Matches in SemOpenAlex for { <https://semopenalex.org/work/W4367314767> ?p ?o ?g. }
- W4367314767 endingPage "941" @default.
- W4367314767 startingPage "941" @default.
- W4367314767 abstract "The design of fluid machinery is a complex task that requires careful consideration of various factors that are interdependent. The correlation between performance parameters and geometric parameters is highly intricate and sensitive, displaying strong nonlinear characteristics. Machine learning techniques have proven to be effective in assisting with optimal fluid machinery design. However, there is a scarcity of literature on this subject. This study aims to present a state-of-the-art review on the optimal design of fluid machinery using machine learning techniques. Machine learning applications primarily involve constructing surrogate models or reduced-order models to explore the correlation between design variables or the relationship between design variables and performance. This paper provides a comprehensive summary of the research status of fluid machinery optimization design, machine learning methods, and the current application of machine learning in fluid machinery optimization design. Additionally, it offers insights into future research directions and recommendations for machine learning techniques in optimal fluid machinery design." @default.
- W4367314767 created "2023-04-29" @default.
- W4367314767 creator A5002177355 @default.
- W4367314767 creator A5008051380 @default.
- W4367314767 creator A5011656472 @default.
- W4367314767 creator A5050301562 @default.
- W4367314767 creator A5072539599 @default.
- W4367314767 creator A5080907004 @default.
- W4367314767 date "2023-04-28" @default.
- W4367314767 modified "2023-10-18" @default.
- W4367314767 title "A Review on Optimal Design of Fluid Machinery Using Machine Learning Techniques" @default.
- W4367314767 cites W2440930599 @default.
- W4367314767 cites W2581524296 @default.
- W4367314767 cites W2613854265 @default.
- W4367314767 cites W2796278176 @default.
- W4367314767 cites W2809647086 @default.
- W4367314767 cites W2809704540 @default.
- W4367314767 cites W2884481898 @default.
- W4367314767 cites W2898515927 @default.
- W4367314767 cites W2899322822 @default.
- W4367314767 cites W2911214324 @default.
- W4367314767 cites W2912581782 @default.
- W4367314767 cites W2922479016 @default.
- W4367314767 cites W2931260581 @default.
- W4367314767 cites W2943364687 @default.
- W4367314767 cites W2944831586 @default.
- W4367314767 cites W2944987817 @default.
- W4367314767 cites W2967663338 @default.
- W4367314767 cites W2969918588 @default.
- W4367314767 cites W2982508689 @default.
- W4367314767 cites W2986794290 @default.
- W4367314767 cites W2991128382 @default.
- W4367314767 cites W2992578100 @default.
- W4367314767 cites W2997566831 @default.
- W4367314767 cites W2998702171 @default.
- W4367314767 cites W3004540924 @default.
- W4367314767 cites W3004670854 @default.
- W4367314767 cites W3006335093 @default.
- W4367314767 cites W3009350620 @default.
- W4367314767 cites W3011321148 @default.
- W4367314767 cites W3012253434 @default.
- W4367314767 cites W3016192360 @default.
- W4367314767 cites W3019420398 @default.
- W4367314767 cites W3020845342 @default.
- W4367314767 cites W3024218944 @default.
- W4367314767 cites W3024824293 @default.
- W4367314767 cites W3040657692 @default.
- W4367314767 cites W3046517760 @default.
- W4367314767 cites W3046812802 @default.
- W4367314767 cites W3049683529 @default.
- W4367314767 cites W3083707628 @default.
- W4367314767 cites W3094453699 @default.
- W4367314767 cites W3097198725 @default.
- W4367314767 cites W3097931262 @default.
- W4367314767 cites W3099688890 @default.
- W4367314767 cites W3100068159 @default.
- W4367314767 cites W3102140816 @default.
- W4367314767 cites W3113558818 @default.
- W4367314767 cites W3117237870 @default.
- W4367314767 cites W3128669180 @default.
- W4367314767 cites W3145806293 @default.
- W4367314767 cites W3158714549 @default.
- W4367314767 cites W3162669665 @default.
- W4367314767 cites W3163562723 @default.
- W4367314767 cites W3166288931 @default.
- W4367314767 cites W3172576596 @default.
- W4367314767 cites W3181519820 @default.
- W4367314767 cites W3196586297 @default.
- W4367314767 cites W3197933363 @default.
- W4367314767 cites W3198558306 @default.
- W4367314767 cites W3199463048 @default.
- W4367314767 cites W3200042244 @default.
- W4367314767 cites W3200900716 @default.
- W4367314767 cites W3201612401 @default.
- W4367314767 cites W3210477968 @default.
- W4367314767 cites W3213098209 @default.
- W4367314767 cites W4207065110 @default.
- W4367314767 cites W4224279659 @default.
- W4367314767 cites W4281650286 @default.
- W4367314767 cites W4281966168 @default.
- W4367314767 cites W4282831419 @default.
- W4367314767 cites W4295995163 @default.
- W4367314767 cites W4313328480 @default.
- W4367314767 cites W4313592108 @default.
- W4367314767 doi "https://doi.org/10.3390/jmse11050941" @default.
- W4367314767 hasPublicationYear "2023" @default.
- W4367314767 type Work @default.
- W4367314767 citedByCount "0" @default.
- W4367314767 crossrefType "journal-article" @default.
- W4367314767 hasAuthorship W4367314767A5002177355 @default.
- W4367314767 hasAuthorship W4367314767A5008051380 @default.
- W4367314767 hasAuthorship W4367314767A5011656472 @default.
- W4367314767 hasAuthorship W4367314767A5050301562 @default.
- W4367314767 hasAuthorship W4367314767A5072539599 @default.
- W4367314767 hasAuthorship W4367314767A5080907004 @default.
- W4367314767 hasBestOaLocation W43673147671 @default.
- W4367314767 hasConcept C119857082 @default.
- W4367314767 hasConcept C127413603 @default.