An adaptive multi-fidelity optimization framework based on co-Kriging surrogate models and stochastic sampling with application to coastal aquifer management

Christelis, Vasileios; Kopsiaftis, George; Regis, Rommel G.; Mantoglou, Aristotelis

doi:10.1016/j.advwatres.2023.104537

Advances in Water Resources

2023

DOI: 10.1016/j.advwatres.2023.104537

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An adaptive multi-fidelity optimization framework based on co-Kriging surrogate models and stochastic sampling with application to coastal aquifer management

Vasileios Christelis,

George Kopsiaftis,

Rommel G. Regis

et al.

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Cited by 3 publications

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A multi-fidelity framework for power prediction of wind farm under yaw misalignment

Tu,

Chen,

Zhang

et al. 2025

Applied Energy

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A multi-fidelity framework for power prediction of wind farm under yaw misalignment

Tu,

Chen,

Zhang

et al. 2025

Applied Energy

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Development of a novel multi-fidelity meta modeling approach for robust multi-objective optimization of a natural gas-hydrogen/diesel dual fuel engine

Sehili,

Cerdoun,

Tarabet

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part D:

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Multi-fidelity modeling (MFM) is an evolving field that matches low-fidelity models (LFM) and high-fidelity models (HFM) to get better solutions with low computational cost. However, improving the duality between accuracy and computational cost remain challenging, particularly for complex problems such as dual fuel engines. This paper contributes to the MF modeling cost-effectiveness improvement by proposing a new approach to solve large-dimensional multi-objective optimization problems. The first step is to build a meta-model based on the LF model, which will be subjected to a comet-governed analysis to detect potential areas where the uncertainty on the LF model is relatively high. Then, a design of experiment (DOE) will be developed based on the results of this analysis to construct an initial HF model. Finally, an iterative loop will be activated to improve the accuracy of the MF model using a well-weighed combination of the details delivered by the LF model correction via the HF model and the HF meta-model. The developed approach is validated on four different mathematical benchmarks with different difficulties, compared with four different MF modeling strategies. This validation shows that the proposed MF modeling is competitive and can produce solutions as accurate as the HF model while reducing significantly the overall computation time by up to 50%. As an engineering application, the operating conditions in a natural gas-hydrogen/diesel dual fuel engine in terms of compression ratio, pilot injection timing, and EGR are optimized. A reduction of 46%, 68%, and 96% was achieved for HC, NOx, and the knocking index, respectively, while an increase in thermal efficiency of about 5% was obtained.

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Inverse problem assisted multivariate geostatistical model for identification of transmissivity fields

Kapoor,

Kashyap

2024

Front. Water

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Groundwater models often require transmissivity (T) fields as an input. These T fields are commonly generated by performing univariate interpolation of the T data. This T data is derived from pumping tests and is generally limited due to the large costs and logistical requirements. Hence T fields generated using this limited data may not be representative for a whole study region. Groundwater models often require transmissivity (T) fields as an input. These T fields are commonly generated by performing univariate interpolation (using kriging, IDW etc.) of the T data. This T data is derived from pumping tests and is generally limited due to the large costs and logistical requirements. Hence, the T fields generated using this limited data may not be representative for the whole study region. This study presents a novel cokriging based methodology to generate credible T fields. Cokriging - a multivariate geostatistical interpolation method permits incorporation of additional correlated auxiliary variables for the generation of enhanced fields. Here abundantly available litholog derived saturated thickness data has been used as secondary (auxiliary) data given its correlation with the primary T data. Additionally, the proposed methodology addresses two operational problems of traditional cokriging procedure. The first operational problem is the poor estimation of variogram and cross-variogram parameters due to sparse T data. The second problem is the determination of relative contributions of primary and secondary variable in the estimation process. These two problems have been resolved by proposing a set of novel non-bias conditions, and linking the interpolator with a head based inverse problem solution for credible estimation of these parameters. The proposed methodology has been applied to Bist doab region in Punjab (India). Additionally, base line studies have been performed to elucidate the superiority of the proposed cokriging based methodology over kriging in terms of head reproducibility.

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An adaptive multi-fidelity optimization framework based on co-Kriging surrogate models and stochastic sampling with application to coastal aquifer management

Cited by 3 publications

References 82 publications

A multi-fidelity framework for power prediction of wind farm under yaw misalignment

A multi-fidelity framework for power prediction of wind farm under yaw misalignment

Development of a novel multi-fidelity meta modeling approach for robust multi-objective optimization of a natural gas-hydrogen/diesel dual fuel engine

Inverse problem assisted multivariate geostatistical model for identification of transmissivity fields

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