One-dimensional non-linear consolidation of soft foundation with upper crust with depth-dependent additional stress

2015 ◽  
Vol 19 (sup9) ◽  
pp. S9-198-S9-204
Author(s):  
J. H. Zhang ◽  
Y. L. Wang ◽  
G. M. Cen ◽  
W. W. Jiang ◽  
J. L. Zheng ◽  
...  
2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Junhui Zhang ◽  
Guangming Cen ◽  
Weizheng Liu ◽  
Houxuan Wu

A model for one-dimensional consolidation of a double-layered foundation considering the depth-dependent initial excess pore pressure and additional stress and time-dependent loading under different drainage conditions was presented in this study and its general analytical solution was deduced. The consolidation solutions of several special cases of single-drained and double-drained conditions under an instantaneous loading and a single-level uniform loading were derived. Then, the average degree of consolidation of the double-layered foundation defined by settlement was gained and verified. Finally, the effects of the initial excess pore pressure distributions, depth-dependent additional stress, and loading modes on the consolidation of the soft foundation with an upper crust with different drainage conditions were revealed. The results show that the distributions of initial excess pore pressure and additional stress with depth and loading rates have a significant influence on the consolidation process of the soft foundation with an upper crust. This influence is larger with the single-drained condition than that with the double-drained condition. Comparing the consolidation rate with a uniform initial pore pressure and additional stress, their decreasing distribution with depth quickens the consolidation at the former and middle stages. Moreover, the larger the loading rate is, the quicker the consolidation of the soft foundation with an upper crust is.


Author(s):  
Vincent Kather ◽  
Finn Lückoff ◽  
Christian O. Paschereit ◽  
Kilian Oberleithner

The generation and turbulent transport of temporal equivalence ratio fluctuations in a swirl combustor are experimentally investigated and compared to a one-dimensional transport model. These fluctuations are generated by acoustic perturbations at the fuel injector and play a crucial role in the feedback loop leading to thermoacoustic instabilities. The focus of this investigation lies on the interplay between fuel fluctuations and coherent vortical structures that are both affected by the acoustic forcing. To this end, optical diagnostics are applied inside the mixing duct and in the combustion chamber, housing a turbulent swirl flame. The flame was acoustically perturbed to obtain phase-averaged spatially resolved flow and equivalence ratio fluctuations, which allow the determination of flux-based local and global mixing transfer functions. Measurements show that the mode-conversion model that predicts the generation of equivalence ratio fluctuations at the injector holds for linear acoustic forcing amplitudes, but it fails for non-linear amplitudes. The global (radially integrated) transport of fuel fluctuations from the injector to the flame is reasonably well approximated by a one-dimensional transport model with an effective diffusivity that accounts for turbulent diffusion and dispersion. This approach however, fails to recover critical details of the mixing transfer function, which is caused by non-local interaction of flow and fuel fluctuations. This effect becomes even more pronounced for non-linear forcing amplitudes where strong coherent fluctuations induce a non-trivial frequency dependence of the mixing process. The mechanisms resolved in this study suggest that non-local interference of fuel fluctuations and coherent flow fluctuations is significant for the transport of global equivalence ratio fluctuations at linear acoustic amplitudes and crucial for non-linear amplitudes. To improve future predictions and facilitate a satisfactory modelling, a non-local, two-dimensional approach is necessary.


Autoregressive (AR) random fields are widely use to describe changes in the status of real-physical objects and implemented for analyzing linear & non-linear models. AR models are Markov processes with a higher order dependence for one-dimensional time series. Actually, various estimation methods were used in order to evaluate the autoregression parameters. Although in many applications background knowledge can often shed light on the search for a suitable model, but other applications lack this knowledge and often require the type of trial errors to choose a model. This article presents a brief survey of the literatures related to the linear and non-linear autoregression models, including several extensions of the main mode models and the models developed. The use of autoregression to describe such system requires that they be of sufficiently high orders which leads to increase the computational costs.


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