IP Library › Granted Patent US 12,508,853
Granted Patent B2
US 12,508,853 · App. 17/449,106 · Granted Dec 30, 2025

Proactive cooling system

Inventors: Venkata Vara Prasad Karri (Visakhapatnam, IN); Saraswathi Sailaja Perumalla (Visakhapatnam, IN); Sarbajit K. Rakshit (Kolkata, IN); Suresh Nagulakonda (Visakhapatnam, IN)
Assignee: International Business Machines Corporation
B60C23/19B60C23/20G01C21/3461G01C21/3605G06N20/00
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Quick Facts
Patent No.
US 12,508,853
App. No.
17/449,106
Granted
Dec 30, 2025
Kind
B2
Abstract

Aspects of this disclosure include a method for proactive cooling, a vehicle, and a computer program product for a proactive cooling system. One embodiment of the method may comprise creating a machine learning model of a physical object, and instrumenting the physical object with sensors to generate real-time data about the physical object and its operational environment. The method may further comprise generating, with the machine learning model, a predicted future temperature for the physical object using the real-time data about the physical object and its operational environment, and in response to the predicted future temperature exceeding a threshold temperature within a threshold time period, proactively cooling the physical object.

Claims (37)

1 . A method for proactive cooling, comprising:

creating a machine learning model of a physical object;

instrumenting the physical object with sensors to generate real-time data about the physical object and its operational environment;

generating, with the machine learning model, a predicted future temperature for the physical object using the real-time data about the physical object and its operational environment; and

in response to the predicted future temperature exceeding a threshold temperature within a threshold time period, proactively cooling the physical object.

2 . The method of claim 1 , wherein the physical object comprises a tire for a vehicle.

3 . The method of claim 1 , further comprising:

receiving a destination from a driver of the vehicle; and

calculating a route to the destination, wherein the real-time data includes a future weather parameter along the route and a future road parameter along the route.

4 . The method of claim 3 , wherein the real-time data further includes a number of turns in a segment of the route.

5 . The method of claim 2 , wherein creating the machine learning model comprises creating a digital twin model of the tire, wherein:

the digital twin model simulates heat flux into the tire and heat generated by the tire; and

the threshold temperature is a temperature at which the tire will be at risk of damage.

6 . The method of claim 5 , further comprising, in response to the digital twin model predicting a future temperature above a temperature at which the tire will be damaged, proactively enabling a venturi effect-based air jet cooling system against the tire, wherein the proactive cooling provides thermal stability of the tire.

7 . The method of claim 6 , wherein proactively enabling the venturi effect-based air jet cooling system comprises opening an aperture to increase an amount air flowing through the venturi effect-based air jet cooling system.

8 . The method of claim 7 , wherein the venturi effect-based air jet cooling system is directed tangentially to the tire below a center of rotation.

9 . The method of claim 7 , further comprising calculating, by the digital twin model, how long the venturi effect-based cooling system is to be applied to the tire, and in response, closing the aperture.

10 . The method of claim 9 , wherein calculating how long the venturi effect-based cooling system is active is based at least in part on a current speed of the vehicle.

11 . The method of claim 6 , wherein proactively enabling the venturi effect-based air jet cooling system comprises dynamically increasing a number of jets in the venturi effect-based air jet cooling system applying cooling to the tire.

12 . The method of claim 6 , wherein the venturi effect-based air jet cooling system comprises an array of venturi tunnels in front of the vehicle.

13 . The method of claim 3 , further comprising displaying a thermal parameter of the tire to the driver before and after proactively cooling the physical object.

14 . The method of claim 13 , further comprising recommending that the driver stop based on a current temperature of the tire and a rate at which the tire is heating.

15 . The method of claim 13 , further comprising recommending proactive maintenance based on a change in a pattern of the thermal parameter of the tire.

16 . The method of claim 2 , wherein proactively cooling the tire of the vehicle comprises activating a consumable-free cooling system.

17 . The method of claim 1 , wherein the physical object comprises a brake assembly for a vehicle.

18 . A vehicle, comprising:

a wheel assembly;

a plurality of sensors that generate real-time data about the wheel assembly and its operational environment;

a processor; and

a memory coupled to the processor, wherein the memory contains program instructions executable by the processor to cause the processor to:

generate, with a digital twin model of the wheel assembly, a predicted future temperature for the wheel assembly using the real-time data about the wheel assembly and its operational environment; and

in response to the predicted future temperature exceeding a threshold temperature within a threshold time period, proactively cooling the wheel assembly.

19 . The vehicle of claim 18 , wherein proactively cooling the wheel assembly comprises selectably enabling a venturi effect-based air jet cooling system to direct cooled air against the wheel assembly.

20 . A computer program product for a proactive cooling system, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:

receive data from a plurality of sensors associated with a physical object, wherein the plurality of sensors generate real-time data about the physical object and its operational environment;

generate, with a digital twin model of the physical object, a predicted future temperature for the physical object using the real-time data about the physical object and its operational environment; and

in response to the predicted future temperature exceeding a threshold temperature within a threshold time period, proactively cool the physical object.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 28, 2021
From: PERUMALLA, SARASWATHI SAILAJA; RAKSHIT, SARBAJIT K.; NAGULAKONDA, SURESH
To: INTERNATIONAL BUSINESS MACHINES CORPORATION
Reel/Frame 057619/0648 →
Continuity (1)
Related Publication 20230102845A1 · Mar 30, 2023
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