A reduced order model for turbulent flows in the urban environment using machine learning
Tài liệu tham khảo
Wang, 2018, Model identification of reduced order fluid dynamics systems using deep learning, Int. J. Numer. Methods Fluid., 86, 255, 10.1002/fld.4416
Grinstein, 1899, On integrating large eddy simulation and laboratory turbulent flow experiments, Phil. Trans. Roy. Soc. Lond.: Math. Phys. Eng. Sci., 367, 2931
Blocken, 2015, Computational fluid dynamics for urban physics: importance, scales, possibilities, limitations and ten tips and tricks towards accurate and reliable simulations, Build. Environ., 91, 219, 10.1016/j.buildenv.2015.02.015
Vonlanthen, 2017, Urban climate multiscale interaction between a cluster of buildings and the abl developing over a real terrain, Urban Clim., 20, 1, 10.1016/j.uclim.2017.02.009
Lateb, 2016, On the use of numerical modelling for near-field pollutant dispersion in urban environments - a review, Environ. Pollut., 208, 271, 10.1016/j.envpol.2015.07.039
Aristodemou, 2018, How tall buildings affect turbulent air flows and dispersion of pollution within a neighbourhood, Environ. Pollut., 233, 782, 10.1016/j.envpol.2017.10.041
Song, 2018, Natural ventilation in cities: the implications of fluid mechanics, Build. Res. Inf., 0, 1
Omrani, 2017, Natural ventilation in multi-storey buildings: design process and review of evaluation tool, Build. Environ., 116, 182, 10.1016/j.buildenv.2017.02.012
Guo, 2015, Study on natural ventilation design optimization based on cfd simulation for green buildings, vol. 121, 573
Xie, 2005, Impact of building configuration on air quality in street canyon, Atmos. Environ., 39, 4519, 10.1016/j.atmosenv.2005.03.043
Blocken, 2014, 50 years of computational wind engineering: past, present and future, J. Wind Eng. Ind. Aerod., 129, 69, 10.1016/j.jweia.2014.03.008
Salim, 2011, Comparison of RANS, URANS and LES in the prediction of airflow and pollutant dispersion
Gousseau, 2011, CFD simulation of near-field pollutant dispersion on a high-resolution grid: a case study be LES and RANS for a building group in downtown Montreal, Atmos. Environ., 45, 428, 10.1016/j.atmosenv.2010.09.065
Ramponi, 2012, CFD simulation of cross-ventilation for a generic isolated building: impact of computational parameters, Build. Environ., 53, 34, 10.1016/j.buildenv.2012.01.004
Koutsourakis, 2012, Evaluation of Reynolds stress, k-ε and RNG k-ε turbulence models in street canyon flows using various experimental datasets, Environ. Fluid Mech., 12, 379, 10.1007/s10652-012-9240-9
Van Hooff, 2017, On the accuracy of CFD simulations of cross-ventilation flows for a generic isolated building: comparison of RANS, LES and experiments, Build. Environ., 114, 148, 10.1016/j.buildenv.2016.12.019
Cao, 2012, On the construction and use of linear low-dimensional ventilation models, Indoor Air, 22, 427, 10.1111/j.1600-0668.2012.00771.x
Liu, 2016, Implementation of a fast fluid dynamics model in OpenFOAM for simulating indoor airflow, Numer. Heat Tran., Part A: Applications, 69, 748, 10.1080/10407782.2015.1090780
Tallet, 2015, POD approach to determine in real-time the temperature distribution in a cavity, Build. Environ., 93, 34, 10.1016/j.buildenv.2015.07.007
Xiao, 2015, Non-intrusive reduced order modelling of the Navier–Stokes equations, Comput. Methods Appl. Mech. Eng., 293, 522, 10.1016/j.cma.2015.05.015
Stefanescu, 2013, POD/DEIM Nonlinear model order reduction of an ADI implicit shallow water equations model, J. Comput. Phys., 237, 95, 10.1016/j.jcp.2012.11.035
Stefanescu, 2014, Comparison of POD reduced order strategies for the nonlinear 2D shallow water equations, Int. J. Numer. Methods Fluid., 76, 497, 10.1002/fld.3946
Fukunaga, 1990, Introduction to statistical recognition, 5
Xiao, 2013, Non-linear Petrov–Galerkin methods for reduced order modelling of the Navier–Stokes equations using a mixed finite element pair, Comput. Methods Appl. Mech. Eng., 255, 147, 10.1016/j.cma.2012.11.002
Xiao, 2015, Non-intrusive reduced order modelling of the Navier-Stokes equations based on RBF interpolation, Int. J. Numer. Methods Fluid., 79, 580, 10.1002/fld.4066
Fang, 2014, Reduced order modelling of an unstructured mesh air pollution model and application in 2D/3D urban street canyons, Atmos. Environ., 96, 96, 10.1016/j.atmosenv.2014.07.021
Diez, 2015, Design-space dimensionality reduction in shape optimization by Karhunen–Loève expansion, Comput. Methods Appl. Mech. Eng., 283, 1525, 10.1016/j.cma.2014.10.042
Manzoni, 2015, Reduced Basis Isogeometric Methods (RB-IGA) for the real-time simulation of potential flows about parametrized NACA airfoils, Comput. Methods Appl. Mech. Eng., 284, 1147, 10.1016/j.cma.2014.11.037
Chen, 2012, A dual-weighted trust-region adaptive POD 4-D var applied to a finite-volume shallow water equations model on the sphere, Int. J. Numer. Methods Fluid., 68, 377, 10.1002/fld.2523
Bistrian, 2015, An improved algorithm for the shallow water equations model reduction: dynamic Mode Decomposition vs POD, Int. J. Numer. Methods Fluid., 78, 552, 10.1002/fld.4029
Fang, 2013, Non-linear Petrov–Galerkin methods for reduced order hyperbolic equations and discontinuous finite element methods, J. Comput. Phys., 234, 540, 10.1016/j.jcp.2012.10.011
Sabetghadam, 2012, α regularization of the POD-Galerkin dynamical systems of the Kuramoto–Sivashinsky equation, Appl. Math. Comput., 218, 6012, 10.1016/j.amc.2011.11.083
Carlberg, 2011, Efficient non-linear model reduction via a least-squares Petrov–Galerkin projection and compressive tensor approximations, Int. J. Numer. Methods Eng., 86, 155, 10.1002/nme.3050
Chu, 2011, State-preserving nonlinear model reduction procedure, Chem. Eng. Sci., 66, 3907, 10.1016/j.ces.2011.05.012
Willcox, 2003, Model reduction for large-scale linear applications, 1431
Barrault, 2004, An ‘empirical interpolation’ method: application to efficient reduced-basis discretization of partial differential equations, Compt. Rendus Math., 339, 667, 10.1016/j.crma.2004.08.006
Chaturantabut, 2010, Nonlinear model reduction via discrete empirical interpolation, SIAM J. Sci. Comput., 32, 2737, 10.1137/090766498
Carlberg, 2013, The GNAT method for nonlinear model reduction: effective implementation and application to computational fluid dynamics and turbulent flows, J. Comput. Phys., 242, 623, 10.1016/j.jcp.2013.02.028
Fang, 2009, Reduced-order modelling of an adaptive mesh ocean model, Int. J. Numer. Methods Fluid., 59, 827, 10.1002/fld.1841
Du, 2013, POD reduced-order unstructured mesh modeling applied to 2D and 3D fluid flow, Comput. Math. Appl., 65, 362, 10.1016/j.camwa.2012.06.009
Xiao, 2014, Non-linear model reduction for the Navier–Stokes equations using residual DEIM method, J. Comput. Phys., 263
Wu, 2018
Vervecken, 2015, Stable reduced-order models for pollutant dispersion in the built environment, Build. Environ., 92, 360, 10.1016/j.buildenv.2015.05.008
Cao, 2015, Fast prediction of indoor pollutant dispersion based on reduced-order ventilation models, vol. 8, 415
Chen, 2012
Wirtz, 2012, Efficient a-posteriori error estimation for nonlinear kernel-based reduced systems, Syst. Contr. Lett., 61, 203, 10.1016/j.sysconle.2011.10.012
Wirtz, 2013
Audouze, 2013, Nonintrusive reduced-order modeling of parametrized time-dependent partial differential equations, Numer. Methods Part. Differ. Equ., 29, 1587, 10.1002/num.21768
Walton, 2013, Reduced order modelling for unsteady fluid flow using proper orthogonal decomposition and radial basis functions, Appl. Math. Model., 37, 8930, 10.1016/j.apm.2013.04.025
Noori, 2013, Development and application of reduced-order neural network model based on proper orthogonal decomposition for BOD5 monitoring: active and online prediction, Environ. Prog. Sustain. Energy, 32, 120, 10.1002/ep.10611
Noack, 2011, vol. 528
Xiao, 2016, Non-intrusive reduced order modeling of fluid-structure interactions, Comput. Methods Appl. Mech. Eng., 303, 35, 10.1016/j.cma.2015.12.029
Krizhevsky, 2012, Imagenet classification with deep convolutional neural networks, 1097
Wieland, 2014, Performance evaluation of machine learning algorithms for urban pattern recognition from multi-spectral satellite images, Rem. Sens., 6, 2912, 10.3390/rs6042912
Waldrop, 2015, No drivers required, Nature, 518, 20, 10.1038/518020a
Liu, 2016
Kim, 2018, Personal comfort models: predicting individuals' thermal preference using occupant heating and cooling behavior and machine learning, Build. Environ., 129, 96, 10.1016/j.buildenv.2017.12.011
Hinton, 2012, Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups, IEEE Signal Process. Mag., 29, 82, 10.1109/MSP.2012.2205597
Jean, 2014
Duriez, 2017, vol. 123
Abadi, 2016, TensorFlow: a system for large-scale machine learning, 16, 265
Chollet, 2015
Xiao, 2017, Non-intrusive reduced-order modeling for multiphase porous media flows using Smolyak sparse grids, Int. J. Numer. Methods Fluid., 83, 205, 10.1002/fld.4263
Rasmussen, 2004, Gaussian processes in machine learning, 63
Wang, 2017, Evaluation of Bayesian source estimation methods with Prairie Grass observations and Gaussian plume model: a comparison of likelihood functions and distance measures, Atmos. Environ., 152, 519, 10.1016/j.atmosenv.2017.01.014
Aristodemou, 2009, A comparison of mesh-adaptive LES with wind tunnel data for flow past buildings: mean flows and velocity fluctuations, Atmos. Environ., 43, 6238, 10.1016/j.atmosenv.2009.07.014
Xiao, 2017, A non-intrusive reduced-order model for compressible fluid and fractured solid coupling and its application to blasting, J. Comput. Phys., 330, 221, 10.1016/j.jcp.2016.10.068
Rasmussen, 2006
Yao, 2007, Is a direct numerical simulation of chaos possible? A study of a model nonlinearity, Int. J. Heat Mass Tran., 50, 2200, 10.1016/j.ijheatmasstransfer.2006.10.051
Pain, 2005, Three-dimensional unstructured mesh ocean modelling, Ocean Model., 10, 5, 10.1016/j.ocemod.2004.07.005
Pain, 2001, Tetrahedral mesh optimisation and adaptivity for steady-state and transient finite element calculations, Comput. Methods Appl. Mech. Eng., 190, 3771, 10.1016/S0045-7825(00)00294-2
Bentham, 2004
Franke, 2007
Pavlidis, 2010, Synthetic-eddy method for urban atmospheric flow modelling, Boundary-Layer Meteorol., 136, 285, 10.1007/s10546-010-9508-x