Physical field estimation from CFD database and sparse sensor observations

This paper presents a new approach to estimate one physical field from off-line computational fluid dynamics (CFD) database and real-time sparse sensor observations. Firstly, we determine the proper orthogonal decomposition (POD) modes from the CFD database. Then, we use extreme learning machine (ELM) to build a regression model between the boundary conditions of physical fields and their POD coefficients. With this model, we can directly estimate the physical field of interest. Next, we modify the estimated physical field based on sparse sensor observations with the help of the dominant POD modes. The modified physical field is shown more accurate than the physical field estimated from either the regression model or sensor observations. Finally, we provide a simple example to show the effectiveness of the proposed approach.

[1]  Marcus M. Keane,et al.  Formal calibration methodology for CFD models of naturally ventilated indoor environments , 2013 .

[2]  Leon R. Glicksman,et al.  Design analysis of single-sided natural ventilation , 2003 .

[3]  Xiaoying Wu,et al.  Integrated analysis of CFD data with K-means clustering algorithm and extreme learning machine for localized HVAC control , 2015 .

[4]  L. Sirovich.  Turbulence and the dynamics of coherent structures. I. Coherent structures , 1987 .

[5]  Jeong-Hoon Yang,et al.  Supply airflow control algorithm of a floor-standing room air-conditioner to achieve thermal comfort for residential housing in summer , 2013 .

[6]  Siaw Kiang Chou,et al.  Achieving better energy-efficient air conditioning - A review of technologies and strategies , 2013 .

[7]  B. R. Noack.  Turbulence, Coherent Structures, Dynamical Systems and Symmetry , 2013 .

[8]  H. P. Lee,et al.  PROPER ORTHOGONAL DECOMPOSITION AND ITS APPLICATIONS—PART I: THEORY , 2002 .

[9]  Hua Li,et al.  Sensor Placement by Maximal Projection on Minimum Eigenspace for Linear Inverse Problems , 2015, IEEE Transactions on Signal Processing.

[10]  H. Tran,et al.  Modeling and control of physical processes using proper orthogonal decomposition , 2001 .

[11]  K. Willcox.  Unsteady Flow Sensing and Estimation via the Gappy Proper Orthogonal Decomposition , 2004 .

[12]  K. Willcox,et al.  Aerodynamic Data Reconstruction and Inverse Design Using Proper Orthogonal Decomposition , 2004 .

[13]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[14]  Haralambos Sarimveis,et al.  Development of a computational tool to quantify architectural-design effects on thermal comfort in naturally ventilated rural houses , 2010 .

[15]  Adnan Qamar,et al.  Steady supersonic flow-field predictions using proper orthogonal decomposition technique , 2009 .

[16]  Karen Willcox,et al.  Proper orthogonal decomposition extensions for parametric applications in compressible aerodynamics , 2003 .

[17]  Shi Liu,et al.  Temperature field reconstruction from the partial measurement data using the gappy proper orthogonal decomposition , 2013 .

[18]  Guang-Bin Huang,et al.  An Insight into Extreme Learning Machines: Random Neurons, Random Features and Kernels , 2014, Cognitive Computation.

[19]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[20]  Siep Weiland,et al.  Missing Point Estimation in Models Described by Proper Orthogonal Decomposition , 2004, IEEE Transactions on Automatic Control.