Impeller geometry optimization using a machine learning-based algorithm with physics embedded dynamic sampling method for a high-speed miniature pump

This paper proposes an optimization approach leveraging machine learning, integrated with physics-informed dynamic sampling, to improve the hydraulic efficiency of highspeed pumps utilized in aerospace. These pumps are difficult to optimize due to their sensitivity to various impeller geometrical pa...

Full description

Saved in:
Bibliographic Details
Published in:Journal of mechanical science and technology Vol. 39; no. 7; pp. 4043 - 4065
Main Authors: Song, Xueyi, Zheng, Kexin, Luo, Xianwu
Format: Journal Article
Language:English
Published: Seoul Korean Society of Mechanical Engineers 01.07.2025
Springer Nature B.V
대한기계학회
Subjects:
Algorithms
Control
Dynamical Systems
Efficiency
Engineering
Flow separation
High speed
Hydraulics
Impellers
Industrial and Production Engineering
Machine learning
Mechanical Engineering
Optimization
Original Article
Parameter sensitivity
Physics
Pumps
Sampling methods
Suction
Uniform flow
Vibration
Vorticity
기계공학
Machine learning-based algorithm
Physics embedded dynamic sampling method
High-speed miniature pump
Optimization
ISSN:1738-494X, 1976-3824
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract This paper proposes an optimization approach leveraging machine learning, integrated with physics-informed dynamic sampling, to improve the hydraulic efficiency of highspeed pumps utilized in aerospace. These pumps are difficult to optimize due to their sensitivity to various impeller geometrical parameters. To address the large design space and reduce computational cost, the study introduces fundamental design physics equations and integrates a distance criterion within the optimization algorithm. The optimization process focuses on 13 key design variables and utilizes CFD simulations to predict hydraulic performance, with the goal of maximizing efficiency while ensuring the pump head within specified limits. The results show a 4.35 % increase in hydraulic efficiency and improved flow uniformity. Analysis of entropy generation rates and boundary vorticity flux reveals more uniform flow along the blades’ suction side and reduced vorticity near the trailing edge, indicating reduced flow separation and turbulence. This study offers an effective tool for optimizing high-speed miniature pumps, providing insights for future pump designs.
AbstractList This paper proposes an optimization approach leveraging machine learning, integrated with physics-informed dynamic sampling, to improve the hydraulic efficiency of highspeed pumps utilized in aerospace. These pumps are difficult to optimize due to their sensitivity to various impeller geometrical parameters. To address the large design space and reduce computational cost, the study introduces fundamental design physics equations and integrates a distance criterion within the optimization algorithm. The optimization process focuses on 13 key design variables and utilizes CFD simulations to predict hydraulic performance, with the goal of maximizing efficiency while ensuring the pump head within specified limits. The results show a 4.35 % increase in hydraulic efficiency and improved flow uniformity. Analysis of entropy generation rates and boundary vorticity flux reveals more uniform flow along the blades’ suction side and reduced vorticity near the trailing edge, indicating reduced flow separation and turbulence. This study offers an effective tool for optimizing high-speed miniature pumps, providing insights for future pump designs.
This paper proposes an optimization approach leveraging machine learning, integrated with physics-informed dynamic sampling, to improve the hydraulic efficiency of highspeed pumps utilized in aerospace. These pumps are difficult to optimize due to their sensitivity to various impeller geometrical parameters. To address the large design space and reduce computational cost, the study introduces fundamental design physics equations and integrates a distance criterion within the optimization algorithm. The optimization process focuses on 13 key design variables and utilizes CFD simulations to predict hydraulic performance, with the goal of maximizing efficiency while ensuring the pump head within specified limits. The results show a 4.35 % increase in hydraulic efficiency and improved flow uniformity. Analysis of entropy generation rates and boundary vorticity flux reveals more uniform flow along the blades’ suction side and reduced vorticity near the trailing edge, indicating reduced flow separation and turbulence. This study offers an effective tool for optimizing high-speed miniature pumps, providing insights for future pump designs. KCI Citation Count: 0
Author Song, Xueyi
Zheng, Kexin
Luo, Xianwu
Author_xml – sequence: 1
  givenname: Xueyi
  surname: Song
  fullname: Song, Xueyi
  organization: State Key Laboratory of Hydroscience and Engineering, Department of Energy and Power Engineering, Tsinghua University
– sequence: 2
  givenname: Kexin
  surname: Zheng
  fullname: Zheng, Kexin
  organization: State Key Laboratory of Hydroscience and Engineering, Department of Energy and Power Engineering, Tsinghua University
– sequence: 3
  givenname: Xianwu
  surname: Luo
  fullname: Luo, Xianwu
  email: luoxw@tsinghua.edu.cn
  organization: State Key Laboratory of Hydroscience and Engineering, Department of Energy and Power Engineering, Tsinghua University, Beijing Key Laboratory of CO2 Utilization and Reduction Technology, Department of Energy and Power Engineering, Tsinghua University
BackLink https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART003224667$$DAccess content in National Research Foundation of Korea (NRF)
BookMark eNp9kctq3TAQhk1JoLn0AboTdFdQq6tlL0Po5UCgUBLoTkiybCuxLpVsyul79H2jUxe6ajczw-j7_xH8l81ZiME2zWuM3mGExPuCCUEtRIRD1JIOohfNBe5FC2lH2FmdBe0g69m3l81lKY-oQgzji-bXwSe7LDaDyUZv13wEMa3Ou59qdTGArbgwAQW8MrMLFixW5VBXUKtiB6CWKWa3zh78qBWk-VicKcB6bYehvg_HoLwzoCiflpNTPTHHAYwxV9PZTTMsyVbQu-DUumUL0ubTdXM-qqXYV3_6VfPw8cP97Wd49-XT4fbmDhpK6Qrx2PaYMMY5F9oMRqC2FWa0rdIGUWr5SDXqheaaMtQyog1hGuleCMYGM2h61bzdfUMe5ZNxMir3u09RPmV58_X-IDESlGPRVvjNDqccv2-2rPIxbjnU_0lKBKdM8Ir-lyJdRViHKoV3yuRYSrajTNl5lY_1nDwFKvdAZQ1UngKVJw3ZNaWyYbL5r_O_Rc_C-aZ_
Cites_doi 10.1007/s12206-024-0521-2
10.1177/1687814017745251
10.3390/jmse9020236
10.1017/dsj.2021.25
10.1016/j.energy.2020.117582
10.1007/s12206-023-0120-7
10.1016/j.enconman.2014.12.006
10.1080/0305215X.2021.2015585
10.1126/sciadv.abi7203
10.1115/1.4043056
10.1016/j.energy.2024.132691
10.1016/j.applthermaleng.2018.06.067
10.1155/2014/234615
10.1088/1742-6596/1909/1/012072
10.1016/j.compfluid.2008.01.002
10.1111/aor.13366
10.1016/j.flowmeasinst.2024.102521
10.1007/s00158-019-02280-0
10.1007/s00158-019-02367-8
10.1080/19942060.2011.11015351
10.1080/19942060.2023.2227686
10.1155/2014/464363
10.1515/tjj-2020-0008
10.1142/S0217984923501178
10.1007/s10047-018-1072-z
10.1016/j.ast.2015.12.003
10.1016/S1001-6058(16)60816-8
10.1016/j.renene.2021.10.094
10.1007/s11431-015-5865-5
10.1115/1.4049178
10.1016/j.energy.2023.126677
10.3901/CJME.2015.0116.016
10.1016/j.apenergy.2021.116455
10.1002/er.3845
10.3390/pr7050246
10.3390/app14104313
10.3390/en17040853
ContentType Journal Article
Copyright The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2025
The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2025.
Copyright_xml – notice: The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2025
– notice: The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2025.
DBID AAYXX
CITATION
7TB
8FD
FR3
ACYCR
DOI 10.1007/s12206-025-0628-0
DatabaseName CrossRef
Mechanical & Transportation Engineering Abstracts
Technology Research Database
Engineering Research Database
Korean Citation Index
DatabaseTitle CrossRef
Technology Research Database
Mechanical & Transportation Engineering Abstracts
Engineering Research Database
DatabaseTitleList Technology Research Database


Technology Research Database
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
Physics
EISSN 1976-3824
EndPage 4065
ExternalDocumentID oai_kci_go_kr_ARTI_10735176
10_1007_s12206_025_0628_0
GroupedDBID -Y2
.86
.VR
06D
0R~
0VY
1N0
2.D
203
29L
29~
2J2
2JN
2JY
2KG
2KM
2LR
2VQ
2~H
30V
4.4
406
408
40D
40E
5GY
5VS
6NX
8FE
8FG
8UJ
95-
95.
95~
96X
9ZL
AAAVM
AABHQ
AACDK
AAHNG
AAIAL
AAJBT
AAJKR
AANZL
AAPKM
AARHV
AARTL
AASML
AATNV
AATVU
AAUYE
AAWCG
AAYIU
AAYQN
AAYTO
AAYZH
ABAKF
ABDBE
ABDZT
ABECU
ABFSG
ABFTD
ABFTV
ABHQN
ABJCF
ABJNI
ABJOX
ABKCH
ABMNI
ABMQK
ABNWP
ABQBU
ABQSL
ABSXP
ABTEG
ABTHY
ABTKH
ABTMW
ABWNU
ABXPI
ACAOD
ACBXY
ACDTI
ACGFS
ACHSB
ACHXU
ACIWK
ACKNC
ACMDZ
ACMLO
ACOKC
ACOMO
ACPIV
ACSNA
ACSTC
ACZOJ
ADHIR
ADHKG
ADKNI
ADKPE
ADMLS
ADRFC
ADTPH
ADURQ
ADYFF
ADZKW
AEBTG
AEFQL
AEGAL
AEGNC
AEJHL
AEJRE
AEKMD
AEMSY
AENEX
AEOHA
AEPYU
AESKC
AETLH
AEVLU
AEXYK
AEZWR
AFBBN
AFDZB
AFGCZ
AFHIU
AFKRA
AFLOW
AFOHR
AFQWF
AFWTZ
AFZKB
AGAYW
AGDGC
AGJBK
AGMZJ
AGQEE
AGQMX
AGQPQ
AGRTI
AGWIL
AGWZB
AGYKE
AHAVH
AHBYD
AHKAY
AHPBZ
AHSBF
AHWEU
AHYZX
AIAKS
AIGIU
AIIXL
AILAN
AITGF
AIXLP
AJBLW
AJRNO
ALMA_UNASSIGNED_HOLDINGS
ALWAN
AMKLP
AMXSW
AMYLF
AOCGG
ARCEE
ARMRJ
ASPBG
ATHPR
AVWKF
AXYYD
AYJHY
AZFZN
B-.
BA0
BDATZ
BENPR
BGLVJ
CAG
CCPQU
COF
CS3
CSCUP
DBRKI
DDRTE
DNIVK
DPUIP
EBLON
EBS
EIOEI
EJD
ESBYG
FEDTE
FERAY
FFXSO
FIGPU
FINBP
FNLPD
FRRFC
FSGXE
FWDCC
GGCAI
GGRSB
GJIRD
GNWQR
GQ7
GW5
H13
HCIFZ
HF~
HG6
HMJXF
HRMNR
HVGLF
HZ~
I-F
IJ-
IKXTQ
IWAJR
IXC
IXD
I~X
I~Z
J-C
J0Z
JBSCW
JZLTJ
KOV
KVFHK
L6V
LLZTM
M7S
MA-
MK~
ML~
MZR
NDZJH
NF0
NPVJJ
NQJWS
O9-
P9P
PF0
PHGZM
PHGZT
PQGLB
PT4
PTHSS
Q2X
QOS
R89
R9I
RHV
ROL
RPX
RSV
S0W
S16
S1Z
S26
S27
S28
S3B
SAP
SCLPG
SDH
SEG
SHX
SISQX
SJYHP
SNE
SNPRN
SNX
SOHCF
SOJ
SPISZ
SRMVM
SSLCW
STPWE
SZN
T13
T16
TDB
TSG
TSV
TUC
TUS
U2A
UG4
UOJIU
UTJUX
UZXMN
VC2
VFIZW
W48
WK8
YLTOR
Z45
ZMTXR
ZZE
~A9
AAYXX
ABRTQ
AFFHD
ARAPS
CITATION
7TB
8FD
FR3
ACYCR
ID FETCH-LOGICAL-c333t-1f6912445557bcdc70667cfe6abc033e5f3b097b5b340642bc24b0b97744dcdb3
IEDL.DBID RSV
ISICitedReferencesCount 0
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001520722900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 1738-494X
IngestDate Sun Jul 13 03:14:50 EDT 2025
Thu Nov 27 04:24:12 EST 2025
Thu Sep 18 00:02:06 EDT 2025
Sat Nov 29 07:48:44 EST 2025
Thu Jul 10 06:07:02 EDT 2025
IsPeerReviewed true
IsScholarly true
Issue 7
Keywords Machine learning-based algorithm
Physics embedded dynamic sampling method
High-speed miniature pump
Optimization
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c333t-1f6912445557bcdc70667cfe6abc033e5f3b097b5b340642bc24b0b97744dcdb3
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
PQID 3228507480
PQPubID 326249
PageCount 23
ParticipantIDs nrf_kci_oai_kci_go_kr_ARTI_10735176
proquest_journals_3275347507
proquest_journals_3228507480
crossref_primary_10_1007_s12206_025_0628_0
springer_journals_10_1007_s12206_025_0628_0
PublicationCentury 2000
PublicationDate 2025-07-01
PublicationDateYYYYMMDD 2025-07-01
PublicationDate_xml – month: 07
  year: 2025
  text: 2025-07-01
  day: 01
PublicationDecade 2020
PublicationPlace Seoul
PublicationPlace_xml – name: Seoul
– name: Heidelberg
PublicationTitle Journal of mechanical science and technology
PublicationTitleAbbrev J Mech Sci Technol
PublicationYear 2025
Publisher Korean Society of Mechanical Engineers
Springer Nature B.V
대한기계학회
Publisher_xml – name: Korean Society of Mechanical Engineers
– name: Springer Nature B.V
– name: 대한기계학회
References X Song (628_CR38) 2022; 2217
M H Shojaeefard (628_CR24) 2019; 60
B Zhao (628_CR34) 2015; 28
628_CR4
Q Liu (628_CR8) 2024; 96
J Yang (628_CR39) 2008; 39
X Song (628_CR5) 2024; 38
B Ghadimi (628_CR32) 2019; 43
O Owoyele (628_CR37) 2021; 285
Y Gu (628_CR10) 2024; 307
H Xiang (628_CR13) 2022; 39
628_CR11
628_CR12
B Ghadimi (628_CR25) 2019; 22
628_CR35
D D Yand (628_CR41) 2022; 65
O Owoyele (628_CR36) 2021; 143
K Ekradi (628_CR15) 2020; 201
H Safikhani (628_CR22) 2011; 5
L Zhang (628_CR6) 2023; 268
K Xu (628_CR23) 2021; 9
Y X Zhang (628_CR43) 2012; 15
M J Montazeri (628_CR16) 2016; 49
S Massoudi (628_CR27) 2022; 8
A R Starke (628_CR19) 2018; 142
D Yang (628_CR40) 2021; 40
A Yu (628_CR42) 2022; 183
X Gan (628_CR31) 2023; 55
X Song (628_CR7) 2022; 2217
X Song (628_CR30) 2024; 2707
B Cui (628_CR9) 2023; 37
R Lamba (628_CR18) 2018; 42
M H Ahmadi (628_CR20) 2015; 91
628_CR21
R F Huang (628_CR26) 2015; 58
628_CR44
Y Yuan (628_CR14) 2017; 9
J Pei (628_CR28) 2019; 141
M Zhou (628_CR3) 2021; 7
S Derakhshan (628_CR29) 2008; 37
H Li (628_CR2) 2023; 37
628_CR1
X Han (628_CR33) 2020; 61
J Zhang (628_CR17) 2017; 29
References_xml – volume: 38
  start-page: 3009
  issue: 6
  year: 2024
  ident: 628_CR5
  publication-title: Journal of Mechanical Science and Technology
  doi: 10.1007/s12206-024-0521-2
– volume: 9
  start-page: 1
  issue: 12
  year: 2017
  ident: 628_CR14
  publication-title: Advances in Mechanical Engineering
  doi: 10.1177/1687814017745251
– volume: 9
  start-page: 1
  issue: 2
  year: 2021
  ident: 628_CR23
  publication-title: Journal of Marine Science and Engineering
  doi: 10.3390/jmse9020236
– volume: 8
  start-page: 1
  issue: 2009
  year: 2022
  ident: 628_CR27
  publication-title: Design Science
  doi: 10.1017/dsj.2021.25
– volume: 201
  start-page: 117582
  year: 2020
  ident: 628_CR15
  publication-title: Energy
  doi: 10.1016/j.energy.2020.117582
– volume: 37
  start-page: 767
  issue: 2
  year: 2023
  ident: 628_CR2
  publication-title: Journal of Mechanical Science and Technology
  doi: 10.1007/s12206-023-0120-7
– volume: 2217
  start-page: 012049
  issue: 1
  year: 2022
  ident: 628_CR7
  publication-title: Journal of Physics: Conference Series
– volume: 91
  start-page: 315
  year: 2015
  ident: 628_CR20
  publication-title: Energy Conversion and Management
  doi: 10.1016/j.enconman.2014.12.006
– volume: 55
  start-page: 580
  issue: 4
  year: 2023
  ident: 628_CR31
  publication-title: Engineering Optimization
  doi: 10.1080/0305215X.2021.2015585
– volume: 2707
  start-page: 012154
  issue: 1
  year: 2024
  ident: 628_CR30
  publication-title: Journal of Physics: Conference Series
– volume: 7
  start-page: 1
  issue: 44
  year: 2021
  ident: 628_CR3
  publication-title: Science Advances
  doi: 10.1126/sciadv.abi7203
– volume: 141
  start-page: 061108
  issue: 6
  year: 2019
  ident: 628_CR28
  publication-title: Journal of Fluids Engineering, Transactions of the ASME
  doi: 10.1115/1.4043056
– volume: 307
  start-page: 132691
  year: 2024
  ident: 628_CR10
  publication-title: Energy
  doi: 10.1016/j.energy.2024.132691
– volume: 142
  start-page: 118
  year: 2018
  ident: 628_CR19
  publication-title: Applied Thermal Engineering
  doi: 10.1016/j.applthermaleng.2018.06.067
– ident: 628_CR21
  doi: 10.1155/2014/234615
– ident: 628_CR12
  doi: 10.1088/1742-6596/1909/1/012072
– volume: 37
  start-page: 1354
  issue: 10
  year: 2008
  ident: 628_CR29
  publication-title: Computers and Fluids
  doi: 10.1016/j.compfluid.2008.01.002
– volume: 43
  start-page: E76
  issue: 5
  year: 2019
  ident: 628_CR32
  publication-title: Artificial Organs
  doi: 10.1111/aor.13366
– volume: 96
  start-page: 102521
  year: 2024
  ident: 628_CR8
  publication-title: Flow Measurement and Instrumentation
  doi: 10.1016/j.flowmeasinst.2024.102521
– volume: 60
  start-page: 1509
  issue: 4
  year: 2019
  ident: 628_CR24
  publication-title: Structural and Multidisciplinary Optimization
  doi: 10.1007/s00158-019-02280-0
– volume: 61
  start-page: 381
  issue: 1
  year: 2020
  ident: 628_CR33
  publication-title: Structural and Multidisciplinary Optimization
  doi: 10.1007/s00158-019-02367-8
– volume: 39
  start-page: 89
  issue: 12
  year: 2008
  ident: 628_CR39
  publication-title: Nongye Jixie Xuebao/Transactions of the Chinese Society of Agricultural Machinery
– volume: 5
  start-page: 37
  issue: 1
  year: 2011
  ident: 628_CR22
  publication-title: Engineering Applications of Computational Fluid Mechanics
  doi: 10.1080/19942060.2011.11015351
– ident: 628_CR11
  doi: 10.1080/19942060.2023.2227686
– volume: 65
  start-page: 157
  issue: 1
  year: 2022
  ident: 628_CR41
  publication-title: Science China Technological Sciences
– volume: 2217
  start-page: 012009
  issue: 1
  year: 2022
  ident: 628_CR38
  publication-title: Journal of Physics: Conference Series
– ident: 628_CR44
  doi: 10.1155/2014/464363
– volume: 39
  start-page: 549
  issue: 4
  year: 2022
  ident: 628_CR13
  publication-title: International Journal of Turbo and Jet Engines
  doi: 10.1515/tjj-2020-0008
– volume: 37
  start-page: 1
  issue: 36
  year: 2023
  ident: 628_CR9
  publication-title: Modern Physics Letters B
  doi: 10.1142/S0217984923501178
– volume: 22
  start-page: 29
  issue: 1
  year: 2019
  ident: 628_CR25
  publication-title: Journal of Artificial Organs
  doi: 10.1007/s10047-018-1072-z
– volume: 49
  start-page: 185
  year: 2016
  ident: 628_CR16
  publication-title: Aerospace Science and Technology
  doi: 10.1016/j.ast.2015.12.003
– volume: 29
  start-page: 1023
  issue: 6
  year: 2017
  ident: 628_CR17
  publication-title: Journal of Hydrodynamics
  doi: 10.1016/S1001-6058(16)60816-8
– volume: 183
  start-page: 447
  year: 2022
  ident: 628_CR42
  publication-title: Renewable Energy
  doi: 10.1016/j.renene.2021.10.094
– volume: 15
  start-page: 032024
  issue: PART3
  year: 2012
  ident: 628_CR43
  publication-title: IOP Conference Series: Earth and Environmental Science
– volume: 58
  start-page: 2122
  issue: 12
  year: 2015
  ident: 628_CR26
  publication-title: Sci. China Tech. Sci.
  doi: 10.1007/s11431-015-5865-5
– volume: 143
  start-page: 1
  issue: 3
  year: 2021
  ident: 628_CR36
  publication-title: Journal of Energy Resources Technology, Transactions of the ASME
  doi: 10.1115/1.4049178
– volume: 268
  start-page: 126677
  year: 2023
  ident: 628_CR6
  publication-title: Energy
  doi: 10.1016/j.energy.2023.126677
– volume: 28
  start-page: 634
  issue: 3
  year: 2015
  ident: 628_CR34
  publication-title: Chinese Journal of Mechanical Engineering (English Edition)
  doi: 10.3901/CJME.2015.0116.016
– volume: 285
  start-page: 116455
  year: 2021
  ident: 628_CR37
  publication-title: Applied Energy
  doi: 10.1016/j.apenergy.2021.116455
– volume: 42
  start-page: 633
  issue: 2
  year: 2018
  ident: 628_CR18
  publication-title: International Journal of Energy Research
  doi: 10.1002/er.3845
– ident: 628_CR35
  doi: 10.3390/pr7050246
– ident: 628_CR1
  doi: 10.3390/app14104313
– ident: 628_CR4
  doi: 10.3390/en17040853
– volume: 40
  start-page: 95
  issue: 7
  year: 2021
  ident: 628_CR40
  publication-title: Shuili Fadian Xuebao/Journal of Hydroelectric Engineering
SSID ssj0062411
Score 2.3649533
Snippet This paper proposes an optimization approach leveraging machine learning, integrated with physics-informed dynamic sampling, to improve the hydraulic...
SourceID nrf
proquest
crossref
springer
SourceType Open Website
Aggregation Database
Index Database
Publisher
StartPage 4043
SubjectTerms Algorithms
Control
Dynamical Systems
Efficiency
Engineering
Flow separation
High speed
Hydraulics
Impellers
Industrial and Production Engineering
Machine learning
Mechanical Engineering
Optimization
Original Article
Parameter sensitivity
Physics
Pumps
Sampling methods
Suction
Uniform flow
Vibration
Vorticity
기계공학
Title Impeller geometry optimization using a machine learning-based algorithm with physics embedded dynamic sampling method for a high-speed miniature pump
URI https://link.springer.com/article/10.1007/s12206-025-0628-0
https://www.proquest.com/docview/3228507480
https://www.proquest.com/docview/3275347507
https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART003224667
Volume 39
WOSCitedRecordID wos001520722900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
ispartofPNX Journal of Mechanical Science and Technology, 2025, 39(7), , pp.4043-4065
journalDatabaseRights – providerCode: PRVAVX
  databaseName: SpringerLINK Contemporary 1997-Present
  customDbUrl:
  eissn: 1976-3824
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0062411
  issn: 1738-494X
  databaseCode: RSV
  dateStart: 19970101
  isFulltext: true
  titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22
  providerName: Springer Nature
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9QwELZKy4EeSnmJbUtlCU4gS9nYiZMjQq3opUItoL1ZGT_Cqs1mlaSV-kP4v8zkoaWoHOCUgx-x7PHMfJ4XY-9yF2uXJ1IgnECAkvpCZCF4YcEiRQULHqK-2IQ-P88Wi_zLGMfdTt7uk0my59SbYLc4JvQbJ4Li_gTi9B2UdhnVa7i4_D6x3xRFUo-yNN5klavFZMp8aIp7wujRqgn39Mw_TKO9xDl9-l9r3Wd7o4LJPw4U8Yxt-dVztvtb2sEX7OdZRTYX3_DS15XvmjteI-eoxpBMTr7wJS941Ttaej5WligFiTzHi-uybpbdj4rTIy4f3kZa7ivwyMUcd0ORe94W5K2OMw1Vqjmqxzgp5UcW7RqFJqe0Jn1eUb5GonrJvp2efP30WYzlGYSVUnZiHtKctIMkSTRYZzX5y9rg0wJsJKVPgoQo15CAVARzwMYKIiCFUznrQL5i26t65V8zXqh5AO0taBmU97YAcBDreQ4ZDs-iGXs_nZNZD1k4zCbfMu21wb02tNcGO7_FkzRXdmkodzZ9y9pcNQYRwhmO0TKZ63TGjqaTNuO1bQ1ytwwVZEV_fLAZwZ1CHUvP2Ifp4DfNf13RwT_1PmRP4p5yyCf4iG13zY1_wx7b227ZNsc9sf8C3Bb8nA
linkProvider Springer Nature
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9QwELagIAEH3qgLBSzBCWQpGztxckSIqivKCkFBe7MyfqSrks0qSZH4IfxfZvLQUlQOcMrBj1j2eGY-z4uxl7mLtcsTKRBOIEBJfSGyELywYJGiggUPUV9sQi-X2WqVfxzjuNvJ230ySfacehfsFseEfuNEUNyfQJx-TaHAooT5nz5_ndhviiKpR1kab7LK1WoyZV42xQVhdHXThAt65h-m0V7iHN75r7XeZbdHBZO_GSjiHrviN_fZrd_SDj5gPxcV2Vx8w0tfV75rfvAaOUc1hmRy8oUvecGr3tHS87GyRClI5DlefCvrZt2dVpwecfnwNtJyX4FHLua4G4rc87Ygb3WcaahSzVE9xkkpP7Jotyg0OaU16fOK8i0S1UP25fDdydsjMZZnEFZK2Yl5SHPSDpIk0WCd1eQva4NPC7CRlD4JEqJcQwJSEcwBGyuIgBRO5awD-YjtbeqN32e8UPMA2lvQMijvbQHgINbzHDIcnkUz9mo6J7MdsnCYXb5l2muDe21orw12foEnac7s2lDubPqWtTlrDCKEBY7RMpnrdMYOppM247VtDXK3DBVkRX-8tBnBnUIdS8_Y6-ngd81_XdHjf-r9nN04OvlwbI4Xy_dP2M24pyLyDz5ge11z7p-y6_Z7t26bZz3h_wK6Nf-A
linkToPdf http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Li9RAEG50FdGDb3F01QY9Kc1m0p10chR1cFCGBR_MrUn1IzusSYYkCvtD_L9WZRLGlfUgnnLoR5ru6qr6ul6MvchdrF2eSIFwAgFK6guRheCFBYsUFSx4iIZiE3q1ytbr_Hisc9pN3u6TSXIX00BZmur-aOvC0T7wLY4JCceJoBhAgZj9iiI_eoLrn75OrDhF8TQgLo23WuVqPZk1L5rinGC6XLfhnM75h5l0kD6LW_-97tvs5qh48tc7SrnDLvn6LrvxWzrCe-znsiJbjG956ZvK9-0Zb5CjVGOoJicf-ZIXvBocMD0fK06UgkSh48W3smk3_UnF6XGX795MOu4r8MjdHHdndVFtLO8K8mLHmXbVqzmqzTgp5U0W3RaFKad0J0O-Ub5FYrvPvizefX7zXoxlG4SVUvZiHtKctIYkSTRYZzX50drg0wJsJKVPgoQo15CAVAR_wMYKIiBFVDnrQD5gB3VT-4eMF2oeQHsLWgblvS0AHMR6nkOGw7Noxl5OZ2a2u-wcZp-Hmfba4F4b2muDnZ_jqZpTuzGUU5u-ZWNOW4PIYYljtEzmOp2xw-nUzXidO4NcL0PFWdEfL2xG0KdQ99Iz9moign3zX1f06J96P2PXjt8uzMfl6sNjdj0eiIjchg_ZQd9-90_YVfuj33Tt0-EO_AL5BAhz
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Impeller+geometry+optimization+using+a+machine+learning-based+algorithm+with+physics+embedded+dynamic+sampling+method+for+a+high-speed+miniature+pump&rft.jtitle=Journal+of+mechanical+science+and+technology&rft.date=2025-07-01&rft.pub=%EB%8C%80%ED%95%9C%EA%B8%B0%EA%B3%84%ED%95%99%ED%9A%8C&rft.issn=1738-494X&rft.eissn=1976-3824&rft.spage=4043&rft.epage=4065&rft_id=info:doi/10.1007%2Fs12206-025-0628-0&rft.externalDBID=n%2Fa&rft.externalDocID=oai_kci_go_kr_ARTI_10735176
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1738-494X&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1738-494X&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1738-494X&client=summon