Jianyong Ao¹, Yanping Li¹, Shengqing Hu¹, Songyu Gao², Qi Yao^2,*

Energy Engineering, Vol.121, No.12, pp. 3825-3841, 2024, DOI:10.32604/ee.2024.055250 - 22 November 2024

Abstract Blades are essential components of wind turbines. Reducing their fatigue loads during operation helps to extend their lifespan, but it is difficult to quickly and accurately calculate the fatigue loads of blades. To solve this problem, this paper innovatively designs a data-driven blade load modeling method based on a deep learning framework through mechanism analysis, feature selection, and model construction. In the mechanism analysis part, the generation mechanism of blade loads and the load theoretical calculation method based on material damage theory are analyzed, and four measurable operating state parameters related to blade loads are… More >

Jiefeng Chen¹, Lisha Ding¹, Pengyu Wang¹, Weijin Zhang², Jie Li³, Badr A. Mohamed⁴, Jie Chen¹, Songqi Leng¹, Tonggui Liu¹, Lijian Leng^2,*, Wenguang Zhou^1,*

Journal of Renewable Materials, Vol.10, No.6, pp. 1555-1574, 2022, DOI:10.32604/jrm.2022.018625 - 20 January 2022

Abstract Biomass is a carbon-neutral renewable energy resource. Biochar produced from biomass pyrolysis exhibits preferable characteristics and potential for fossil fuel substitution. For time- and cost-saving, it is vital to establish predictive models to predict biochar properties. However, limited studies focused on the accurate prediction of HHV of biochar by using proximate and ultimate analysis results of various biochar. Therefore, the multi-linear regression (MLR) and the machine learning (ML) models were developed to predict the measured HHV of biochar from the experiment data of this study. In detail, 52 types of biochars were produced by pyrolysis… More >

Kyungsuk Jang¹, Gun Jin Yun^2,*

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1091-1120, 2021, DOI:10.32604/cmc.2020.012911 - 26 November 2020

Abstract This paper first proposes a new self-learning data-driven methodology that can develop the failure criteria of unknown anisotropic ductile materials from the minimal number of experimental tests. Establishing failure criteria of anisotropic ductile materials requires time-consuming tests and manual data evaluation. The proposed method can overcome such practical challenges. The methodology is formalized by combining four ideas: 1) The deep learning neural network (DLNN)-based material constitutive model, 2) Self-learning inverse finite element (SELIFE) simulation, 3) Algorithmic identification of failure points from the self-learned stress-strain curves and 4) Derivation of the failure criteria through symbolic regression More >