Phase change material (PCM)-based latent heat storage (LHS) systems help address the mismatch between renewable energy supply and thermal demand. However, their practical implementation is constrained by the strongly nonlinear and multiphysics nature of phase change, which makes high-fidelity simulations and real-time applications computationally expensive. This review examines mathematical reduced-order modeling (ROM) as an effective strategy to overcome this limitation by combining physics-based simplifications, projection methods, interpolation techniques, and data-driven models for PCM-based LHS systems. While physical simplifications (such as dimensional reduction and effective property approximations) represent an important first layer of model reduction, the primary focus of this work is on the mathematical ROM methodologies that operate on the governing equations after such physical simplifications have been applied. The review covers approaches including two-temperature non-equilibrium and analytical thermal-resistance models, Proper Orthogonal Decomposition (POD), CFD-derived look-up tables, kriging and ε-NTU grey/black-box metamodels, and machine-learning methods such as artificial neural networks and gradient-boosted regressors trained from CFD data. These ROM techniques have been applied to packed beds, PCM-integrated heat exchangers, finned enclosures, triplex-tube systems, and solar thermal components, achieving speed-ups from tens to over 80,000 times faster than full CFD simulations while maintaining prediction errors typically below 5% or within sub-Kelvin temperature deviations. A critical comparative analysis exposes the fundamental trade-off between interpretability, data dependence, and computational efficiency, leading to a practical decision-making framework that guides method selection for specific applications such as design optimization, real-time control, and system-level simulation. Remaining challenges—including accurate representation of phase change nonlinearity, moving phase boundaries, multi-timescale dynamics, generalization across geometries, experimental validation, and integration into industrial workflows—motivate a structured roadmap for future hybrid physics–machine learning developments, standardized validation protocols, and pathways toward industrial deployment.

An HHO generator is a device that electrolyzes water to produce a mixture of hydrogen and oxygen gases, which can be used as a fuel source for various applications such as welding, cutting, and combustion engines. The efficiency and production rate of HHO generators are influenced by various parameters, including electrode materials, voltage, current, and electrolyte solutions. This study aimed to evaluate the oxyhydrogen production of an HHO generator by conducting parametric analysis. A complete setup consisting of a modular HHO generator, a bubbler, and a device for measuring the volume flow rate was constructed and used in a series of experiments to determine the effects of electrode material, electrolyte solution, applied current, and plate geometry on oxyhydrogen production. The results were evaluated and analyzed using the Pareto principle, which indicated that plate geometry and input current were the most significant factors, while the other two were considered less critical. The surface area of the plates was the most significant factor affecting oxyhydrogen production, while the type of material used as an electrode was the least significant. The highest oxyhydrogen production rate, averaging 0.504 L/min over three trials, was achieved using grooved stainless-steel 316L plates in a Potassium Hydroxide (KOH) solution with a 280-ampere current. Corrosion tests indicated that stainless steel 316L in KOH solution had the lowest corrosion rate (5.043 × 10-4 MPY), while stainless steel 304 had the highest (1.009 × 10-3 MPY).