Intelligent Geothermal Energy Storage: Integration of Nano-Enhanced Phase Change Materials, Sub-Surface Diagnostics, and Edge-Native Deep Learning for Real-Time Structural Health Monitoring

The global transition toward grid-scale thermal energy storage (TES) necessitates advanced media capable of overcoming volumetric constraints inherent in sensible heat reservoirs. This report evaluates a comprehensive materials and computational framework for latent heat thermal energy storage (LHTES), utilizing engineered porous sedimentary and micro-fractured igneous rock matrices infiltrated with nanocomposite phase change materials (Nano-PCMs). While Nano-PCMs substantially elevate volumetric energy density via isothermal phase transitions, their integration into subterranean formations introduces critical vulnerabilities, including thermomechanical fatigue, micro-cracking, and accelerated chemical leaching during high-temperature cycling. This study details the synthesis of carbon- and metal-oxide-doped Nano-PCMs, rigorously defining dispersion parameters to maximize phonon transport while suppressing agglomeration. A specific procedure for geological matrix infiltration via vacuum impregnation is established, combined with an in-situ supramolecular lock shell encapsulation and silica-nanofluid wettability alteration to mitigate primary silicate dissolution. To continuously monitor reservoir integrity, a hybrid diagnostic architecture is formulated, multiplexing Fiber Bragg Grating (FBG) optical sensors with Electrochemical Impedance Spectroscopy (EIS). These disparate data streams are ingested by a decentralized Edge AI platform utilizing NVIDIA Jetson Nano architectures. A Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) topology processes the multivariate time-series data to predict incipient structural failure. To rectify acute class imbalance in failure data, Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) are deployed. The framework yields a ۲۱.۴% enhancement in base thermal conductivity, near-zero leakage, and predictive anomaly detection achieving an Area Under the Curve (AUC) of ۰.۹۷, establishing a resilient architecture for next-generation geothermal energy storage.

کلیدواژه ها:

Geothermal Energy ، EnergyStorage ، Nano-PCMs ، Edge AI ، Structural Health Monitoring ، CNN-LSTM