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Ge Xiang

Bio: Ge Xiang is an academic researcher from University of Electronic Science and Technology of China. The author has contributed to research in topics: Surrogate model & Adaptive sampling. The author has an hindex of 1, co-authored 3 publications receiving 4 citations.

Papers
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Journal ArticleDOI
TL;DR: In this article, an optimization design framework is proposed to minimize the maximum temperature difference (MTD) of automotive lithium battery pack, which has certain guiding significance for the liquid cooling design of the battery packs.

37 citations

Journal ArticleDOI
TL;DR: The benchmark test functions and an application problem that deals with driving arm base of palletizing robot show that the proposed method can effectively improve the global and local prediction accuracy of the surrogate model.
Abstract: An ensemble of surrogate models with high robustness and accuracy can effectively avoid the difficult choice of surrogate model. However, most of the existing ensembles of surrogate models are constructed with static sampling methods. In this paper, we propose an ensemble of adaptive surrogate models by applying adaptive sampling strategy based on expected local errors. In the proposed method, local error expectations of the surrogate models are calculated. Then according to local error expectations, the new sample points are added within the dominating radius of the samples. Constructed by the RBF and Kriging models, the ensemble of adaptive surrogate models is proposed by combining the adaptive sampling strategy. The benchmark test functions and an application problem that deals with driving arm base of palletizing robot show that the proposed method can effectively improve the global and local prediction accuracy of the surrogate model.

2 citations

Journal ArticleDOI
TL;DR: In this article, an adaptive sampling method based on distance density and local complexity is proposed for the selection of sample points in the process of establishing a high-precision surrogate model, which can make the new sample point more distributed in the key area of sample space.

2 citations


Cited by
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Journal ArticleDOI
TL;DR: The combination of passive and active cooling/heating methods is promising to meet the stringent thermal requirements, particularly under dynamic conditions with drastic power fluctuations, as well as the remaining challenges and perspectives of thermal management systems with high efficiency and durability.

66 citations

Journal ArticleDOI
TL;DR: In this article, a battery thermal management system (BTMS) for cooling of battery pack, using phase change materials (PCM) and mini-channel cold plates (MCPs), has been designed and numerically studied.

57 citations

Journal ArticleDOI
TL;DR: In this article, a novel battery thermal management system consisting of metal foam-paraffin PCM composite, nanofluid cooling, and heat sink, which all were subjected to a magnetic field was designed.

22 citations

Journal ArticleDOI
TL;DR: In this paper, the authors investigated the capability of the universal Kriging (UK) model for single-objective global optimization applied within an efficient global optimization (EGO) framework.
Abstract: In this paper, we investigate the capability of the universal Kriging (UK) model for single-objective global optimization applied within an efficient global optimization (EGO) framework. We implemented this combined UK-EGO framework and studied four variants of the UK methods, that is, a UK with a first-order polynomial, a UK with a second-order polynomial, a blind Kriging (BK) implementation from the ooDACE toolbox, and a polynomial-chaos Kriging (PCK) implementation. The UK-EGO framework with automatic trend function selection derived from the BK and PCK models works by building a UK surrogate model and then performing optimizations via expected improvement criteria on the Kriging model with the lowest leave-one-out cross-validation error. Next, we studied and compared the UK-EGO variants and standard EGO using five synthetic test functions and one aerodynamic problem. Our results show that the proper choice for the trend function through automatic feature selection can improve the optimization performance of UK-EGO relative to EGO. From our results, we found that PCK-EGO was the best variant, as it had more robust performance as compared to the rest of the UK-EGO schemes; however, total-order expansion should be used to generate the candidate trend function set for high-dimensional problems. Note that, for some test functions, the UK with predetermined polynomial trend functions performed better than that of BK and PCK, indicating that the use of automatic trend function selection does not always lead to the best quality solutions. We also found that although some variants of UK are not as globally accurate as the ordinary Kriging (OK), they can still identify better-optimized solutions due to the addition of the trend function, which helps the optimizer locate the global optimum.

18 citations

Journal ArticleDOI
TL;DR: In this article , a new manifold immersion (MI) cooling structure was proposed for battery thermal management, and the local convective heat transfer coefficients of the lateral and baffle surfaces were analyzed separately.

15 citations