Evidence map›Paper›PMID 37763351›Full record

ReviewMaterials (Basel, Switzerland)2023

A Mini Review on Fluid Topology Optimization.

He Li, Cong Wang, Xuyu Zhang, Jie Li, Jianhu Shen, Shiwei Zhou

Abstract readReview
In one paragraph

Review in Materials (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

He LiSchool of Engineering, RMIT University, Melbourne 3001, Australia.
Cong WangSchool of Engineering, RMIT University, Melbourne 3001, Australia.
Xuyu ZhangSchool of Engineering, RMIT University, Melbourne 3001, Australia.
Jie LiSchool of Engineering, RMIT University, Melbourne 3001, Australia.ORCID 0000-0002-0344-2707
Jianhu ShenSchool of Engineering, RMIT University, Melbourne 3001, Australia.
Shiwei ZhouSchool of Engineering, RMIT University, Melbourne 3001, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Topology optimization holds great potential to achieve the best performance for various fluid-related applications like aircraft components and microfluidic mixers. This paper reviews comprehensively the technical progress of this field over the last decade from the viewpoint of structural expression. The density-based approach has been widely adopted to design structures due to its simple concept, ease of implementation, and robustness. Different designs using such a pointwise method for systems under Stokes, laminar Navier-Stokes, turbulent, non-Newtonian, and steady-state/unsteady-state fluid flows are reviewed and discussed in depth. Both isogeometric analysis and the moving morphable components/voids methods will demonstrate their advantages regarding integration with computer-aided design. The moving morphable components/voids method also significantly reduces computing costs. From the viewpoint of boundary smoothness, we are most concerned about whether smoother boundaries can reduce objective functions such as energy dissipation even lower. Therefore, this work also concentrates on level set and spline expression methods. Furthermore, we identify isogeometric analysis and machine learning in shaping the field's future. In addition, the review highlights the following two challenges: achieving accurate fluid model construction and the relatively limited experimental validation of fluid topology optimization outcomes.

Indexed as

fluid topology optimizationisogeometric analysisNavier–Stokes flows

Identifiers

PMID37763351
PMCPMC10647552

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.