The increasing complexity of modern energy systems has intensified the need for advanced engineering tools capable of handling multi-physics interactions, large design spaces, and high computational costs. Artificial Intelligence (AI) has emerged as a powerful enabler for engineering problem solving by augmenting traditional physics-based models with data-driven intelligence. This study explores the integration of AI tools into the analysis and optimization of concentrating solar power (CSP) systems, with particular focus on linear Fresnel reflector (LFR) technology and advanced receiver designs.

Linear Fresnel systems offer a cost-effective and structurally simple alternative to parabolic trough collectors; however, their performance is strongly influenced by non-uniform solar flux distribution, optical losses, and complex heat transfer mechanisms within the receiver cavity. Conventional optical ray-tracing, computational fluid dynamics (CFD), and experimental approaches provide high-fidelity results but remain computationally intensive, especially when evaluating multiple geometries, operating conditions, and geographical locations.

In this context, AI-based methodologies are introduced as complementary tools to accelerate design exploration and enhance system understanding. Machine learning surrogate models can be trained using data generated from optical simulations to rapidly predict optical efficiency and circumferential heat-flux distribution for different absorber configurations, such as asymmetric tubes and concave-wall designs. These models enable fast evaluation of receiver performance under varying solar positions and climatic conditions, significantly reducing reliance on repeated ray-tracing simulations.

Furthermore, AI techniques can be integrated with CFD-based thermal analysis to predict temperature fields, airflow patterns inside the receiver cavity, and global heat losses. By learning the nonlinear relationships between geometry, heat flux, and thermal behavior, data-driven models can approximate complex heat transfer phenomena with reduced computational cost while maintaining acceptable accuracy. When combined with experimental measurements, AI also supports model calibration, uncertainty reduction, and real-time performance monitoring.

The proposed AI-assisted framework demonstrates how data-driven tools can support multi-objective optimization of LFR receivers by simultaneously improving flux uniformity, thermal stability, and overall efficiency. The results highlight the potential of AI to function as a digital engineering assistant, enabling faster design iterations, improved decision-making, and enhanced performance of concentrating solar systems. This work illustrates how the integration of AI tools with established engineering methods can address critical challenges in renewable energy system design and contribute to the development of next-generation CSP technologies. The integration of AI with established engineering principles represents a critical step toward smarter, more resilient, and economically competitive solar thermal power plants.