{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T10:54:47Z","timestamp":1791888887779,"version":"4.3.4"},"reference-count":211,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T00:00:00Z","timestamp":1722297600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T00:00:00Z","timestamp":1722297600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100013076","name":"National Major Science and Technology Projects of China","doi-asserted-by":"publisher","award":["J2019-II-0012-0032"],"award-info":[{"award-number":["J2019-II-0012-0032"]}],"id":[{"id":"10.13039\/501100013076","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Turbomachinery plays a vital role in energy conversion systems, with aerodynamic issues being integral to its entire lifecycle, spanning the period of design, validation, and maintenance. Conventionally, the reliance on skilled aerodynamic engineers has been pivotal in the successful development of turbomachines. However, in the current era of burgeoning artificial intelligence (AI) technology, researchers are increasingly turning to AI to replace human expertise and decision-making in these aerodynamic issues and to solve previously intractable aerodynamic problems. This paper presents a systematic literature review of the latest advancements in applying AI to turbomachinery aerodynamics, encompassing the design, validation, and maintenance of compressors and turbines. It underscores how AI is revolutionizing the research paradigm of turbomachinery aerodynamics. AI\u2019s powerful learning capability facilitates more precise and convenient aerodynamic analyses and inspires innovative aerodynamic design ideas that go beyond the capabilities of classical design techniques. Additionally, AI\u2019s autonomous decision-making capability can be employed for aerodynamic optimization and active flow control of turbomachines, generating optimal aerodynamic solutions and complex control strategies that surpass human brains. As a main contribution, we provide a detailed exposition of the future intelligent turbomachinery research and development (R &amp;D) system, along with highlighting potential challenges such as physics embedding, interactive 3D design optimization, and real-time prognoses. It is anticipated that harnessing AI\u2019s full potential will lead to a comprehensive AI-based turbomachinery R &amp;D system in the future.<\/jats:p>","DOI":"10.1007\/s10462-024-10867-3","type":"journal-article","created":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T08:49:17Z","timestamp":1722934157000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Application of artificial intelligence in turbomachinery aerodynamics: progresses and challenges"],"prefix":"10.1007","volume":"57","author":[{"given":"Zhengping","family":"Zou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengcheng","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiming","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lichao","family":"Yao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,7,30]]},"reference":[{"key":"10867_CR1","first-page":"10","volume":"2013","author":"V Ageeva","year":"2013","unstructured":"Ageeva V, Stratoudaki T, Clark M et al (2013) Integrative solution for in-situ ultrasonic inspection of aero-engine blades using endoscopic cheap optical transducers (CHOTs). J Nondestruct Test 2013:10","journal-title":"J Nondestruct Test"},{"key":"10867_CR2","first-page":"134","volume-title":"European conference on artificial life","author":"C Alberto","year":"1992","unstructured":"Alberto C, Marco D, Vittorio M (1992) Distributed optimization by ant colonies. European conference on artificial life. MIT Press, Cambridge, pp 134\u2013142"},{"issue":"1","key":"10867_CR3","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.ijpe.2004.06.057","volume":"105","author":"I Alsyouf","year":"2007","unstructured":"Alsyouf I (2007) The role of maintenance in improving companies\u2019 productivity and profitability. Int J Prod Econ 105(1):70\u201378. https:\/\/doi.org\/10.1016\/j.ijpe.2004.06.057","journal-title":"Int J Prod Econ"},{"key":"10867_CR4","doi-asserted-by":"publisher","unstructured":"Amstad P, So KK, Fischer M (2022) Machine-learning assisted optimization of generalized K-Omega (GEKO) turbulence model parameters for turbocharger radial compressor. In: ASME Turbo Expo 2022: turbomachinery technical conference and exposition. American Society of Mechanical Engineers, Rotterdam, The Netherlands, p V10DT34A002, https:\/\/doi.org\/10.1115\/GT2022-78306","DOI":"10.1115\/GT2022-78306"},{"issue":"11","key":"10867_CR5","doi-asserted-by":"publisher","DOI":"10.1115\/1.4044935","volume":"141","author":"G Angelini","year":"2019","unstructured":"Angelini G, Corsini A, Delibra G et al (2019) A multidimensional extension of balje chart for axial flow turbomachinery using artificial intelligence-based meta-models. J Eng Gas Turb Power 141(11):111012. https:\/\/doi.org\/10.1115\/1.4044935","journal-title":"J Eng Gas Turb Power"},{"key":"10867_CR6","doi-asserted-by":"publisher","unstructured":"Angelini G, Corsini A, Delibra G et al (2021) Identification of losses in turbomachinery with machine learning. In: Proceedings of the ASME Turbo Expo 2021: turbomachinery technical conference and exposition. American Society of Mechanical Engineers, p V001T10A008, https:\/\/doi.org\/10.1115\/GT2020-15337","DOI":"10.1115\/GT2020-15337"},{"key":"10867_CR7","first-page":"87","volume":"C255","author":"M Atkins","year":"1987","unstructured":"Atkins M (1987) Secondary losses and end-wall profiling in a turbine cascade. IMechE Paper C255:87","journal-title":"IMechE Paper"},{"key":"10867_CR8","volume-title":"Turbomachines: a guide to design, selection and theory","author":"OE Balje","year":"1981","unstructured":"Balje OE (1981) Turbomachines: a guide to design, selection and theory. Wiley, Hoboken"},{"issue":"3","key":"10867_CR9","doi-asserted-by":"publisher","first-page":"559","DOI":"10.2514\/1.2703","volume":"20","author":"E Benini","year":"2004","unstructured":"Benini E (2004) Three-dimensional multi-objective design optimization of a transonic compressor rotor. J Propul Power 20(3):559\u2013565. https:\/\/doi.org\/10.2514\/1.2703","journal-title":"J Propul Power"},{"issue":"3","key":"10867_CR10","doi-asserted-by":"publisher","first-page":"544","DOI":"10.2514\/2.5995","volume":"18","author":"E Benini","year":"2002","unstructured":"Benini E, Toffolo A (2002) Development of high-performance airfoils for axial flow compressors using evolutionary computation. J Propul Power 18(3):544\u2013554. https:\/\/doi.org\/10.2514\/2.5995","journal-title":"J Propul Power"},{"key":"10867_CR11","doi-asserted-by":"publisher","unstructured":"Beqiraj K, Perrone A, Sanguineti M et al (2022) Rotor37 Aerodynamic optimization: a machine learning approach. In: Proceedings of the ASME Turbo Expo 2022: turbomachinery technical conference and exposition. American Society of Mechanical Engineers, p V10DT34A021, https:\/\/doi.org\/10.1115\/GT2022-83063","DOI":"10.1115\/GT2022-83063"},{"key":"10867_CR12","doi-asserted-by":"publisher","unstructured":"Bharati P, Pramanik A (2020) Deep learning techniques-R-CNN to Mask R-CNN: a survey. Springer, Singapore, Advances in Intelligent Systems and Computing, pp 657\u2013668, https:\/\/doi.org\/10.1007\/978-981-13-9042-5_56","DOI":"10.1007\/978-981-13-9042-5_56"},{"issue":"6","key":"10867_CR13","doi-asserted-by":"publisher","first-page":"458","DOI":"10.3390\/machines10060458","volume":"10","author":"M Bicchi","year":"2022","unstructured":"Bicchi M, Biliotti D, Marconcini M et al (2022) An AI-based fast design method for new centrifugal compressor families. Machines 10(6):458. https:\/\/doi.org\/10.3390\/machines10060458","journal-title":"Machines"},{"issue":"4","key":"10867_CR14","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1177\/0954408920927658","volume":"234","author":"L Bourabia","year":"2020","unstructured":"Bourabia L, Khalfallah S, Cerdoun M et al (2020) An efficient methodology to generate optimal inputs for the preliminary design of centrifugal compressor impellers. J Process Mech Eng 234(4):353\u2013366. https:\/\/doi.org\/10.1177\/0954408920927658","journal-title":"J Process Mech Eng"},{"issue":"1","key":"10867_CR15","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45(1):5\u201332. https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Mach Learn"},{"key":"10867_CR16","doi-asserted-by":"publisher","unstructured":"Cameron JD, Morris SC (2007) Spatial correlation based stall inception analysis. In: Proceedings of the ASME turbo Expo 2007: power for land, sea, and air. American Society of Mechanical Engineers, Montreal, Canada, pp 433\u2013444, https:\/\/doi.org\/10.1115\/GT2007-28268","DOI":"10.1115\/GT2007-28268"},{"key":"10867_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2019.106024","volume":"137","author":"TP Carvalho","year":"2019","unstructured":"Carvalho TP, Soares FAAMN, Vita R et al (2019) A systematic literature review of machine learning methods applied to predictive maintenance. Comput Ind Eng 137:106024. https:\/\/doi.org\/10.1016\/j.cie.2019.106024","journal-title":"Comput Ind Eng"},{"key":"10867_CR18","doi-asserted-by":"publisher","DOI":"10.1017\/9781108241663","volume-title":"Radial flow turbocompressors: design, analysis, and applications","author":"M Casey","year":"2021","unstructured":"Casey M, Robinson C (2021) Radial flow turbocompressors: design, analysis, and applications. Cambridge University Press, Cambridge"},{"key":"10867_CR19","doi-asserted-by":"publisher","unstructured":"Charles RQ, Su H, Kaichun M et al (2017) Pointnet: Deep learning on point sets for 3d classification and segmentation. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 77\u201385, https:\/\/doi.org\/10.1109\/CVPR.2017.16","DOI":"10.1109\/CVPR.2017.16"},{"key":"10867_CR20","doi-asserted-by":"publisher","DOI":"10.1007\/s42452-022-05132-7","author":"J Chen","year":"2022","unstructured":"Chen J, Liu C (2022) Efficient one-dimensional turbomachinery design method based on transfer learning and Bayesian optimization. SN Appl Sci. https:\/\/doi.org\/10.1007\/s42452-022-05132-7","journal-title":"SN Appl Sci"},{"issue":"03","key":"10867_CR21","doi-asserted-by":"publisher","first-page":"2050012","DOI":"10.1142\/S0219691320500125","volume":"18","author":"L Chen","year":"2020","unstructured":"Chen L, Zou L, Fan C et al (2020) Feature weighting network for aircraft engine defect detection. Int J Wavelets Multiresolut Inf Process 18(03):2050012. https:\/\/doi.org\/10.1142\/S0219691320500125","journal-title":"Int J Wavelets Multiresolut Inf Process"},{"key":"10867_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109352","volume":"252","author":"J Chen","year":"2022","unstructured":"Chen J, Liu C, Xuan L et al (2022) Knowledge-based turbomachinery design system via a deep neural network and multi-output Gaussian process. Knowl Based Syst 252:109352. https:\/\/doi.org\/10.1016\/j.knosys.2022.109352","journal-title":"Knowl Based Syst"},{"key":"10867_CR23","doi-asserted-by":"publisher","unstructured":"Cheng H, Yi W, Ji L (2021) Multi-point optimization design of high pressure-ratio centrifugal impeller based on machine learning. In: Proceedings of the ASME Turbo Expo 2021: turbomachinery technical conference and exposition. American Society of Mechanical Engineers, p V02DT38A009, https:\/\/doi.org\/10.1115\/GT2020-14576","DOI":"10.1115\/GT2020-14576"},{"key":"10867_CR24","volume-title":"Energy efficient engine: low pressure turbine test hardware detailed design report","author":"DGG Cherry","year":"1982","unstructured":"Cherry DGG (1982) Energy efficient engine: low pressure turbine test hardware detailed design report. NASA Lewis Research Center, Cleveland"},{"issue":"8","key":"10867_CR25","doi-asserted-by":"publisher","DOI":"10.1063\/5.0161142","volume":"35","author":"Z Chi","year":"2023","unstructured":"Chi Z, Chu W, Zhang H et al (2023) Stall margin evaluation and data mining based multi-objective optimization design of casing treatment for an axial compressor rotor. Phys Fluids 35(8):086117. https:\/\/doi.org\/10.1063\/5.0161142","journal-title":"Phys Fluids"},{"key":"10867_CR26","unstructured":"Cordier O (1955) \u00c4hnlichkeitsbedingungen f\u00fcr Str\u00f6mungsmaschinen. VDI-Berichte (Band 3):S. 85"},{"issue":"3","key":"10867_CR27","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/BF00994018","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273\u2013297. https:\/\/doi.org\/10.1007\/BF00994018","journal-title":"Mach Learn"},{"issue":"1","key":"10867_CR28","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","volume":"13","author":"T Cover","year":"1967","unstructured":"Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Tran Inf Theory 13(1):21\u201327. https:\/\/doi.org\/10.1109\/TIT.1967.1053964","journal-title":"IEEE Tran Inf Theory"},{"key":"10867_CR29","volume-title":"Compressor aerodynamics","author":"NA Cumpsty","year":"2004","unstructured":"Cumpsty NA (2004) Compressor aerodynamics. Krieger Publishing Company, Malabar, Florida"},{"issue":"3","key":"10867_CR30","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1115\/1.2841154","volume":"119","author":"EM Curtis","year":"1997","unstructured":"Curtis EM, Hodson HP, Banieghbal MR et al (1997) Development of blade profiles for low-pressure turbine applications. J Turbomach 119(3):531\u2013538. https:\/\/doi.org\/10.1115\/1.2841154","journal-title":"J Turbomach"},{"issue":"4","key":"10867_CR31","doi-asserted-by":"publisher","first-page":"814","DOI":"10.2514\/1.27740","volume":"23","author":"K Daneshkhah","year":"2007","unstructured":"Daneshkhah K, Ghaly W (2007) Aerodynamic inverse design for viscous flow in turbomachinery blading. J Propul Power 23(4):814\u2013820. https:\/\/doi.org\/10.2514\/1.27740","journal-title":"J Propul Power"},{"issue":"12","key":"10867_CR32","doi-asserted-by":"publisher","first-page":"2309","DOI":"10.2514\/3.12985","volume":"33","author":"T Dang","year":"1995","unstructured":"Dang T, Isgro V (1995) Euler-based inverse method for turbomachine blades. I - Two-dimensional cascades. AIAA J 33(12):2309\u20132315. https:\/\/doi.org\/10.2514\/3.12985","journal-title":"AIAA J"},{"issue":"24","key":"10867_CR33","doi-asserted-by":"publisher","first-page":"8468","DOI":"10.3390\/en14248468","volume":"14","author":"M de Castro-Cros","year":"2021","unstructured":"de Castro-Cros M, Velasco M, Angulo C (2021) Machine-learning-based condition assessment of gas turbines: a review. Energies 14(24):8468. https:\/\/doi.org\/10.3390\/en14248468","journal-title":"Energies"},{"issue":"4","key":"10867_CR34","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1243\/0957650971537204","volume":"211","author":"A Demeulenaere","year":"1997","unstructured":"Demeulenaere A, L\u00e9onard O, Van den Braembussche R (1997) A two-dimensional Navier-Stokes inverse solver for compressor and turbine blade design. J Power Eng 211(4):299\u2013307. https:\/\/doi.org\/10.1243\/0957650971537204","journal-title":"J Power Eng"},{"key":"10867_CR35","doi-asserted-by":"publisher","DOI":"10.1243\/0954406991522220","author":"JD Denton","year":"1998","unstructured":"Denton JD, Xu L (1998) The exploitation of three-dimensional flow in turbomachinery design. J Mech Eng. https:\/\/doi.org\/10.1243\/0954406991522220","journal-title":"J Mech Eng"},{"issue":"10","key":"10867_CR36","doi-asserted-by":"publisher","first-page":"2022","DOI":"10.1016\/j.compfluid.2010.07.003","volume":"39","author":"S Derakhshan","year":"2010","unstructured":"Derakhshan S, Mohammadi B, Nourbakhsh A (2010) The comparison of incomplete sensitivities and Genetic algorithms applications in 3D radial turbomachinery blade optimization. Comput Fluids 39(10):2022\u20132029. https:\/\/doi.org\/10.1016\/j.compfluid.2010.07.003","journal-title":"Comput Fluids"},{"key":"10867_CR37","doi-asserted-by":"publisher","DOI":"10.1115\/GT2022-82688","author":"V Dipierro","year":"2022","unstructured":"Dipierro V, Martinelli M, Persico G et al (2022) Mean-line design and optimization of axial-flow turbines based on mixed integer nonlinear programming. Am Soc Mech Eng Digital Collect. https:\/\/doi.org\/10.1115\/GT2022-82688","journal-title":"Am Soc Mech Eng Digital Collect"},{"issue":"6","key":"10867_CR38","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1016\/S0967-0661(02)00005-9","volume":"10","author":"I Dremin","year":"2002","unstructured":"Dremin I, Furletov V, Ivanov O et al (2002) Precursors of stall and surge processes in gas turbines revealed by wavelet analysis. Control Eng Practice 10(6):599\u2013604. https:\/\/doi.org\/10.1016\/S0967-0661(02)00005-9","journal-title":"Control Eng Practice"},{"key":"10867_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2022.124351","volume":"254","author":"Q Du","year":"2022","unstructured":"Du Q, Li Y, Yang L et al (2022) Performance prediction and design optimization of turbine blade profile with deep learning method. Energy 254:124351. https:\/\/doi.org\/10.1016\/j.energy.2022.124351","journal-title":"Energy"},{"issue":"1","key":"10867_CR40","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1146\/annurev-fluid-010518-040547","volume":"51","author":"K Duraisamy","year":"2019","unstructured":"Duraisamy K, Iaccarino G, Xiao H (2019) Turbulence modeling in the age of data. Annu Rev Fluid Mech 51(1):357\u2013377. https:\/\/doi.org\/10.1146\/annurev-fluid-010518-040547","journal-title":"Annu Rev Fluid Mech"},{"issue":"1","key":"10867_CR41","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1146\/annurev-fluid-122316-045020","volume":"50","author":"PA Durbin","year":"2018","unstructured":"Durbin PA (2018) Some recent developments in turbulence closure modeling. Annu Rev Fluid Mech 50(1):77\u2013103. https:\/\/doi.org\/10.1146\/annurev-fluid-122316-045020","journal-title":"Annu Rev Fluid Mech"},{"issue":"3","key":"10867_CR42","doi-asserted-by":"publisher","first-page":"390","DOI":"10.1115\/1.3448334","volume":"98","author":"D Eckardt","year":"1976","unstructured":"Eckardt D (1976) Detailed flow investigations within a high-speed centrifugal compressor impeller. J Fluids Eng 98(3):390\u2013399. https:\/\/doi.org\/10.1115\/1.3448334","journal-title":"J Fluids Eng"},{"key":"10867_CR43","doi-asserted-by":"publisher","unstructured":"Elshamy O, Ghizawi N, Yon C et al (2009) An aerodynamic optimization procedure for the preliminary design of centrifugal compressor stages. In: Proceedings of the ASME Turbo Expo 2009: power for land, sea, and air. American Society of Mechanical Engineers, pp 1649\u20131655, https:\/\/doi.org\/10.1115\/GT2008-51154","DOI":"10.1115\/GT2008-51154"},{"issue":"6","key":"10867_CR44","doi-asserted-by":"publisher","first-page":"933","DOI":"10.1007\/s00348-006-0212-z","volume":"41","author":"GE Elsinga","year":"2006","unstructured":"Elsinga GE, Scarano F, Wieneke B et al (2006) Tomographic particle image velocimetry. Exp Fluids 41(6):933\u2013947. https:\/\/doi.org\/10.1007\/s00348-006-0212-z","journal-title":"Exp Fluids"},{"key":"10867_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.compfluid.2022.105354","volume":"238","author":"L Fang","year":"2022","unstructured":"Fang L, Bao TW, Xu WQ et al (2022) Data driven turbulence modeling in turbomachinery: an applicability study. Comput Fluids 238:105354. https:\/\/doi.org\/10.1016\/j.compfluid.2022.105354","journal-title":"Comput Fluids"},{"key":"10867_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.ast.2023.108165","volume":"134","author":"Y Feng","year":"2023","unstructured":"Feng Y, Song X, Yuan W et al (2023) Physics-informed deep learning cascade loss model. Aerosp Sci Technol 134:108165. https:\/\/doi.org\/10.1016\/j.ast.2023.108165","journal-title":"Aerosp Sci Technol"},{"key":"10867_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.compfluid.2020.104474","volume":"201","author":"A Ferrero","year":"2020","unstructured":"Ferrero A, Iollo A, Larocca F (2020) Field inversion for data-augmented RANS modelling in turbomachinery flows. Comput Fluids 201:104474. https:\/\/doi.org\/10.1016\/j.compfluid.2020.104474","journal-title":"Comput Fluids"},{"key":"10867_CR48","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1016\/B978-0-08-096676-2.10011-6","volume-title":"Forsthoffer\u2019s best practice handbook for rotating machinery","author":"WE Forsthoffer","year":"2011","unstructured":"Forsthoffer WE (2011) Preventive and predictive maintenance best practices. In: Forsthoffer WE (ed) Forsthoffer\u2019s best practice handbook for rotating machinery. Butterworth-Heinemann, Boston, pp 563\u2013576. https:\/\/doi.org\/10.1016\/B978-0-08-096676-2.10011-6"},{"key":"10867_CR49","doi-asserted-by":"publisher","unstructured":"Friso R, Oliani S, Casari N et al (2021) Towards a Machine Learning Based Design for Fouling of an Axial Turbine Vane. In: Proceedings of the ASME Turbo Expo 2021: turbomachinery technical conference and exposition. American Society of Mechanical Engineers, Virtual, Online, p V02CT34A027, https:\/\/doi.org\/10.1115\/GT2021-59520","DOI":"10.1115\/GT2021-59520"},{"issue":"2","key":"10867_CR50","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1007\/s00348-005-1019-z","volume":"39","author":"B Ganapathisubramani","year":"2005","unstructured":"Ganapathisubramani B, Longmire EK, Marusic I et al (2005) Dual-plane PIV technique to determine the complete velocity gradient tensor in a turbulent boundary layer. Exp Fluids 39(2):222\u2013231. https:\/\/doi.org\/10.1007\/s00348-005-1019-z","journal-title":"Exp Fluids"},{"issue":"1115\/1","key":"10867_CR51","first-page":"4052301","volume":"10","author":"S Ghosh","year":"2021","unstructured":"Ghosh S, Anantha Padmanabha G, Peng C et al (2021) Inverse aerodynamic design of gas turbine blades using probabilistic machine learning. J Mech Des doi 10(1115\/1):4052301","journal-title":"J Mech Des doi"},{"issue":"3","key":"10867_CR52","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1145\/175247.175259","volume":"37","author":"DE Goldberg","year":"1994","unstructured":"Goldberg DE (1994) Genetic and evolutionary algorithms come of age. Commun ACM 37(3):113\u2013119. https:\/\/doi.org\/10.1145\/175247.175259","journal-title":"Commun ACM"},{"issue":"2","key":"10867_CR53","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1115\/1.3241725","volume":"103","author":"EM Greitzer","year":"1981","unstructured":"Greitzer EM (1981) The stability of pumping systems-the 1980 freeman scholar lecture. J Fluids Eng 103(2):193\u2013242. https:\/\/doi.org\/10.1115\/1.3241725","journal-title":"J Fluids Eng"},{"issue":"1","key":"10867_CR54","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.ymssp.2013.02.005","volume":"44","author":"AA Gubran","year":"2014","unstructured":"Gubran AA, Sinha JK (2014) Shaft instantaneous angular speed for blade vibration in rotating machine. Mech Syst Signal Proc 44(1):47\u201359. https:\/\/doi.org\/10.1016\/j.ymssp.2013.02.005","journal-title":"Mech Syst Signal Proc"},{"key":"10867_CR55","unstructured":"Haarnoja T, Zhou A, Hartikainen K et al (2019) Soft actor-critic algorithms and applications. Preprint at http:\/\/arxiv.org\/abs\/1812.05905"},{"issue":"1","key":"10867_CR56","doi-asserted-by":"publisher","first-page":"100","DOI":"10.2307\/2346830","volume":"28","author":"JA Hartigan","year":"1979","unstructured":"Hartigan JA, Wong MA (1979) A K-means clustering algorithm. J Royal Stat Soc: Ser C (Appl Stat) 28(1):100\u2013108. https:\/\/doi.org\/10.2307\/2346830","journal-title":"J Royal Stat Soc: Ser C (Appl Stat)"},{"issue":"9","key":"10867_CR57","doi-asserted-by":"publisher","DOI":"10.1115\/1.4047026","volume":"142","author":"X He","year":"2020","unstructured":"He X, Zhao F, Vahdati M (2020) Uncertainty quantification of Spalart-Allmaras turbulence model coefficients for simplified compressor flow features. J Fluids Eng 142(9):081007. https:\/\/doi.org\/10.1115\/1.4047026","journal-title":"J Fluids Eng"},{"key":"10867_CR58","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijheatmasstransfer.2022.123148","volume":"195","author":"Q He","year":"2022","unstructured":"He Q, Zhao W, Chi Z et al (2022a) Application of deep-learning method in the conjugate heat transfer optimization of full-coverage film cooling on turbine vanes. Int J Heat Mass Trans 195:123148. https:\/\/doi.org\/10.1016\/j.ijheatmasstransfer.2022.123148","journal-title":"Int J Heat Mass Trans"},{"issue":"12","key":"10867_CR59","doi-asserted-by":"publisher","DOI":"10.1115\/1.4055333","volume":"144","author":"X He","year":"2022","unstructured":"He X, Zhao F, Vahdati M (2022b) A turbo-oriented data-driven modification to the Spalart-Allmaras turbulence model. J Turbomach 144(12):121007. https:\/\/doi.org\/10.1115\/1.4055333","journal-title":"J Turbomach"},{"key":"10867_CR60","volume-title":"The fourth paradigm","author":"T Hey","year":"2009","unstructured":"Hey T (2009) The fourth paradigm. Microsoft Research, Redmond"},{"issue":"7","key":"10867_CR61","doi-asserted-by":"publisher","first-page":"R61","DOI":"10.1088\/0957-0233\/13\/7\/201","volume":"13","author":"KD Hinsch","year":"2002","unstructured":"Hinsch KD (2002) Holographic particle image velocimetry. Meas Sci Technol 13(7):R61. https:\/\/doi.org\/10.1088\/0957-0233\/13\/7\/201","journal-title":"Meas Sci Technol"},{"key":"10867_CR62","doi-asserted-by":"publisher","DOI":"10.1115\/1.4046458","author":"SM Hipple","year":"2020","unstructured":"Hipple SM, Bonilla-Alvarado H, Pezzini P et al (2020) Using machine learning tools to predict compressor stall. J Energy Resour Technol. https:\/\/doi.org\/10.1115\/1.4046458","journal-title":"J Energy Resour Technol"},{"issue":"4","key":"10867_CR63","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1080\/10618560902773379","volume":"23","author":"C Hirsch","year":"2009","unstructured":"Hirsch C, Tartinville B (2009) Reynolds-averaged Navier-Stokes modelling for industrial applications and some challenging issues. Int J Comput Fluid Dyn 23(4):295\u2013303. https:\/\/doi.org\/10.1080\/10618560902773379","journal-title":"Int J Comput Fluid Dyn"},{"issue":"2","key":"10867_CR64","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1115\/1.3239559","volume":"106","author":"DE Hobbs","year":"1984","unstructured":"Hobbs DE, Weingold HD (1984) Development of controlled diffusion airfoils for multistage compressor application. J Eng Gas Turb Power 106(2):271\u2013278. https:\/\/doi.org\/10.1115\/1.3239559","journal-title":"J Eng Gas Turb Power"},{"key":"10867_CR65","volume-title":"Energy efficient engine: high pressure compressor detail design report","author":"PR Hollowny","year":"1982","unstructured":"Hollowny PR, Knight GL, Koch CC et al (1982) Energy efficient engine: high pressure compressor detail design report. NASA Lewis Research Center, Cleveland"},{"key":"10867_CR66","unstructured":"H\u00f6nen H, Arnold T (2003) Development of a surge predicion system for multi stage axial compressors. In: Proceedings of IGTC\u201903 Tokyo, international gas turbine congress 2003. Gas Turbine Society of Japan, Tokyo, Japan"},{"issue":"1","key":"10867_CR67","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1115\/1.1650379","volume":"127","author":"JH Horlock","year":"2005","unstructured":"Horlock JH, Denton JD (2005) A review of some early design practice using computational fluid dynamics and a current perspective. J Turbomach 127(1):5\u201313. https:\/\/doi.org\/10.1115\/1.1650379","journal-title":"J Turbomach"},{"key":"10867_CR68","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1037\/h0071325","volume":"24","author":"H Hotelling","year":"1933","unstructured":"Hotelling H (1933) Analysis of a complex of statistical variables into principal components. J Educ Psychol 24:417\u2013441. https:\/\/doi.org\/10.1037\/h0071325","journal-title":"J Educ Psychol"},{"key":"10867_CR69","doi-asserted-by":"publisher","DOI":"10.1016\/j.ast.2022.107814","volume":"130","author":"H Hu","year":"2022","unstructured":"Hu H, Song Y, Yu J et al (2022) The application of support vector regression and virtual sample generation technique in the optimization design of transonic compressor. Aerosp Sci Technol 130:107814. https:\/\/doi.org\/10.1016\/j.ast.2022.107814","journal-title":"Aerosp Sci Technol"},{"key":"10867_CR70","doi-asserted-by":"publisher","first-page":"13039","DOI":"10.1109\/ACCESS.2021.3052217","volume":"9","author":"X Huang","year":"2021","unstructured":"Huang X, Zhang X, Xiong Y et al (2021) A novel intelligent fault diagnosis approach for early cracks of turbine blades via improved deep belief network using three-dimensional blade tip clearance. IEEE Access 9:13039\u201313051. https:\/\/doi.org\/10.1109\/ACCESS.2021.3052217","journal-title":"IEEE Access"},{"issue":"4","key":"10867_CR71","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1007\/s10921-022-00907-9","volume":"41","author":"BE Jaeger","year":"2022","unstructured":"Jaeger BE, Schmid S, Grosse CU et al (2022) Infrared thermal imaging-based turbine blade crack classification using deep learning. J Nondestruct Eval 41(4):74. https:\/\/doi.org\/10.1007\/s10921-022-00907-9","journal-title":"J Nondestruct Eval"},{"key":"10867_CR72","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/BFb0103920","volume-title":"Computational mathematics driven by industrial problems","author":"A Jameson","year":"2000","unstructured":"Jameson A, Martinelli L (2000) Aerodynamic shape optimization techniques based on control theory. In: Burkard RE, Jameson A (eds) Computational mathematics driven by industrial problems. Springer, Berlin, Heidelberg, pp 151\u2013221. https:\/\/doi.org\/10.1007\/BFb0103920"},{"key":"10867_CR73","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1007\/978-3-030-57745-2_25","volume-title":"Advances in asset management and condition monitoring","author":"K Jiang","year":"2020","unstructured":"Jiang K, Xiang Y, Chen T et al (2020) Research on surge control of centrifugal compressor based on reinforcement learning. In: Ball A, Gelman L, Rao BKN (eds) Advances in asset management and condition monitoring. Springer International Publishing, Cham, pp 293\u2013305. https:\/\/doi.org\/10.1007\/978-3-030-57745-2_25"},{"key":"10867_CR74","doi-asserted-by":"publisher","first-page":"1066","DOI":"10.1016\/j.procs.2022.01.135","volume":"199","author":"P Jiang","year":"2022","unstructured":"Jiang P, Ergu D, Liu F et al (2022) A review of yolo algorithm developments. Proc Comput Sci 199:1066\u20131073. https:\/\/doi.org\/10.1016\/j.procs.2022.01.135","journal-title":"Proc Comput Sci"},{"issue":"2","key":"10867_CR75","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1016\/0017-9310(72)90076-2","volume":"15","author":"WP Jones","year":"1972","unstructured":"Jones WP, Launder BE (1972) The prediction of laminarization with a two-equation model of turbulence. Int J Heat Mass Trans 15(2):301\u2013314. https:\/\/doi.org\/10.1016\/0017-9310(72)90076-2","journal-title":"Int J Heat Mass Trans"},{"issue":"1","key":"10867_CR76","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1115\/1.3227240","volume":"104","author":"SC Kacker","year":"1982","unstructured":"Kacker SC, Okapuu U (1982) Mean line prediction method for axial flow turbine efficiency. J Eng Power 104(1):111\u2013119. https:\/\/doi.org\/10.1115\/1.3227240","journal-title":"J Eng Power"},{"issue":"6","key":"10867_CR77","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1038\/s42254-021-00314-5","volume":"3","author":"GE Karniadakis","year":"2021","unstructured":"Karniadakis GE, Kevrekidis IG, Lu L et al (2021) Physics-informed machine learning. Nat Rev Phys 3(6):422\u2013440. https:\/\/doi.org\/10.1038\/s42254-021-00314-5","journal-title":"Nat Rev Phys"},{"issue":"2","key":"10867_CR78","doi-asserted-by":"publisher","DOI":"10.1063\/5.0033376","volume":"33","author":"A Kashefi","year":"2021","unstructured":"Kashefi A, Rempe D, Guibas LJ (2021) A point-cloud deep learning framework for prediction of fluid flow fields on irregular geometries. Phys Fluids 33(2):027104. https:\/\/doi.org\/10.1063\/5.0033376","journal-title":"Phys Fluids"},{"issue":"2514\/1","key":"10867_CR79","first-page":"J061544","volume":"10","author":"AJ Keane","year":"2022","unstructured":"Keane AJ, Voutchkov II (2022) Embedded parameter information in conditional generative adversarial networks for compressor airfoil design. AIAA J 10(2514\/1):J061544","journal-title":"AIAA J"},{"key":"10867_CR80","doi-asserted-by":"publisher","unstructured":"Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN\u201995 - international conference on neural networks, pp 1942\u20131948, https:\/\/doi.org\/10.1109\/ICNN.1995.488968","DOI":"10.1109\/ICNN.1995.488968"},{"key":"10867_CR81","doi-asserted-by":"publisher","unstructured":"Kidikian J, Badrieh C, Reggio M (2021) Mathematical model to describe double circular arc and multiple circular arc compressor blading profiles. In: Proceedings of the ASME Turbo Expo 2021: turbomachinery technical conference and exposition. American Society of Mechanical Engineers, p V02CT34A018, https:\/\/doi.org\/10.1115\/GT2021-59238","DOI":"10.1115\/GT2021-59238"},{"issue":"5\u20136","key":"10867_CR82","doi-asserted-by":"publisher","first-page":"2020","DOI":"10.1177\/1475921719830328","volume":"18","author":"YH Kim","year":"2019","unstructured":"Kim YH, Lee JR (2019) Videoscope-based inspection of turbofan engine blades using convolutional neural networks and image processing. Struct Health Monitor 18(5\u20136):2020\u20132039. https:\/\/doi.org\/10.1177\/1475921719830328. (publisher: SAGE Publications)","journal-title":"Struct Health Monitor"},{"issue":"5","key":"10867_CR83","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1243\/09576509JPE884","volume":"224","author":"J Kim","year":"2010","unstructured":"Kim J, Choi J, Husain A et al (2010) Multi-objective optimization of a centrifugal compressor impeller through evolutionary algorithms. Proc Inst Mech Eng Part A: J Power Eng 224(5):711\u2013721. https:\/\/doi.org\/10.1243\/09576509JPE884","journal-title":"Proc Inst Mech Eng Part A: J Power Eng"},{"issue":"10","key":"10867_CR84","doi-asserted-by":"publisher","DOI":"10.1115\/1.4004906","volume":"133","author":"J Kim","year":"2011","unstructured":"Kim J, Kim J, Kim K (2011) Axial-flow ventilation fan design through multi-objective optimization to enhance aerodynamic performance. J Fluids Eng 133(10):101101. https:\/\/doi.org\/10.1115\/1.4004906","journal-title":"J Fluids Eng"},{"issue":"13","key":"10867_CR85","doi-asserted-by":"publisher","first-page":"3521","DOI":"10.1073\/pnas.1611835114","volume":"114","author":"J Kirkpatrick","year":"2017","unstructured":"Kirkpatrick J, Pascanu R, Rabinowitz N et al (2017) Overcoming catastrophic forgetting in neural networks. Proc Natl Acad Sci 114(13):3521\u20133526. https:\/\/doi.org\/10.1073\/pnas.1611835114","journal-title":"Proc Natl Acad Sci"},{"issue":"3","key":"10867_CR86","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1115\/1.3446202","volume":"98","author":"CC Koch","year":"1976","unstructured":"Koch CC, Smith LH (1976) Loss sources and magnitudes in axial-flow compressors. J Eng Power 98(3):411. https:\/\/doi.org\/10.1115\/1.3446202","journal-title":"J Eng Power"},{"issue":"21","key":"10867_CR87","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2101784118","volume":"118","author":"D Kochkov","year":"2021","unstructured":"Kochkov D, Smith JA, Alieva A et al (2021) Machine learning-accelerated computational fluid dynamics. Proc Natl Acad Sci 118(21):e2101784118. https:\/\/doi.org\/10.1073\/pnas.2101784118","journal-title":"Proc Natl Acad Sci"},{"key":"10867_CR88","doi-asserted-by":"publisher","unstructured":"K\u00f6ller U, M\u00f6nig R, K\u00fcsters B et al (1999) Development of advanced compressor airfoils for heavy-duty gas turbines: part i - design and optimization. In: Proceedings of the ASME turbo expo 1999: power for land, sea, and air. American Society of Mechanical Engineers, New York, USA, p V001T03A021, https:\/\/doi.org\/10.1115\/99-GT-095","DOI":"10.1115\/99-GT-095"},{"issue":"1115\/1","key":"10867_CR89","first-page":"3103923","volume":"10","author":"K Kosowski","year":"2009","unstructured":"Kosowski K, Tucki K, Kosowski A (2009) Application of artificial neural networks in investigations of steam turbine cascades. J Turbomach 10(1115\/1):3103923","journal-title":"J Turbomach"},{"key":"10867_CR90","doi-asserted-by":"publisher","unstructured":"Krain H, Hoffman W (1989) Verification of an impeller design by laser measurements and 3D-viscous flow calculations. In: ASME. Turbo Expo 1989: power for land, sea, and air, 79139, p V001T01A064, https:\/\/doi.org\/10.1115\/89-GT-159","DOI":"10.1115\/89-GT-159"},{"issue":"2","key":"10867_CR91","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/0045-7825(74)90029-2","volume":"3","author":"BE Launder","year":"1974","unstructured":"Launder BE, Spalding DB (1974) The numerical computation of turbulent flows. Comput Meth Appl Mech Eng 3(2):269\u2013289. https:\/\/doi.org\/10.1016\/0045-7825(74)90029-2","journal-title":"Comput Meth Appl Mech Eng"},{"issue":"4","key":"10867_CR92","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","volume":"1","author":"Y LeCun","year":"1989","unstructured":"LeCun Y, Boser B, Denker JS et al (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1(4):541\u2013551. https:\/\/doi.org\/10.1162\/neco.1989.1.4.541","journal-title":"Neural Comput"},{"issue":"9","key":"10867_CR93","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.1007\/BF03185803","volume":"14","author":"S Lee","year":"2000","unstructured":"Lee S, Kim K (2000) Design optimization of axial flow compressor blades with three-dimensional Navier-Stokes solver. KSME Int J 14(9):1005\u20131012. https:\/\/doi.org\/10.1007\/BF03185803","journal-title":"KSME Int J"},{"issue":"5","key":"10867_CR94","doi-asserted-by":"publisher","DOI":"10.1115\/1.4039051","volume":"140","author":"KB Lee","year":"2018","unstructured":"Lee KB, Wilson M, Vahdati M (2018) Validation of a numerical model for predicting stalled flows in a low-speed fan-part I: modification of Spalart-Allmaras turbulence model. J Turbomach 140(5):051008. https:\/\/doi.org\/10.1115\/1.4039051","journal-title":"J Turbomach"},{"issue":"2","key":"10867_CR95","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1007\/s11630-997-0024-3","volume":"6","author":"J Li","year":"1997","unstructured":"Li J, Feng Z, Chang J et al (1997) Aerodynamic optimum design of transonic turbine cascades using Genetic Algorithms. J Therm Sci 6(2):111\u2013116. https:\/\/doi.org\/10.1007\/s11630-997-0024-3","journal-title":"J Therm Sci"},{"key":"10867_CR103","doi-asserted-by":"publisher","unstructured":"Li C, Lei Y, Fu R (2011) Aerodynamic instability detection in compressor based on Hilbert-Huang transform. In: 2011 IEEE international conference on computer science and automation engineering, pp 355\u2013358, https:\/\/doi.org\/10.1109\/CSAE.2011.5952867","DOI":"10.1109\/CSAE.2011.5952867"},{"issue":"7","key":"10867_CR96","doi-asserted-by":"publisher","first-page":"3398","DOI":"10.1177\/09544062211039878","volume":"236","author":"X Li","year":"2022","unstructured":"Li X, Liu Z, Zhao Y (2022a) Redesign of casing treatment for a transonic centrifugal compressor based on a hybrid global optimization method. Proc Inst Mech Eng, Part C: J Mech Eng Sci 236(7):3398\u20133417. https:\/\/doi.org\/10.1177\/09544062211039878. (publisher: IMECHE)","journal-title":"Proc Inst Mech Eng, Part C: J Mech Eng Sci"},{"issue":"14","key":"10867_CR97","doi-asserted-by":"publisher","first-page":"7235","DOI":"10.3390\/app12147235","volume":"12","author":"X Li","year":"2022","unstructured":"Li X, Wang C, Ju H et al (2022b) Surface defect detection model for aero-engine components based on improved YOLOv5. Appl Sci 12(14):7235. https:\/\/doi.org\/10.3390\/app12147235","journal-title":"Appl Sci"},{"key":"10867_CR98","doi-asserted-by":"publisher","DOI":"10.1016\/j.paerosci.2022.100849","volume":"134","author":"J Li","year":"2022","unstructured":"Li J, Du X, Martins JRRA (2022c) Machine learning in aerodynamic shape optimization. Progress Aerosp Sci 134:100849. https:\/\/doi.org\/10.1016\/j.paerosci.2022.100849","journal-title":"Progress Aerosp Sci"},{"key":"10867_CR99","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2022.124440","volume":"254","author":"J Li","year":"2022","unstructured":"Li J, Liu T, Wang Y et al (2022d) Integrated graph deep learning framework for flow field reconstruction and performance prediction of turbomachinery. Energy 254:124440. https:\/\/doi.org\/10.1016\/j.energy.2022.124440","journal-title":"Energy"},{"key":"10867_CR100","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2024.130452","volume":"291","author":"B Li","year":"2024","unstructured":"Li B, Xie H, Sun L et al (2024) Optimization design of radial inflow turbine combined with mean-line model and CFD analysis for geothermal power generation. Energy 291:130452. https:\/\/doi.org\/10.1016\/j.energy.2024.130452","journal-title":"Energy"},{"issue":"4","key":"10867_CR101","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1007\/s00348-015-1947-1","volume":"56","author":"Y Liang","year":"2015","unstructured":"Liang Y, Zou Z, Liu H et al (2015) Experimental investigation on the effects of wake passing frequency on boundary layer transition in high-lift low-pressure turbines. Exp Fluids 56(4):81. https:\/\/doi.org\/10.1007\/s00348-015-1947-1","journal-title":"Exp Fluids"},{"key":"10867_CR102","doi-asserted-by":"publisher","unstructured":"Libeyre F, Bainier F, Alas P (2021) A comprehensive modeling of centrifugal compressor vibrations for early fault detection. In: Proceedings of the ASME turbo expo 2020: turbomachinery technical conference and exposition. American Society of Mechanical Engineers, Virtual, Online, p V005T05A022, https:\/\/doi.org\/10.1115\/GT2020-15641","DOI":"10.1115\/GT2020-15641"},{"key":"10867_CR104","unstructured":"Lillicrap TP, Hunt JJ, Pritzel A et al (2019) Continuous control with deep reinforcement learning. Preprint http:\/\/arxiv.org\/abs\/1509.02971"},{"key":"10867_CR105","doi-asserted-by":"publisher","unstructured":"Lin TY, Doll\u00e1r P, Girshick R et al (2017) Feature pyramid networks for object detection. pp 936\u2013944, https:\/\/doi.org\/10.1109\/CVPR.2017.106","DOI":"10.1109\/CVPR.2017.106"},{"key":"10867_CR106","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.neucom.2019.02.007","volume":"338","author":"P Lin","year":"2019","unstructured":"Lin P, Wang M, Wang C et al (2019) Abrupt stall detection for axial compressors with non-uniform inflow via deterministic learning. Neurocomputing 338:163\u2013171. https:\/\/doi.org\/10.1016\/j.neucom.2019.02.007","journal-title":"Neurocomputing"},{"key":"10867_CR107","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1017\/jfm.2016.615","volume":"807","author":"J Ling","year":"2016","unstructured":"Ling J, Kurzawski A, Templeton J (2016) Reynolds averaged turbulence modelling using deep neural networks with embedded invariance. J Fluid Mech 807:155\u2013166. https:\/\/doi.org\/10.1017\/jfm.2016.615","journal-title":"J Fluid Mech"},{"issue":"12","key":"10867_CR108","doi-asserted-by":"publisher","first-page":"2433","DOI":"10.1177\/09544100211063115","volume":"236","author":"Y Liu","year":"2022","unstructured":"Liu Y, Chen J, Cheng J (2022) Mean line aerodynamic design of an axial compressor using a novel design approach based on reinforcement learning. Proc Inst Mech Eng Part G: J Aerosp Eng 236(12):2433\u20132446. https:\/\/doi.org\/10.1177\/09544100211063115","journal-title":"Proc Inst Mech Eng Part G: J Aerosp Eng"},{"key":"10867_CR109","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2024.131327","volume":"297","author":"C Liu","year":"2024","unstructured":"Liu C, Zou Z, Xu P et al (2024) Development of helium turbine loss model based on knowledge transfer with neural network and its application on aerodynamic design. Energy 297:131327. https:\/\/doi.org\/10.1016\/j.energy.2024.131327","journal-title":"Energy"},{"key":"10867_CR110","volume-title":"Inlet distortion and compressor stability","author":"JP Longley","year":"1988","unstructured":"Longley JP (1988) Inlet distortion and compressor stability. University of Cambridge, London"},{"issue":"1","key":"10867_CR111","doi-asserted-by":"publisher","DOI":"10.1115\/1.4052136","volume":"144","author":"DI Lopez","year":"2021","unstructured":"Lopez DI, Ghisu T, Shahpar S (2021) Global optimization of a transonic fan blade through AI-enabled active subspaces. J Turbomach 144(1):011013. https:\/\/doi.org\/10.1115\/1.4052136","journal-title":"J Turbomach"},{"key":"10867_CR112","doi-asserted-by":"publisher","DOI":"10.1016\/j.ast.2021.106893","volume":"116","author":"J Luo","year":"2021","unstructured":"Luo J, Xia Z, Liu F (2021) Robust design optimization considering inlet flow angle variations of a turbine cascade. Aerosp Sci Technol 116:106893. https:\/\/doi.org\/10.1016\/j.ast.2021.106893","journal-title":"Aerosp Sci Technol"},{"key":"10867_CR113","doi-asserted-by":"publisher","first-page":"909","DOI":"10.3390\/pr9060909","volume":"9","author":"Q Lv","year":"2021","unstructured":"Lv Q, Yu X, Ma H et al (2021) Applications of machine learning to reciprocating compressor fault diagnosis: a review. Processes 9:909. https:\/\/doi.org\/10.3390\/pr9060909","journal-title":"Processes"},{"key":"10867_CR114","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijheatmasstransfer.2019.07.069","volume":"143","author":"H Maral","year":"2019","unstructured":"Maral H, Alpman E, Kavurmac\u0131o\u011flu L et al (2019) A genetic algorithm based aerothermal optimization of tip carving for an axial turbine blade. Int J Heat Mass Tans 143:118419. https:\/\/doi.org\/10.1016\/j.ijheatmasstransfer.2019.07.069","journal-title":"Int J Heat Mass Tans"},{"issue":"11","key":"10867_CR115","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1260\/0957456001498723","volume":"31","author":"KP Maynard","year":"2000","unstructured":"Maynard KP, Trethewey M (2000) Blade and shaft crack detection using torsional vibration measurements part 1: feasibility studies. Noise Vib Worldw 31(11):9\u201315. https:\/\/doi.org\/10.1260\/0957456001498723","journal-title":"Noise Vib Worldw"},{"issue":"1","key":"10867_CR116","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1038\/s41597-021-01034-2","volume":"8","author":"R McConkey","year":"2021","unstructured":"McConkey R, Yee E, Lien FS (2021) A curated dataset for data-driven turbulence modelling. Sci Data 8(1):255. https:\/\/doi.org\/10.1038\/s41597-021-01034-2","journal-title":"Sci Data"},{"issue":"19","key":"10867_CR117","doi-asserted-by":"publisher","first-page":"7304","DOI":"10.3390\/en15197304","volume":"15","author":"N Menga","year":"2022","unstructured":"Menga N, Mothakani A, De Giorgi MG et al (2022) Extreme learning machine-based diagnostics for component degradation in a microturbine. Energies 15(19):7304. https:\/\/doi.org\/10.3390\/en15197304","journal-title":"Energies"},{"issue":"8","key":"10867_CR118","doi-asserted-by":"publisher","first-page":"1598","DOI":"10.2514\/3.12149","volume":"32","author":"FR Menter","year":"1994","unstructured":"Menter FR (1994) Two-equation eddy-viscosity turbulence models for engineering applications. AIAA J 32(8):1598\u20131605. https:\/\/doi.org\/10.2514\/3.12149","journal-title":"AIAA J"},{"issue":"7540","key":"10867_CR119","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1038\/nature14236","volume":"518","author":"V Mnih","year":"2015","unstructured":"Mnih V, Kavukcuoglu K, Silver D et al (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529\u2013533. https:\/\/doi.org\/10.1038\/nature14236","journal-title":"Nature"},{"issue":"1","key":"10867_CR120","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1146\/annurev.fluid.30.1.539","volume":"30","author":"P Moin","year":"1998","unstructured":"Moin P, Mahesh K (1998) Direct numerical simulation: a tool in turbulence research. Annu Rev Fluid Mech 30(1):539\u2013578. https:\/\/doi.org\/10.1146\/annurev.fluid.30.1.539","journal-title":"Annu Rev Fluid Mech"},{"issue":"1115\/1","key":"10867_CR121","first-page":"4006668","volume":"10","author":"H Okui","year":"2013","unstructured":"Okui H, Verstraete T, Van den Braembussche RA et al (2013) Three-dimensional design and optimization of a transonic rotor in axial flow compressors. J Turbomach doi 10(1115\/1):4006668","journal-title":"J Turbomach doi"},{"key":"10867_CR122","volume-title":"Aerodynamic and mechanical design of an 8: 1 pressure ratio centrifugal compressor","author":"C Osborne","year":"1974","unstructured":"Osborne C, Runstadler PW Jr, Stacy WD (1974) Aerodynamic and mechanical design of an 8: 1 pressure ratio centrifugal compressor. NASA Lewis Research Center, Cleveland"},{"issue":"4","key":"10867_CR123","doi-asserted-by":"publisher","first-page":"612","DOI":"10.2514\/1.2290","volume":"20","author":"A Oyama","year":"2004","unstructured":"Oyama A, Liou MS, Obayashi S (2004) Transonic axial-flow blade optimization: evolutionary algorithms\/three-dimensional Navier-Stokes solver. J Propul Power 20(4):612\u2013619. https:\/\/doi.org\/10.2514\/1.2290","journal-title":"J Propul Power"},{"issue":"10","key":"10867_CR124","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Know Data Eng 22(10):1345\u20131359. https:\/\/doi.org\/10.1109\/TKDE.2009.191","journal-title":"IEEE Trans Know Data Eng"},{"issue":"2","key":"10867_CR125","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1109\/TNN.2010.2091281","volume":"22","author":"SJ Pan","year":"2011","unstructured":"Pan SJ, Tsang IW, Kwok JT et al (2011) Domain adaptation via transfer component analysis. IEEE Trans Neural Netw 22(2):199\u2013210. https:\/\/doi.org\/10.1109\/TNN.2010.2091281","journal-title":"IEEE Trans Neural Netw"},{"key":"10867_CR126","doi-asserted-by":"publisher","first-page":"758","DOI":"10.1016\/j.jcp.2015.11.012","volume":"305","author":"EJ Parish","year":"2016","unstructured":"Parish EJ, Duraisamy K (2016) A paradigm for data-driven predictive modeling using field inversion and machine learning. J Comput Phys 305:758\u2013774. https:\/\/doi.org\/10.1016\/j.jcp.2015.11.012","journal-title":"J Comput Phys"},{"key":"10867_CR127","volume-title":"Unsteady disturbance structures in axial flow compressor stall inception","author":"HG Park","year":"1994","unstructured":"Park HG (1994) Unsteady disturbance structures in axial flow compressor stall inception. Massachusetts Institute of Technology, Massachusetts"},{"issue":"2","key":"10867_CR128","doi-asserted-by":"publisher","first-page":"15","DOI":"10.3390\/designs4020015","volume":"4","author":"A Parvaresh","year":"2020","unstructured":"Parvaresh A, Abrazeh S, Mohseni SR et al (2020) A novel deep learning backstepping controller-based digital twins technology for pitch angle control of variable speed wind turbine. Designs 4(2):15. https:\/\/doi.org\/10.3390\/designs4020015","journal-title":"Designs"},{"issue":"2","key":"10867_CR129","doi-asserted-by":"publisher","first-page":"326","DOI":"10.1115\/1.2841318","volume":"121","author":"S Pierret","year":"1999","unstructured":"Pierret S, Van den Braembussche RA (1999) Turbomachinery blade design using a Navier-Stokes solver and artificial neural network. J Turbomach 121(2):326\u2013332. https:\/\/doi.org\/10.1115\/1.2841318","journal-title":"J Turbomach"},{"key":"10867_CR130","doi-asserted-by":"publisher","unstructured":"Pongetti J, Kipouros T, Emmanuelli M et al (2021) Using autoencoders and output consolidation to improve machine learning models for turbomachinery applications. In: Proceedings of the ASME Turbo Expo 2021: turbomachinery technical conference and exposition. American Society of Mechanical Engineers, p V02DT36A018, https:\/\/doi.org\/10.1115\/GT2021-60158","DOI":"10.1115\/GT2021-60158"},{"issue":"2","key":"10867_CR131","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1017\/S0022112075003382","volume":"72","author":"SB Pope","year":"1975","unstructured":"Pope SB (1975) A more general effective-viscosity hypothesis. J Fluid Mech 72(2):331\u2013340. https:\/\/doi.org\/10.1017\/S0022112075003382","journal-title":"J Fluid Mech"},{"key":"10867_CR132","doi-asserted-by":"publisher","unstructured":"Post P, Winhart B, di Mare F (2022) Investigation of physics-informed neural networks based solution techniques for internal flows. In: ASME Turbo Expo 2022: turbomachinery technical conference and exposition. American Society of Mechanical Engineers, Rotterdam, The Netherlands, p V10CT32A011, https:\/\/doi.org\/10.1115\/GT2022-80960","DOI":"10.1115\/GT2022-80960"},{"issue":"6","key":"10867_CR133","doi-asserted-by":"publisher","DOI":"10.1115\/1.4024796","volume":"135","author":"TJ Praisner","year":"2013","unstructured":"Praisner TJ, Grover EA, Knezevici DC et al (2013) Toward the expansion of low-pressure-turbine airfoil design space. J Turbomach 135(6):061007. https:\/\/doi.org\/10.1115\/1.4024796","journal-title":"J Turbomach"},{"key":"10867_CR134","doi-asserted-by":"publisher","unstructured":"Pritchard LJ (2015) An eleven parameter axial turbine airfoil geometry model. In: Proceedings of the ASME Turbo Expo 2015: power for land, sea, and air. American Society of Mechanical Engineers, p V001T03A058, https:\/\/doi.org\/10.1115\/85-GT-219","DOI":"10.1115\/85-GT-219"},{"key":"10867_CR135","first-page":"10","volume":"41","author":"HC Pusey","year":"2007","unstructured":"Pusey HC (2007) Turbomachinery condition monitoring and failure prognosis. Sound Vib 41:10\u201315","journal-title":"Sound Vib"},{"key":"10867_CR136","doi-asserted-by":"publisher","unstructured":"Raghavan MS, Palatel A, Simon J (2020) Artificial intelligence based gas turbine compressor wash: a predictive approach. In: Proceedings of the ASME 2019: gas turbine india conference. American Society of Mechanical Engineers, Chennai, Tamil Nadu, India, p V002T09A006, https:\/\/doi.org\/10.1115\/GTINDIA2019-2434","DOI":"10.1115\/GTINDIA2019-2434"},{"issue":"1","key":"10867_CR137","first-page":"932","volume":"19","author":"M Raissi","year":"2018","unstructured":"Raissi M (2018) Deep hidden physics models: deep learning of nonlinear partial differential equations. J Mach Learn Res 19(1):932\u2013955","journal-title":"J Mach Learn Res"},{"key":"10867_CR138","unstructured":"Raissi M, Perdikaris P, Karniadakis GE (2017a) Physics informed deep learning (Part I): data-driven solutions of nonlinear partial differential equations. Preprint at http:\/\/arxiv.org\/abs\/1711.10561"},{"key":"10867_CR139","unstructured":"Raissi M, Perdikaris P, Karniadakis GE (2017b) Physics informed deep learning (Part II): data-driven discovery of nonlinear partial differential equations. Preprint at http:\/\/arxiv.org\/abs\/1711.10566"},{"issue":"1","key":"10867_CR140","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12206-018-1201-x","volume":"33","author":"S Rani","year":"2019","unstructured":"Rani S, Agrawal AK, Rastogi V (2019) Vibration analysis for detecting failure mode and crack location in first stage gas turbine blade. J Mech Sci Technol 33(1):1\u201310. https:\/\/doi.org\/10.1007\/s12206-018-1201-x","journal-title":"J Mech Sci Technol"},{"key":"10867_CR141","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/3206.001.0001","volume-title":"Gaussian processes for machine learning","author":"CE Rasmussen","year":"2005","unstructured":"Rasmussen CE, Williams CKI (2005) Gaussian processes for machine learning. The MIT Press, Cambridge"},{"key":"10867_CR142","doi-asserted-by":"publisher","unstructured":"Ren P, Stuart C, Spence S et al (2022) Using machine learning for loss prediction in a hybrid meanline modeling method to deliver improved radial turbine performance prediction. In: Proceedings of the ASME Turbo Expo 2022: turbomachinery technical conference and exposition. American Society of Mechanical Engineers, p V10BT35A004, https:\/\/doi.org\/10.1115\/GT2022-80186","DOI":"10.1115\/GT2022-80186"},{"key":"10867_CR143","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1037\/h0042519","volume":"65","author":"F Rosenblatt","year":"1958","unstructured":"Rosenblatt F (1958) The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 65:386\u2013408. https:\/\/doi.org\/10.1037\/h0042519","journal-title":"Psychol Rev"},{"key":"10867_CR144","doi-asserted-by":"publisher","DOI":"10.1115\/1.3106002","author":"B Rosic","year":"2012","unstructured":"Rosic B, Xu L (2012) Blade lean and shroud leakage flows in low aspect ratio turbines. J Turbomach. https:\/\/doi.org\/10.1115\/1.3106002","journal-title":"J Turbomach"},{"key":"10867_CR145","unstructured":"Ruder S (2017) An overview of gradient descent optimization algorithms. Preprint at http:\/\/arxiv.org\/abs\/1609.04747"},{"key":"10867_CR146","volume-title":"Artificial intelligence: a modern approach","author":"S Russell","year":"2009","unstructured":"Russell S, Norvig P (2009) Artificial intelligence: a modern approach, 3rd edn. Pearson, Upper Saddle River","edition":"3"},{"issue":"2","key":"10867_CR147","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1115\/1.3227410","volume":"105","author":"NL Sanger","year":"1983","unstructured":"Sanger NL (1983) The use of optimization techniques to design-controlled diffusion compressor blading. J Eng Power 105(2):256\u2013264. https:\/\/doi.org\/10.1115\/1.3227410","journal-title":"J Eng Power"},{"issue":"4","key":"10867_CR148","doi-asserted-by":"publisher","first-page":"563","DOI":"10.14429\/dsj.54.2069","volume":"54","author":"B Sasi","year":"2004","unstructured":"Sasi B, Rao BPC, Jayakumar T (2004) Dual-frequency eddy current non-destructive detection of fatigue cracks in compressor discs of aero engines. Defence Sci J 54(4):563\u2013570. https:\/\/doi.org\/10.14429\/dsj.54.2069","journal-title":"Defence Sci J"},{"key":"10867_CR149","doi-asserted-by":"publisher","DOI":"10.1115\/1.4035075","author":"M Schnoes","year":"2017","unstructured":"Schnoes M, Nicke E (2017) A database of optimal airfoils for axial compressor throughflow design. J Turbomach. https:\/\/doi.org\/10.1115\/1.4035075","journal-title":"J Turbomach"},{"key":"10867_CR150","unstructured":"Schulman J, Levine S, Moritz P et al (2015) Trust region policy optimization. In: Proceedings of the 32nd international conference on international conference on machine learning. JMLR.org, Lille, France, ICML\u201915, pp 1889\u20131897"},{"issue":"4","key":"10867_CR151","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1145\/15886.15903","volume":"20","author":"TW Sederberg","year":"1986","unstructured":"Sederberg TW, Parry SR (1986) Free-form deformation of solid geometric models. SIGGRAPH Comput Graph 20(4):151\u2013160. https:\/\/doi.org\/10.1145\/15886.15903","journal-title":"SIGGRAPH Comput Graph"},{"issue":"6","key":"10867_CR152","doi-asserted-by":"publisher","first-page":"1170","DOI":"10.1177\/09576509231163350","volume":"237","author":"A Shahrabi Farahani","year":"2023","unstructured":"Shahrabi Farahani A, Mohammadi E, Alizadeh M (2023) Utilizing artificial intelligence to develop an advanced compressor airfoil family for industrial, aero-derivative, and heavy-duty gas turbines. Proc Inst Mech Eng Part A: J Power Energy 237(6):1170\u20131187. https:\/\/doi.org\/10.1177\/09576509231163350. (publisher: IMECHE)","journal-title":"Proc Inst Mech Eng Part A: J Power Energy"},{"key":"10867_CR153","doi-asserted-by":"publisher","DOI":"10.1016\/j.applthermaleng.2023.121733","volume":"236","author":"A Shahrabi Farahani","year":"2024","unstructured":"Shahrabi Farahani A, Kohandel H, Moradtabrizi H et al (2024) Power generation gas turbine performance enhancement in hot ambient temperature conditions through axial compressor design optimization. Appl Therm Eng 236:121733. https:\/\/doi.org\/10.1016\/j.applthermaleng.2023.121733","journal-title":"Appl Therm Eng"},{"key":"10867_CR154","doi-asserted-by":"publisher","unstructured":"Shen Z, Wan X, Ye F et al (2019) Deep learning based framework for automatic damage detection in aircraft engine borescope inspection. pp 1005\u20131010, https:\/\/doi.org\/10.1109\/ICCNC.2019.8685593","DOI":"10.1109\/ICCNC.2019.8685593"},{"key":"10867_CR155","doi-asserted-by":"publisher","unstructured":"Simoni D, Lengani D, Petronio D et al (2020) A Bayesian approach for the identification of cascade loss model strategy. In: Proceedings of the ASME Turbo Expo 2020: turbomachinery technical conference and exposition. American Society of Mechanical Engineers, Virtual, Online, p V02BT33A010, https:\/\/doi.org\/10.1115\/GT2020-14625","DOI":"10.1115\/GT2020-14625"},{"issue":"4","key":"10867_CR156","doi-asserted-by":"publisher","DOI":"10.1063\/1.4947045","volume":"28","author":"AP Singh","year":"2016","unstructured":"Singh AP, Duraisamy K (2016) Using field inversion to quantify functional errors in turbulence closures. Phys Fluids 28(4):045110. https:\/\/doi.org\/10.1063\/1.4947045","journal-title":"Phys Fluids"},{"issue":"7","key":"10867_CR157","doi-asserted-by":"publisher","first-page":"2215","DOI":"10.2514\/1.J055595","volume":"55","author":"AP Singh","year":"2017","unstructured":"Singh AP, Medida S, Duraisamy K (2017) Machine-learning-augmented predictive modeling of turbulent separated flows over airfoils. AIAA J 55(7):2215\u20132227. https:\/\/doi.org\/10.2514\/1.J055595","journal-title":"AIAA J"},{"issue":"655","key":"10867_CR158","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1017\/S0001924000059108","volume":"69","author":"SF Smith","year":"1965","unstructured":"Smith SF (1965) A simple correlation of turbine efficiency. J Aeronaut Soc 69(655):467\u2013470. https:\/\/doi.org\/10.1017\/S0001924000059108","journal-title":"J Aeronaut Soc"},{"key":"10867_CR159","doi-asserted-by":"publisher","unstructured":"Spalart P, Allmaras S (1992) A one-equation turbulence model for aerodynamic flows. In: 30th aerospace sciences meeting and exhibit. American Institute of Aeronautics and Astronautics, p 439, https:\/\/doi.org\/10.2514\/6.1992-439","DOI":"10.2514\/6.1992-439"},{"key":"10867_CR160","doi-asserted-by":"publisher","unstructured":"Stadtm\u00fcller P, Fottner L (2001) A test case for the numerical investigation of wake passing effects on a highly loaded LP turbine cascade blade. In: ASME Turbo Expo 2001: power for land, sea, and air. American Society of Mechanical Engineers, p V001T03A015, https:\/\/doi.org\/10.1115\/2001-GT-0311","DOI":"10.1115\/2001-GT-0311"},{"issue":"3","key":"10867_CR161","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1007\/s007780050040","volume":"6","author":"M Steinbrunn","year":"1997","unstructured":"Steinbrunn M, Moerkotte G, Kemper A (1997) Heuristic and randomized optimization for the join ordering problem. VLDB J 6(3):191\u2013208. https:\/\/doi.org\/10.1007\/s007780050040","journal-title":"VLDB J"},{"key":"10867_CR162","doi-asserted-by":"publisher","first-page":"620","DOI":"10.1016\/j.renene.2018.10.047","volume":"133","author":"A Stetco","year":"2019","unstructured":"Stetco A, Dinmohammadi F, Zhao X et al (2019) Machine learning methods for wind turbine condition monitoring: a review. Renew Energy 133:620\u2013635. https:\/\/doi.org\/10.1016\/j.renene.2018.10.047","journal-title":"Renew Energy"},{"issue":"4","key":"10867_CR163","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1023\/A:1008202821328","volume":"11","author":"R Storn","year":"1997","unstructured":"Storn R, Price K (1997) Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341\u2013359. https:\/\/doi.org\/10.1023\/A:1008202821328","journal-title":"J Glob Optim"},{"key":"10867_CR164","volume-title":"Laser anemometer measurements in a transonic axial-flow fan rotor","author":"AJ Strazisar","year":"1989","unstructured":"Strazisar AJ, Wood JR, Hathaway MD et al (1989) Laser anemometer measurements in a transonic axial-flow fan rotor. NASA Lewis Research Center, Cleveland"},{"key":"10867_CR165","volume-title":"Introduction to reinforcement learning","author":"RS Sutton","year":"1998","unstructured":"Sutton RS, Barto AG (1998) Introduction to reinforcement learning, 1st edn. MIT press, Cambridge","edition":"1"},{"key":"10867_CR166","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.104900","volume":"113","author":"SS Talebi","year":"2022","unstructured":"Talebi SS, Madadi A, Tousi AM et al (2022) Micro gas turbine fault detection and isolation with a combination of artificial neural network and off-design performance analysis. Eng Appl Artif Intell 113:104900. https:\/\/doi.org\/10.1016\/j.engappai.2022.104900","journal-title":"Eng Appl Artif Intell"},{"issue":"5","key":"10867_CR167","doi-asserted-by":"publisher","DOI":"10.1115\/1.4046658","volume":"142","author":"JV Taylor","year":"2020","unstructured":"Taylor JV, Conduit B, Dickens A et al (2020) Predicting the operability of damaged compressors using machine learning. J Turbomach 142(5):051010. https:\/\/doi.org\/10.1115\/1.4046658","journal-title":"J Turbomach"},{"key":"10867_CR168","doi-asserted-by":"publisher","unstructured":"Thatte A, Vurimi G, Borate P et al (2021) An artificial intelligence based method for performance prediction and inverse design of hydraulic turbochargers. In: Proceedings of the ASME Turbo Expo 2021: turbomachinery technical conference and exposition. American Society of Mechanical Engineers, p V008T20A027, https:\/\/doi.org\/10.1115\/GT2020-16012","DOI":"10.1115\/GT2020-16012"},{"issue":"2","key":"10867_CR169","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1115\/1.3227277","volume":"104","author":"WT Thompkins Jr","year":"1982","unstructured":"Thompkins WT Jr, Tong SS (1982) Inverse or design calculations for nonpotential flow in turbomachinery blade passages. J Eng Power 104(2):281\u2013285. https:\/\/doi.org\/10.1115\/1.3227277","journal-title":"J Eng Power"},{"key":"10867_CR170","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1007\/978-1-4615-5529-2_8","volume-title":"Learning to learn","author":"S Thrun","year":"1998","unstructured":"Thrun S (1998) Lifelong learning algorithms. In: Thrun S, Pratt L (eds) Learning to learn. Springer, Boston, pp 181\u2013209. https:\/\/doi.org\/10.1007\/978-1-4615-5529-2_8"},{"issue":"1","key":"10867_CR171","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1115\/1.2835644","volume":"117","author":"M Tryfonidis","year":"1995","unstructured":"Tryfonidis M, Etchevers O, Paduano JD et al (1995) Prestall behavior of several high-speed compressors. J Turbomach 117(1):62\u201380. https:\/\/doi.org\/10.1115\/1.2835644","journal-title":"J Turbomach"},{"key":"10867_CR172","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.energy.2019.03.140","volume":"176","author":"X Wang","year":"2019","unstructured":"Wang X, Zou Z (2019) Uncertainty analysis of impact of geometric variations on turbine blade performance. Energy 176:67\u201380. https:\/\/doi.org\/10.1016\/j.energy.2019.03.140","journal-title":"Energy"},{"issue":"1","key":"10867_CR173","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.jppr.2012.10.004","volume":"2","author":"P Wang","year":"2013","unstructured":"Wang P, Li Y, Zou Z et al (2013) Conjugate heat transfer investigation of cooled turbine using the preconditioned density-based algorithm. Propuls Power Res 2(1):56\u201369. https:\/\/doi.org\/10.1016\/j.jppr.2012.10.004","journal-title":"Propuls Power Res"},{"key":"10867_CR174","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/j.engfailanal.2013.10.006","volume":"36","author":"Y Wang","year":"2014","unstructured":"Wang Y, Deng C, Wu J et al (2014) A corrective maintenance scheme for engineering equipment. Eng Fail Anal 36:269\u2013283. https:\/\/doi.org\/10.1016\/j.engfailanal.2013.10.006","journal-title":"Eng Fail Anal"},{"issue":"5","key":"10867_CR175","doi-asserted-by":"publisher","first-page":"3447","DOI":"10.1007\/s10462-019-09768-7","volume":"53","author":"Y Wang","year":"2020","unstructured":"Wang Y, Yu Y, Cao S et al (2020) A review of applications of artificial intelligent algorithms in wind farms. Artif Intell Rev 53(5):3447\u20133500. https:\/\/doi.org\/10.1007\/s10462-019-09768-7","journal-title":"Artif Intell Rev"},{"key":"10867_CR176","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2020.118878","volume":"214","author":"Q Wang","year":"2021","unstructured":"Wang Q, Yang L, Rao Y (2021) Establishment of a generalizable model on a small-scale dataset to predict the surface pressure distribution of gas turbine blades. Energy 214:118878. https:\/\/doi.org\/10.1016\/j.energy.2020.118878","journal-title":"Energy"},{"issue":"1","key":"10867_CR177","doi-asserted-by":"publisher","DOI":"10.1063\/5.0078143","volume":"34","author":"H Wang","year":"2022","unstructured":"Wang H, Liu Y, Wang S (2022a) Dense velocity reconstruction from particle image velocimetry\/particle tracking velocimetry using a physics-informed neural network. Phys Fluids 34(1):017116. https:\/\/doi.org\/10.1063\/5.0078143","journal-title":"Phys Fluids"},{"key":"10867_CR178","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2022.123373","volume":"246","author":"Q Wang","year":"2022","unstructured":"Wang Q, Yang L, Huang K (2022b) Fast prediction and sensitivity analysis of gas turbine cooling performance using supervised learning approaches. Energy 246:123373. https:\/\/doi.org\/10.1016\/j.energy.2022.123373","journal-title":"Energy"},{"key":"10867_CR179","doi-asserted-by":"publisher","DOI":"10.1016\/j.ast.2022.107851","volume":"129","author":"X Wang","year":"2022","unstructured":"Wang X, Yao L, Zou Z (2022c) Effect of loading level and axial distribution on uncertainty performance of turbine blade with geometric variations. Aerosp Sci Technol 129:107851. https:\/\/doi.org\/10.1016\/j.ast.2022.107851","journal-title":"Aerosp Sci Technol"},{"key":"10867_CR180","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2021.110922","volume":"452","author":"F Waschkowski","year":"2022","unstructured":"Waschkowski F, Zhao Y, Sandberg R et al (2022) Multi-objective CFD-driven development of coupled turbulence closure models. J Comput Phys 452:110922. https:\/\/doi.org\/10.1016\/j.jcp.2021.110922","journal-title":"J Comput Phys"},{"key":"10867_CR181","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.jcp.2016.08.015","volume":"325","author":"J Weatheritt","year":"2016","unstructured":"Weatheritt J, Sandberg R (2016) A novel evolutionary algorithm applied to algebraic modifications of the RANS stress-strain relationship. J Comput Phys 325:22\u201337. https:\/\/doi.org\/10.1016\/j.jcp.2016.08.015","journal-title":"J Comput Phys"},{"key":"10867_CR182","volume-title":"Design of highly loaded axial-flow fans and compressors","author":"AJ Wennerstrom","year":"2001","unstructured":"Wennerstrom AJ (2001) Design of highly loaded axial-flow fans and compressors. Concepts ETI Inc, White River Junction"},{"issue":"2","key":"10867_CR183","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1115\/1.1343465","volume":"123","author":"MP Wernet","year":"2000","unstructured":"Wernet MP, Bright MM, Skoch GJ (2000) An investigation of surge in a high-speed centrifugal compressor using digital PIV. J Turbomach 123(2):418\u2013428. https:\/\/doi.org\/10.1115\/1.1343465","journal-title":"J Turbomach"},{"issue":"1","key":"10867_CR184","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1146\/annurev-fluid-120710-101204","volume":"45","author":"J Westerweel","year":"2013","unstructured":"Westerweel J, Elsinga GE, Adrian RJ (2013) Particle image velocimetry for complex and turbulent flows. Annu Rev Fluid Mech 45(1):409\u2013436. https:\/\/doi.org\/10.1146\/annurev-fluid-120710-101204","journal-title":"Annu Rev Fluid Mech"},{"key":"10867_CR185","volume-title":"Turbulence modeling for CFD","author":"DC Wilcox","year":"2006","unstructured":"Wilcox DC (2006) Turbulence modeling for CFD, 3rd edn. D C W Industries, La C\u0169nada, Calif","edition":"3"},{"issue":"11","key":"10867_CR186","doi-asserted-by":"publisher","first-page":"2823","DOI":"10.2514\/1.36541","volume":"46","author":"DC Wilcox","year":"2008","unstructured":"Wilcox DC (2008) Formulation of the k-w turbulence model revisited. AIAA J 46(11):2823\u20132838. https:\/\/doi.org\/10.2514\/1.36541","journal-title":"AIAA J"},{"key":"10867_CR187","doi-asserted-by":"publisher","unstructured":"Wong CY, Seshadri P, Parks GT (2021) Automatic borescope damage assessments for gas turbine blades via deep learning. In: AIAA Scitech 2021 Forum. American Institute of Aeronautics and Astronautics, p 1488, https:\/\/doi.org\/10.2514\/6.2021-1488","DOI":"10.2514\/6.2021-1488"},{"issue":"3","key":"10867_CR188","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1002\/qre.698","volume":"22","author":"WK Wong","year":"2006","unstructured":"Wong WK, Ng SH, Xu K (2006) A statistical investigation and optimization of an industrial radiography inspection process for aero-engine components. Qual Rel Eng Int 22(3):321\u2013334. https:\/\/doi.org\/10.1002\/qre.698","journal-title":"Qual Rel Eng Int"},{"key":"10867_CR189","unstructured":"Wright P, Miller D (1992) An improved compressor performance prediction model. In: Proceedings of the European conference of turbomachinery: latest developments in a changing scene. Institution of Mechanical Engineers, C423\/028"},{"key":"10867_CR190","doi-asserted-by":"publisher","DOI":"10.1115\/1.4016114","volume-title":"A general theory of three-dimensional flow in subsonic and supersonic turbomachines of axial, radial, and mixed-flow types","author":"C Wu","year":"1952","unstructured":"Wu C (1952) A general theory of three-dimensional flow in subsonic and supersonic turbomachines of axial, radial, and mixed-flow types. Lewis Flight Propulsion Laboratory, Cleveland"},{"key":"10867_CR191","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.jcp.2016.07.038","volume":"324","author":"H Xiao","year":"2016","unstructured":"Xiao H, Wu JL, Wang JX et al (2016) Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier-Stokes simulations: a data-driven, physics-informed Bayesian approach. J Comput Phys 324:115\u2013136. https:\/\/doi.org\/10.1016\/j.jcp.2016.07.038","journal-title":"J Comput Phys"},{"issue":"8","key":"10867_CR192","doi-asserted-by":"publisher","DOI":"10.1063\/1.5110788","volume":"31","author":"C Xie","year":"2019","unstructured":"Xie C, Wang J, Li H et al (2019) Artificial neural network mixed model for large eddy simulation of compressible isotropic turbulence. Phys Fluids 31(8):085112. https:\/\/doi.org\/10.1063\/1.5110788","journal-title":"Phys Fluids"},{"key":"10867_CR193","doi-asserted-by":"publisher","first-page":"200","DOI":"10.33737\/jgpps\/152462","volume":"6","author":"P Xu","year":"2022","unstructured":"Xu P, Zou Z (2022) A study of real-gas effect on SCO2 compressor performance using similitude method. J Glob Power Propuls Soc 6:200\u2013212. https:\/\/doi.org\/10.33737\/jgpps\/152462","journal-title":"J Glob Power Propuls Soc"},{"key":"10867_CR194","doi-asserted-by":"publisher","DOI":"10.1016\/j.ast.2020.106358","volume":"108","author":"P Xu","year":"2021","unstructured":"Xu P, Zou Z, Xuan L (2021a) A hybrid performance prediction method for centrifugal compressors based on single-zone and two-zone models. Aerosp Sci Technol 108:106358. https:\/\/doi.org\/10.1016\/j.ast.2020.106358","journal-title":"Aerosp Sci Technol"},{"key":"10867_CR195","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2021.114747","volume":"247","author":"P Xu","year":"2021","unstructured":"Xu P, Zou Z, Yao L (2021b) A unified performance conversion method for similar compressors working with different gases based on polytropic analysis and deep-learning improvement. Energy Conv Manag 247:114747. https:\/\/doi.org\/10.1016\/j.enconman.2021.114747","journal-title":"Energy Conv Manag"},{"key":"10867_CR196","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2022.116318","volume":"271","author":"P Xu","year":"2022","unstructured":"Xu P, Zou Z, Fu C (2022) Aerodynamic design considerations for supercritical CO2 centrifugal compressor with real-gas effects. Energy Conv Manag 271:116318. https:\/\/doi.org\/10.1016\/j.enconman.2022.116318","journal-title":"Energy Conv Manag"},{"issue":"10","key":"10867_CR197","doi-asserted-by":"publisher","DOI":"10.1063\/5.0022561","volume":"32","author":"Y Yin","year":"2020","unstructured":"Yin Y, Yang P, Zhang Y et al (2020) Feature selection and processing of turbulence modeling based on an artificial neural network. Phys Fluids 32(10):105117. https:\/\/doi.org\/10.1063\/5.0022561","journal-title":"Phys Fluids"},{"issue":"11","key":"10867_CR198","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.cja.2022.05.013","volume":"35","author":"Z Yue","year":"2022","unstructured":"Yue Z, Zhou C, Jin D et al (2022) A spanwise loss model for axial compressor stator based on machine learning. Chin J Aeronaut 35(11):74\u201384. https:\/\/doi.org\/10.1016\/j.cja.2022.05.013","journal-title":"Chin J Aeronaut"},{"key":"10867_CR199","doi-asserted-by":"publisher","first-page":"06008","DOI":"10.1051\/matecconf\/201925506008","volume":"255","author":"MD Yusoff","year":"2019","unstructured":"Yusoff MD, Ooi CS, Lim MH et al (2019) A hybrid k-means-GMM machine learning technique for turbomachinery condition monitoring. MATEC Web Conf 255:06008. https:\/\/doi.org\/10.1051\/matecconf\/201925506008","journal-title":"MATEC Web Conf"},{"issue":"5","key":"10867_CR200","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1002\/fld.1650130505","volume":"13","author":"M Zangeneh","year":"1991","unstructured":"Zangeneh M (1991) A compressible three-dimensional design method for radial and mixed flow turbomachinery blades. Int J Numer Methods Fluids 13(5):599\u2013624. https:\/\/doi.org\/10.1002\/fld.1650130505","journal-title":"Int J Numer Methods Fluids"},{"issue":"2","key":"10867_CR201","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1115\/1.2928362","volume":"116","author":"M Zangeneh","year":"1994","unstructured":"Zangeneh M (1994) Inviscid-viscous interaction method for three-dimensional inverse design of centrifugal impellers. J Turbomach 116(2):280\u2013290. https:\/\/doi.org\/10.1115\/1.2928362","journal-title":"J Turbomach"},{"issue":"6","key":"10867_CR202","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1007\/s42241-019-0089-y","volume":"31","author":"X Zhang","year":"2019","unstructured":"Zhang X, Wu J, Coutier Delgosha O et al (2019) Recent progress in augmenting turbulence models with physics-informed machine learning. J Hydrodyn 31(6):1153\u20131158. https:\/\/doi.org\/10.1007\/s42241-019-0089-y","journal-title":"J Hydrodyn"},{"key":"10867_CR203","doi-asserted-by":"publisher","DOI":"10.1177\/09544100221097586","author":"X Zhang","year":"2022","unstructured":"Zhang X, Zhang T, Li L et al (2022) Real-time prediction for the surge of turboshaft engine using multi-branch feature fusion neural network. J Aerosp Eng. https:\/\/doi.org\/10.1177\/09544100221097586","journal-title":"J Aerosp Eng"},{"issue":"1","key":"10867_CR204","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1693\/1\/012028","volume":"1693","author":"H Zhao","year":"2020","unstructured":"Zhao H, Quan F, Xia W et al (2020a) Detection of spike-type stall of axial compressors based on dilated causal convolutional neural networks. J Phys: Conf Ser 1693(1):012028. https:\/\/doi.org\/10.1088\/1742-6596\/1693\/1\/012028","journal-title":"J Phys: Conf Ser"},{"key":"10867_CR205","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2020.109413","volume":"411","author":"Y Zhao","year":"2020","unstructured":"Zhao Y, Akolekar HD, Weatheritt J et al (2020b) RANS turbulence model development using CFD-driven machine learning. J Comput Phys 411:109413. https:\/\/doi.org\/10.1016\/j.jcp.2020.109413","journal-title":"J Comput Phys"},{"issue":"1","key":"10867_CR206","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1115\/1.1516814","volume":"125","author":"KU Ziegler","year":"2003","unstructured":"Ziegler KU, Gallus HE, Niehuis R (2003) A study on impeller-diffuser interaction-Part I: influence on the performance. J Turbomach 125(1):173\u2013182. https:\/\/doi.org\/10.1115\/1.1516814","journal-title":"J Turbomach"},{"key":"10867_CR207","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2020.106889","volume":"150","author":"T Zonta","year":"2020","unstructured":"Zonta T, da Costa CA, da Rosa Righi R et al (2020) Predictive maintenance in the Industry 4.0: a systematic literature review. Comput Ind Eng 150:106889. https:\/\/doi.org\/10.1016\/j.cie.2020.106889","journal-title":"Comput Ind Eng"},{"key":"10867_CR208","doi-asserted-by":"publisher","unstructured":"Zou F (2020) Review of aero-engine defect detection technology. pp 1524\u20131527, https:\/\/doi.org\/10.1109\/ITNEC48623.2020.9084849","DOI":"10.1109\/ITNEC48623.2020.9084849"},{"key":"10867_CR209","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.energy.2017.07.047","volume":"138","author":"Z Zou","year":"2017","unstructured":"Zou Z, Shao F, Li Y et al (2017) Dominant flow structure in the squealer tip gap and its impact on turbine aerodynamic performance. Energy 138:167\u2013184. https:\/\/doi.org\/10.1016\/j.energy.2017.07.047","journal-title":"Energy"},{"key":"10867_CR210","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-10-5750-2","volume-title":"Axial turbine aerodynamics for aero-engines: flow analysis and aerodynamics design","author":"Z Zou","year":"2018","unstructured":"Zou Z, Wang S, Liu H et al (2018) Axial turbine aerodynamics for aero-engines: flow analysis and aerodynamics design, 1st edn. Springer, Singapore","edition":"1"},{"key":"10867_CR211","doi-asserted-by":"publisher","DOI":"10.1016\/j.icheatmasstransfer.2020.104588","volume":"114","author":"Z Zou","year":"2020","unstructured":"Zou Z, Xuan L, Chen Y et al (2020) Effects of flow structure on heat transfer of squealer tip in a turbine rotor blade. Int Commun Heat Mass Transf 114:104588. https:\/\/doi.org\/10.1016\/j.icheatmasstransfer.2020.104588","journal-title":"Int Commun Heat Mass Transf"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-024-10867-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-024-10867-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-024-10867-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,17]],"date-time":"2024-08-17T05:23:01Z","timestamp":1723872181000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-024-10867-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,30]]},"references-count":211,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["10867"],"URL":"https:\/\/doi.org\/10.1007\/s10462-024-10867-3","relation":{},"ISSN":["1573-7462"],"issn-type":[{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,30]]},"assertion":[{"value":"20 July 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 July 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to infuence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"222"}}