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Reduced-Order Modeling and Active Subspace to Support Shape Optimization of Centrifugal Pumps

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Publicado en:Aerospace vol. 12, no. 11 (2025), p. 1007-1030
Autor principal: Gedda Giacomo
Otros Autores: Ferrero, Andrea, Masseni Filippo, Mariani Massimo, Pastrone Dario
Publicado:
MDPI AG
Materias:
Aeronautics
Reduced order models
Fluid dynamics
Adaptive sampling
Parameter sensitivity
Optimization techniques
Hydrogen
Decomposition
Parameter modification
Hypercubes
Quasi Newton methods
Aerospace engineering
Subspaces
Proper Orthogonal Decomposition
Model reduction
Efficiency
Aircraft
Design optimization
Simulation
Parameter identification
Torque
Genetic algorithms
Radial basis function
Shape optimization
Interpolation
Neural networks
Impellers
Design parameters
Diffusers
Geometry
Centrifugal pumps
Latin hypercube sampling
Acceso en línea:Citation/Abstract
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Resumen:This study presents a reduced-order modeling framework for the shape optimization of a centrifugal pump. A database of CFD solutions is generated using Latin Hypercube Sampling over five design parameters to construct a reduced-order model based on proper orthogonal decomposition with radial basis function interpolation. The model predicts the flow field at the impeller–diffuser interface and pump outlet, enabling the estimation of impeller torque and total pressure rise. The active subspaces method is applied to reduce the dimensionality of the input space from five to four modified parameters. The sensitivity of the ROM is assessed with respect to further dimensionality reductions in the parameter space, POD mode truncation, and adaptive sampling. The model is then used to perform pump shape optimization via a quasi-Newton method, identifying the combination of the parameters that minimizes the impeller torque while satisfying a constraint on the head. The optimal result is validated through CFD analysis and compared against the Pareto front generated by a genetic algorithm. The work highlights the potential of model-order reduction techniques in centrifugal pump optimization.
ISSN:2226-4310
DOI:10.3390/aerospace12111007
Fuente:Advanced Technologies & Aerospace Database