Wind field reconstruction using dimension-reduction of CFD data with experimental validation

Qin, Li; Shi, Liyi; Li, Teng; Shahzad, Muhammad Ali; Schlaberg, H. Iñaki; Yan, Song An

doi:10.1016/j.energy.2018.02.141

Cited by 13 publications

(2 citation statements)

References 17 publications

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“…For example, Jiang et al [33] employed POD to combine sparse sensor observations with the CFD database to quickly estimate the airflow field. Qin et al [34] employed singular value decomposition (SVD) and principal component analysis (PCA) to reduce the dimensions of the wind speed database and combined sparse sensor measurements to achieve high-precision wind field reconstruction. Zhang et al [35] proposed a sensor and CFD database fusion technology based on Tucker decomposition, which achieved the purpose of obtaining a 3D wind field from sparse measurements.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of Unsteady Wind Field Based on CFD and Reduced-Order Model

Zhang

Liu

2023

Mathematics

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Short-term wind power forecasting is crucial for updating the wind power trading strategy, equipment protection and control regulation. To solve the difficulty surrounding the instability of the statistical model and the time-consuming nature of the physical model in short-term wind power forecasting, two innovative wind field reconstruction methods combining CFD and a reduced-order model were developed. In this study, POD and Tucker decomposition were employed to obtain the spatial–temporal information correlation of 2D and 3D wind fields, and their inverse processes were combined with sparse sensing to reconstruct multi-dimensional unsteady wind fields. Simulation and detailed discussion were performed to verify the practicability of the proposed algorithms. The simulation results indicate that the wind speed distributions could be reconstructed with reasonably high accuracy (where the absolute velocity relative error was less than 0.8%) using 20 sensors (which only accounted for 0.04% of the total data in the 3D wind field) based on the proposed algorithms. The factors influencing the results of reconstruction were systematically analyzed, including all-time steps, the number of basis vectors and 4-mode dimensions, the diversity of CFD databases, and the reconstruction time. The results indicated that the reconstruction time could be shortened to the time interval of data acquisition to synchronize data acquisition with wind field reconstruction, which is of great significance in the reconstruction of unsteady wind fields. Although there are still many studies to be carried out to achieve short-term predictions, both unsteady reconstruction methods proposed in this paper enable a new direction for short-term wind field prediction.

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Section: Introductionmentioning

confidence: 99%

Reconstruction of Unsteady Wind Field Based on CFD and Reduced-Order Model

Zhang

Liu

2023

Mathematics

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“…Since then, it has been widely used in flow field analysis [22,23]. Allery used it to investigate particle dispersion [24], and Qin and Sun used it in flow field reconstruction [25,26]. The method has also been used in oceanography [27] and aerodynamics studies [28,29].…”

Section: Introductionmentioning

confidence: 99%

Rapid Reconstruction of Simulated and Experimental Temperature Fields Based on Proper Orthogonal Decomposition

et al. 2020

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Temperature information has a certain significance in thermal energy systems, especially in gas combustion systems. Generally, measurements and numerical calculations are used to acquire temperature information, but both of these approaches have their limitations. Constrained by cost and conditions, measurement methods are difficult to use to reconstruct the temperature field. Numerical methods are able to estimate the temperature field; however, the calculation process in numerical methods is very complex, so these methods cannot be used in real time. For the purpose of solving these problems, a two-dimensional temperature field reconstruction method based on the proper orthogonal decomposition (POD) algorithm is proposed in this study. In the proposed method, the temperature field reconstruction task is transformed into an optimization problem. Theoretical analysis and simulations show that the proposed method is feasible. Gas combustion experiments were also performed to validate this method. Results indicate that the proposed method can yield a reliable reconstruction solution and can be applied to real-time applications.

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Development of self-adaptive low-dimension ventilation models using OpenFOAM: Towards the application of AI based on CFD data

Ren

Cao

2020

Building and Environment

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Wind field reconstruction using dimension-reduction of CFD data with experimental validation

Cited by 13 publications

References 17 publications

Reconstruction of Unsteady Wind Field Based on CFD and Reduced-Order Model

Reconstruction of Unsteady Wind Field Based on CFD and Reduced-Order Model

Rapid Reconstruction of Simulated and Experimental Temperature Fields Based on Proper Orthogonal Decomposition

Development of self-adaptive low-dimension ventilation models using OpenFOAM: Towards the application of AI based on CFD data

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