A variable fidelity information fusion method based on radial basis function. Issue 2 (April 2017)
- Record Type:
- Journal Article
- Title:
- A variable fidelity information fusion method based on radial basis function. Issue 2 (April 2017)
- Main Title:
- A variable fidelity information fusion method based on radial basis function
- Authors:
- Zhou, Qi
Jiang, Ping
Shao, Xinyu
Hu, Jiexiang
Cao, Longchao
Wan, Li - Abstract:
- Highlights: A variable-fidelity information fusion approach based on RBF is proposed. The low-fidelity output is taken as a prior-knowledge of the studied system. Cases study show the applicability and efficiency of the proposed approaches. Abstract: Radial basis function (RBF) model has been widely used in complex engineering design process to replace the computational-intensive simulation models. This paper proposes a variable-fidelity metamodeling (VFM) approach based on RBF, in which different levels fidelity information can be integrated and fully exploited. In the proposed VFM approach, a RBF metamodel is constructed for the low-fidelity (LF) model as a start. Then by taking the constructed LF metamodel as a prior-knowledge and mapping the output space of the LF metamodel to that of the studied high-fidelity (HF) model, a variable fidelity (VF) metamodel is created to approximate the relationships between the design variables and corresponding output responses. A numerical illustrative example is adopted to make a detailed comparison between the VFM approach developed in this research and three existing scaling function based VFM approaches, considering different sample sizes and sample noises. Results illustrate that the proposed VFM approach outperforms the scaling function based VFM approaches both in global and local accuracy. Then the proposed VFM approach is applied to two engineering problems, modeling aerodynamic data for a three-dimensional aircraft and theHighlights: A variable-fidelity information fusion approach based on RBF is proposed. The low-fidelity output is taken as a prior-knowledge of the studied system. Cases study show the applicability and efficiency of the proposed approaches. Abstract: Radial basis function (RBF) model has been widely used in complex engineering design process to replace the computational-intensive simulation models. This paper proposes a variable-fidelity metamodeling (VFM) approach based on RBF, in which different levels fidelity information can be integrated and fully exploited. In the proposed VFM approach, a RBF metamodel is constructed for the low-fidelity (LF) model as a start. Then by taking the constructed LF metamodel as a prior-knowledge and mapping the output space of the LF metamodel to that of the studied high-fidelity (HF) model, a variable fidelity (VF) metamodel is created to approximate the relationships between the design variables and corresponding output responses. A numerical illustrative example is adopted to make a detailed comparison between the VFM approach developed in this research and three existing scaling function based VFM approaches, considering different sample sizes and sample noises. Results illustrate that the proposed VFM approach outperforms the scaling function based VFM approaches both in global and local accuracy. Then the proposed VFM approach is applied to two engineering problems, modeling aerodynamic data for a three-dimensional aircraft and the prediction of weld bead profile in laser welding, to illustrate its ability in support of complex engineering design. … (more)
- Is Part Of:
- Advanced engineering informatics. Volume 32:Issue 2(2017:Apr.)
- Journal:
- Advanced engineering informatics
- Issue:
- Volume 32:Issue 2(2017:Apr.)
- Issue Display:
- Volume 32, Issue 2 (2017)
- Year:
- 2017
- Volume:
- 32
- Issue:
- 2
- Issue Sort Value:
- 2017-0032-0002-0000
- Page Start:
- 26
- Page End:
- 39
- Publication Date:
- 2017-04
- Subjects:
- Variable fidelity -- Information fusion -- Radial basis function -- Simulation-based design -- Metamodel
Computer-aided engineering -- Periodicals
Engineering -- Data processing -- Periodicals
620.00285 - Journal URLs:
- http://www.sciencedirect.com/science/journal/14740346 ↗
http://books.google.com/books?id=KhFVAAAAMAAJ ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.aei.2016.12.005 ↗
- Languages:
- English
- ISSNs:
- 1474-0346
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 0696.851100
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 2132.xml