Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (4)
  • Open Access

    ARTICLE

    Air-Side Heat Transfer Performance Prediction for Microchannel Heat Exchangers Using Data-Driven Models with Dimensionless Numbers

    Long Huang1,2,3,*, Junjia Zou3, Baoqing Liu1, Zhijiang Jin1,2, Jinyuan Qian1

    Frontiers in Heat and Mass Transfer, Vol.22, No.6, pp. 1613-1643, 2024, DOI:10.32604/fhmt.2024.058231 - 19 December 2024

    Abstract This study explores the effectiveness of machine learning models in predicting the air-side performance of microchannel heat exchangers. The data were generated by experimentally validated Computational Fluid Dynamics (CFD) simulations of air-to-water microchannel heat exchangers. A distinctive aspect of this research is the comparative analysis of four diverse machine learning algorithms: Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forest (RF), and Gaussian Process Regression (GPR). These models are adeptly applied to predict air-side heat transfer performance with high precision, with ANN and GPR exhibiting notably superior accuracy. Additionally, this research further delves into… More >

  • Open Access

    ARTICLE

    Data-Driven Modeling for Wind Turbine Blade Loads Based on Deep Neural Network

    Jianyong Ao1, Yanping Li1, Shengqing Hu1, Songyu Gao2, Qi Yao2,*

    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 >

  • Open Access

    ARTICLE

    The Estimation of the Higher Heating Value of Biochar by Data-Driven Modeling

    Jiefeng Chen1, Lisha Ding1, Pengyu Wang1, Weijin Zhang2, Jie Li3, Badr A. Mohamed4, Jie Chen1, Songqi Leng1, Tonggui Liu1, Lijian Leng2,*, Wenguang Zhou1,*

    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 >

  • Open Access

    ARTICLE

    A Self-Learning Data-Driven Development of Failure Criteria of Unknown Anisotropic Ductile Materials with Deep Learning Neural Network

    Kyungsuk Jang1, Gun Jin Yun2,*

    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 >

Displaying 1-10 on page 1 of 4. Per Page