Enhancement of Mixing Performance of Two-Layer Crossing Micromixer through Surrogate-Based Optimization
Optimum configuration of a micromixer with two-layer crossing microstructure was performed using mixing analysis, surrogate modeling, along with an optimization algorithm. Mixing performance was used to determine the optimum designs at Reynolds number 40. A surrogate modeling method based on a radia...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Micromachines |
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| Online Access: | https://www.mdpi.com/2072-666X/12/2/211 |
| _version_ | 1797396006319423488 |
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| author | Shakhawat Hossain Nass Toufiq Tayeb Farzana Islam Mosab Kaseem P.D.H. Bui M.M.K. Bhuiya Muhammad Aslam Kwang-Yong Kim |
| author_facet | Shakhawat Hossain Nass Toufiq Tayeb Farzana Islam Mosab Kaseem P.D.H. Bui M.M.K. Bhuiya Muhammad Aslam Kwang-Yong Kim |
| author_sort | Shakhawat Hossain |
| collection | DOAJ |
| description | Optimum configuration of a micromixer with two-layer crossing microstructure was performed using mixing analysis, surrogate modeling, along with an optimization algorithm. Mixing performance was used to determine the optimum designs at Reynolds number 40. A surrogate modeling method based on a radial basis neural network (RBNN) was used to approximate the value of the objective function. The optimization study was carried out with three design variables; viz., the ratio of the main channel thickness to the pitch length (H/PI), the ratio of the thickness of the diagonal channel to the pitch length (W/PI), and the ratio of the depth of the channel to the pitch length (d/PI). Through a primary parametric study, the design space was constrained. The design points surrounded by the design constraints were chosen using a well-known technique called Latin hypercube sampling (LHS). The optimal design confirmed a 32.0% enhancement of the mixing index as compared to the reference design. |
| first_indexed | 2024-03-09T00:44:03Z |
| format | Article |
| id | doaj.art-bf4fb6937c0540d1a5694ca4a763af60 |
| institution | Directory Open Access Journal |
| issn | 2072-666X |
| language | English |
| last_indexed | 2024-03-09T00:44:03Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj.art-bf4fb6937c0540d1a5694ca4a763af602023-12-11T17:38:06ZengMDPI AGMicromachines2072-666X2021-02-0112221110.3390/mi12020211Enhancement of Mixing Performance of Two-Layer Crossing Micromixer through Surrogate-Based OptimizationShakhawat Hossain0Nass Toufiq Tayeb1Farzana Islam2Mosab Kaseem3P.D.H. Bui4M.M.K. Bhuiya5Muhammad Aslam6Kwang-Yong Kim7Department of Industrial and Production Engineering, Jashore University of Science and Technology, Jashore 7408, BangladeshGas Turbine Joint Research Team, University of Djelfa, Djelfa 17000, AlgeriaDepartment of Nanotechnology and Advanced Materials Engineering, Sejong University, Seoul 05006, KoreaDepartment of Nanotechnology and Advanced Materials Engineering, Sejong University, Seoul 05006, KoreaDepartment of Mechanical Engineering, University of Tulsa, Tulsa, OK 74104, USADepartment of Mechanical Engineering, Chittagong University of Engineering & Technology (CUET), Chittagong 4349, BangladeshDepartment of Chemical Engineering, Lahore Campus, COMSATS University Islamabad (CUI), Lahore 53720, PakistanDepartment of Mechanical Engineering, Inha University, 100 Inha-ro, Michuhol-gu, Incheon 22212, KoreaOptimum configuration of a micromixer with two-layer crossing microstructure was performed using mixing analysis, surrogate modeling, along with an optimization algorithm. Mixing performance was used to determine the optimum designs at Reynolds number 40. A surrogate modeling method based on a radial basis neural network (RBNN) was used to approximate the value of the objective function. The optimization study was carried out with three design variables; viz., the ratio of the main channel thickness to the pitch length (H/PI), the ratio of the thickness of the diagonal channel to the pitch length (W/PI), and the ratio of the depth of the channel to the pitch length (d/PI). Through a primary parametric study, the design space was constrained. The design points surrounded by the design constraints were chosen using a well-known technique called Latin hypercube sampling (LHS). The optimal design confirmed a 32.0% enhancement of the mixing index as compared to the reference design.https://www.mdpi.com/2072-666X/12/2/211Navier–Stokes equationsmixing indexpassive micromixersoptimizationRBNN |
| spellingShingle | Shakhawat Hossain Nass Toufiq Tayeb Farzana Islam Mosab Kaseem P.D.H. Bui M.M.K. Bhuiya Muhammad Aslam Kwang-Yong Kim Enhancement of Mixing Performance of Two-Layer Crossing Micromixer through Surrogate-Based Optimization Micromachines Navier–Stokes equations mixing index passive micromixers optimization RBNN |
| title | Enhancement of Mixing Performance of Two-Layer Crossing Micromixer through Surrogate-Based Optimization |
| title_full | Enhancement of Mixing Performance of Two-Layer Crossing Micromixer through Surrogate-Based Optimization |
| title_fullStr | Enhancement of Mixing Performance of Two-Layer Crossing Micromixer through Surrogate-Based Optimization |
| title_full_unstemmed | Enhancement of Mixing Performance of Two-Layer Crossing Micromixer through Surrogate-Based Optimization |
| title_short | Enhancement of Mixing Performance of Two-Layer Crossing Micromixer through Surrogate-Based Optimization |
| title_sort | enhancement of mixing performance of two layer crossing micromixer through surrogate based optimization |
| topic | Navier–Stokes equations mixing index passive micromixers optimization RBNN |
| url | https://www.mdpi.com/2072-666X/12/2/211 |
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