{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T22:10:06Z","timestamp":1768515006437,"version":"3.49.0"},"reference-count":30,"publisher":"ASME International","issue":"3","license":[{"start":{"date-parts":[[2022,8,8]],"date-time":"2022-08-08T00:00:00Z","timestamp":1659916800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.asme.org\/publications-submissions\/publishing-information\/legal-policies"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51935007"],"award-info":[{"award-number":["51935007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["asmedigitalcollection.asme.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Modeling and characterizing high-dimensional aerodynamic pressures, on the elevator in the hoistway, is very challenging. An accurate model is helpful to understand and analyze the pressure characteristics, which is a vital consideration in the design of a competitive elevator. The current full-order models are usually governed by the Navier\u2013Stokes equations and have low computational efficiency. A reduced-order model is thus necessary to improve computational efficiency. This work aims at investigating two data-driven approaches, for modeling and characterizing the aerodynamic pressures, i.e., proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD) that are able to extract temporal\u2013spatial structures from the data. A three-dimensional (3D) model of the realistic elevator is first built and simulation data of the aerodynamic pressures during the motion of the elevator in the hoistway is generated via computational fluid dynamics (CFD). Then, POD and DMD are employed to analyze the simulation data. It is found that through clustering techniques and since there exist local aerodynamic pressure pulses during the operation of the elevator, the aerodynamic pressure on the elevator has some distinct patterns. Therefore, cluster-based POD and DMD are further employed in the analysis. The results demonstrate that the cluster-based POD and DMD can achieve lower reconstruction errors than POD and DMD.<\/jats:p>","DOI":"10.1115\/1.4054869","type":"journal-article","created":{"date-parts":[[2022,6,24]],"date-time":"2022-06-24T07:18:40Z","timestamp":1656055120000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":9,"title":["Data-Driven Approaches for Characterization of Aerodynamics on Super High-Speed Elevators"],"prefix":"10.1115","volume":"23","author":[{"given":"Jingren","family":"Xie","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University School of Mechanical Engineering;, MoE Key Lab of Artificial Intelligence, AI Institute, , Shanghai 200240 , China"}]},{"given":"Shuai","family":"Mao","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University School of Mechanical Engineering, , Shanghai 200240 , China"}]},{"given":"Zhinan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University School of Mechanical Engineering; State Key Laboratory of Mechanical System and Vibration, , Shanghai 200240 , China"}]},{"given":"Chengliang","family":"Liu","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University School of Mechanical Engineering;, State Key Laboratory of Mechanical System and Vibration;, MoE Key Lab of Artificial Intelligence, AI Institute, , Shanghai 200240 , China"}]}],"member":"33","published-online":{"date-parts":[[2022,8,8]]},"reference":[{"key":"2022080814343191800_CIT0001","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1016\/j.jweia.2019.02.012","article-title":"Theoretical Modeling and Sensitivity Analysis of the Car-Induced Unsteady Airflow in Super High-Speed Elevator","volume":"188","author":"Qiao","year":"2019","journal-title":"J. Wind Eng. Ind. Aerodyn."},{"issue":"6","key":"2022080814343191800_CIT0002","doi-asserted-by":"publisher","first-page":"720","DOI":"10.1016\/S1001-6058(08)60009-8","article-title":"Numerical Simulation of Unsteady Turbulent Flow Induced by Two-Dimensional Elevator Car and Counter Weight System","volume":"19","author":"Shi","year":"2007","journal-title":"J. Hydrodyn."},{"issue":"4","key":"2022080814343191800_CIT0003","doi-asserted-by":"publisher","first-page":"765","DOI":"10.1007\/s12273-018-0430-3","article-title":"Gas Flow Behavior and Flow Transition in Elevator Shafts Considering Elevator Motion During a Building Fire","volume":"11","author":"Chen","year":"2018","journal-title":"Build. Simul."},{"key":"2022080814343191800_CIT0004","doi-asserted-by":"publisher","first-page":"104545","DOI":"10.1016\/j.jweia.2021.104545","article-title":"Higher Order Dynamic Mode Decomposition of Wind Pressures on Square Buildings","volume":"211","author":"Zhou","year":"2020","journal-title":"J. Wind Eng. Ind. Aerodyn."},{"key":"2022080814343191800_CIT0005","doi-asserted-by":"publisher","first-page":"104278","DOI":"10.1016\/j.jweia.2020.104278","article-title":"Dynamic Mode Decomposition on Pressure Flow Field Analysis: Flow Field Reconstruction, Accuracy, and Practical Significance","volume":"205","author":"Li","year":"2020","journal-title":"J. Wind Eng. Ind. Aerodyn."},{"key":"2022080814343191800_CIT0006","volume-title":"Stochastic Tools in Turbulence","author":"Lumley","year":"1970"},{"issue":"12","key":"2022080814343191800_CIT0007","doi-asserted-by":"publisher","first-page":"2426","DOI":"10.2514\/3.12309","article-title":"Eigenanalysis of Unsteady Flows About Airfoils, Cascades, and Wings","volume":"32","author":"Hall","year":"1994","journal-title":"AIAA J."},{"issue":"8","key":"2022080814343191800_CIT0008","doi-asserted-by":"publisher","first-page":"1578","DOI":"10.2514\/3.13274","article-title":"Eigenmode Analysis in Unsteady Aerodynamics: Reduced-Order Models","volume":"34","author":"Dowell","year":"1996","journal-title":"AIAA J."},{"key":"2022080814343191800_CIT0009","author":"Andrianne","year":"2009"},{"key":"2022080814343191800_CIT0010","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1017\/S0022112010001217","article-title":"Dynamic Mode Decomposition of Numerical and Experimental Data","volume":"656","author":"Schmid","year":"2010","journal-title":"J. Fluid Mech."},{"issue":"12","key":"2022080814343191800_CIT0011","doi-asserted-by":"publisher","first-page":"4013","DOI":"10.2514\/1.J056060","article-title":"Modal Analysis of Fluid Flows: An Overview","volume":"55","author":"Taira","year":"2017","journal-title":"AIAA J."},{"issue":"3","key":"2022080814343191800_CIT0012","doi-asserted-by":"publisher","first-page":"997","DOI":"10.1142\/S0218127405012429","article-title":"Model Reduction for Fluids, Using Balanced Proper Orthogonal Decomposition","volume":"15","author":"Rowley","year":"2005","journal-title":"Int. J. Bifurcat. Chaos"},{"key":"2022080814343191800_CIT0013","doi-asserted-by":"publisher","first-page":"821","DOI":"10.1017\/jfm.2018.283","article-title":"Spectral Proper Orthogonal Decomposition and Its Relationship to Dynamic Mode Decomposition and Resolvent Analysis","volume":"847","author":"Towne","year":"2018","journal-title":"J. Fluid Mech."},{"issue":"6","key":"2022080814343191800_CIT0014","doi-asserted-by":"publisher","first-page":"887","DOI":"10.1007\/s00332-012-9130-9","article-title":"Variants of Dynamic Mode Decomposition: Boundary Condition, Koopman, and Fourier Analyses","volume":"22","author":"Chen","year":"2012","journal-title":"J. Nonlinear Sci."},{"key":"2022080814343191800_CIT0015","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1017\/jfm.2013.426","article-title":"Optimal Mode Decomposition for Unsteady Flows","volume":"733","author":"Wynn","year":"2013","journal-title":"J. Fluid Mech."},{"issue":"2","key":"2022080814343191800_CIT0016","doi-asserted-by":"publisher","first-page":"024103","DOI":"10.1063\/1.4863670","article-title":"Sparsity-Promoting Dynamic Mode Decomposition","volume":"26","author":"Jovanovi\u0107","year":"2014","journal-title":"Phys. Fluids"},{"issue":"6","key":"2022080814343191800_CIT0017","doi-asserted-by":"publisher","first-page":"1307","DOI":"10.1007\/s00332-015-9258-5","article-title":"A Data\u2013Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition","volume":"25","author":"Williams","year":"2015","journal-title":"J. Nonlinear Sci."},{"issue":"2","key":"2022080814343191800_CIT0018","doi-asserted-by":"publisher","first-page":"165","DOI":"10.3934\/jcd.2015002","article-title":"Compressed Sensing and Dynamic Mode Decomposition","volume":"2","author":"Brunton","year":"2015","journal-title":"J. Comput. Dyn."},{"issue":"2","key":"2022080814343191800_CIT0019","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1137\/15M1023543","article-title":"Multiresolution Dynamic Mode Decomposition","volume":"15","author":"Kutz","year":"2016","journal-title":"SIAM J. Appl. Dyn. Syst."},{"issue":"11","key":"2022080814343191800_CIT0020","doi-asserted-by":"publisher","first-page":"111701","DOI":"10.1063\/1.4901016","article-title":"Dynamic Mode Decomposition for Large and Streaming Datasets","volume":"26","author":"Hemati","year":"2014","journal-title":"Phys. Fluids"},{"key":"2022080814343191800_CIT0021","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jneumeth.2015.10.010","article-title":"Extracting Spatial-Temporal Coherent Patterns in Large-Scale Neural Recordings Using Dynamic Mode Decomposition","volume":"258","author":"Brunton","year":"2016","journal-title":"J. Neurosci. Methods"},{"issue":"3","key":"2022080814343191800_CIT0022","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00348-016-2127-7","article-title":"Characterizing and Correcting for the Effect of Sensor Noise in the Dynamic Mode Decomposition","volume":"57","author":"Dawson","year":"2016","journal-title":"Exp. Fluids"},{"issue":"2","key":"2022080814343191800_CIT0023","doi-asserted-by":"publisher","first-page":"1164","DOI":"10.1137\/15M104565X","article-title":"Sparse Sensing and DMD-Based Identification of Flow Regimes and Bifurcations in Complex Flows","volume":"16","author":"Kramer","year":"2017","journal-title":"SIAM J. Appl. Dyn. Syst."},{"issue":"3","key":"2022080814343191800_CIT0024","doi-asserted-by":"publisher","first-page":"032108","DOI":"10.1063\/1.5138932","article-title":"Characterizing Magnetized Plasmas With Dynamic Mode Decomposition","volume":"27","author":"Kaptanoglu","year":"2020","journal-title":"Phys. Plasmas"},{"key":"2022080814343191800_CIT0025","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1017\/jfm.2014.355","article-title":"Cluster-Based Reduced-Order Modelling of a Mixing Layer","volume":"754","author":"Kaiser","year":"2014","journal-title":"J. Fluid Mech."},{"issue":"1","key":"2022080814343191800_CIT0026","doi-asserted-by":"publisher","first-page":"1016","DOI":"10.1038\/s41467-021-21331-z","article-title":"Learning Dominant Physical Processes With Data-Driven Balance Models","volume":"12","author":"Callaham","year":"2021","journal-title":"Nat. Commun."},{"key":"2022080814343191800_CIT0027","article-title":"Incorporating Symmetry Into Deep Dynamics Models for Improved Generalization","author":"Wang","year":"2020"},{"issue":"1","key":"2022080814343191800_CIT0028","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.jneumeth.2012.08.016","article-title":"A Correlation-Matrix-Based Hierarchical Clustering Method for Functional Connectivity Analysis","volume":"211","author":"Liu","year":"2012","journal-title":"J. Neurosci. Methods"},{"issue":"2","key":"2022080814343191800_CIT0029","doi-asserted-by":"publisher","first-page":"391","DOI":"10.3934\/jcd.2014.1.391","article-title":"On Dynamic Mode Decomposition: Theory and Applications","volume":"1","author":"Tu","year":"2014","journal-title":"J. Comput. Dyn."},{"issue":"13","key":"2022080814343191800_CIT0030","doi-asserted-by":"publisher","first-page":"2964","DOI":"10.1002\/nme.6342","article-title":"The Substructuring-Based Topology Optimization for Maximizing the First Eigenvalue of Hierarchical Lattice Structure","volume":"121","author":"Wu","year":"2020","journal-title":"Int. J. Numer. Methods Eng."}],"container-title":["Journal of Computing and Information Science in Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/asmedigitalcollection.asme.org\/computingengineering\/article-pdf\/23\/3\/031004\/6907839\/jcise_23_3_031004.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/asmedigitalcollection.asme.org\/computingengineering\/article-pdf\/23\/3\/031004\/6907839\/jcise_23_3_031004.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,8]],"date-time":"2022-08-08T14:34:40Z","timestamp":1659969280000},"score":1,"resource":{"primary":{"URL":"https:\/\/asmedigitalcollection.asme.org\/computingengineering\/article\/23\/3\/031004\/1141848\/Data-Driven-Approaches-for-Characterization-of"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,8]]},"references-count":30,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,6,1]]}},"URL":"https:\/\/doi.org\/10.1115\/1.4054869","relation":{},"ISSN":["1530-9827","1944-7078"],"issn-type":[{"value":"1530-9827","type":"print"},{"value":"1944-7078","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,8]]},"article-number":"031004"}}