Big data for microstructure‐property relationships: A case study of predicting effective conductivities

The analysis of big data is changing industries, businesses and research as large amounts of data are available nowadays. In the area of microstructures, acquisition of (3‐D tomographic image) data is difficult and time‐consuming. It is shown that large amounts of data representing the geometry of virtual, but realistic 3‐D microstructures can be generated using stochastic microstructure modeling. Combining the model output with physical simulations and data mining techniques, microstructure‐property relationships can be quantitatively characterized. Exemplarily, we aim to predict effective conductivities given the microstructure characteristics volume fraction, mean geodesic tortuosity, and constrictivity. Therefore, we analyze 8119 microstructures generated by two different stochastic 3‐D microstructure models. This is—to the best of our knowledge—by far the largest set of microstructures that has ever been analyzed. Fitting artificial neural networks, random forests and classical equations, the prediction of effective conductivities based on geometric microstructure characteristics is possible. © 2017 American Institute of Chemical Engineers AIChE J, 63: 4224–4232, 2017     

Quantitative relationships between microstructure and effective transport properties based on virtual materials testing

The microstructure influence on conductive transport processes is described in terms of volume fraction ε, tortuosity τ, and constrictivity β. Virtual microstructures with different parameter constellations are produced using methods from stochastic geometry. Effective conductivities are obtained from solving the diffusion equation in a finite element model. In this way, a large database is generated which is used to test expressions describing different micro–macro relationships such as Archie's law, tortuosity, and constrictivity equations. It turns out that the constrictivity equation has the highest accuracy indicating that all three parameters are necessary to capture the microstructure influence correctly. The predictive capability of the constrictivity equation is improved by introducing modifications of it and using error‐minimization, which leads to the following expression: with intrinsic conductivity . The equation is important for future studies in, for example, batteries, fuel cells, and for transport processes in porous materials. © 2014 American Institute of Chemical Engineers AIChE J, 60: 1983–1999, 2014     

基于导热形状因子的泡沫金属导热特性分析

通过理论分析引入用于定向计算泡沫金属等效热导率的导热形状因子（m）,并基于文献报道的大量实验数据对m进行了计算和分析。研究发现,m随泡沫金属材质、孔隙率及孔密度变化呈显著随机波动现象,无固定趋势或规律可循;泡沫金属等效热导率的准确预测需纳入多孔泡沫结构定向形变效应影响。鉴于此,通过直接数值模拟获得了m随孔胞形变参数（即沿泡沫金属宏观传热方向与其垂直方向的胞径比）变化的无量纲准则关联式,进而提出了基于m定向预测泡沫金属等效热导率的新方法。对比文献报道实验数据及基于各向同性结构假设的理论模型预测结果发现,上述方法可提高等效热导率的预测精度（平均偏差为0.77%）。     

开孔泡沫金属复合材料有效热导率的研究进展

有效热导率是开孔泡沫金属复合材料热传输热性的重要参数,基于三维结构的复杂性,从边界模型和晶胞分析模型两个方面出发,较为全面地概述了有效热导率的研究现状。指出边界模型以均质化方法宏观分析热传导问题而忽略了微观孔结构的影响,重点阐述晶胞分析模型中立方体模型和开尔文模型的经验相关性分析方法,指出其关键点在于以孔隙率形式将多孔结构形状参数拟合成可调参数表达式。此外,3D断层扫描与数值模拟相结合,阐述lattice-Boltzmann方法对开孔泡沫结构的研究,突出真实孔结构对有效热导率的影响和规律。展望后期研究重点是经验相关模型的精确拟合方式及特征关联式的统一化,高精度数值模拟计算中的简化对比分析模型。     

Macroporous mullite materials prepared by novel shaping strategies based on starch thermogelation for thermal insulation

A rigorous microstructural analysis of porous mullite materials developed using novel shaping strategies based on the starch consolidation casting, and their thermal properties in relation to the processing and starch type were accomplished in view of their use as thermal insulators. In order to characterize the size and morphology of pore, basic size and 2D shape factors, and global 3D stereological parameters were determined using microscopy techniques. Results indicated that the porosity volume, pore connectivity degree, and mean free path were the determining factors of the lowest heat transfer by conduction registered in materials prepared with cassava starch. This material is the best candidate to be used in thermal insulation.     

颗粒内部毛细凝聚对气体有效扩散系数的影响

采用改进型Wicke-Kallenbath稳态扩散池,在373～413 K及0.4～1.0 MPa条件下测定了氢气在某工业催化剂CuO/ZnO/γ-Al₂O₃内部的有效扩散系数。实验采用逐步增加环己烷流量至某最大值而后逐步降低回到初始点的方法,测定了催化剂孔内发生毛细凝聚时氢气有效扩散系数随环己烷蒸气相对压力的变化。实验结果表明:在 373 K下环己烷蒸气相对压力为0.42时开始发生毛细凝聚,而在413 K下则延迟到0.6才开始出现这一现象。此外,滞后环宽度随着温度的升高而变窄,并且氢气的有效扩散系数随环己烷蒸气相对压力变化出现多重滞后环。建立了催化剂部分润湿时氢气的有效扩散系数与内部润湿分率的关联式,与实验测量值符合较好。     

环路热管中Ti64ELI毛细芯传热能力的实验研究

在环路热管（LHP）中,蒸发段的结构最为复杂,而其中的毛细芯是直接影响热管工作性能的部分,因此有很大的研究价值。为模拟LHP中毛细芯的真实运行情况,设计了一台在常温下测定毛细芯工作时表面温度的实验装置,对环路热管蒸发段毛细芯的传热能力进行独立的实验研究,分别采用乙醇和水为工质,在不同的加热功率下,计算毛细芯的有效热导率。研究发现,在低加热功率下,含乙醇的毛细芯的有效热导率要高于含水的毛细芯的有效热导率;而在高加热功率下,实验结果正好相反。本次实验的结论对于毛细芯传热性能的评估具有一定参考价值,也可以为LHP蒸发器的模拟仿真提供一定的实验数据。     

三类随机分形结构下干土壤有效热导率的介观研究

地埋管换热器的换热能力是设计地源热泵系统的关键,而环境土壤的有效热导率是影响地下传热量的重要参数。为探究土壤的介观结构参数对有效热导率的影响,提出三类随机分形结构,并结合格子玻尔兹曼方法,对土壤类多孔材料的传热特性进行了基础研究。通过对热探针实验结果和三类重构结构下模拟结果的对比分析,发现孔隙率仍然是影响干土壤有效热导率的主要因素,分形维度数和粒径比的影响则较小;干土壤介观结构的随机性对有效热导率有较大的影响,随机颗粒分布的微小变化会导致差异高达到24.5%。     

孔隙随机结构对非均质多孔泡沫导热性能的影响

多孔泡沫是一类低密度、高比表面积、具有独特性能的新型功能材料。实际多孔泡沫材料通常是非均质的,即其孔隙结构分布是随机的。为研究非均质多孔泡沫材料的导热性能,提出用孔隙均匀度作为表征孔隙结构分布随机性的参数,以多孔石墨泡沫为例,分析孔隙均匀度对多孔泡沫有效热导率的影响。数值计算结果表明：孔隙结构分布越不均匀,多孔泡沫材料的导热性能越差。根据计算结果提出了非均质石墨泡沫相对有效热导率的预测式,并与现有文献报道的结果进行了比较,发现当前结果呈现了孔隙结构随机性对材料有效热导率的影响,与ORNL实验结果更吻合。