Evidence map›Paper›PMID 39472484›Full record

ArticleScientific reports2024

Jet mixing optimization using a bio-inspired evolution of hardware and control.

Tamir Shaqarin, Zhutao Jiang, Tianyu Wang, Chang Hou, Guy Y Cornejo Maceda, Nan Deng, Nan Gao, Bernd R Noack

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

8 authors.

Tamir Shaqarin *Department of Mechanical Engineering, Tafila Technical University, Tafila, 66110, Jordan.
Zhutao Jiang *Chair of Artificial Intelligence and Aerodynamics, School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, 518055, People's Republic of China.
Tianyu WangChair of Artificial Intelligence and Aerodynamics, School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, 518055, People's Republic of China.
Chang HouChair of Artificial Intelligence and Aerodynamics, School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, 518055, People's Republic of China.
Guy Y Cornejo MacedaChair of Artificial Intelligence and Aerodynamics, School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, 518055, People's Republic of China. yoslan@hit.edu.cn.
Nan DengChair of Artificial Intelligence and Aerodynamics, School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, 518055, People's Republic of China.
Nan GaoChair of Artificial Intelligence and Aerodynamics, School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, 518055, People's Republic of China.
Bernd R NoackChair of Artificial Intelligence and Aerodynamics, School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, 518055, People's Republic of China. bernd.noack@hit.edu.cn.

Funding

Guangdong Basic and Applied Research Foundation 2022A1515011492National Natural Science Foundation of China 12172109National Natural Science Foundation of China 12172111National Natural Science Foundation of China 12202121National Natural Science Foundation of China 12302293Shenzhen Science and Technology Program JCYJ20220531095605012
6 · The paper itself

Abstract

Jet mixing is a critical factor in various engineering applications, influencing pollutant dispersion, chemical processes, medical treatments, and combustion enhancement. Hitherto, jet mixing has typically been optimized by either passive or active control techniques. In this experimental study, we combine simultaneous optimization of active control with 12 inward-pointing minijets and a tuneable nozzle exit shape commanded by 12 stepper motors. Jet mixing is monitored at the end of the potential core with an array of 7 × 7 Pitot tubes. This high-dimensional actuation space is conquered with Particle Swarm Optimization through Targeted, Position-Mutated Elitism. Our results underscore the significant impact of combining control techniques, illustrating the complex interactions of both passive and active control on jet flow dynamics. The mixing area of the combined control optimization is 4.5 times larger than the area of the unforced state. This mixing increase significantly outperforms the effect of shape optimization of the nozzle alone. Our study points at the potential of optimization in high-dimensional design spaces for shapes as well as passive and active control-leveraging the rapid development of flow control hardware and the increasingly powerful tools of artificial intelligence for optimization.

Indexed as

Bio-inspired optimizationFlow controlJet mixingSmart nozzle

Identifiers

PMID39472484
PMCPMC11522276

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Registered trials

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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.