Mechanoelectrical flexible hub-beam model of ionic-type solvent-free nanofluids

Hu, Weipeng; Huai, Yulu; Xu, Mengbo; Feng, Xi‐Qiao; Jiang, Ruisong; Zheng, Youqi; Deng, Zichen

doi:10.1016/j.ymssp.2021.107833

Cited by 64 publications

(16 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Generally, in the context of computational analysis, the nonlinear activation analyses have been widely used to address forward (e.g., dynamic analyses) and reverse (e.g., fault diagnosis) problems across various application domains in the literature. Some highlighted examples include the nonlinear semi-continuum model for material analysis [42], a mechanoelectrical flexible hubbeam model for fluid analysis [31] and First Order Approximate Coupling (FOAC) model for hub-beam dynamic analysis [20]. Specifically, in the context of deep learning, the aim of a model is to get linearly related separable features from nonlinearly separable input instances by performing multiple transformations over the number of layers [24].…”

Section: Introductionmentioning

confidence: 99%

Prediction of hydraulic blockage at culverts from a single image using deep learning

Iqbal

Barthélemy

Perez

2022

Neural Comput & Applic

View full text Add to dashboard Cite

Cross-drainage hydraulic structures such as culverts and bridges in urban landscapes are prone to get blocked by the transported debris (e.g., urban, vegetated), which often reduces their hydraulic capacity and triggers flash floods. Unavailability of relevant data from blockage-originated flooding events and complex nature of debris accumulation are highlighted factors hindering the research within the blockage management domain. Wollongong City Council (WCC) blockage conduit policy is the leading formal guidelines to incorporate blockage into design guidelines; however, are criticized by the hydraulic engineers for its dependence on the post-flood visual inspections (i.e., visual blockage) instead of peak floods hydraulic investigations (i.e., hydraulic blockage). Apparently, no quantifiable relationship is reported between the visual blockage and hydraulic blockage; therefore, many consider WCC blockage guidelines invalid. This paper exploits the power of Artificial Intelligence (AI), motivated by its recent success, and attempts to relate visual blockage with hydraulic blockage by proposing a deep learning pipeline to predict hydraulic blockage from an image of the culvert. Two experiments are performed where the conventional pipeline and end-to-end learning approaches are implemented and compared in the context of predicting hydraulic blockage from a single image. In experiment one, the conventional deep learning pipeline approach (i.e., feature extraction using CNN and regression using ANN) is adopted. In contrast, in experiment two, end-to-end deep learning models (i.e., E2E_ MobileNet, E2E_ BlockageNet) are trained and compared with the conventional pipeline approach. Dataset (i.e., Hydraulics-Lab Blockage Dataset (HBD), Visual Hydraulics-Lab Dataset (VHD)) used in this research were collected from laboratory experiments performed using scaled physical models of culverts. E2E_ BlockageNet model was reported best in predicting hydraulic blockage with $$R^2$$ R 2 score of 0.91 and indicated that hydraulic blockage could be interrelated with the visual features at the culvert.

show abstract

Section: Introductionmentioning

confidence: 99%

Prediction of hydraulic blockage at culverts from a single image using deep learning

Iqbal

Barthélemy

Perez

2022

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…To this end, it is of significance to investigate the switching dynamic behaviors of the rheological property of the solvent-free nanofluids. Recently, a mechanoelectrical flexible hub-beam model [17] was proposed based on the classic hub-beam model [18][19][20][21] to obtain the critical disturbance of the liquid-like behaviors of ionic-type solvent-free nanofluids by using the structure-preserving method [22][23][24][25][26][27][28][29][30][31][32] . However, the previous model has neglected the specific forms (such as the electric field, the magnetic field, and temperature) of disturbance acting on the solvent-free nanofluids [17] .…”

Section: Introductionmentioning

confidence: 99%

“…Recently, a mechanoelectrical flexible hub-beam model [17] was proposed based on the classic hub-beam model [18][19][20][21] to obtain the critical disturbance of the liquid-like behaviors of ionic-type solvent-free nanofluids by using the structure-preserving method [22][23][24][25][26][27][28][29][30][31][32] . However, the previous model has neglected the specific forms (such as the electric field, the magnetic field, and temperature) of disturbance acting on the solvent-free nanofluids [17] . Therefore, the obtained critical disturbance described by the initial deflection of the corona branch cannot be directly used in the control strategy.…”

Section: Introductionmentioning

confidence: 99%

Effects of temperature change on the rheological property of modified multiwall carbon nanotubes

Wang

Huai

et al. 2022

Appl. Math. Mech.-Engl. Ed.

Self Cite

View full text Add to dashboard Cite

Solvent-free nanofluids hold promise for many technologically significant applications. The liquid-like behavior, a typical rheological property of solvent-free nanofluids, has aroused considerable interests. However, there has been still lack of efficient methods to predict and control the liquid-like behavior of solvent-free nanofluids. In this paper, we propose a semi-discrete dynamic system with stochastic excitation describing the temperature change effects on the rheological property of multiwall carbon nanotubes (MWCNTs) modified by grafting sulfonic acid terminated organosilanes as corona and tertiary amine as canopy, which is a typical covalent-type solvent-free nanofluid system. The vibration of the grafting branches is simulated by employing a structure-preserving approach, and the shear force of grafting branches at the fixed end is computed subsequently. By taking the shear forces as an excitation acting on the MWCNTs, the axial motion of the MWCNTs is solved with the 7-point Gauss-Kronrod quadrature rule. The critical temperature associated with the appearance of the liquid-like behavior as well as the upper bound of the moving speed of the modified MWCNTs is determined, which can be used to predict and control the liquid-like behavior of the modified MWCNTs in engineering applications.

show abstract

“…e systematic and in-depth understandings of the mechanical properties of these devices and their key micro/nanocomponents are needed to provide theoretical guidance for the mechanical-based design and regulation [3,4]. So far, there have been numerous investigations on this topic, especially the studies on the mechanical behavior of uniform nanomaterials and structures based on the molecular dynamic simulation or nonclassical continuum approaches [5][6][7][8][9]. ese results play an important role in perfecting performances and promoting the industrialization of MEMS/NEMS [10] and cellular mechanics [11].…”

Section: Introductionmentioning

confidence: 99%

A Nonlocal Strain Gradient Approach for Out‐of‐Plane Vibration of Axially Moving Functionally Graded Nanoplates in a Hygrothermal Environment

Zhu

Yan

Wang

et al. 2021

Shock and Vibration

View full text Add to dashboard Cite

Vibration analyses on axially moving functionally graded nanoplates exposed to hygrothermal environments are presented. The theoretical model of the nanoplate is described via the Kirchhoff plate theory in conjunction with the concept of the physical neutral layer. By employing the nonlocal strain gradient theory, the governing equation of motion is derived based on Hamilton’s principle. The composite beam function method, as well as the complex modal approach, is utilized to obtain the vibration frequencies of axially moving functionally graded nanoplates. Some benchmark results related to the effects of temperature changing, moisture concentration, axial speed, aspect ratio, nonlocal parameter, and the material characteristic scale parameter on the stiffness of axially moving functionally graded nanoplates are obtained. The results reveal that with increasing the nonlocal parameter, gradient index, temperature changing, moisture concentration, and axial speed, the vibration frequencies decrease. The frequencies increase while increasing the material characteristic scale parameter and aspect ratio. Moreover, there is an interaction between the nonlocal parameter and material characteristic scale parameter, influencing and restricting each other.

show abstract

Mechanoelectrical flexible hub-beam model of ionic-type solvent-free nanofluids

Cited by 64 publications

References 41 publications

Prediction of hydraulic blockage at culverts from a single image using deep learning

Prediction of hydraulic blockage at culverts from a single image using deep learning

Effects of temperature change on the rheological property of modified multiwall carbon nanotubes

A Nonlocal Strain Gradient Approach for Out‐of‐Plane Vibration of Axially Moving Functionally Graded Nanoplates in a Hygrothermal Environment

Contact Info

Product

Resources

About