unstructured mesh
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2022 ◽  
Vol 166 ◽  
pp. 108807
Author(s):  
Kun Zhuang ◽  
Yong Zheng ◽  
Fei Xu ◽  
Ting Li ◽  
Wei Lu ◽  
...  

2022 ◽  
Author(s):  
Coleman Blakely ◽  
Guoming Ling ◽  
William James Pringle ◽  
María Teresa Contreras ◽  
Damrongsak Wirasaet ◽  
...  

2021 ◽  
Vol 9 (12) ◽  
pp. 1398
Author(s):  
Tao Song ◽  
Jiarong Wang ◽  
Danya Xu ◽  
Wei Wei ◽  
Runsheng Han ◽  
...  

Physical oceanography models rely heavily on grid discretization. It is known that unstructured grids perform well in dealing with boundary fitting problems in complex nearshore regions. However, it is time-consuming to find a set of unstructured grids in specific ocean areas, particularly in the case of land areas that are frequently changed by human construction. In this work, an attempt was made to use machine learning for the optimization of the unstructured triangular meshes formed with Delaunay triangulation in the global ocean field, so that the triangles in the triangular mesh were closer to equilateral triangles, the long, narrow triangles in the triangular mesh were reduced, and the mesh quality was improved. Specifically, we used Delaunay triangulation to generate the unstructured grid, and then developed a K-means clustering-based algorithm to optimize the unstructured grid. With the proposed method, unstructured meshes were generated and optimized for global oceans, small sea areas, and the South China Sea estuary to carry out data experiments. The results suggested that the proportion of triangles with a triangle shape factor greater than 0.7 amounted to 77.80%, 79.78%, and 79.78%, respectively, in the unstructured mesh. Meanwhile, the proportion of long, narrow triangles in the unstructured mesh was decreased to 8.99%, 3.46%, and 4.12%, respectively.


Author(s):  
Kevin Raynaldo ◽  
Steven Darmawan ◽  
Agus Halim

Remotely Operated Vehicle (ROV) is an underwater robot that designed by UNTAR Robotics Team and has been competed in Singapore Robotics Games (SRG) 2020. Evaluation that conducted from the competition is the need of optimization in thrust and maneuverability so it can move more flexible and stable. Based on the problem, investigation of thruster’s configuration by adding kort nozzle to existing propeller is implemented to increase thrust and performance. Consideration in using open water characteristics for analysis is elaborated in this investigation. The existing propeller has 3-blade with 35 mm diameter; 1,4 pitch diameter ratio; and 0,511 expanded blade area ratio which is used as thruster of ROV 2020. It utilizes CFD approach in ANSYS CFX 2020 R1 software with moving reference frame (MRF) method. Meanwhile, general mesh or unstructured mesh arrangements is used as computational mesh with 165.201 nodes. The MRF implements frozen rotor concept as frame change/mixing to observe fluid flow. The CFD with shear stress transport (SST) k-omega model is conducted. The simulation is done at 300 rpm and J = 0,473 for ROV’s operating condition. The result shows that thruster equipped by kort nozzle is able to increase the thrust for 2,253% and reduce the propeller required torque for 6,633%. Furthermore, the configuration can also reduce wake phenomenon as result of rotating propeller which represents better maneuver chance. Keywords: ROV, kort nozzle, open water characteristics, CFD, performanceAbstrakRemotely Operated Vehicle (ROV) merupakan sebuah underwater robot yang didesain oleh Tim Robotik UNTAR dan telah berkompetisi dalam Singapore Robotics Games (SRG) 2020. Evaluasi yang dilakukan terhadap hasil kompetisi tersebut adalah terdapat kebutuhan untuk melakukan optimasi dalam thrust dan kemampuan bermanuver sehingga ROV dapat bergerak lebih fleksibel dan stabil. Berdasarkan permasalahan tersebut, investigasi pada konfigurasi thruster dengan penambahan kort nozzle terhadap existing propeller diimplementasikan untuk meningkatkan thrust dan unjuk kerja. Pertimbangan dalam penggunaan open water characteristics sebagai dasar analisis diuraikan dalam investigasi ini. Existing propeller memiliki 3 buah blade dengan diameter 35 mm; pitch diameter ratio sebesar 1,4; dan expanded blade area ratio sebesar 0,511 yang mana digunakan sebagai thruster ROV 2020. Investigasi tersebut menggunakan pendekatan CFD dalam software ANSYS CFX 2020 R1 dengan metode moving reference frame (MRF). Sementara itu, computational mesh menggunakan jenis general mesh atau unstructured mesh arrangements dengan total 165.201 nodes. MRF mengimplementasikan konsep frozen rotor sebagai frame change/mixing untuk mengamati aliran fluida. CFD dilakukan dengan menggunakan model shear stress transport (SST) k-omega. Simulasi tersebut dilakukan pada 300 rpm dan J = 0,473 sebagai operating condition ROV. Hasil simulasi menunjukkan bahwa thruster yang dilengkapi kort nozzle mampu meningkatkan thrust sebesar 2,253% dan mengurangi torsi yang dibutuhkan propeller sebesar 6,633%. Lebih lanjut, konfigurasi ini juga dapat mengurangi fenomena wake sebagai akibat dari putaran propeller yang mana merepresentasikan peluang manuver yang lebih baik.


Author(s):  
Mostafa Abobaker ◽  
Sogair Addeep ◽  
Lukmon O Afolabi ◽  
Abdulhafid M Elfaghi

Mesh type and quality play a significant role in the accuracy and stability of the numerical computation. A computational method for two-dimensional subsonic flow over NACA 0012 airfoil at angles of attack from 0o to 10o and operating Reynolds number of 6×106 is presented with structured and unstructured meshes. Steady-state governing equations of continuity and momentum conservation are solved and combined with k-v shear stress transport (SST-omega) turbulence model to obtain the flow. The effect of structured and unstructured mesh types on lift and drag coefficients are illustrated. Calculations are done for constant velocity and a range of angles of attack using Ansys Fluent CFD software. The results are validated through a comparison of the predictions and experimental measurements for the selected airfoil. The calculations showed that the structured mesh results are closer to experimental data for this airfoil and under studied operating conditions.


2021 ◽  
pp. 193-224
Author(s):  
George Qin
Keyword(s):  

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