2020/11/19 by Rebecca Ward, Ward, Rebecca, Ruchi Choudhary +7
Decision Sciences · Engineering · #Advanced Control Systems Optimization #Computational Engineering #FOS: Computer and information sciences #Fault Detection and Control Systems #Finance #J.2 #Simulation Techniques and Applications #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.2011.09810
openalex publication_date 2020/11/19 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
Assimilation of continuously streamed monitored data is an essential\ncomponent of a digital twin; the assimilated data are used to ensure the\ndigital twin is a true representation of the monitored system. One way this is\nachieved is by calibration of simulation models, whether data-derived or\nphysics-based, or a combination of both. Traditional manual calibration is not\npossible in this context hence new methods are required for continuous\ncalibration. In this paper, a particle filter methodology for continuous\ncalibration of the physics-based model element of a digital twin is presented\nand applied to an example of an underground farm. The methodology is applied to\na synthetic problem with known calibration parameter values prior to being used\nin conjunction with monitored data. The proposed methodology is compared\nagainst static and sequential Bayesian calibration approaches and compares\nfavourably in terms of determination of the distribution of parameter values\nand analysis run-times, both essential requirements. The methodology is shown\nto be potentially useful as a means to ensure continuing model fidelity.\n