The pursuit for sustainability and a reduction in reliance on non-renewable energy sources has fueled the growing use of solar energy systems worldwide. This study assesses the feasibility of using domestic solar heating systems in Kirkuk, Iraq. The study utilized T*SOL software focused on three different solar collector systems: Thermital, Eraslan, and Apricus, chosen based on their type of collector loop (open loop or closed loop), type of collector (flat plate or evacuated tube), and collector size. The impact of varying the size of the hot water storage tank was also analyzed. The aim was to find a cost-effective solution that maximizes energy efficiency, reduces initial and maintenance costs, and cuts CO ₂ emissions. The results of the economic analysis showed that the energy demand for the case study was 1430 kWh. Both open loop and closed loop systems were found to achieve a solar fraction of 86% using evacuated tube collectors , but the open loop system required a smaller storage tank. The amount of CO ₂ emissions avoided was estimated to be between 689.5 and 1316 kg/year. The study concludes that domestic solar heating systems in Kirkuk, Iraq offer a sustainable and cost-effective energy solution, eliminating the need for an electrical water heater and resulting in substantial energy savings.

Purpose: To develop a hybrid machine-learning framework that integrates a Physics-Informed Neural Network (PINN) with Particle Swarm Optimization (PSO)–tuned regression models to deliver fast, accurate, and physically consistent pressure-drop predictions in metal-foam boiling applications. Design/methodology/approach: • A novel PINN is constructed by embedding the full set of governing thermal–hydraulic equations (mass, momentum, and energy conservation) directly into its loss function. • Five regression models (SVR, AdaBoost, Polynomial Regression, XGBoost and Random Forest) are hyperparameter-tuned via PSO. • Both the PSO-optimized regressors and the PINN are trained and tested on synthetic datasets representative of subcooled boiling flows in metal-foam tubes to assess accuracy, robustness, and computational efficiency. Findings: The PINN model outperforms all baseline PSO-tuned regressors, achieving an R2 of 0.9947 on test data. This demonstrates that enforcing physical laws during training yields superior generalization and predictive performance compared with purely data-driven approaches. Research limitations/implications: - Validated on synthetic and experimental data, providing practical confidence.- Extensible to diverse foam geometries and fluids for scalable applicability.- Compatible with sensor networks and real-time control for accurate, responsive pressure-drop forecasts. Practical implications: High precision RF, PSO tuned XGBoost and PINN models integrate into digital twins and control loops for dynamic pressure drop prediction, anomaly detection, and adaptive setpoint adjustment. This reduces downtime, optimizes heat exchanger design, lowers maintenance costs, and enhances system reliability. Social implications: Enables safer, more efficient boiling systems with less downtime and environmental impact. Originality/value: This work uniquely combines PSO-optimized ensemble learners with a physics-driven PINN architecture that explicitly embeds conservation laws into the learning process, an innovation not previously applied to subcooled boiling in porous media. It establishes a new benchmark for constraint-aware, data-driven modeling in thermal-hydraulic research.