Steering Drilling Wellbore Trajectory Prediction Based on the NOA-LSTM-FCNN Method DOI Creative Commons
Yi Gao, Na Wang, Fei Li

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Ноя. 25, 2024

Abstract Aiming at the problem that it is difficult to accurately predict wellbore trajectory under complex geological conditions, NOA-LSTM-FCNN prediction method for steering drilling proposed by combining nutcracker optimization algorithm (NOA), long short-term memory network (LSTM) and fully connected neural (FCNN). This adopts an LSTM layer receive input data capture long-term dependencies within data, extracting important information. The FCNN performs nonlinear mapping on output of further extracts relevant features enhance accuracy. NOA employed hyperparameter LSTM-FCNN model. Through experimental validation, has shown significant improvement in accuracy strong adaptability compared traditional machine learning deep methods. In addition, applies various types effectively enhancing capabilities conditions.

Язык: Английский

A hyperelastic strain energy function for isotropic rubberlike materials DOI
Nurul Hassan Shah, Shaikh Faruque Ali

International Journal of Mechanical Sciences, Год журнала: 2024, Номер 279, С. 109472 - 109472

Опубликована: Окт. 1, 2024

Язык: Английский

Процитировано

8
Design strategy of curved-beam based metamaterial with unprecedented lateral deformation mode transition DOI

Jinyu Ji,

Kai Zhang, Xiaogang Guo

и другие.

International Journal of Mechanical Sciences, Год журнала: 2025, Номер unknown, С. 110136 - 110136

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

1
Nature-inspired miniaturized magnetic soft robotic swimmers DOI
R. Pramanik, Roel Verstappen, Patrick R. Onck

и другие.

Applied Physics Reviews, Год журнала: 2024, Номер 11(2)

Опубликована: Апрель 24, 2024

State-of-the-art biomedical applications such as targeted drug delivery and laparoscopic surgery are extremely challenging because of the small length scales, requirements wireless manipulation, operational accuracy, precise localization. In this regard, miniaturized magnetic soft robotic swimmers (MSRS) attractive candidates since they offer a contactless mode operation for path maneuvering. Inspired by nature, researchers have designed these small-scale intelligent machines to demonstrate enhanced swimming performance through viscous fluidic media using different modes propulsion. review paper, we identify classify nature-inspired basic that been optimized over large evolutionary timescales. For example, ciliary like Paramecium Coleps covered with tiny hairlike filaments (cilia) beat rhythmically coordinated wave movements propulsion gather food. Undulatory spermatozoa midge larvae use traveling body waves push surrounding fluid effective highly environments. Helical bacteria rotate their slender whiskers (flagella) locomotion stagnant viscid fluids. Essentially, all three employ nonreciprocal motion achieve spatial asymmetry. We provide mechanistic understanding magnetic-field-induced spatiotemporal symmetry-breaking principles adopted MSRS at scales. Furthermore, theoretical computational tools can precisely predict magnetically driven deformation fluid–structure interaction discussed. Here, present holistic descriptive recent developments in smart material systems covering wide spectrum fabrication techniques, design, applications, strategies, actuation, modeling approaches. Finally, future prospects promising systems. Specifically, synchronous tracking noninvasive imaging external agents during vivo clinical still remains daunting task. experimental demonstrations mostly limited vitro ex phantom models where dynamics testing conditions quite compared conditions. Additionally, multi-shape morphing multi-stimuli-responsive modalities active structures demand further advancements 4D printing avenues. Their multi-state configuration an solid-fluid continuum would require development multi-scale models. Eventually, adding multiple levels intelligence enhance adaptivity, functionalities, reliability critical applications.

Язык: Английский

Процитировано

6
Machine learning-encoded multiscale modelling and Bayesian optimization framework to design programmable metamaterials DOI
Yang Liu, Xiaoyan Li, Yuli Chen

и другие.

Acta Mechanica Sinica, Год журнала: 2024, Номер 41(1)

Опубликована: Авг. 22, 2024

Язык: Английский

Процитировано

4
Prediction of the compressive strength and carpet plot for cross-material CFRP laminate based on deep transfer learning DOI
Zhicen Song, Yunwen Feng, Cheng Lu

и другие.

Materials Science and Engineering A, Год журнала: 2025, Номер 924, С. 147792 - 147792

Опубликована: Янв. 5, 2025

Язык: Английский

Процитировано

0
Design Strategy of Curved-Beam Based Metamaterial with Unprecedented Lateral Deformation Mode Transition DOI

Jinyu Ji,

Kai Zhang, Xiaogang Guo

и другие.

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Steering drilling wellbore trajectory prediction based on the NOA-LSTM-FCNN method DOI Creative Commons
Yi Gao, Na Wang, Fei Li

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 12, 2025

Aiming at the problem that it is difficult to accurately predict wellbore trajectory under complex geological conditions, NOA-LSTM-FCNN prediction method for steering drilling proposed by combining NOA, LSTM and FCNN. This adopts layer receive input data capture long-term dependencies within data, extracting important information. The FCNN performs nonlinear mapping on output of further extracts relevant features enhance accuracy. NOA employed hyperparameter optimization LSTM-FCNN model. experimental results show effect better than other methods. Taking well deviation angle H21 as an example, compared with traditional machine learning methods (LR, SVM BP) deep (CNN, GRU), evaluation index R² this was improved 0.17887, 0.03129, 0.0259, 0.00054, 0.00032 0.00031 respectively, showing significant accuracy advantages strong adaptability. In addition, applies various types effectively enhancing capabilities conditions.

Язык: Английский

Процитировано

0
Inverse design of programmable shape-morphing kirigami structures DOI Creative Commons

Xiaoyuan Ying,

Dilum Fernando, Marcelo A. Dias

и другие.

International Journal of Mechanical Sciences, Год журнала: 2024, Номер unknown, С. 109840 - 109840

Опубликована: Ноя. 1, 2024

Язык: Английский

Процитировано

1
A novel Bézier planar beam modeling method based on absolute nodal coordinate formulation DOI
Guozheng Kang, Dingguo Zhang, Bin Wang

и другие.

Applied Mathematical Modelling, Год журнала: 2024, Номер unknown, С. 115922 - 115922

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

1
Steering Drilling Wellbore Trajectory Prediction Based on the NOA-LSTM-FCNN Method DOI Creative Commons
Yi Gao, Na Wang, Fei Li

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Ноя. 25, 2024

Abstract Aiming at the problem that it is difficult to accurately predict wellbore trajectory under complex geological conditions, NOA-LSTM-FCNN prediction method for steering drilling proposed by combining nutcracker optimization algorithm (NOA), long short-term memory network (LSTM) and fully connected neural (FCNN). This adopts an LSTM layer receive input data capture long-term dependencies within data, extracting important information. The FCNN performs nonlinear mapping on output of further extracts relevant features enhance accuracy. NOA employed hyperparameter LSTM-FCNN model. Through experimental validation, has shown significant improvement in accuracy strong adaptability compared traditional machine learning deep methods. In addition, applies various types effectively enhancing capabilities conditions.

Язык: Английский

Процитировано

0