{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T03:21:05Z","timestamp":1771039265124,"version":"3.50.1"},"reference-count":134,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T00:00:00Z","timestamp":1751241600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Escuela Polit\u00e9cnica Nacional","award":["PIIF-23-04"],"award-info":[{"award-number":["PIIF-23-04"]}]},{"name":"Escuela Polit\u00e9cnica Nacional","award":["EAPA_0001\/2022"],"award-info":[{"award-number":["EAPA_0001\/2022"]}]},{"name":"INTERREG ATLANTIC AREA PROGRAMME","award":["PIIF-23-04"],"award-info":[{"award-number":["PIIF-23-04"]}]},{"name":"INTERREG ATLANTIC AREA PROGRAMME","award":["EAPA_0001\/2022"],"award-info":[{"award-number":["EAPA_0001\/2022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Water"],"abstract":"<jats:p>Optimizing the design of impellers in turbomachinery is crucial for improving its energy efficiency, structural integrity, and hydraulic performance in various engineering applications. This work proposes a novel modular framework for impeller optimization that integrates high-fidelity CFD and FEM simulations, AI-based surrogate modeling, and multi-objective evolutionary algorithms. A comprehensive analysis of over one hundred recent studies was conducted, with a focus on advanced computational and hybrid optimization techniques, CFD, FEM, surrogate modeling, evolutionary algorithms, and machine learning approaches. Emphasis is placed on multi-objective and data-driven strategies that integrate high-fidelity simulations with metamodels and experimental validation. The findings demonstrate that hybrid methodologies such as combining response surface methodology (RSM), Box\u2013Behnken design (BBD), non-dominated sorting genetic algorithm II (NSGA-II), and XGBoost lead to significant improvements in hydraulic efficiency (up to 6.7%), mass reduction (over 30%), and cavitation mitigation. This study introduces a modular decision-making framework for impeller optimization which considers design objectives, simulation constraints, and the physical characteristics of turbomachinery. Furthermore, emerging trends in open-source tools, additive manufacturing, and the application of deep neural networks are discussed as key enablers for future advancements in both research and industrial applications. This work provides a practical, results-oriented framework for engineers and researchers seeking to enhance the design of impellers in the next generation of turbomachinery.<\/jats:p>","DOI":"10.3390\/w17131976","type":"journal-article","created":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T12:10:31Z","timestamp":1751285431000},"page":"1976","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Hybrid Optimization Approaches for Impeller Design in Turbomachinery: Methods, Metrics, and Design Strategies"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6863-4104","authenticated-orcid":false,"given":"Abel","family":"Remache","sequence":"first","affiliation":[{"name":"Industrial Design Department, Facultad de Ingenier\u00eda y Ciencias Aplicadas, Universidad Central del Ecuador, Quito 170129, Ecuador"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8316-7778","authenticated-orcid":false,"given":"Modesto","family":"P\u00e9rez-S\u00e1nchez","sequence":"additional","affiliation":[{"name":"Hydraulic Engineering and Environmental Department, Universitat Polit\u00e8cnica de Val\u00e8ncia, 46022 Valencia, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4732-2421","authenticated-orcid":false,"given":"V\u00edctor Hugo","family":"Hidalgo","sequence":"additional","affiliation":[{"name":"Laboratorio de Mec\u00e1nica Inform\u00e1tica, Facultad de Ingenier\u00eda Mec\u00e1nica, Escuela Polit\u00e9cnica Nacional, Quito 170517, Ecuador"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9028-9711","authenticated-orcid":false,"given":"Helena M.","family":"Ramos","sequence":"additional","affiliation":[{"name":"Civil Engineering Research and Innovation for Sustainability (CERIS), Instituto Superior T\u00e9cnico, Department of Civil Engineering, Architecture and Environment, University of Lisbon, 1049-001 Lisbon, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gambini, M., and Vellini, M. (2021). Turbomachinery Selection. Turbomachinery: Fundamentals, Selection and Preliminary Design, Springer International Publishing.","DOI":"10.1007\/978-3-030-51299-6"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ji, Y., Song, H., Xue, Z., Li, Z., Tong, M., and Li, H. (2023). A Review of the Efficiency Improvement of Hydraulic Turbines in Energy Recovery. Processes, 11.","DOI":"10.3390\/pr11061815"},{"key":"ref_3","first-page":"V006T09A002","article-title":"Numerical Analysis of Reversible Radial-Flow Turbomachinery for Energy Storage Applications","volume":"Volume 86991","author":"Parisi","year":"2023","journal-title":"Turbo Expo: Power for Land, Sea, and Air"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"101211","DOI":"10.1115\/1.4047539","article-title":"Design Optimization of the Impeller and Volute of a Centrifugal Pump to Improve the Hydraulic Performance and Flow Stability","volume":"142","author":"Shim","year":"2020","journal-title":"J. Fluids Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"111358","DOI":"10.1016\/j.est.2024.111358","article-title":"Investigation on the Unstable Flow Characteristic and Its Alleviation Methods by Modifying the Impeller Blade Tailing Edge in a Centrifugal Pump","volume":"86","author":"Ye","year":"2024","journal-title":"J. Energy Storage"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Shi, L., Zhu, J., Tang, F., and Wang, C. (2020). Multi-Disciplinary Optimization Design of Axial-Flow Pump Impellers Based on the Approximation Model. Energies, 13.","DOI":"10.3390\/en13040779"},{"key":"ref_7","first-page":"V05CT20A001","article-title":"Multi-Objective Optimization of the Cooling Configuration of a High Pressure Turbine Blade","volume":"Volume 51104","author":"Wagner","year":"2018","journal-title":"Turbo Expo: Power for Land, Sea, and Air"},{"key":"ref_8","first-page":"1974","article-title":"Energy-Saving Oriented Optimization Design of the Impeller and Volute of a Multi-Stage Double-Suction Centrifugal Pump Using Artificial Neural Network","volume":"16","author":"Zhao","year":"2022","journal-title":"Eng. Appl. Comput. Fluid Mech."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hammond, J., Pepper, N., Montomoli, F., and Michelassi, V. (2022). Machine Learning Methods in CFD for Turbomachinery: A Review. Int. J. Turbomach. Propuls. Power, 7.","DOI":"10.3390\/ijtpp7020016"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1007\/s10462-024-10867-3","article-title":"Application of Artificial Intelligence in Turbomachinery Aerodynamics: Progresses and Challenges","volume":"57","author":"Zou","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"143","DOI":"10.5293\/IJFMS.2016.9.2.143","article-title":"A Study on the Multi-Objective Optimization of Impeller for High-Power Centrifugal Compressor","volume":"9","author":"Kang","year":"2016","journal-title":"Int. J. Fluid Mach. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4853","DOI":"10.1007\/s12206-017-0933-3","article-title":"Design and Optimization of Meridional Profiles for the Impeller of Centrifugal Compressors","volume":"31","author":"Mojaddam","year":"2017","journal-title":"J. Mech. Sci. Technol."},{"key":"ref_13","first-page":"11","article-title":"Topology Optimization of Static Turbomachinery Components","volume":"23","author":"Buonamici","year":"2023","journal-title":"Manuf. Technol. J."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Mojaddam, M., and Pullen, K.R. (2019). Optimization of a Centrifugal Compressor Using the Design of Experiment Technique. Appl. Sci., 9.","DOI":"10.3390\/app9020291"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"124","DOI":"10.33737\/jgpps\/150663","article-title":"Machine Learning Based Design Optimization of Centrifugal Impellers","volume":"6","author":"Zhang","year":"2022","journal-title":"J. Glob. Power Propuls. Soc."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"122818","DOI":"10.1016\/j.renene.2025.122818","article-title":"Multi-Objective Optimization Design for Broadening the High Efficiency Region of Hydrodynamic Turbine with Forward-Curved Impeller and Energy Loss Analysis","volume":"245","author":"Yang","year":"2025","journal-title":"Renew. Energy"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6405","DOI":"10.1177\/09544062231221625","article-title":"A Review on Aerodynamic Optimization of Turbomachinery Using Adjoint Method","volume":"238","author":"Lavimi","year":"2024","journal-title":"Proc. Inst. Mech. Eng. C J. Mech. Eng. Sci."},{"key":"ref_18","unstructured":"Gomersall, P., and Jain, D. (2024). Robust Design and Optimization of Turbo-Machinery Compressors. Catal. Propelling Sch. Forw., 2."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"103473","DOI":"10.1016\/j.cja.2025.103473","article-title":"A Panoramic Aerodynamic Performance Prediction Method for Turbomachinery Cascades Using Transformer-Enhanced Neural Operator","volume":"38","author":"Wang","year":"2025","journal-title":"Chin. J. Aeronaut."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.aej.2024.06.041","article-title":"Collaborative Design Method for Multi-Impeller Turbomachinery Based on Variable Sectional-Area Distribution","volume":"104","author":"Ke","year":"2024","journal-title":"Alex. Eng. J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"121799","DOI":"10.1016\/j.ijheatmasstransfer.2021.121799","article-title":"Aerodynamic Robustness Optimization and Design Exploration of Centrifugal Compressor Impeller under Uncertainties","volume":"180","author":"Tang","year":"2021","journal-title":"Int. J. Heat Mass Transf."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, M., Li, Y., Yuan, J., and Osman, F.K. (2021). Matching Optimization of a Mixed Flow Pump Impeller and Diffuser Based on the Inverse Design Method. Processes, 9.","DOI":"10.3390\/pr9020260"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liu, Q., Zhuang, S., Bao, H., He, Z., Wang, K., and Liu, H. (2022). The Optimization of a First-Stage Liquid-Sealing Impeller Structure for a Turbopump Based on Response Surface Methodology. Processes, 10.","DOI":"10.3390\/pr10101999"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"12444","DOI":"10.1016\/j.ijhydene.2022.11.312","article-title":"Novel Multidisciplinary Design and Multi-Objective Optimization of Centrifugal Compressor Used for Hydrogen Fuel Cells","volume":"48","author":"Chen","year":"2023","journal-title":"Int. J. Hydrogen Energy"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"15597","DOI":"10.1007\/s13369-024-09134-y","article-title":"The Fan Design Optimization for Totally Enclosed Type Induction Motor with Experimentally Verified CFD-Based MOGA Simulations","volume":"49","year":"2024","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"127497","DOI":"10.1016\/j.cej.2020.127497","article-title":"Impeller Shape-Optimization of Stirred-Tank Reactor: CFD and Fluid Structure Interaction Analyses","volume":"413","author":"Hoseini","year":"2021","journal-title":"Chem. Eng. J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3193","DOI":"10.1002\/qre.3584","article-title":"Fatigue Reliability Evaluation for Impellers with Consideration of Multi-source Uncertainties Using a WOA-XGBoost Surrogate Model","volume":"40","author":"Qian","year":"2024","journal-title":"Qual. Reliab. Eng. Int."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yang, Y., Shu, B., and Zhao, X. (2023, January 11\u201313). Automatic Detection of Surface Defects of Submersible Pump Impellers by Machine Learning Algorithm. Proceedings of the 2023 IEEE International Conference on Image Processing and Computer Applications (ICIPCA), Changchun, China.","DOI":"10.1109\/ICIPCA59209.2023.10257869"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"60259","DOI":"10.1109\/ACCESS.2020.2982841","article-title":"A General Framework for Designing 3D Impellers Using Topology Optimization and Additive Manufacturing","volume":"8","author":"Meli","year":"2020","journal-title":"IEEE Access"},{"key":"ref_30","unstructured":"Corbo, S., Iurisci, G., Gangioli, F., Boccini, E., Meli, E., and Rindi, A. (2018). Additive Manufacturing and Topology Optimization Applied to Impeller to Enhance Mechanical Performance. Proceedings of the 47th Turbomachinery Symposium, Turbomachinery Laboratory, Texas A&M Engineering Experiment Station."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"12026","DOI":"10.1088\/1742-6596\/2252\/1\/012026","article-title":"Topology Optimization Design with Addictive Manufacturing Constraints for Centrifugal Impeller","volume":"2252","author":"Liu","year":"2022","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"D\u00f6nmez, A.H., Yumurtaci, Z., and Kavurmacio\u011flu, L. (2024). Cavitation Performance Enhancement of a Centrifugal Pump Impeller Based on Taguchi\u2019s Orthogonal Optimization. Arab. J. Sci. Eng.","DOI":"10.1007\/s13369-024-09579-1"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1007\/s12206-022-1217-0","article-title":"Hydrodynamic Optimization of the Impeller and Diffuser Vane of an Axial-Flow Pump","volume":"37","author":"Nguyen","year":"2023","journal-title":"J. Mech. Sci. Technol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"123057","DOI":"10.1016\/j.apenergy.2024.123057","article-title":"Multi-Objective Optimization for Impeller Structure Parameters of Fuel Cell Air Compressor Using Linear-Based Boosting Model and Reference Vector Guided Evolutionary Algorithm","volume":"363","author":"Fu","year":"2024","journal-title":"Appl. Energy"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1007\/s00158-024-03746-6","article-title":"Modal Analysis and Structural Optimization of Integrated Bladed Disks and Centrifugal Compressor Impellers","volume":"67","author":"Lima","year":"2024","journal-title":"Struct. Multidiscip. Optim."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1007\/s00158-019-02367-8","article-title":"Centrifugal Pump Impeller and Volute Shape Optimization via Combined NUMECA, Genetic Algorithm, and Back Propagation Neural Network","volume":"61","author":"Han","year":"2020","journal-title":"Struct. Multidiscip. Optim."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"117411","DOI":"10.1016\/j.ces.2021.117411","article-title":"Computational Prediction of the Just-Suspended Speed, Njs, in Stirred Vessels Using the Lattice Boltzmann Method (LBM) Coupled with a Novel Mathematical Approach","volume":"251","author":"Sirasitthichoke","year":"2022","journal-title":"Chem Eng Sci"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2733","DOI":"10.1016\/j.cjche.2020.06.036","article-title":"CFD Simulation of Impeller Shape Effect on Quality of Mixing in Two-Phase Gas\u2013Liquid Agitated Vessel","volume":"28","author":"Heidari","year":"2020","journal-title":"Chin J Chem Eng"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"107013","DOI":"10.1016\/j.apacoust.2019.107013","article-title":"Validating Impeller Geometry Optimization for Sound Quality Based on Psychoacoustics Metrics","volume":"157","author":"Ogris","year":"2020","journal-title":"Appl. Acoust."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.ijnaoe.2019.07.002","article-title":"Multi-Condition Optimization and Experimental Verification of Impeller for a Marine Centrifugal Pump","volume":"12","author":"Wang","year":"2020","journal-title":"Int. J. Nav. Archit. Ocean Eng."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Omidi, M., Liu, S.-J., Mohtaram, S., Lu, H.-T., and Zhang, H.-C. (2019). Improving Centrifugal Compressor Performance by Optimizing the Design of Impellers Using Genetic Algorithm and Computational Fluid Dynamics Methods. Sustainability, 11.","DOI":"10.3390\/su11195409"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Lu, R., Yuan, J., Wei, G., Zhang, Y., Lei, X., and Si, Q. (2021). Optimization Design of Energy-Saving Mixed Flow Pump Based on MIGA-RBF Algorithm. Machines, 9.","DOI":"10.3390\/machines9120365"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Na, S.J., Kim, Y.S., and Jeon, E.S. (2022). Analysis of Erosion Minimization for a Slurry Pump Using Discrete Phase Model Simulations. Appl. Sci., 12.","DOI":"10.3390\/app12031597"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Dr\u0103gan, V., Dumitrescu, O., Dobromirescu, C., and Popa, I.F. (2024). Satellite Thermal Management Pump Impeller Design and Optimization. Inventions, 9.","DOI":"10.3390\/inventions9030054"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"124032","DOI":"10.1016\/j.energy.2022.124032","article-title":"Optimal Design and Performance Improvement of an Electric Submersible Pump Impeller Based on Taguchi Approach","volume":"252","author":"Bai","year":"2022","journal-title":"Energy"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Aliuly, A., Amanzholov, T., Seitov, A., Momysh, N., Jaichibekov, N., and Kaltayev, A. (2024). Hydraulic Design and CFD-Based Parametric Study for Optimizing Centrifugal Pump Impeller Performance. Appl. Sci., 14.","DOI":"10.3390\/app142210161"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"111005","DOI":"10.1115\/1.4048022","article-title":"Development, Validation, and Application of an Optimization Scheme for Impellers of Centrifugal Fans Using Computational Fluid Dynamics-Trained Metamodels","volume":"142","author":"Bamberger","year":"2020","journal-title":"J. Turbomach."},{"key":"ref_48","unstructured":"Wu, M. (2025, June 26). Efficient CFD-Based Multi-Objective Optimization of Impeller Geometries for Homogenization Processes in Stirred Tanks: The Mean Age Theory Approach. Available online: https:\/\/depositonce.tu-berlin.de\/items\/8e77b8cf-3f2f-42b1-9128-406b6673d873."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1007\/s12650-024-00998-8","article-title":"Assessment of the Predictive Capabilities of Various Turbulence Models for the Simulation of Rotating Stall in the Centrifugal Pump Impeller","volume":"27","author":"Park","year":"2024","journal-title":"J. Vis."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"108466","DOI":"10.1016\/j.ast.2023.108466","article-title":"Instabilities Identification Based on a New Centrifugal 3D Impeller Outflow Model","volume":"140","author":"Fan","year":"2023","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.1007\/s12206-024-0223-9","article-title":"Optimization of Impeller Blades of an Electric Water Pump via Computational Fluid Dynamics","volume":"38","author":"Teng","year":"2024","journal-title":"J. Mech. Sci. Technol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/s42241-023-0001-7","article-title":"Some Notes on Numerical Investigation of Three Cavitation Models through a Verification and Validation Procedure","volume":"35","author":"Deng","year":"2023","journal-title":"J. Hydrodyn."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1016\/j.egyr.2022.10.043","article-title":"Optimization of Energy Recovery Turbine in Demineralized Water Treatment System of Power Station by Box\u2013Behnken Design Method","volume":"8","author":"Ji","year":"2022","journal-title":"Energy Rep."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"106734","DOI":"10.1016\/j.icheatmasstransfer.2023.106734","article-title":"Multi-Objective Optimization of a Regenerative Pump with S-Shaped Impeller Using Response Surface Methodology","volume":"143","author":"Salimi","year":"2023","journal-title":"Int. Commun. Heat Mass Transf."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Cao, W., Wang, H., Yang, X., and Leng, X. (2023). Optimization of Guide Vane Centrifugal Pumps Based on Response Surface Methodology and Study of Internal Flow Characteristics. J. Mar. Sci. Eng., 11.","DOI":"10.3390\/jmse11101917"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Meng, F.N., Dong, Q.L., Wang, P.F., and Wang, Y. (2014). Multiobjective Optimization for the Impeller of Centrifugal Fan Based on Response Surface Methodology with Grey Relational Analysis Method. Adv. Mech. Eng.","DOI":"10.1155\/2014\/614581"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1007\/s40430-022-03441-x","article-title":"A New Expert System for Active Vibration Control (AVC) for High-Speed Train Moving on a Flexible Structure and PID Optimization Using MOGA and NSGA-II Algorithms","volume":"44","year":"2022","journal-title":"J. Braz. Soc. Mech. Sci. Eng."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"47635","DOI":"10.1021\/acsomega.3c05762","article-title":"Optimized Design of Solid\u2013Liquid Dual-Impeller Mixing Systems for Enhanced Efficiency","volume":"8","author":"Xia","year":"2023","journal-title":"ACS Omega"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Zhang, D., Zhao, Z., Ouyang, H., Wu, Z., and Han, X. (2023). An Efficient Reliability Analysis Method Based on the Improved Radial Basis Function Neural Network. J. Mech. Des., 145.","DOI":"10.1115\/1.4062584"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"104196","DOI":"10.1016\/j.apor.2024.104196","article-title":"The Surrogate Model for Short-Term Extreme Response Prediction Based on ANN and Kriging Algorithm","volume":"152","author":"Zhao","year":"2024","journal-title":"Appl. Ocean Res."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"3777","DOI":"10.2514\/1.J059332","article-title":"Kriging-Based Aeroelastic Gust Response Analysis at High Angles of Attack","volume":"58","author":"Mallik","year":"2020","journal-title":"AIAA J."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Borisut, P., and Nuchitprasittichai, A. (2023). Adaptive Latin Hypercube Sampling for a Surrogate-Based Optimization with Artificial Neural Network. Processes, 11.","DOI":"10.3390\/pr11113232"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"16878140211072944","DOI":"10.1177\/16878140211072944","article-title":"Research on Cooperative Optimization of Multiphase Pump Impeller and Diffuser Based on Adaptive Refined Response Surface Method","volume":"14","author":"Cancan","year":"2022","journal-title":"Adv. Mech. Eng."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.1080\/15435075.2021.1904942","article-title":"Multi-Objective Optimization of the Centrifugal Compressor Impeller in 130 KW PEMFC through Coupling SVM with NSGA -III Algorithms","volume":"18","author":"Ma","year":"2021","journal-title":"Int. J. Green Energy"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"3823","DOI":"10.1007\/s42107-024-01014-y","article-title":"Time-Cost Trade-off Optimization Model for Retrofitting Planning Projects Using MOGA","volume":"25","author":"Patil","year":"2024","journal-title":"Asian J. Civil. Eng."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Gupta, K., and Gupta, M.K. (2020). Application of Multi-Objective Genetic Algorithm (MOGA) Optimization in Machining Processes. Optimization of Manufacturing Processes, Springer International Publishing.","DOI":"10.1007\/978-3-030-19638-7"},{"key":"ref_67","first-page":"V006T10A008","article-title":"Multi-Point Aerodynamic Optimization of a Backward-Curved Impeller Fan","volume":"Volume 87981","author":"Tabor","year":"2024","journal-title":"Turbo Expo: Power for Land, Sea, and Air"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"121","DOI":"10.33645\/cnc.2022.7.44.7.121","article-title":"A Study on Suction Pump Impeller Form Optimization for Ballast Water Treatment System","volume":"25","author":"Lee","year":"2022","journal-title":"J. Korean Soc. Ind. Converg."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1395","DOI":"10.1080\/0305215X.2021.1932867","article-title":"Surrogate-Based Design Optimization of a Centrifugal Pump Impeller","volume":"54","author":"Jaiswal","year":"2022","journal-title":"Eng. Optim."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1503","DOI":"10.1007\/s12530-024-09574-9","article-title":"Enhancing CNN Structure and Learning through NSGA-II-Based Multi-Objective Optimization","volume":"15","author":"Elghazi","year":"2024","journal-title":"Evol. Syst."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1553","DOI":"10.1016\/j.matpr.2021.12.161","article-title":"A Comparison of the MOGA and NSGA-II Optimization Techniques to Reduce the Cost of a Biomass Supply Network","volume":"57","author":"Arya","year":"2022","journal-title":"Mater. Today Proc."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Wang, M., Li, Y., Yuan, J., Meng, F., Appiah, D., and Chen, J. (2020). Comprehensive Improvement of Mixed-Flow Pump Impeller Based on Multi-Objective Optimization. Processes, 8.","DOI":"10.3390\/pr8080905"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1016\/j.promfg.2017.07.148","article-title":"The Role of Additive Manufacturing in the Era of Industry 4.0","volume":"11","author":"Dilberoglu","year":"2017","journal-title":"Procedia Manuf."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Wang, W., Sun, J., Liu, J., Zhao, J., Pei, J., and Wang, J. (2022). Introducing Non-Hierarchical RSM and MIGA for Performance Prediction and Optimization of a Centrifugal Pump under the Nominal Condition. Processes, 10.","DOI":"10.20944\/preprints202207.0354.v1"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"053308","DOI":"10.1063\/5.0204518","article-title":"Effect of Parameter Optimization on the Flow Characteristics of Venturi-Self-Excited Oscillation Mixer Based on Response Surface Model and Multi-Island Genetic Algorithm","volume":"36","author":"Nie","year":"2024","journal-title":"Phys. Fluids"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"16878132221104576","DOI":"10.1177\/16878132221104576","article-title":"Optimization of the Impeller for Hydraulic Performance Improvement of a High-Speed Magnetic Drive Pump","volume":"14","author":"Xu","year":"2022","journal-title":"Adv. Mech. Eng."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1177\/09576509211049613","article-title":"Optimization of a Vacuum Cleaner Fan Suction and Shaft Power Using Kriging Surrogate Model and MIGA","volume":"236","author":"Almasi","year":"2021","journal-title":"Proc. Inst. Mech. Eng. Part A J. Power Energy"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Santana-Cabrera, J., Monz\u00f3n-Verona, J.M., Santana-Mart\u00edn, F.J., Garc\u00eda-Alonso, S., and Montiel-Nelson, J.A. (2015). Optimization of the Dimensionless Model of an Electrostatic Microswitch Based on AMGA Algorithm. Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences, Springer.","DOI":"10.1007\/978-3-319-11541-2_33"},{"key":"ref_79","first-page":"V02CT42A030","article-title":"Development of a Multi-Objective Preliminary Design Optimization Approach for Axial Flow Compressors","volume":"Volume 51012","author":"He","year":"2018","journal-title":"Turbo Expo: Power for Land, Sea, and Air"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"051703","DOI":"10.1115\/1.4036372","article-title":"Multi-Objective Shape Optimization Design for Liquefied Natural Gas Cryogenic Helical Corrugated Steel Pipe","volume":"139","author":"Yang","year":"2017","journal-title":"J. Offshore Mech. Arct. Eng."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Ding, Y., and Li, L. (2024). Optimization Method for Digital Twin Manufacturing System Based on NSGA-II. Int. J. Adv. Comput. Sci. Appl., 15.","DOI":"10.14569\/IJACSA.2024.01504100"},{"key":"ref_82","first-page":"1","article-title":"MOEA\/D vs. NSGA-II: A Comprehensive Comparison for Multi\/Many Objective Analog\/RF Circuit Optimization through a Generic Benchmark","volume":"29","author":"Saundefinedlican","year":"2023","journal-title":"ACM Trans. Des. Autom. Electron. Syst."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"105941","DOI":"10.1016\/j.csite.2025.105941","article-title":"An Experimental Study on the Agitating Efficiency and Power Consumption for Viscoelastic-Based Nanofluids: Elasticity, Impeller Effects, and Artificial Neural Network Approach","volume":"68","author":"Hassanlouei","year":"2025","journal-title":"Case Stud. Therm. Eng."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"203741","DOI":"10.1016\/j.wear.2021.203741","article-title":"Neural Network Prediction of Slurry Erosion of Heavy-Duty Pump Impeller\/Casing Materials 18Cr-8Ni, 16Cr-10Ni-2Mo, Super Duplex 24Cr-6Ni-3Mo-N, and Grey Cast Iron","volume":"476","author":"Singh","year":"2021","journal-title":"Wear"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"106193","DOI":"10.1016\/j.engfailanal.2022.106193","article-title":"VHCF Evaluation with BP Neural Network for Centrifugal Impeller Material Affected by Internal Inclusion and GBF Region","volume":"136","author":"Jinlong","year":"2022","journal-title":"Eng Fail Anal"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"126701","DOI":"10.1016\/j.energy.2023.126701","article-title":"Operational Performance Estimation of Vehicle Electric Coolant Pump Based on the ISSA-BP Neural Network","volume":"268","author":"Zhang","year":"2023","journal-title":"Energy"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Yi, X., Wang, Z., Liu, S., Hou, X., and Tang, Q. (2022). An Accelerated Degradation Durability Evaluation Model for the Turbine Impeller of a Turbine Based on a Genetic Algorithms Back-Propagation Neural Network. Appl. Sci., 12.","DOI":"10.3390\/app12189302"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1016\/j.cma.2010.11.014","article-title":"Multi-Objective Optimization of Turbomachinery Using Improved NSGA-II and Approximation Model","volume":"200","author":"Wang","year":"2011","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s12206-023-1202-2","article-title":"Random Forest-Based Multi-Faults Classification Modeling and Analysis for Intelligent Centrifugal Pump System","volume":"38","author":"Chang","year":"2024","journal-title":"J. Mech. Sci. Technol."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Rahimzadeh, A., Ranjbarrad, S., Ein-Mozaffari, F., and Lohi, A. (2024). Application of Machine Learning Models in Coaxial Bioreactors: Classification and Torque Prediction. ChemEngineering, 8.","DOI":"10.3390\/chemengineering8020042"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Xu, B., Deng, J., Liu, X., Chang, A., Chen, J., and Zhang, D. (2023). A Review on Optimal Design of Fluid Machinery Using Machine Learning Techniques. J. Mar. Sci. Eng., 11.","DOI":"10.3390\/jmse11050941"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Huang, R., Ni, J., Qiao, P., Wang, Q., Shi, X., and Yin, Q. (2023). An Explainable Prediction Model for Aerodynamic Noise of an Engine Turbocharger Compressor Using an Ensemble Learning and Shapley Additive Explanations Approach. Sustainability, 15.","DOI":"10.3390\/su151813405"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"110654","DOI":"10.1016\/j.ress.2024.110654","article-title":"Aerodynamic Robustness Optimization of Aeroengine Fan Performance Based on an Interpretable Dynamic Machine Learning Method","volume":"254","author":"Cheng","year":"2025","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"110897","DOI":"10.1016\/j.measurement.2022.110897","article-title":"An Acoustic Signal Cavitation Detection Framework Based on XGBoost with Adaptive Selection Feature Engineering","volume":"192","author":"Sha","year":"2022","journal-title":"Measurement"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"121460","DOI":"10.1109\/ACCESS.2020.3006499","article-title":"Analysis and Application of Grey Wolf Optimizer-Long Short-Term Memory","volume":"8","author":"Pan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_96","first-page":"012061","article-title":"An Improved Grey Wolf Optimizer (IGWO) Algorithm for Optimization of Centrifugal Pump with Guide Vane","volume":"2854","author":"Jian","year":"2024","journal-title":"Journal of Physics: Conference Series"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Jing, T., Sun, H., Cheng, J., and Zhou, L. (2023). Optimization of a Screw Centrifugal Blood Pump Based on Random Forest and Multi-Objective Gray Wolf Optimization Algorithm. Micromachines, 14.","DOI":"10.3390\/mi14020406"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"106869","DOI":"10.1016\/j.ast.2021.106869","article-title":"Dual-Convolutional Neural Network Based Aerodynamic Prediction and Multi-Objective Optimization of a Compact Turbine Rotor","volume":"116","author":"Wang","year":"2021","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.euromechflu.2023.12.005","article-title":"Off-Design Performance Analysis of a Radial Fan Using Experimental, Computational, and Artificial Intelligence Approaches","volume":"104","author":"Moradihaji","year":"2024","journal-title":"Eur. J. Mech.-B\/Fluids"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"124440","DOI":"10.1016\/j.energy.2022.124440","article-title":"Integrated Graph Deep Learning Framework for Flow Field Reconstruction and Performance Prediction of Turbomachinery","volume":"254","author":"Li","year":"2022","journal-title":"Energy"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Cui, H., Li, Z., Sun, B., Fan, T., Li, Y., Luo, L., Zhang, Y., and Wang, J. (2022). A New Ice Quality Prediction Method of Wind Turbine Impeller Based on the Deep Neural Network. Energies, 15.","DOI":"10.3390\/en15228454"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"100067","DOI":"10.1016\/j.bcra.2022.100067","article-title":"A Survey on Blockchain Technology and Its Security","volume":"3","author":"Guo","year":"2022","journal-title":"Blockchain Res. Appl."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"112101","DOI":"10.1115\/1.4033185","article-title":"Optimization of a Centrifugal Compressor Impeller for Robustness to Manufacturing Uncertainties","volume":"138","author":"Javed","year":"2016","journal-title":"J. Eng. Gas. Turbine Power"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"127289","DOI":"10.1016\/j.energy.2023.127289","article-title":"Uncertainty Quantification and Aerodynamic Robust Optimization of Turbomachinery Based on Graph Learning Methods","volume":"273","author":"Li","year":"2023","journal-title":"Energy"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"2715","DOI":"10.1007\/s13369-024-09229-6","article-title":"Optimization Design and Internal Flow Characteristics Analysis Based on Latin Hypercube Sampling Method","volume":"50","author":"Peng","year":"2025","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Ayremlouzadeh, H., Jafarmadar, S., and Niaki, S.R.A. (2022). Investigation on the Effect of Impeller Design Parameters on Performance of a Low Specific Speed Centrifugal Pump Using Taguchi Optimization Method. Int. J. Fluid Power, 161\u2013182.","DOI":"10.13052\/ijfp1439-9776.2322"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"3759","DOI":"10.1016\/j.aej.2021.02.045","article-title":"Mixing Optimization with Inward Flow Configuration Contra-Rotating Impeller, Baffle-Free Tank","volume":"60","author":"Satjaritanun","year":"2021","journal-title":"Alex. Eng. J."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"106787","DOI":"10.1016\/j.ast.2021.106787","article-title":"Aerodynamic Analysis and Design Optimization of a Centrifugal Compressor Impeller Considering Realistic Manufacturing Uncertainties","volume":"115","author":"Ju","year":"2021","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Jamimoghaddam, M., Sadighi, A., and Araste, Z. (2020, January 23\u201324). ESA-Based Anomaly Detection of a Centrifugal Pump Using Self-Organizing Map. Proceedings of the 2020 6th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS), Mashhad, Iran.","DOI":"10.1109\/ICSPIS51611.2020.9349579"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cja.2021.07.019","article-title":"A Gradient-Based Method Assisted by Surrogate Model for Robust Optimization of Turbomachinery Blades","volume":"35","author":"Jiaqi","year":"2022","journal-title":"Chin. J. Aeronaut."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1007\/s00158-019-02227-5","article-title":"A Novel Global Optimization Algorithm and Data-Mining Methods for Turbomachinery Design","volume":"60","author":"Li","year":"2019","journal-title":"Struct. Multidiscip. Optim."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"12136","DOI":"10.1088\/1742-6596\/2752\/1\/012136","article-title":"Experimental and Numerical Study on Erosive Wear in a Dredge Pump Impeller","volume":"2752","author":"Guo","year":"2024","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Yang, X., Xi, T., Qin, Y., Zhang, H., and Wang, Y. (2024). Computational Fluid Dynamics\u2013Discrete Phase Method Simulations in Process Engineering: A Review of Recent Progress. Appl. Sci., 14.","DOI":"10.3390\/app14093856"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"012074","DOI":"10.1088\/1755-1315\/1079\/1\/012074","article-title":"Lattice Boltzmann Simulation of the ERCOFTAC Pump Impeller","volume":"1079","author":"Casartelli","year":"2022","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"2045","DOI":"10.1007\/s00158-018-1966-7","article-title":"Topology Optimization Applied to the Development of Small Scale Pump","volume":"57","author":"Romero","year":"2018","journal-title":"Struct. Multidiscip. Optim."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"151863","DOI":"10.1016\/j.cej.2024.151863","article-title":"Novel Designs of Blade Mixer Impellers from the Discrete Element Method and Topology Optimization","volume":"490","author":"Castro","year":"2024","journal-title":"Chem. Eng. J."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"121972","DOI":"10.1016\/j.apenergy.2023.121972","article-title":"Robust Optimization and Uncertainty Quantification of a Micro Axial Compressor for Unmanned Aerial Vehicles","volume":"352","author":"Cheng","year":"2023","journal-title":"Appl. Energy"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"092602","DOI":"10.1115\/1.4029882","article-title":"Multi-Objective Aerodynamic Optimization Design and Data Mining of a High Pressure Ratio Centrifugal Impeller","volume":"137","author":"Guo","year":"2015","journal-title":"J. Eng. Gas Turbine Power"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"111102","DOI":"10.1016\/j.vacuum.2022.111102","article-title":"Liquid-Vapor Two-Phase Flow in Centrifugal Pump: Cavitation, Mass Transfer, and Impeller Structure Optimization","volume":"201","author":"Li","year":"2022","journal-title":"Vacuum"},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1007\/s40964-023-00469-w","article-title":"Interlayer Bonding Improvement and Optimization of Printing Parameters of FFF Polyphenylene Sulfide Parts Using GRA Method","volume":"9","author":"Zirak","year":"2024","journal-title":"Prog. Addit. Manuf."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1007\/s10499-024-01758-z","article-title":"Multi-Objective Optimization of a Pond Aeration System Using Taguchi-Based Gray Relational Analysis","volume":"33","author":"Arici","year":"2024","journal-title":"Aquac. Int."},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Kumaresan, G., Shanmugam, N.S., and Dhinakaran, V. (2021). Optimizing Performance Characteristics of Blower for Combustion Process Using Taguchi Based Grey Relational Analysis. Advances in Materials Research, Springer Nature.","DOI":"10.1007\/978-981-15-8319-3"},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Sinagra, M., Picone, C., Aric\u00f2, C., Pantano, A., Tucciarelli, T., Hannachi, M., and Driss, Z. (2021). Impeller Optimization in Crossflow Hydraulic Turbines. Water, 13.","DOI":"10.3390\/w13030313"},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"012154","DOI":"10.1088\/1742-6596\/2707\/1\/012154","article-title":"Impeller Optimization Using a Machine Learning-Based Algorithm with Dynamic Sampling Method and Flow Analysis for an Axial Flow Pump","volume":"2707","author":"Song","year":"2024","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"7363","DOI":"10.1177\/09544062211028264","article-title":"A Machine-Learning Approach to Predicting the Energy Conversion Performance of Centrifugal Pump Impeller Influenced by Blade Profile","volume":"235","author":"Wu","year":"2021","journal-title":"Proc. Inst. Mech. Eng. C J. Mech. Eng. Sci."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1080\/10426914.2019.1605177","article-title":"Integrated Energy-Efficient Machining of Rotary Impellers and Multi-Objective Optimization","volume":"35","author":"Serin","year":"2020","journal-title":"Mater. Manuf. Process."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"091003","DOI":"10.1115\/1.4065050","article-title":"Adaptive Batch Sampling Strategy for the Hundreds-Dimensional Aerodynamic Optimization of the Centrifugal Impeller","volume":"146","author":"Ji","year":"2024","journal-title":"J. Turbomach."},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Iyer, S., Deshmukh, S.M., and Tapre, R.W. (2025). Enhancing Impeller Design Parameters for Optimal Pump Efficiency and Performance in Supercritical Thermal Power Projects. Energy Technol., 2500022.","DOI":"10.1002\/ente.202500022"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"2153","DOI":"10.1016\/j.matpr.2020.07.637","article-title":"Impeller Design and CFD Analysis of Fluid Flow in Rotodynamic Pumps","volume":"37","author":"Chandrasekaran","year":"2021","journal-title":"Mater. Today Proc."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"109129","DOI":"10.1016\/j.ijheatfluidflow.2023.109129","article-title":"Optimization of a Double-Intake Squirrel Cage Fan Using OpenFoam and Metamodels","volume":"101","author":"Poncet","year":"2023","journal-title":"Int. J. Heat Fluid Flow"},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"075189","DOI":"10.1063\/5.0217560","article-title":"Characterization of the Flow through a Centrifugal Pump under Different Inflow Conditions Based on OpenFOAM and Sparsity-Promoting Dynamic Mode Decomposition","volume":"36","author":"Li","year":"2024","journal-title":"Phys. Fluids"},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Remache, A., P\u00e9rez-S\u00e1nchez, M., Hidalgo, V.H., Ramos, H.M., and S\u00e1nchez-Romero, F.-J. (2024). Towards Sustainability in Hydraulic Machinery Manufacturing by 3D Printing. Processes, 12.","DOI":"10.3390\/pr12122664"},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"021202","DOI":"10.1115\/1.4044965","article-title":"Optimization of a Centrifugal Impeller on Blade Thickness Distribution to Reduce Hydro-Induced Vibration","volume":"142","author":"Qian","year":"2020","journal-title":"J. Fluids Eng."},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Kim, B., Siddique, M.H., Samad, A., Hu, G., and Lee, D.-E. (2022). Optimization of Centrifugal Pump Impeller for Pumping Viscous Fluids Using Direct Design Optimization Technique. Machines, 10.","DOI":"10.3390\/machines10090774"}],"container-title":["Water"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-4441\/17\/13\/1976\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:01:52Z","timestamp":1760032912000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-4441\/17\/13\/1976"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,30]]},"references-count":134,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["w17131976"],"URL":"https:\/\/doi.org\/10.3390\/w17131976","relation":{},"ISSN":["2073-4441"],"issn-type":[{"value":"2073-4441","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,30]]}}}