Systems and methods for automatically monitoring coolants of immersion cooling systems

Abstract

A computer-implemented method for monitoring a coolant of an immersion cooling system includes receiving, by a coolant monitoring system, sensor data captured by one or more sensors of the immersion coolant system, the sensor data reflective of one or more detectable properties of the coolant, receiving, by the coolant monitoring system, enrichment data characterizing a relationship between historical sensor data and one or more coolant parameters of one or more historical coolants, integrating, by the coolant monitoring system, the sensor data with the enrichment data to provide enriched data estimating one or more of the coolant parameters of the coolant, and providing, by the coolant monitoring system, information to a user that is based on or contains the enriched data.

Description

SYSTEMS AND METHODS FOR AUTOMATICALLY MONITORING COOLANTS OF IMMERSION COOLING SYSTEMSCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims benefit of U.S. provisional patent application No.63 / 852,597 filed July 28, 2025, entitled “Systems and Methods for Automatically Monitoring Coolants of Immersion Cooling Systems”, and U.S. provisional patent application No. 63 / 737,828 filed December 23, 2024, entitled “Systems and Methods for Automatically Monitoring Lubricants of Industrial Assets”, both of which are hereby incorporated herein in their entirety for all purposes.STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0002] Not applicable.BACKGROUND

[0003] Data centers are specialized facilities that house the computing, storage and networking hardware underpinning modern digital services. At their core, they comprise computing or IT equipment such as server racks and the like populated with blade or rack-mounted servers, interconnected via high-speed switches, and supported by uninterruptible power supplies (UPS), generators, and sophisticated power-distribution units. Cooling infrastructure removes the substantial heat generated by tightly packed electronics, while fire-suppression, security and monitoring systems ensure reliability and uptime. As the backbone of cloud computing, streaming platforms, financial trading and emerging artificial intelligence (Al) workloads, data centers blend mechanical, electrical and IT disciplines into a highly optimized environment.BRIEF SUMMARY OF THE DISCLOSURE

[0004] An embodiment of a computer-implemented method for monitoring a coolant of an immersion cooling system comprises (a) receiving, by a coolant monitoring system, sensor data captured by one or more sensors of the immersion coolant system, the sensor data reflective of one or more detectable properties of the coolant, (b) receiving, by the coolant monitoring system, enrichment data characterizing a relationship between historical sensor data and one or more coolant parameters ofone or more historical coolants, (c) integrating, by the coolant monitoring system, the sensor data with the enrichment data to provide enriched data estimating one or more of the coolant parameters of the coolant, and (d) providing, by the coolant monitoring system, information to a user that is based on or contains the enriched data. In some embodiments, (d) comprises providing by the coolant monitoring system a coolant alert to a user that is based on the one or more coolant parameters. In some embodiments, the coolant alert is provided to the user in at least one of real-time or near real-time following (a). In certain embodiments, (d) comprises providing by a trained predictive coolant model of the coolant monitoring system the coolant alert. In certain embodiments, the trained predictive coolant model is trained at least partially using the enrichment data. In some embodiments, the one or more sensors are in contact with the coolant. In some embodiments, the one or more sensors are configured to emit a signal that travels through the coolant. In certain embodiments, the one or more sensors comprise at least one of an infrared sensor, a piezoelectric sensor, an optical sensor, or an electromagnetic transducer. In certain embodiments, the one or more coolant parameters comprises at least one of a dielectric constant, an electrical conductivity, a turbidity, a contaminant content, an acidity, or a colorimetric parameter. In some embodiments, the coolant comprises a dielectric fluid.

[0005] An embodiment of a computer-implemented method for monitoring a coolant of an immersion cooling system comprises (a) receiving, by a coolant monitoring system, sensor data captured by one or more sensors of the immersion coolant system, the sensor data reflective of one or more detectable properties of the coolant, (b) receiving, by the coolant monitoring system, enrichment data characterizing a relationship between historical sensor data and one or more coolant parameters of one or more historical coolants, (c) integrating, by the coolant monitoring system, the sensor data with the enrichment data to provide enriched data estimating one or more of the coolant parameters of the coolant, and (d) determining, by the coolant monitoring system, a coolant score based on the one or more coolant parameters. In some embodiments, the enrichment data comprises at least one of laboratory data containing historical coolant data, or asset data containing historical data pertaining to the immersion cooling system besides coolant data. In some embodiments, (d) comprises providing by the coolant monitoring system the coolant score to a user. In certain embodiments, the coolant score is provided to the user in at least one of realtime or near real-time following (a).

[0006] An embodiment of a computer-readable medium storing executable code which, when executed by a processor, causes the processor to receive by a coolant monitoring system sensor data captured by the one or more sensors of an immersion coolant system, the sensor data reflective of one or more detectable properties of the coolant, receive by the coolant monitoring system enrichment data characterizing a relationship between historical sensor data and one or more coolant parameters of one or more historical coolants, and integrate by the coolant monitoring system the sensor data with the enrichment data to provide enriched data estimating one or more of the coolant parameters of the coolant. In certain embodiments, the executable code, when executed by the processor, causes the processor to provide by the coolant monitoring system information to a user that is based on or contains the enriched data. In some embodiments, the executable, when executed by the processor, causes the processor to provide a coolant alert to a user that is based on the one or more coolant parameters. In some embodiments, the executable code, when executed by the processor, causes the processor to determine a coolant score based on the one or more coolant parameters. An embodiment of an immersion cooling system for cooling electrical equipment comprises a tank having an interior tillable with a coolant and configured to receive electrical equipment whereby the electrical equipment is immersible in the coolant within the interior, a heat exchanger in fluid communication with the interior of the tank, the heat exchanger configured to exchange heat between the coolant and an external heat sink, a circulation device coupled between the tank and the heat exchanger to circulate the coolant between the tank and the heat exchanger, one or more sensors to output sensor data reflective of one or more detectable properties of the coolant, and a computer system comprising a processor and the computer-readable medium. In certain embodiments, the one or more sensors comprise at least one of an infrared sensor, a piezoelectric sensor, an optical sensor, or an electromagnetic transducer.

[0007] Embodiments described herein comprise a combination of features and characteristics intended to address various shortcomings associated with certain prior devices, systems, and methods. The foregoing has outlined rather broadly the features and technical characteristics of the disclosed embodiments in order that the detailed description that follows may be better understood. The various characteristics and features described above, as well as others, will be readily apparent to those skilled in the art upon reading the following detailed description, and by referring to theaccompanying drawings. It should be appreciated that the conception and the specific embodiments disclosed may be readily utilized as a basis for modifying or designing other structures for carrying out the same purposes as the disclosed embodiments. It should also be realized that such equivalent constructions do not depart from the spirit and scope of the principles disclosed herein.BRIEF DESCRIPTION OF THE DRAWINGS

[0008] For a detailed description of exemplary embodiments of the disclosure, reference will now be made to the accompanying drawings in which:

[0009] FIGS. 1 and 2 depict block diagrams of immersion cooling systems according to some embodiments;

[0010] FIG. 3 depicts a block diagram of a computer system according to some embodiments; and

[0011] FIGS. 4 and 5 depict flowcharts of computer-implemented method for monitoring a coolant of an immersion cooling system according to some embodiments.DETAILED DESCRIPTION OF THE DISCLOSED EMBODIMENTS

[0012] The following discussion is directed to various exemplary embodiments. However, one skilled in the art will understand that the examples disclosed herein have broad application, and that the discussion of any embodiment is meant only to be exemplary of that embodiment, and not intended to suggest that the scope of the disclosure, including the claims, is limited to that embodiment.

[0013] Certain terms are used throughout the following description and claims to refer to particular features or components. As one skilled in the art will appreciate, different persons may refer to the same feature or component by different names. This document does not intend to distinguish between components or features that differ in name but not function. The drawing figures are not necessarily to scale. Certain features and components herein may be shown exaggerated in scale or in somewhat schematic form and some details of conventional elements may not be shown in interest of clarity and conciseness.

[0014] In the following discussion and in the claims, the terms "including" and "comprising" are used in an open-ended fashion, and thus should be interpreted to mean "including, but not limited to...” Also, the term "couple" or "couples" is intended to mean either an indirect or direct connection. Thus, if a first device couples to a second device, that connection may be through a direct connection, or through anindirect connection via other devices, components, and connections. In addition, as used herein, the terms "axial" and "axially" generally mean along or parallel to a central axis (e.g., central axis of a body or a port), while the terms "radial" and "radially" generally mean perpendicular to the central axis. For instance, an axial distance refers to a distance measured along or parallel to the central axis, and a radial distance means a distance measured perpendicular to the central axis.

[0015] As described above, data centers comprise specialized facilities that house sophisticated and powerful computer systems such as large quantity of IT equipment that consumes vast amounts of electrical power while generating substantial amounts of heat. Demand for processing power and storage has driven explosive growth in both hyperscale and enterprise data centers. This expansion reflects the proliferation of AI / ML training, 5G edge deployments, big-data analytics and the ongoing shift of enterprise workloads to colocation and cloud-provider facilities. Al in particular can be significantly power intensive generating significant amounts of heat that require more robust cooling solutions.

[0016] In order to manage the increasing amounts of heat generated by the computer systems of data centers, immersion cooling systems have been incorporated into such in which IT equipment of the data center are submerged directly into a coolant, such as a dielectric fluid. Immersion cooling systems may comprise single-phase systems where the coolant comprises and remains a liquid (the coolant does not undergo a phase change during operation), and two-phase configurations where at least a portion of the liquid coolant may evaporate during operation to leverage evaporation and condensation (e.g., facilitated by a separate heat exchanger) in transferring heat the from the IT equipment. As an example, in single-phase immersion cooling systems, dielectric liquids in the form of thermally conductive oils circulate around hot components, carrying heat to an external heat exchanger. Additionally, two-phase immersion cooling systems typically use an evaporative fluid such as a refrigerant that vaporizes at chip-level temperatures, rises to a condenser, then condenses and returns by gravity.

[0017] In addition, immersion cooling systems may utilize enclosed chassis, open baths, or hybrid techniques. Enclosed chassis immersion cooling systems typically include a plurality of separate chasses each having its own dielectric fluid or mechanism for independently controlling the liquid level within the given chassis such that multiple racks of servers stacked vertically therein. Such enclosed chassis maybe individually removed (e.g., horizontally) from a surrounding support structure for servicing of the IT equipment contained therein. Conversely, open bath immersion cooling systems typically include a common bath or tank in which different kinds of IT equipment share a common volume of dielectric fluid. Rather than being individually removable from a surrounding support structure and instead may be opened (e.g., from the top) to provide access to the IT equipment contained therein.

[0018] The condition of the dielectric fluid may be critical to the performance of the immersion cooling system in providing adequate cooling to the IT equipment of a given data center while maintaining appropriate electrical insulation of the IT equipment immersed in the dielectric fluid. Particularly, in addition to transferring heat from the IT equipment, the dielectric fluid must electrically insulate the IT equipment to prevent, for instance, electrical arcing and the like between electrically conductive components of the IT equipment. The dielectric constant (measured, e.g., in kilovolts per mm (kV / mm)) is often used to judge such electric ability of the dielectric fluid to prevent electrical arcing and related issues and defines the maximum electric field the dielectric fluid can withstand without breakdown. Over time, dissolved gases, water ingress, and / or byproducts of material leaching can reduce dielectric constant of the dielectric fluid, gradually lowering the breakdown threshold and ultimately necessitating replacement thereof.

[0019] With respect to heat transfer, thermal conductivity (measured, e.g., in Watts per meters-Kelvin (W / m-K)) and specific heat capacity (measured, e.g., in kilojoules per kilograms-Kelvin (kJ / kg-K)) generally define the capacity of the dielectric fluid to transfer heat from the IT equipment during operation. Over extended operation, particle contamination, fluid oxidation, and / or other issues can form micro-impurities that impede heat flow, reducing these values and impairing cooling performance unless addressed by filtration, fluid conditioning, or replacement. Thus, the presence of contaminants, whether resulting through ingress from an external source or produced by the dielectric fluid itself, can reduce the ability of the dielectric fluid to resist electrical breakdown and to transfer heat from the IT equipment immersed therein.

[0020] Other parameters in addition to those relating directly to electrical stability and heat transfer may impact the performance of the dielectric fluid of an immersion cooling system. For instance, minimum parameters for both flash point and auto ignition must generally be maintained for safe operation of the immersion coolingsystem. In addition, threshold parameters for acidity (e.g., hydrocarbon, natural esters, synthetic esters, fluorocarbons) must also generally be maintained for the dielectric fluid to prevent damaging the IT equipment or other components that come into direct contact with the dielectric fluid.

[0021] Given that immersion cooling systems generally comprise sealed or closed-loop systems that are inconvenient to access on the fly (e.g., without avoiding an interruption in the operation of the IT equipment cooled by said immersion cooling system) monitoring the condition of the dielectric fluid over time is a difficult and often impractical task. Due to these issues, conventionally, operators of immersion cooling systems may periodically condition and / or replace the dielectric fluid thereof periodically based off of limited laboratory testing and historical or institutional knowledge without directly monitoring the condition of the dielectric fluid during operation of the immersion cooling system. However, in order to avoid risking failure of the dielectric fluid, operators of immersion cooling systems are typically conservative when scheduling conditioning or replacement of the dielectric fluid thereof given their limited awareness of the current condition of the dielectric fluid. This may lead to the dielectric fluid being replaced before replacement is warranted based on its current condition, resulting in unnecessary shutdowns and other disturbances to the operation of the immersion cooling system which ultimately negatively impact the performance of the immersion cooling system in providing uninterrupted cooling to the IT equipment of a data center.

[0022] Accordingly, embodiments of immersion cooling systems including computer-implemented, automated coolant monitoring systems and methods are disclosed herein intended to permit the “online” monitoring of the condition of the dielectric fluid of immersion cooling systems without needing to interrupt or otherwise disturb the operation of the immersion cooling system. In this manner, conditioning or replacement of the dielectric fluid (and the concomitant interruption to the operation of the immersion cooling system) may be performed only when warranted based on the current or projected future condition of the dielectric fluid in view of the online monitoring of the condition of the dielectric fluid. Particularly, embodiments of coolant monitoring systems disclosed herein may actively monitor in real-time or near realtime (e.g., every second, every minute, every hour, and the like) a variety of parameters of the dielectric fluid including, for example, dielectric constant, volume resistivity, flash point, turbidity, auto ignition point, Sulphur content, acidity, thermalconductivity, specific heat capacity, and the like. Moreover, embodiments of coolant monitoring systems disclosed herein may permit or facilitate the projection of such parameters of the dielectric fluid at selected points in the future such that conditioning or replacement of the dielectric fluid may be conveniently scheduled beforehand to provide the data center with forewarning before interruption to the immersion cooing system thereof.

[0023] Referring initially to FIG. 1, an embodiment of an immersion cooling system 10 is shown for cooling IT equipment 3 of a data center 1. Immersion cooling system 10 generally includes an immersion vessel 12 filled at least partially with a coolant 14 (e.g., a dielectric fluid or liquid), a heat exchanger 20, a cooling unit 22, one or more coolant sensors 24, one or more sensor data transmitters 28, and a coolant monitoring system 50 in signal communication with the sensor data transmitter 28. IT equipment 3 of the data center 1 is received in the immersion vessel 12 and is at least partially submerged in the coolant 14 whereby heat generated by the IT equipment 3 may be transferred to the coolant 14. In some embodiments, immersion vessel 12 comprises one or more separate enclosed chasses, an open bath, or a hybrid solution. Additionally, in some embodiments, immersion cooling system 10 comprises a singlephase system in which coolant 14 remains as a liquid and does not undergo a phase change during the operation thereof. In other embodiments, immersion cooling system 10 comprises a to-phase immersion system in which at least a portion of the coolant 14 undergoes a phase change (e.g., evaporating and condensing) during the operation thereof.

[0024] During the operation of immersion cooling system 10, coolant 14 flows along a coolant loop 15 defined by the immersion vessel 12, a plurality of fluid conduits 16, and the heat exchanger 20. Particularly, heat is transferred from the IT equipment 3 received in immersion vessel 12 to the coolant 14, with heated coolant 14 flowing from the immersion vessel 12, through fluid conduits 16, and into the heat exchanger 20 where the heated coolant 14 is cooled or chilled. Subsequently, the chilled coolant 14 flows through fluid conduits 16 and returns to the immersion vessel 12 for cooling the IT equipment 3 therein. Additionally, a secondary coolant (e.g., water) is supplied to the heat exchanger 20 by the cooling unit 22 of immersion cooling system 10. Heat is transferred from the heated coolant 14 to the secondary coolant within heat exchanger 20, with the heated secondary coolant returning to the cooling unit 22 to be cooled therein. Heat may be transferred in cooling unit 22 from the heated secondary coolantto a heatsink such as the environment surrounding cooling unit 22. For instance, cooling unit 22 may comprise a cooling tower in some embodiments where heat from the heated secondary coolant is transferred to the ambient air.

[0025] In this exemplary embodiment, coolant sensor 24 is positioned along one or more of the fluid conduits 16 such that coolant 14 may pass by or through the coolant sensor 24 whereby the coolant sensor 24 may capture coolant data 25 that is reflective of one or more detectable properties of the coolant 14. Although coolant sensor 24 is positioned along fluid conduits 16 in this exemplary embodiment, in other embodiments, coolant sensor 24 may be positioned in alternative arrangements (e.g., within immersion vessel 12) whereby the coolant sensor 24 may measure detectable properties of the coolant sensor 24. Additionally, as used herein, the term “detectable properties” includes information directly gathered by the coolant sensor 24 from the coolant 14. The detectable properties captured in the coolant data 25 provided by coolant sensor 24 are associated with one or more coolant parameters 52 of the coolant 14 including, among others, a dielectric constant, an electrical conductivity, a turbidity, a contaminant content, an acidity, and a colorimetric parameter of the coolant 14. In some embodiments, coolant parameters 52 may quantify the presence of water, debris, and / or other contaminants in the coolant 14. In certain embodiments, coolant parameters 52 may also include such parameters as total acid number (TAN) and total base number (TBN) of the coolant 14. As will be discussed further herein, the coolant monitoring system 50 may estimate the coolant parameters 52 in real-time or near real-time based on the coolant data 25 provided by coolant sensor 24 and by utilizing enrichment data 40.

[0026] In some embodiments, the coolant sensor 24 may come into direct contact with the coolant 14 while in other embodiments coolant sensor 24 may not come into direct contact with coolant 14. The configuration and operation of the coolant sensor 24 may vary depending on the needs of the given application. In some embodiments, the coolant sensor 24 comprises an infrared sensor, a piezoelectric sensor, an optical sensor, and / or an electromagnetic transducer. Additionally, coolant sensor 24 may comprise a direct sensor that is installed directly inline with the flow of coolant 14 along coolant loop 15. In other embodiments, coolant sensor 24 may comprise a sensor box having a kidney loop system for circulating the coolant 14 therethrough. In still other embodiments, the coolant sensor 24 comprises an infrared sensor box including a Fourier-transform infrared spectroscopy (FTIR) sensor.

[0027] In some embodiments, the detectable properties of the coolant 14 measured by the coolant sensor 24 include, among others, temperature, humidity, bulk resistance, impedance, viscosity, dielectric constant, ferrous and non-ferrous contaminants, oxidation, nitration, sulfonation, and the like. In some embodiments, immersion cooling system 10 may include sensors in addition to coolant sensor 24 for measuring other parameters of immersion cooling system 10 including, for example, liquid level of coolant 14 within immersion vessel 12, flowrate of coolant 14 along coolant loop 15, and temperature within immersion vessel 12.

[0028] In this exemplary embodiment, coolant monitoring system 50 receives coolant data 25 from the coolant sensor 24 from which the coolant monitoring system 50 determines one or more coolant parameters 52 of the coolant 14 using enrichment data 40 (e.g., stored in a datastore or other structure of, or in signal communication with, coolant monitoring system 50). In some embodiments, the coolant monitoring system 50 may be at least partially located at the physical site of the immersion cooling system 10 while, in other embodiments, coolant monitoring system 50 may be at least partially located at a remote facility.

[0029] The coolant parameters 52 may be provided to a user 2 (e.g., an operator or other personnel associated with the immersion cooling system 10 and / or data center 1) of immersion cooling system 10 who may be located at the data center 1 or remote from the data center 1. Additionally, coolant sensor 24 is in signal communication with a sensor data transmitter 28 whereby the coolant data 25 may be provided or communicated to the coolant monitoring system 50 (e.g., wirelessly) across a network 30 such as, for example, the Internet. In this manner, sensor data transmitter 28 provides a local data transmitter through which each of the coolant data 25 and / or other sensor data of immersion cooling system 10 may be provided (e.g., in real-time or near real-time) to coolant monitoring system 50 generally irrespective of the physical locations of data center 1 / immersion cooling system 10 and coolant monitoring system 50.

[0030] In this exemplary embodiment, coolant monitoring system 50 receives via the network 30 the coolant data 25 captured by coolant sensor 24 of immersion cooling system 10. Although only a single data center 1 is shown in FIG. 1, coolant monitoring system 50 may be similarly connected via network 30 with a plurality (e.g., tens, hundreds, or thousands) of different data centers 1 each having its own immersion cooling system 10 and which may vary in configuration from the data center 1 shownin FIG. 1. In this arrangement, coolant monitoring system 50 may receive in parallel and in real-time or near real-time a plurality of separate sensor datastreams associated with different immersion cooling systems including the immersion cooling system 10 shown in FIG. 1.

[0031] Generally, coolant monitoring system 50 generates or determines the coolant parameters 52 of the coolant 14 (and / or other coolants of immersion cooling system 10 and / or other data centers besides immersion cooling system 10) based on both the collection of coolant data 25 obtained from the coolant sensor 24 of immersion cooling system 10 along with enrichment data 40 that may be obtained separately from the particular immersion cooling system 10. In other words, coolant monitoring system 50 may determine the coolant parameters 52 of coolant 14 by integrating the coolant data 25 obtained from coolant sensor 24 with the enrichment data 40 to thereby enrich the coolant data 25 to permit the determination of coolant parameters 52. For example, in some embodiments, coolant monitoring system 50 comprises a predictive model for determining the coolant parameters 52 based on coolant data 25 where the predictive model may be trained using the enrichment data 40 or otherwise configured to integrate the enrichment data 40 with the coolant data 25. Thus, in some embodiments, at least some of the enrichment data 40 may configure a predictive model (e.g., define weights of the predictive model) of the coolant monitoring system 50 for determining coolant parameters 52 in real-time (e.g., in intervals less than 15 minutes in duration) or near real-time (e.g., in intervals approximately between 15 minutes and one hour in duration) based on the coolant data 25 (and / or other tertiary sensor data).

[0032] In some embodiments, the different coolant parameters 52 may be presented or indicated (e.g., via a computer-implemented user interface) as a nondimensional coolant score 54 (e.g., a dielectric score, a thermal conductivity score, a specific heat capacity score, an acidity score, a water content score, a debris score, a viscosity score, a thermal conductivity score, and the like) to the user 2 rather than as a dimensional unit to make the coolant scores 54 more readily intelligible to the user 2. For instance, the coolant parameters 52 may each be scaled using a baseline parameter. The baseline parameter may be indicative of issue free performance of the coolant 14 prior to any degradation to the coolant 14 whereby the baseline parameter may be used as a benchmark with the coolant scores 54 reflecting a degree of divergence from the benchmark established by the baseline parameter.However, the configuration of the baseline parameter may vary in other embodiments. Additionally, the coolant scores 54 may be updated automatically in real-time or near real-time as coolant data 25 is received by the coolant monitoring system 50 from the immersion cooling system 10. In some embodiments, the coolant scores 54 (or the coolant parameters 52 themselves) may comprise rolling averages having a temporal window size defined by the provider of coolant monitoring system 50 and / or by the user 2.

[0033] In some embodiments, coolant monitoring system 50 may additionally be configured to provide the user 2 with a coolant alert 56 in response to one of the coolant scores 54 equaling or exceeding a predefined threshold. For instance, a dielectric alert may be provided automatically (e.g., in real-time or near real-time) in response to a dielectric constant or related dielectric parameters of coolant parameters 52 equally or falling below a predefined threshold to thereby alert the user 2 of the issue before damage may occur to the IT equipment 3 cooled by the immersion cooling system 10.

[0034] As described above, coolant monitoring system 50 leverages enrichment data 40 in order to determine the coolant parameters 52 from the immersion cooling system 10 supplied coolant data 25. Particularly, the enrichment data 40 may include data characterizing (e.g., correlating) the relationships between the sensor data obtained from immersion cooling system 10 with the coolant parameters 52. For example, enrichment data 40 may model, based on historical data obtained through previous offsite laboratory testing, the relationship between data similar to coolant data 25 (e.g., temperature, humidity, bulk resistance, impedance, viscosity, dielectric constant, ferrous and non-ferrous contaminants, oxidation, nitration, sulfonation, and the like) and coolant parameters 52. Thus, in some embodiments, at least some of the enrichment data 40 provided to coolant monitoring system 50 comprises coolant enrichment data that models or otherwise characterizes the coolant 14 itself and may be obtained via historical data collected through past testing (e.g., performed using an offsite laboratory) of coolant 14 or other coolants having at least some features in common with coolant 14.

[0035] Referring to FIG. 2, another embodiment of an immersion cooling system 100 is shown. Immersion cooling system 100 may include features in common with the immersion cooling system 10 shown in FIG. 1, and shared features are labeled similarly. In this exemplary embodiment, immersion cooling system 100 generallyincludes one or more coolant sensors 104 each associated with coolant 106 of the immersion cooling system 100 (e.g., cooling IT equipment of a data center such as data center 1 shown in FIG. 1), and a coolant monitoring system 150 in signal communication with the coolant sensors 104 and the asset sensors 108 via, for example, the network 30. Coolant sensors 104 and asset sensors 108 may each be located at (e.g., co-located with) the immersion cooling system 100 such as being located at a physical facility defining the immersion cooling system 100. Conversely, in this exemplary embodiment, coolant monitoring system 150 may be at least partially located at one or more sites other than the site of the immersion cooling system 100 (e.g., a data center housing the immersion cooling system 100). In other embodiments, coolant monitoring system 150 may instead be entirely located at the site (e.g., the physical facility) of immersion cooling system 100.

[0036] Coolant sensors 104 may measure coolant data (e.g., coolant 25 shown in FIG.1) of the coolant 106. Additionally, while only a single immersion cooling system 100 is shown in FIG. 2, immersion cooling system 100 may include multiple sets of coolant sensors 104 associated with a plurality of separate data centers 102. In some embodiments, coolant sensors 104 may include coolant sensor 24 shown in FIG. 1; however, in other embodiments, the configuration of coolant sensors 104 may vary.

[0037] In this exemplary embodiment, immersion cooling system 100 additionally includes a coolant or sensor data transmitter 120 connected or otherwise in signal communication with each of the coolant sensors 104 of the immersion cooling system 100. Sensor data transmitter 120 may facilitate the transmission of coolant data captured by coolant sensors 104 across the network 30 to the coolant monitoring system 150. For example, the sensor data transmitter 120 may selectably adjust a sampling rate (e.g., reduce the sampling rate to limit computational demands on the ingestion engine 152 or network bandwidth limitations across network 30), a transmission or network protocol, or other parameters of the data captured by coolant sensors 104 as part of facilitating the external transmission and / or eventual storage of the captured coolant data. In some embodiments, the sensor data transmitter 120 comprises an edge computing gateway that provides the coolant data captured by coolant sensors 104 to the network 30 using a selected network protocol such as MQTT and the like. In addition, sensor data transmitter 120 may aggregate the various datastreams or sets captured by the coolant sensors 104 when facilitating the transmission of said captured coolant data to the coolant monitoring system 150.

[0038] In this exemplary embodiment, the coolant monitoring system 150 of immersion cooling system 100 generally includes a coolant data ingestion engine 152, a coolant data enrichment engine 170, a predictive coolant model 185, and a user interface 190 that provides a gateway for users 2 of immersion cooling system 100 to access the coolant monitoring system 150. For instance, the user interface 190 may comprise one or more input / output devices using which the user 2 may access information from the coolant monitoring system 150 and / or provide information (e.g., one or more selected user inputs) to the coolant monitoring system 150.

[0039] The coolant data ingestion engine 152 (or simply “ingestion engine 152”) of coolant monitoring system 150 receives information provided by the sensor data transmitter 120 (e.g., via network 30) as an aggregated datastream (indicated by arrow 122 in FIG. 2) that captures and aggregates the coolant data captured by coolant sensors 104. Although only a single aggregated coolant datastream 122 is shown in FIG. 2, in some embodiments, ingestion engine 152 may receive a plurality of separate aggregated coolant datastreams 122 each associated with, for example, a unique immersion cooling system 100. Aggregated coolant datastream 122 may aggregate various kinds of different coolant data including both equipment data captured by coolant sensors 104.

[0040] In this exemplary embodiment, ingestion engine 152 generally includes a raw data ingestion engine or ingester 154, a data enriching engine or enricher 158, and an enriched data ingestion engine or ingester 160. Raw data ingester 154 receives aggregated coolant datastreams 122 from the coolant data transmitters 120 of one or more different data centers 102. Raw data ingester 154 may initially process the received aggregated coolant datastreams 122 whereby said aggregated coolant datastreams 122 may be transformed and / or filtered to provide a raw or ingested coolant datastream (indicated by arrows 155 in FIG. 2) for further processing and / or storage. In this exemplary embodiment, raw data ingester 153 stores the ingested datastream 155 in a raw data store 156 of the ingestion engine 152. In some embodiments, aggregated coolant datastream 122 may be provided to the raw data ingester 154 continuously or in batches of a predefined (e.g., user-selected) temporal size or duration (e.g., an hourly batch, a daily batch, and the like).

[0041] Raw data ingester 154 provides the ingested datastream 155 to the data enricher 158 which produces an enriched datastream or simply “enriched data” (indicated by arrow 159 in FIG. 2) from the ingested datastream 155. As used herein,the terms “enriched data” and “enriched datastream” is defined herein as data or datastreams that have been filtered to eliminate or at least mitigate spurious or erroneous data. For instance, enriched data may be generated from raw data by averaging a predefined number of datapoints of the raw data to produce a plurality of condensed datapoints of the enriched data. As another example, predefined data limits may be utilized to filter datapoints of the raw data that fall outside of said data limits which indicate that the filtered raw data is likely spurious. As a further example, rate-of-change analysis may be applied to the datapoints of the raw data to identify datapoints that exceed a predefined rate-of-change threshold. Such identified datapoints may generate an alert or may be filtered from the enriched data.

[0042] In some embodiments, the enriched datastream 159 includes estimates of one or more coolant parameters of one or more coolants of one or more corresponding data centers 102 and which are indicative of the state, quality, and / or current performance of the coolant. For instance, the estimated coolant parameters captured in the enriched datastream 159 may include, amount other parameters, dielectric constant, an electrical conductivity, a turbidity, a contaminant content, an acidity, a colorimetric parameter, a TAN, and / or TBN.

[0043] As will be discussed further herein, the data enricher 158 additionally receives enrichment datastream 180 from the coolant data enrichment engine 170 (or simply “enrichment engine 170”) of coolant monitoring system 150. Particularly, data enricher 158 integrates the ingested datastream 155 received from raw data ingester 154 with the enrichment datastream 180 provided by enrichment engine 170 to produce the enriched datastream 159 containing the estimated coolant parameters. For example, the data enricher 158 may apply the enrichment datastream 180 to the ingested datastream 155 and / or vice-a-versa to produce the enriched datastream 159. In some embodiments, the enriched datastream 159 is provided in real-time or near real-time following the original collection of the coolant data by the coolant sensors 104 of the given immersion cooling system 100.

[0044] In this exemplary embodiment, the enriched data ingester 160 of ingestion engine 152 receives the enriched datastream 159 produced by data enricher 158 and subjects the enriched datastream 159 to additional processing whereby said enriched datastream 159 may be analyzed, transformed, and / or filtered to provide an output datastream (indicated by arrows 161 in FIG. 2) that forms an output of the ingestion engine 152. In this exemplary embodiment, enriched data ingester 160 stores theoutput datastream 161 in an output data store 162 of the ingestion engine 152 that may, in certain embodiments, be externally accessible such as via network 30. In some embodiments, enriched datastream 159 may be provided to the enriched data ingester 160 continuously or in batches of a predefined (e.g., user-selected) temporal size or duration (e.g., an hourly batch, a daily batch, and the like).

[0045] The output datastream 161 is based on the enriched datastream 159 and may include the estimated coolant parameters captured in the enriched datastream 159. Additionally, in some embodiments, output datastream 161 may include information beyond that contained in the enriched datastream 159 including, for example, one or more coolant scores associated with the given coolant of the immersion cooling system 100 and / or one or more coolant alerts also associated with the given coolant. For example, the output datastream 161 may include coolant scores similar to the coolant scores 54 shown in FIG. 1. Similarly, in certain embodiments, the output datastream 161 may include coolant alerts similar to the coolant alerts 56 shown in FIG. 1.

[0046] In this exemplary embodiment, the output datastream 161 produced by enriched data ingester 160 is provided to the user interface 190 whereby it may be accessed by the user 2. For example, the user 2 may utilize the user interface 190 to receive one or more coolant alarms, and / or to review one or more coolant scores or coolant parameters captured in the output datastream 161. Additionally, in some embodiments, the user 2 may input via the user interface 190 information to the coolant monitoring system 150 including the ingestion engine 152. For instance, the user 2 may provide one or more user-selected thresholds used as triggers for the one or more coolant alerts contained in the output datastream 161. In another example, the user 2, via the user interface 190, may define a batch size of the enriched datastream 159 and / or other operational parameters of the ingestion engine 152 including providing user-selected parameters used to determine coolant scores contained in the output datastream 161. In a further example, the user 2, via the user interface 190, may define a window size (e.g., a temporal window size having a defined, user-selected duration) for determining the one or more coolant scores from the enriched datastream 159.

[0047] In this exemplary embodiment, the enrichment engine 170 of coolant monitoring engine 170 generally includes one or more enrichment function 178 that provide, as an output of the enrichment engine 170, the enrichment datastream 180 tothe data enricher 158 of ingestion engine 152. Additionally, although predictive coolant model 185 is shown as separate from enrichment engine 170 in FIG. 2, in other embodiments, predictive coolant model 185 may comprise a component or feature of the enrichment engine 170. For example, the enrichment function 178 and predictive coolant model 185 may work together or jointly to produce the enrichment datastream 180 provided to ingestion engine 152.

[0048] Enrichment function 178 receives secondary, tertiary, and / or other contextual information from which enrichment datastream 180 is based or determined. For instance, in this exemplary embodiment, enrichment function 178 receives (or is permitted to access) a laboratory dataset 172, an asset dataset 174, and / or an enrichment ruleset 176. In other embodiments, the sources of contextual information from which enrichment function 178 draws may vary from that shown in FIG. 2. For example, in other embodiments, enrichment function 178 may not receive laboratory dataset 172 and / or asset dataset 174, and / or may receive additional datasets not shown in FIG. 2. In some embodiments, the laboratory dataset 172 includes historical data obtained, for example, through previous or historical offsite laboratory testing that models or otherwise relates coolant data such as previously captured historical coolant data (e.g., historical coolant data associated with one or more historical coolants) similar in nature to the coolant data captured in the aggregated coolant datastream 122 and ingested datastream 155. In other words, the laboratory dataset 172 may at least partially explain or characterize the relationship between coolant data (e.g., coolant data captured by sensors like coolant sensors 104) and coolant parameters of a selected coolant of the immersion cooling system 100.

[0049] The asset dataset 174 also includes historical data but unlike the laboratory dataset 172 which is directly relates to the selected coolant (or historical data associated with similar coolants), asset dataset 174 pertains instead generally to the immersion cooling system 100 (including selected coolant 106 thereof) rather than specifically to coolant the contained in the immersion cooling system 100. In other words, the historical data specific to the selected immersion cooling system 100 contained in asset dataset 174 may contextualize the relationships (e.g., between coolant data and coolant parameters) characterized by laboratory dataset 172.

[0050] For instance, the operating conditions of immersion cooling system 100 generally as well as the operating conditions of selected pieces of coolant 106 may, in addition to the coolant data, affect the coolant parameters of a given coolant of theimmersion cooling system 100. As an example, the (historically and / or currently) operating temperature, pressure, and the like of coolant 106 may impact the coolant parameters of coolant of the coolant 106 in ways not fully captured or explained by coolant data collected from the given coolant. As an additional example, the (historically and / or current) RH, temperature, and the like of immersion cooling system 100 may also impact the coolant parameters of coolant of the coolant 106 in ways not fully captured or explained by coolant data collected from the given coolant 106. The relationships between such secondary, tertiary, and / or other contextual data (e.g., operating parameters beyond coolant data of coolant 106, generalized or ambient conditions of immersion cooling system 100) and the current coolant parameters of a selected coolant of the immersion cooling system 100 may be at least partially explained or characterized by asset dataset 174 to assist in calibrating estimates of the current coolant parameters of coolant 106 that are obtained using the laboratory dataset 172.

[0051] The enrichment ruleset 176 may contain predefined (e.g., at least partially user-selected) instructions for the enrichment function 178 of enrichment engine 170 for integrating the information contained in the laboratory dataset 172 and asset dataset 174 in producing enrichment datastream 180. For example, enrichment ruleset 176 may define the influence to be accorded to the different datasets 172 and 174 in producing enrichment datastream 180, and determine when particular relationships or models between variables (e.g., relationships between selected primary, secondary, and / or tertiary data and one or more coolant parameters) should be leveraged in producing enrichment datastream 180.

[0052] Although enrichment ruleset 176 is shown in FIG. 2 as separate from enrichment function 178, in other embodiments, enrichment ruleset 176 may comprise a feature or component of enrichment function 178. Additionally, in other embodiments, enrichment engine 170 may not include both laboratory dataset 172 and asset dataset 174, and instead, enrichment datastream 180 may be based only on the laboratory dataset 172 or the asset dataset 174. In still other embodiments, additional datasets other than datasets 172 and 174 may be leveraged by enrichment function 178 in producing enrichment data 180.

[0053] In this exemplary embodiment, predictive coolant model 185 is additionally leveraged by enrichment engine 170 in producing enrichment data 180. In some embodiments, predictive coolant model 185 comprises a machine learning (ML) orartificial intelligence (Al) model such as a classification model and the like that is trained on data provided by enrichment engine 170. For example, predictive coolant model 185 may be trained using the historical data contained in the laboratory dataset 172, asset dataset 174, and / or additional datasets provided by enrichment engine 170. In some embodiments, predictive coolant model 185 may be trained or tuned using additional sources of data including, for example, ingested datastream 155, enriched datastream 159, and / or output datastream 161. In this exemplary embodiment, predictive coolant model 185 may provide its own coolant alerts (indicated by arrow 186 in FIG. 2) to the user interface 190 to warn the user 2 of significant or material changes to one or more coolant parameters of the coolant 106 of immersion cooling system 100. Additionally, in some embodiments, the user 2 may interact or adjust parameters (e.g., hyperparameters) of the predictive coolant model 185 via the user interface 190.

[0054] As described above, the user 2 may receive coolant alerts from the ingestion engine 152 and / or predictive coolant model 185 in this exemplary embodiment whereby the end-user may swiftly act to address the issues raised in the coolant alerts. For example, the user 2 may adjust the operation of the immersion cooling system 100 such as by, for instance, adjusting one or more operational parameters of coolant 106 of immersion cooling system 100. In another example, the user 2 may replace or redress the coolant of immersion cooling system 100 to which the coolant alert pertains in response to receiving such via user interface 190. In this manner, the user may act substantially more quickly (e.g., in real-time or near real-time) to limit or prevent undesirable issues associated with the coolant alerts provided thereto compared to conventional laboratory analysis of the selected coolants.

[0055] Referring now to FIG. 3, an embodiment of a computer system 300 is shown suitable for implementing one or more components disclosed herein. As an example, computer system 300 may be used to execute various embodiments of coolant monitoring systems (e.g., coolant monitoring systems 50 and 150 shown in FIGS. 1 and 2, respectively) disclosed herein.

[0056] The computer system 300 of FIG. 9 generally includes a processor 302 (which may be referred to as a central processor unit or CPU) that is in communication with memory devices including secondary storage 304, read only memory (ROM) 306, random access memory (RAM) 308, input / output (I / O) devices 310, and network connectivity devices 312. The processor 302 may be implemented as one or moreCPU chips. It is understood that by programming and / or loading executable instructions onto the computer system 300, at least one of the CPU 302, the RAM 308, and the ROM 306 are changed, transforming the computer system 300 in part into a particular machine or apparatus having the novel functionality taught by the present disclosure.

[0057] Additionally, after the computer system 300 is turned on or booted, the CPU 302 may execute a computer program or application. For example, the CPU 302 may execute software or firmware stored in the ROM 306 or stored in the RAM 308. In some cases, on boot and / or when the application is initiated, the CPU 302 may copy the application or portions of the application from the secondary storage 304 to the RAM 308 or to memory space within the CPU 302 itself, and the CPU 302 may then execute instructions that the application is comprised of. In some cases, the CPU 302 may copy the application or portions of the application from memory accessed via the network connectivity devices 312 or via the I / O devices 310 to the RAM 308 or to memory space within the CPU 302, and the CPU 302 may then execute instructions that the application is comprised of. During execution, an application may load instructions into the CPU 302, for example load some of the instructions of the application into a cache of the CPU 302. In some contexts, an application that is executed may be said to configure the CPU 302 to do something, e.g., to configure the CPU 302 to perform the function or functions promoted by the subject application. When the CPU 302 is configured in this way by the application, the CPU 302 becomes a specific purpose computer or a specific purpose machine.

[0058] Secondary storage 304 may be used to store programs which are loaded into RAM 308 when such programs are selected for execution. The ROM 306 is used to store instructions and perhaps data which are read during program execution. ROM 306 is a non-volatile memory device which typically has a small memory capacity relative to the larger memory capacity of secondary storage 304. The secondary storage 304, the RAM 308, and / or the ROM 306 may be referred to in some contexts as computer readable storage media and / or non -transitory computer readable media. I / O devices 310 may include printers, video monitors, liquid crystal displays (LCDs), touch screen displays, keyboards, keypads, switches, dials, mice, track balls, voice recognizers, card readers, paper tape readers, or other well-known input devices.

[0059] The network connectivity devices 312 may take the form of modems, modem banks, Ethernet cards, universal serial bus (USB) interface cards, wireless local areanetwork (WLAN) cards, radio transceiver cards, and / or other well-known network devices. The network connectivity devices 312 may provide wired communication links and / or wireless communication links. These network connectivity devices 312 may enable the processor 302 to communicate with the Internet or one or more intranets. With such a network connection, it is contemplated that the processor 302 might receive information from the network, or might output information to the network. Such information, which may include data or instructions to be executed using processor 302 for example, may be received from and outputted to the network, for example, in the form of a computer data baseband signal or signal embodied in a carrier wave.

[0060] The processor 302 executes instructions, codes, computer programs, scripts which it accesses from hard disk, floppy disk, optical disk, flash drive, ROM 306, RAM 308, or the network connectivity devices 312. While only one processor 302 is shown, multiple processors may be present. Thus, while instructions may be discussed as executed by a processor, the instructions may be executed simultaneously, serially, or otherwise executed by one or multiple processors. Instructions, codes, computer programs, scripts, and / or data that may be accessed from the secondary storage 304, for example, hard drives, floppy disks, optical disks, and / or other device, the ROM 306, and / or the RAM 308 may be referred to in some contexts as non-transitory instructions and / or non-transitory information.

[0061] In an embodiment, the computer system 300 may comprise two or more computers in communication with each other that collaborate to perform a task. For example, but not by way of limitation, an application may be partitioned in such a way as to permit concurrent and / or parallel processing of the instructions of the application. Alternatively, the data processed by the application may be partitioned in such a way as to permit concurrent and / or parallel processing of different portions of a dataset by the two or more computers. In an embodiment, the functionality disclosed above may be provided by executing the application and / or applications in a cloud computing environment. Cloud computing may comprise providing computing services via a network connection using dynamically scalable computing resources.

[0062] Referring now to FIG. 4, an embodiment of a computer-implemented method 350 for monitoring a coolant (e.g., coolant 14 shown in FIG. 1, coolant 106 shown in FIG. 2) of an immersion cooling system (e.g., immersion cooling system 10 shown in FIG. 1, immersion cooling system 100 shown in FIG. 2). Initially, at block 352, method350 comprises receiving, by a coolant monitoring system (e.g., coolant monitoring system 50 shown in FIG. 1, coolant monitoring system 150 shown in FIG. 2), sensor data (e.g., coolant data 25 shown in FIG. 1, data of coolant 106 shown in FIG. 2) captured by one or more sensors (e.g., coolant sensor 24 shown in FIG. 1, coolant sensors 104 shown in FIG. 2) of the immersion coolant system, the sensor data reflective of one or more detectable properties of the coolant.

[0063] At block 354, method 350 comprises receiving by the coolant monitoring system enrichment data (e.g., enrichment data 40 shown in FIG. 1, enrichment datastream 180 shown in FIG. 2) characterizing a relationship between historical sensor data and one or more coolant parameters (e.g., coolant parameters 52 shown in FIG. 1) of one or more historical coolants. At block 356, method 350 comprises integrating by the coolant monitoring system the sensor data with the enrichment data (e.g., enriched datastream 159 shown in FIG. 2) to provide enriched data estimating one or more of the coolant parameters of the coolant. At block 358, method 350 comprises providing by the coolant monitoring system information to a user that is based on or contains the enriched data.

[0064] Referring now to FIG. 5, another embodiment of a computer-implemented method 370 for monitoring a coolant (e.g., coolant 14 shown in FIG. 1, coolant 106 shown in FIG. 2) of an immersion cooling system (e.g., immersion cooling system 10 shown in FIG. 1, immersion cooling system 100 shown in FIG. 2). Initially, at block 372, method 370 comprises receiving, by a coolant monitoring system (e.g., coolant monitoring system 50 shown in FIG. 1, coolant monitoring system 150 shown in FIG.2), sensor data (e.g., coolant data 25 shown in FIG. 1, data of coolant 106 shown in FIG. 2) captured by one or more sensors (e.g., coolant sensor 24 shown in FIG. 1, coolant sensors 104 shown in FIG. 2) of the immersion coolant system, the sensor data reflective of one or more detectable properties of the coolant.

[0065] At block 374, method 370 comprises receiving by the coolant monitoring system enrichment data (e.g., enrichment data 40 shown in FIG. 1, enrichment datastream 180 shown in FIG. 2) characterizing a relationship between historical sensor data and one or more coolant parameters (e.g., coolant parameters 52 shown in FIG. 1) of one or more historical coolants. At block 376, method 370 comprises integrating by the coolant monitoring system the sensor data with the enrichment data to provide enriched data (e.g., enriched datastream 159 shown in FIG. 2) estimating one or more of the coolant parameters of the coolant. At block 378, method 370comprises determining by the coolant monitoring system a coolant score (e.g., coolant score 54 shown in FIG. 1) based on the one or more coolant parameters.

[0066] While embodiments of the disclosure have been shown and described, modifications thereof can be made by one skilled in the art without departing from the scope or teachings herein. The embodiments described herein are exemplary only and are not limiting. Many variations and modifications of the systems, apparatus, and processes described herein are possible and are within the scope of the disclosure. For example, the relative dimensions of various parts, the materials from which the various parts are made, and other parameters can be varied. Accordingly, the scope of protection is not limited to the embodiments described herein, but is only limited by the claims that follow, the scope of which shall include all equivalents of the subject matter of the claims. Unless expressly stated otherwise, the steps in a method claim may be performed in any order. The recitation of identifiers such as (a), (b), (c) or (1), (2), (3) before steps in a method claim are not intended to and do not specify a particular order to the steps, but rather are used to simplify subsequent reference to such steps.

Claims

1. CLAIMSWhat is claimed is:

1. A computer-implemented method for monitoring a coolant of an immersion cooling system, the method comprising:(a) receiving, by a coolant monitoring system, sensor data captured by one or more sensors of an immersion coolant system, the sensor data reflective of one or more detectable properties of the coolant;(b) receiving, by the coolant monitoring system, enrichment data characterizing a relationship between historical sensor data and one or more coolant parameters of one or more historical coolants;(c) integrating, by the coolant monitoring system, the sensor data with the enrichment data to provide enriched data estimating one or more of the coolant parameters of the coolant; and(d) providing, by the coolant monitoring system, information to a user that is based on or contains the enriched data.

2. The method of claim 1, wherein (d) comprises providing by the coolant monitoring system a coolant alert to a user that is based on the one or more coolant parameters.

3. The method of claim 2, wherein the coolant alert is provided to the user in at least one of real-time or near real-time following (a).

4. The method of claim 2, wherein (d) comprises providing by a trained predictive coolant model of the coolant monitoring system the coolant alert.

5. The method of claim 4, wherein the trained predictive coolant model is trained at least partially using the enrichment data.

6. The method of claim 1 , wherein the one or more sensors are in contact with the coolant.

7. The method of claim 1 , wherein the one or more sensors are configured to emit a signal that travels through the coolant.

8. The method of claim 1 , wherein the one or more sensors comprise at least one of an infrared sensor, a piezoelectric sensor, an optical sensor, or an electromagnetic transducer.

9. The method of claim 1 , wherein the one or more coolant parameters comprises at least one of a dielectric constant, an electrical conductivity, a turbidity, a contaminant content, an acidity, or a colorimetric parameter.

10. The method of claim 1 , wherein the coolant comprises a dielectric fluid.

11. A computer-implemented method for monitoring a coolant of an immersion cooling system, the method comprising:(a) receiving, by a coolant monitoring system, sensor data captured by one or more sensors of an immersion coolant system, the sensor data reflective of one or more detectable properties of the coolant;(b) receiving, by the coolant monitoring system, enrichment data characterizing a relationship between historical sensor data and one or more coolant parameters of one or more historical coolants;(c) integrating, by the coolant monitoring system, the sensor data with the enrichment data to provide enriched data estimating one or more of the coolant parameters of the coolant; and(d) determining, by the coolant monitoring system, a coolant score based on the one or more coolant parameters.

12. The method of claim 11 , wherein the enrichment data comprises at least one of laboratory data containing historical coolant data, or asset data containing historical data pertaining to the immersion cooling system other than the coolant data.

13. The method of claim 11, wherein (d) comprises providing by the coolant monitoring system the coolant score to a user.

14. The method of claim 13, wherein the coolant score is provided to the user in at least one of real-time or near real-time following (a).

15. A computer-readable medium storing executable code which, when executed by a processor, causes the processor to:receive, by a coolant monitoring system, sensor data captured by one or more sensors of an immersion coolant system, the sensor data reflective of one or more detectable properties of the coolant;receive, by the coolant monitoring system, enrichment data characterizing a relationship between historical sensor data and one or more coolant parameters of one or more historical coolants; andintegrate, by the coolant monitoring system, the sensor data with the enrichment data to provide enriched data estimating one or more of the coolant parameters of the coolant.

16. The computer-readable medium of claim 15, wherein the executable code, when executed by the processor, causes the processor to:Provide, by the coolant monitoring system, information to a user that is based on or contains the enriched data.

17. The computer-readable medium of claim 15, wherein the executable, when executed by the processor, causes the processor to:provide a coolant alert to a user that is based on the one or more coolant parameters.

18. The computer-readable medium of claim 15, wherein the executable code, when executed by the processor, causes the processor to:determine a coolant score based on the one or more coolant parameters.

19. An immersion cooling system for cooling electrical equipment, the immersion cooling system comprising:a tank having an interior tillable with a coolant and configured to receive electrical equipment whereby the electrical equipment is immersible in the coolant within the interior;a heat exchanger in fluid communication with the interior of the tank, the heat exchanger configured to exchange heat between the coolant and an external heat sink;a circulation device coupled between the tank and the heat exchanger to circulate the coolant between the tank and the heat exchanger;one or more sensors to output sensor data reflective of one or more detectable properties of the coolant; anda computer system comprising a processor and the computer-readable medium of claim 15.

20. The immersion cooling system of claim 19, wherein the one or more sensors comprise at least one of an infrared sensor, a piezoelectric sensor, an optical sensor, or an electromagnetic transducer.27

Images