Thermal Performance Optimization of a Phase Change Material (PCM)-Based Thermal Energy Storage System

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@article{1722215,
      author = {Ayushh Kumar, Dr. Raghvendra Kumar Khedle},
      title = {Thermal Performance Optimization of a Phase Change Material (PCM)-Based Thermal Energy Storage System},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {2},
      pages = {737-753},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1722215.pdf},
      abstract = {The mismatch between the availability of thermal energy and demand for it is a principal obstacle to the wider adoption of solar-thermal and waste-heat-recovery systems. Latent heat thermal energy storage (LHTES) using phase change materials (PCMs) offers compact, near-isothermal storage, yet the organic PCMs that store energy well conduct it poorly (0.15–0.25 W/m·K), which throttles charging and discharging power. This study addresses that limitation through a combined numerical, experimental and data-driven optimization of a vertical shell-and-tube LHTES module charged by hot water. A commercial paraffin wax (measured melting range 40.2–44.1 °C, latent heat 178.4 kJ/kg) was characterised by differential scanning calorimetry. Two enhancement strategies—longitudinal copper fins and dispersion of aluminium-oxide nanoparticles—were investigated jointly rather than in isolation. A transient conjugate model using the enthalpy-porosity formulation with buoyancy-driven convection was developed in ANSYS Fluent, made grid- and time-step-independent, and validated against fifteen calibrated thermocouples to within 6.8%. A Box–Behnken design was expanded to 180 cases to train a 4-12-8-2 artificial neural network that predicts complete melting time and stored energy with a testing coefficient of determination of 0.9921. Coupling the surrogate to a non-dominated sorting genetic algorithm (NSGA-II) generated the melting-time–stored-energy Pareto front, from which a preferred design was selected by TOPSIS. The optimum—eight fins of 22.5 mm height with 2.0% nanoparticle loading at 70 °C inlet—reduced complete melting time from 218.0 to 74.6 min (a 65.8% reduction) for only a 4.1% loss of stored energy, raised average charging power from 62 to 173 W, and improved exergy efficiency from 41.2% to 52.7%. Fin geometry and nanoparticle loading interact sub-additively, and the nanoparticle benefit saturates near 2% by mass.},
      keywords = {Phase Change Material, Latent Heat Thermal Energy Storage, Enthalpy-Porosity Method, Nano-Enhanced PCM, Multi-Objective Optimization},
      month = {August},
      doi = {https://doi.org/10.64388/IREV10I2-1722215}
  }