Finite Element Modeling Approaches for Performance Optimization of Emerging Energy Harvesting Systems

The growing demand for decentralized and sustainable power solutions has intensified interest in advanced computational strategies for optimizing emerging energy harvesting systems. This study critically examines finite element modeling (FEM) as a comprehensive framework for enhancing the performance, reliability, and integration of piezoelectric, triboelectric, thermoelectric, electromagnetic, and hybrid energy harvesters. The primary objective was to evaluate how multiphysics simulation techniques support predictive design, resonance tuning, material optimization, and structural durability under realistic operational constraints.

A systematic analytical approach was adopted, synthesizing contemporary modeling methodologies, optimization strategies, and validation frameworks. Emphasis was placed on coupled electromechanical, electrothermal, and magneto-mechanical simulations, alongside parametric analysis, topology optimization, and AI-assisted computational refinement. The review further explored experimental correlation techniques and the integration of digital twin architectures for real-time performance monitoring and adaptive system control.

The findings demonstrate that FEM significantly improves power density, bandwidth adaptability, and lifecycle resilience by enabling high-fidelity representation of nonlinear material behavior and boundary-dependent dynamics. Hybrid and multi-source configurations were identified as particularly promising for enhancing robustness in fluctuating environmental conditions. Moreover, the convergence of FEM with artificial intelligence and secure digital infrastructures was shown to strengthen predictive accuracy, scalability, and sustainability compliance.

In conclusion, simulation-driven optimization has evolved into a strategic design paradigm for next-generation self-powered systems. The study recommends deeper integration of AI-enhanced multiphysics modeling, standardized validation protocols, nanoscale material characterization, and secure digital twin deployment to accelerate innovation and ensure long-term operational resilience within decentralized energy ecosystems.