Kyushu University · 공학
Fei Jiang 교수의 연구실은 지질자원과 환경 문제 해결을 위한 수치 시뮬레이션 및 데이터 기반 모델링 기반 연구를 주요 방향으로 삼고 있습니다. 특히, 미세구조 해석을 위한 고해상도 CT 영상 분석, 라티스 보르츠만 방법을 활용한 다상유동 시뮬레이션, 그리고 딥러닝을 활용한 투과도 예측 워크플로우 개발을 통해 탄소포집·저장(CCS) 및 오일리콜리브리에이션(EOR) 기술의 효율성을 높이고자 합니다. 연구는 자연계의 복잡한 다공구조를 정량적으로 분석하고, 이를 바탕으로 실재 지질 매체의 유체 거동을 예측하는 데 초점을 맞추고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Great endeavors have been dedicated to the development of wound dressing materials. However, there is still a demand for developing a wound dressing hydrogel that integrates natural macromolecules without requiring extra chemical modifications, so as to enable a facile transformation and practical application in wound healing. Herein, a composite hydrogel was prepared with water-soluble polysaccharides from <i>Enteromorpha prolifera</i> (PEP) cross-linked with boric acid and polyacrylamide cross
Abstract Given the world's growing demand for energy, a combination of geological CO 2 sequestration and enhanced oil recovery (EOR) technologies is currently regarded as a promising solution, as it would provide a means of reducing carbon emissions into the atmosphere while also leading to the economic benefit of simultaneously recovering oil. The optimization of injection strategies to maximize CO 2 storage and increase the oil recovery factors requires complicated pore‐scale flow information
The CO_{2} behavior within the reservoirs of carbon capture and storage projects is usually predicted from large-scale simulations of the reservoir. A key parameter in reservoir simulation is relative permeability. However, mineral precipitation alters the pore structure over time, and leads correspondingly to permeability changing with time. In this study, we numerically investigate the influence of carbonate precipitation on relative permeability during CO_{2} storage. The pore spaces in rock
Abstract We develop a numerical simulation that uses the lattice Boltzmann method to directly calculate the characteristics of residual nonwetting‐phase clusters to quantify capillary trapping mechanisms in real sandstone. For this purpose, a digital‐rock‐pore model reconstructed from micro‐CT‐scanned images of Berea sandstone is filtered and segmented into a binary file. The residual‐cluster distribution is generated following simulation of the drainage and imbibition processes. The characteris
Abstract Rock pore geometry has heterogeneous characteristics and is scale dependent. This feature in a geological formation differs significantly from artificial materials and makes it difficult to predict hydrologic and elastic properties. To characterize pore heterogeneity, we propose an evaluation method that exploits the recently developed persistent homology theory. In the proposed method, complex pore geometry is first represented as sphere cloud data using a pore‐network extraction metho
Abstract This study presents a workflow to predict the upscaled absolute permeability of the rock core direct from CT images whose resolution is not sufficient to allow direct pore‐scale permeability computation. This workflow exploits the deep learning technique with the data of raw CT images of rocks and their corresponding permeability value obtained by performing flow simulation on high‐resolution CT images. The permeability map of a much larger region in the rock core is predicted by the tr
Many small insects such as water striders can leap from water surface. Inspired by their jumping capability, we present the design of a novel, miniature, water surface jumping robot in this paper. Jumping from water surface is more challenging than jumping from ground due to the liquid water surface. We address this problem by using carbon fiber strip to store energy, two wings to flap the water surface, a hollow body to initially support the robot, and an intermittent gear train to charge and r
This paper presents a Digital Twin-driven framework for fatigue lifecycle management of steel bridges. A probabilistic multi-scale fatigue deterioration model is proposed to predict the entire fatigue process of steel bridges. Bayesian inference of the deterioration parameters realizes the real-time updating of the predicted lifecycle fatigue evolution process, which provides a good basis for lifecycle optimization. To avoid an empirically predefined repair crack size for maintenance, an optimiz