Integrated AI–PCM–SMA Hybrid Cooling and Energy Recovery System for Hyperscale Data Centers

Abstract

Abstract An integrated system for cooling and energy recovery in hyperscale data centers is disclosed. The invention combines an AI-driven control module, a phase change material (PCM) unit, and a shape memory alloy (SMA) component to create a closed-loop system that dynamically regulates temperature and recovers excess thermal energy. The system is designed for scalable deployment in large data centers and is engineered to interface with standard data center management systems. Abstract

Claims

1. Independent ClaimsClaim 1 (System Claim): An integrated cooling and energy recovery system for a hyperscale data center, comprising:• an AI control module configured to receive, analyze, and interpret real-time operational data from a plurality of sensors and to generate control signals for dynamic adjustment of cooling parameters and energy recovery processes;• a phase change material (PCM) unit adapted to store and release thermal energy via latent heat absorption and release during phase transitions;• a shape memory alloy (SMA) component configured to modulate thermal conduction pathways through controlled, temperature-dependent deformation;• a sensor network operable to continuously monitor environmental parameters including temperature, airflow, and energy consumption; and• an energy recovery mechanism operatively coupled to the PCM unit and the SMA component, the mechanism being configured to capture excess thermal energy and convert it into a recoverable form, wherein the AI control module is further configured to interface with data center management systems via standardized communication protocols.Claim 2 (Method Claim): A method for dynamically cooling and recovering energy in a hyperscale data center comprising:• receiving real-time operational data via a sensor network deployed within the data center;• processing the received data with an AI control module to generate control signals based on predictive deep learning algorithms;• actuating a phase change material unit to store or release thermal energy in accordance with the generated control signals;2025202666  25 Apr 2025• activating a shape memory alloy component to permissively modulate thermal conduction pathways based on said control signals; and• capturing excess thermal energy via an energy recovery mechanism and converting the captured energy into a recoverable form, wherein the steps are performed in a closed-loop system continuously optimizing both cooling efficiency and energy recovery.

2. Dependent ClaimsDependent claims are intended to cover incremental improvements and various embodiments of the invention.A. Enhancements in the AI Control Module• Claim 3: The system of Claim 1, wherein the AI control module further comprises a deep learning algorithm that predicts temperature fluctuations based on historical sensor data and adjusts cooling parameters preemptively.• Claim 4: The system of Claim 1, wherein the AI control module incorporates an adaptive feedback loop that continuously calibrates operational settings based on real-time data deviations from predetermined optimal thresholds.• Claim 5: The system of Claim 1, wherein the AI control module fuses data from disparate sensor types (e.g., thermal, optical, and airflow sensors) to generate a unified operational model for optimal cooling and energy recovery.B. Refinements in the PCM Unit• Claim 6: The system of Claim 1, wherein the phase change material unit comprises micro-encapsulated PCM particles embedded within a polymer matrix to improve heat transfer efficiency and durability during rapid thermal cycling.• Claim 7: The system of Claim 1, wherein the PCM unit includes additives to enhance thermal conductivity and further expedite the rate of latent heat absorption and release.C. Advancements in the SMA Component• Claim 8: The system of Claim 1, wherein the shape memory alloy component is fabricated using a nickel-titanium alloy having a transformation temperature within the range of 30°C to 40°C for optimal responsiveness under data center operating conditions.• Claim 9: The system of Claim 1, wherein the SMA component actuates in response to heat-induced deformation, thereby dynamically modulating thermal conduction pathways within the cooling system.2025202666  25 Apr 2025D. Sensor Networks and Communication Interfaces• Claim 10: The system of Claim 1, wherein the sensor network comprises an array of optical fiber sensors combined with redundant wireless sensor nodes for high-precision continuous monitoring of temperature and airflow.• Claim 11: The system of Claim 1, wherein the sensor network is configured to communicate with the AI control module via encrypted wireless protocols and standardized interfaces such as SNMP, REST APIs, or MQTT.E. Enhancements in Energy Recovery Mechanisms• Claim 12: The system of Claim 1, wherein the energy recovery mechanism further comprises a thermoelectric generator for converting thermal gradients into electrical energy.• Claim 13: The system of Claim 1, further comprising an energy storage unit selected from the group consisting of rechargeable batteries, supercapacitors, or flywheel storage systems for storing energy recovered from excess thermal output.• Claim 14: The system of Claim 1, wherein the energy recovery mechanism includes a dynamic control algorithm that automatically adjusts its conversion efficiency based on the measured temperature differential between the PCM unit and ambient conditions.F. Emerging Embodiments and Future Enhancements• Claim 15: The system of Claim 1, wherein the sensor network is further configured to transmit data in real-time to a cloud-based enterprise resource planning (ERP) system via secure protocols for centralized data consolidation and analytics.• Claim 16: The system of Claim 1, wherein the AI control module integrates an edge computing architecture to process sensor data in proximity to the system for reduced latency and enhanced responsiveness.• Claim 17: The system of Claim 1, further comprising a blockchain-based data logging mechanism configured to record sensor data and system control signals to ensure data integrity and immutability.