On the use of packing models for the prediction of fluvial sediment porosity

IF 2.8 2区 地球科学 Q2 GEOGRAPHY, PHYSICAL
Christoph Rettinger, Mina Tabesh, Ulrich Rüde, Stefan Vollmer, Roy M. Frings
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Abstract

Abstract. Obtaining accurate porosity information of fluvial sediment deposits is helpful and desirable for many tasks of river engineers. Besides direct measurements of single samples and empirical formulas specialized for specific cases, packing models promise efficient predictions due to their theoretical and extensible foundation. The objective of this work is thus to investigate the usability of three such models in order to obtain a suitable porosity prediction method for the challenging case of fluvial sediment packing. There, the complexity originates from wide continuous size distributions, from silt to gravel, and different grain shapes. We use data obtained from extensive numerical packing simulations to determine the required model parameters and to verify the models' accuracy for moderate size ratios. This study reveals systematic deficits in one of the models, which can be attributed to the absence of a built-in mixture packing model. By combining these findings with data from laboratory measurements and extending the model to include cohesive effects, we exemplify that reasonable porosity predictions can be obtained with the Compressible Packing Model for the Rhine river in Germany. Through an additional comparison with data from French rivers, guidelines for a successful prediction in cases with limited prior knowledge of the model parameters are developed. Future model enhancements of the packing models directly, and by incorporating more effects that are known to influence porosity, are expected to improve the predictive performance.
充填模型在河流沉积孔隙度预测中的应用
摘要获得准确的河流沉积物孔隙度信息对河流工程人员的许多工作都是有益的。除了单个样品的直接测量和专门针对特定情况的经验公式外,包装模型由于其理论和可扩展的基础而承诺有效的预测。因此,这项工作的目的是研究三种这样的模型的可用性,以便为具有挑战性的河流沉积物堆积情况获得合适的孔隙度预测方法。在那里,复杂性源于广泛的连续尺寸分布,从淤泥到砾石,以及不同的颗粒形状。我们使用从大量数值填料模拟中获得的数据来确定所需的模型参数,并验证模型在中等尺寸比下的准确性。本研究揭示了其中一个模型的系统性缺陷,这可以归因于缺乏内置的混合包装模型。通过将这些发现与实验室测量数据相结合,并将模型扩展到包括内聚效应,我们举例说明了使用德国莱茵河的可压缩填料模型可以获得合理的孔隙度预测。通过与法国河流数据的额外比较,制定了在模型参数先验知识有限的情况下成功预测的指导方针。未来直接增强充填模型的模型,并纳入更多已知影响孔隙度的效应,有望提高预测性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Earth Surface Dynamics
Earth Surface Dynamics GEOGRAPHY, PHYSICALGEOSCIENCES, MULTIDISCI-GEOSCIENCES, MULTIDISCIPLINARY
CiteScore
5.40
自引率
5.90%
发文量
56
审稿时长
20 weeks
期刊介绍: Earth Surface Dynamics (ESurf) is an international scientific journal dedicated to the publication and discussion of high-quality research on the physical, chemical, and biological processes shaping Earth''s surface and their interactions on all scales.
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