Global sensitivity analysis of asymmetric energy harvesters (2107.04647v2)

Abstract: Parametric variability is inevitable in actual energy harvesters. It can significantly affect crucial aspects of the system performance, especially in harvesting systems that present geometric parameters, material properties, or excitation conditions that are susceptible to small perturbations. This work aims to develop an investigation to identify the most critical parameters in the dynamic behavior of asymmetric bistable energy harvesters with nonlinear piezoelectric coupling, considering the variability of their physical and excitation properties. For this purpose, a global sensitivity analysis based on orthogonal variance decomposition, employing Sobol indices, is performed to quantify the effect of the harvester parameters on the variance of the recovered power. This technique quantifies the variance concerning each parameter individually and collectively regarding the total variation of the model. The results indicate that the frequency and amplitude of excitation, asymmetric terms and electrical proprieties of the piezoelectric coupling are the most critical parameters that affect the mean power harvested. It is also shown that the order of importance of the parameters can change according to the stability of the harvester's dynamic response. In this way, a better understanding of the system under analysis is obtained since the study allows the identification of vital parameters that rule the change of dynamic behavior and therefore constitutes a powerful tool in the robust design, optimization, and response prediction of nonlinear harvesters.

• The paper employs global sensitivity analysis using Sobol indices and polynomial chaos expansion to identify critical parameters influencing power generation in asymmetric energy harvesters.
• Excitation frequency and amplitude, along with piezoelectric coupling coefficients, are identified as significant influencers of harvested power, with their importance contingent on the system's dynamic state.
• The sensitivity analysis findings provide data-driven insights for robust design optimization by prioritizing highly sensitive parameters, demonstrating the computational efficiency of the PCE surrogate model.

Global Sensitivity Analysis of Asymmetric Energy Harvesters: A Comprehensive Investigation

The paper titled "Global sensitivity analysis of asymmetric energy harvesters" by Joao Pedro Norenberg and colleagues provides an in-depth investigation into the understanding of parameter significance in nonlinear piezoelectric energy harvesters. The paper's primary aim is to identify the critical parameters impacting the system performance and explore their implications on design robustness, optimization, and prediction of responses in energy harvesting systems.

Background and Motivation

The research is anchored in the context of micro-scale energy harvesting, a field seeing significant attention due to the increasing demand for self-sustaining autonomous devices. The core of such devices often involves energy harvesters that exploit environmental vibrations, which can be chaotic and variable. The authors highlight the inherent uncertainties in material properties, geometric alignment, and environmental excitation conditions faced during the production and operation of these systems. Traditional deterministic modeling approaches have limitations in accounting for these variabilities, which necessitates a thorough uncertainty quantification (UQ) and sensitivity analysis (SA).

Methodology

The methodology centers on employing a global sensitivity analysis using Sobol indices in conjunction with polynomial chaos expansion (PCE). This approach allows for quantifying the contribution of individual parameters and their interactions concerning the variance of the mean power harvested. The authors adeptly employ a PCE surrogate model to mitigate the computational expenses associated with Monte Carlo simulations, thereby ensuring efficient exploration of the parameter space. The nuanced methodology provides clarity on how variations in geometric, material, and excitation-related parameters influence the system outputs.

Key Findings

Several numerical experiments were conducted, revealing insightful trends about the system behavior under varied conditions:

1. Parametric Influence: Among the examined parameters, excitation frequency and amplitude, as well as piezoelectric coupling coefficients, emerge as significant influencers of the harvested power. The paper delineates shifts in parameter importance contingent on the stability and dynamic characteristics of the energy harvester.
2. Nonlinear Effects: The authors highlight how nonlinear electromechanical coupling and geometrical asymmetries induce complex response behaviors, which are particularly sensitive to the input excitation conditions. Such sensitivities necessitate careful parameter management during design stages to enhance performance predictability.
3. Robust Design Implications: The insights gained from the sensitivity analysis suggest streamlined approaches for robust design by focusing on parameters with high sensitivity indices. This prioritization aids in optimizing energy harvester designs for greater efficiency and resilience in practical applications.
4. Numerical Efficiency: The PCE approach proves to be computationally tenable, offering reduced processing times over traditional exploitation methods while providing accurate sensitivity metrics. The findings validate the surrogate methodology's potential for use in broader nonlinear dynamic system analyses.

Implications and Future Directions

The findings have pronounced implications for the engineering and optimization of energy harvesting systems. By pinpointing the most influential parameters, engineers can focus on controlling key variables during manufacturing, potentially easing design constraints and improving the reliability of harvested power. The paper's use of combined nonlinear dynamic modeling and robust statistical evaluation sets a precedent for future work in this domain.

Going forward, the incorporation of real-world data and further exploration of advanced surrogate modeling techniques can refine the design and operation of harvesting devices. Additionally, expanding the sensitivity analysis framework to encompass more varied environmental conditions and complex material behaviors might enhance the fidelity and robustness of predictive models in stochastic energy systems.

In conclusion, "Global sensitivity analysis of asymmetric energy harvesters" represents a critical advancement in understanding the dynamic complexities of nonlinear energy harvesters. It equips researchers and practitioners with data-driven insights, emphasizing the importance of strategic parameter management in achieving efficient and stable energy harvesting from vibrational sources.