of observations, a contamination level of the coolant. The method also includes initiating an operation, using the processing device, responsive to determining the coolant contamination level.
Claims19
A method, comprising:
- determining, using a processing device, a set of observations from coolant data, the coolant data being received from one or more sensors in an environment associated with a coolant;
- determining, using a machine learning model and the set of observations, a contamination level of the coolant;
- and
- initiating an operation, using the processing device, responsive to determining the coolant contamination level.
A system comprising:
- a processing device to:
- determine a set of observations from coolant data, the coolant data being received from one or more sensors in a computing environment associated with a coolant;
- determine, using a machine learning model and the set of observations, a contamination level of the coolant;
- and
- initiate an operation, using the processing device, responsive to determining the coolant contamination level.
A method, comprising:
- determining, using a processing device, a set of observations from coolant data, the coolant data being received from one or more sensors associated with a first portion of a computing environment associated with a coolant;
- training, using the set of observations, a machine learning model (ML) to determine a cooling efficiency and power efficiency associated with the first portion;
- determining, using the ML model, the cooling efficiency and power efficiency associated with a second portion of the computing environment;
- and
- determining whether to perform an operation at the first portion or the second portion responsive to determining the cooling efficiency and power efficiency associated with the second portion.
Dependencies are inferred from the claim text.