Multi-fidelity Data Fusion(MDF)frameworks have emerged as a prominent approach to producing economical but accurate surrogate models for aerodynamic data modeling by integrating data with different fidelity levels.How...Multi-fidelity Data Fusion(MDF)frameworks have emerged as a prominent approach to producing economical but accurate surrogate models for aerodynamic data modeling by integrating data with different fidelity levels.However,most existing MDF frameworks assume a uniform data structure between sampling data sources;thus,producing an accurate solution at the required level,for cases of non-uniform data structures is challenging.To address this challenge,an Adaptive Multi-fidelity Data Fusion(AMDF)framework is proposed to produce a composite surrogate model which can efficiently model multi-fidelity data featuring non-uniform structures.Firstly,the design space of the input data with non-uniform data structures is decomposed into subdomains containing simplified structures.Secondly,different MDF frameworks and a rule-based selection process are adopted to construct multiple local models for the subdomain data.On the other hand,the Enhanced Local Fidelity Modeling(ELFM)method is proposed to combine the generated local models into a unique and continuous global model.Finally,the resulting model inherits the features of local models and approximates a complete database for the whole design space.The validation of the proposed framework is performed to demonstrate its approximation capabilities in(A)four multi-dimensional analytical problems and(B)a practical engineering case study of constructing an F16C fighter aircraft’s aerodynamic database.Accuracy comparisons of the generated models using the proposed AMDF framework and conventional MDF approaches using a single global modeling algorithm are performed to reveal the adaptability of the proposed approach for fusing multi-fidelity data featuring non-uniform structures.Indeed,the results indicated that the proposed framework outperforms the state-of-the-art MDF approach in the cases of non-uniform data.展开更多
Adaptive sampling is an iterative process for the construction of a global approximation model. Most of engineering analysis tools computes multiple parameters in a single run. This research proposes a novel multi-res...Adaptive sampling is an iterative process for the construction of a global approximation model. Most of engineering analysis tools computes multiple parameters in a single run. This research proposes a novel multi-response adaptive sampling algorithm for simultaneous construction of multiple surrogate models in a time-efficient and accurate manner. The new algorithm uses the Jackknife cross-validation variance and a minimum distance metric to construct a sampling criterion function. A weighted sum of the function is used to consider the characteristics of multiple surrogate models. The proposed algorithm demonstrates good performance on total 22 numerical problems in comparison with three existing adaptive sampling algorithms. The numerical problems include several two-dimensional and six-dimensional functions which are combined into singleresponse and multi-response systems. Application of the proposed algorithm for construction of aerodynamic tables for 2 D airfoil is demonstrated. Scaling-based variable-fidelity modeling is implemented to enhance the accuracy of surrogate modeling. The algorithm succeeds in constructing a system of three highly nonlinear aerodynamic response surfaces within a reasonable amount of time while preserving high accuracy of approximation.展开更多
基金supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.2020R1A6A1A03046811).This paper was also supported by Konkuk University Researcher Fund in 2021.
文摘Multi-fidelity Data Fusion(MDF)frameworks have emerged as a prominent approach to producing economical but accurate surrogate models for aerodynamic data modeling by integrating data with different fidelity levels.However,most existing MDF frameworks assume a uniform data structure between sampling data sources;thus,producing an accurate solution at the required level,for cases of non-uniform data structures is challenging.To address this challenge,an Adaptive Multi-fidelity Data Fusion(AMDF)framework is proposed to produce a composite surrogate model which can efficiently model multi-fidelity data featuring non-uniform structures.Firstly,the design space of the input data with non-uniform data structures is decomposed into subdomains containing simplified structures.Secondly,different MDF frameworks and a rule-based selection process are adopted to construct multiple local models for the subdomain data.On the other hand,the Enhanced Local Fidelity Modeling(ELFM)method is proposed to combine the generated local models into a unique and continuous global model.Finally,the resulting model inherits the features of local models and approximates a complete database for the whole design space.The validation of the proposed framework is performed to demonstrate its approximation capabilities in(A)four multi-dimensional analytical problems and(B)a practical engineering case study of constructing an F16C fighter aircraft’s aerodynamic database.Accuracy comparisons of the generated models using the proposed AMDF framework and conventional MDF approaches using a single global modeling algorithm are performed to reveal the adaptability of the proposed approach for fusing multi-fidelity data featuring non-uniform structures.Indeed,the results indicated that the proposed framework outperforms the state-of-the-art MDF approach in the cases of non-uniform data.
基金supported by the Konkuk University Brain Pool 2018the National Research Foundation of Korea(NRF)[Grant NRF-2018R1D1A1B07046779]funded by the Korean government(MISP)
文摘Adaptive sampling is an iterative process for the construction of a global approximation model. Most of engineering analysis tools computes multiple parameters in a single run. This research proposes a novel multi-response adaptive sampling algorithm for simultaneous construction of multiple surrogate models in a time-efficient and accurate manner. The new algorithm uses the Jackknife cross-validation variance and a minimum distance metric to construct a sampling criterion function. A weighted sum of the function is used to consider the characteristics of multiple surrogate models. The proposed algorithm demonstrates good performance on total 22 numerical problems in comparison with three existing adaptive sampling algorithms. The numerical problems include several two-dimensional and six-dimensional functions which are combined into singleresponse and multi-response systems. Application of the proposed algorithm for construction of aerodynamic tables for 2 D airfoil is demonstrated. Scaling-based variable-fidelity modeling is implemented to enhance the accuracy of surrogate modeling. The algorithm succeeds in constructing a system of three highly nonlinear aerodynamic response surfaces within a reasonable amount of time while preserving high accuracy of approximation.
