Dynamic Surrogate Model-Driven Multi-Objective Shape Optimization for Photovoltaic-Powered Underwater Vehicle Article Swipe
Chenyu Wang
,
Likun Peng
,
Jiabao Chen
,
Wei Pan
,
Jia Chen
,
Huarui Wang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/jmse13081535
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/jmse13081535
In this study, a multi-objective shape optimization framework was established for photovoltaic-powered underwater vehicles (PUVs) to systematically investigate multidisciplinary coupled design methodologies. Specifically, a global sensitivity analysis was conducted to identify four critical design parameters with 24 h energy consumption and cabin volume serving as dual optimization objectives. An integrated automated optimization workflow was constructed by incorporating parametric modeling, computational fluid dynamics (CFD) simulations, and dynamic surrogate models. Additionally, a new phased hybrid adaptive lower confidence bound (PHA-LCB) infill criterion was designed under the consideration of error-driven mechanisms, improvement feedback loops, and iterative attenuation factors to develop high-precision dynamic surrogate models. Coupled with the NSGA-II multi-objective genetic algorithm, this framework generated Pareto-optimal front solutions possessing significant engineering value. Furthermore, an optimal design configuration was ultimately determined through multi-criteria decision analysis. Compared to the initial form, it generates an additional 1148.12 Wh of electrical energy within 24 h, with an 22.36% increase in sailing range and a 2.77% improvement in cabin volume capacity. The proposed closed-loop “modeling–simulation–optimization” framework realized multi-objective optimization of PUV shape parameters, providing methodological paradigms and technical foundations for the engineering design of next-generation autonomous underwater vehicles.
Related Topics
Concepts
Surrogate model
Multi-objective optimization
Multidisciplinary design optimization
Computer science
Sensitivity (control systems)
Parametric statistics
Engineering design process
Optimal design
Computational fluid dynamics
Optimization problem
Genetic algorithm
Engineering optimization
Benchmark (surveying)
Mathematical optimization
Engineering
Aerospace engineering
Mechanical engineering
Mathematics
Electronic engineering
Algorithm
Multidisciplinary approach
Machine learning
Statistics
Geodesy
Sociology
Social science
Geography
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/jmse13081535
- OA Status
- gold
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413225481
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4413225481Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/jmse13081535Digital Object Identifier
- Title
-
Dynamic Surrogate Model-Driven Multi-Objective Shape Optimization for Photovoltaic-Powered Underwater VehicleWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-08-10Full publication date if available
- Authors
-
Chenyu Wang, Likun Peng, Jiabao Chen, Wei Pan, Jia Chen, Huarui WangList of authors in order
- Landing page
-
https://doi.org/10.3390/jmse13081535Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3390/jmse13081535Direct OA link when available
- Concepts
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Surrogate model, Multi-objective optimization, Multidisciplinary design optimization, Computer science, Sensitivity (control systems), Parametric statistics, Engineering design process, Optimal design, Computational fluid dynamics, Optimization problem, Genetic algorithm, Engineering optimization, Benchmark (surveying), Mathematical optimization, Engineering, Aerospace engineering, Mechanical engineering, Mathematics, Electronic engineering, Algorithm, Multidisciplinary approach, Machine learning, Statistics, Geodesy, Sociology, Social science, GeographyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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46Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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