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

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Bibliografiset tiedot
Julkaisussa:Aerospace vol. 12, no. 11 (2025), p. 1007-1030
Päätekijä: Gedda Giacomo
Muut tekijät: Ferrero, Andrea, Masseni Filippo, Mariani Massimo, Pastrone Dario
Julkaistu:
MDPI AG
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022 |a 2226-4310 
024 7 |a 10.3390/aerospace12111007  |2 doi 
035 |a 3275489481 
045 2 |b d20250101  |b d20251231 
084 |a 231330  |2 nlm 
100 1 |a Gedda Giacomo  |u Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Torino, Italy 
245 1 |a Reduced-Order Modeling and Active Subspace to Support Shape Optimization of Centrifugal Pumps 
260 |b MDPI AG  |c 2025 
513 |a Journal Article 
520 3 |a 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. 
653 |a Aeronautics 
653 |a Reduced order models 
653 |a Fluid dynamics 
653 |a Adaptive sampling 
653 |a Parameter sensitivity 
653 |a Optimization techniques 
653 |a Hydrogen 
653 |a Decomposition 
653 |a Parameter modification 
653 |a Hypercubes 
653 |a Quasi Newton methods 
653 |a Aerospace engineering 
653 |a Subspaces 
653 |a Proper Orthogonal Decomposition 
653 |a Model reduction 
653 |a Efficiency 
653 |a Aircraft 
653 |a Design optimization 
653 |a Simulation 
653 |a Parameter identification 
653 |a Torque 
653 |a Genetic algorithms 
653 |a Radial basis function 
653 |a Shape optimization 
653 |a Interpolation 
653 |a Neural networks 
653 |a Impellers 
653 |a Design parameters 
653 |a Diffusers 
653 |a Geometry 
653 |a Centrifugal pumps 
653 |a Latin hypercube sampling 
700 1 |a Ferrero, Andrea  |u Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Torino, Italy 
700 1 |a Masseni Filippo  |u Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Torino, Italy 
700 1 |a Mariani Massimo  |u Vanzetti Engineering SpA, Via dei Mestieri, 3, 12030 Cavallerleone, Italy 
700 1 |a Pastrone Dario  |u Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Torino, Italy 
773 0 |t Aerospace  |g vol. 12, no. 11 (2025), p. 1007-1030 
786 0 |d ProQuest  |t Advanced Technologies & Aerospace Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3275489481/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch 
856 4 0 |3 Full Text + Graphics  |u https://www.proquest.com/docview/3275489481/fulltextwithgraphics/embedded/75I98GEZK8WCJMPQ?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3275489481/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch