resumo
The electric efficiency of vibrational electromagnetic generators is highly dependent on their ability to ensure effective adaptability to uncertain and irregular dynamics of mechanical energy sources. Such adaptive ability demands a planning operation considering future information of the highly nonlinear dynamics of these generators and mechanical excitations patterns. High accurate energy predictions are then mandatory for high energy generation efficiencies. However, on the one hand, high prediction accuracy by analytical modelling from first principles requires high modelling complexity; on the other hand, artificial intelligence models ensuring high prediction accuracy have not yet been explored to enhance the performance of these generators, even though their pre-training holds potential to significantly reduce the energy production costs. We here provide a multifaceted study highlighting the synergies between analytical and artificial intelligence modelling for optimizing the efficiency of vibrational-powered electromagnetic generators. Two main innovations are introduced: (1) development and experimental validation of a time-series forecasting artificial intelligence model based on the deep deterministic policy gradient method; (2) validation of a pre-training scenario by analytical modelling-> artificial intelligence modelling synergy. Both the analytical and artificial intelligence models were able to provide high prediction accuracies to periodic and random 3D motions combining translations and rotations. Moreover, the pre-training scenario, using simulation training data sets, ensures prediction accuracies within the +/- 20% absolute error surfaces, profiling approximately normal distributions centered at approximately null error. These are impacting results in the scope of vibrational electromagnetic generation, holding potential to be extended to innovative self-adaptive electromagnetic generators, including those with ability to absorb complex 6 DOF external mechanical excitations. Besides, it can support the implementation of high-performance AI modelling -> analytical modelling synergies, aiming to re-parameterize the high complex analytical models throughout the EMG operation, such that a superior controllability of the adaptive systems can be achieved.
categoria
Energy & Fuels; Engineering
autores
Vidal, JV; Fonte, TMSL; Lopes, LS; Bernardo, RMC; Carneiro, PMR; Pires, DG; dos Santos, MPS
nossos autores
João Vasco Silvestre Vidal
Investigador Júnioragradecimentos
This work was supported by the Portuguese Foundation for Science and Technology (FCT) (PhD grant references: SFRH/BD/144745/2019 and 2023.01947.BD; UIDB/00481/2020 and UIDP/00481/2020; DOI 10.54499/UIDB/00481/2020, https://doi.org/10.54499/UIDB/004 81/2020, and DOI 10.54499/UIDP/00481/2020, https://doi.org/10 .54499/UIDP/00481/2020) .