3D Numerical Modeling of Hydrodynamic Flow, Sediment Deposition and Transport in Stormwater Ponds and Alluvial Channels

Prediction of flow and sediment transport is an important and challenging problem for stormwater management and river engineering applications. This thesis concerns primarily the computation of flow, sediment deposition and transport processes in stormwater ponds and alluvial channels based on a multiphase flow approach in modeling sediment transport. Starting from an existing hydrodynamic Reynolds Averaged Navier-Stokes flow solver, numerical models are developed to predict flow, sediment deposition and transport under the FLUENT software package. Two types of sediment transport models are formulated to consider quantities of present sediment phase volume fractions: a Discrete Phase Model in a Lagrangian frame where the sediment phase occupies a low volume fraction and particle-particle interactions are neglected; a Eulerian two-phase model where each phase is considered as an interpenetrating continuum and particle-particle interactions are not neglegible. The model is capable to model sediment transport with high volume fractions. The solution methodologies are implemented numerically for different case studies. The Discrete Phase Model approach, together with a standard k – ϵ turbulence model, is applied to stormwater pond modeling studies. The use of computational fluid dynamics to simulate flow fields and sediment depositions in stormwater tanks is beneficial because one may compare different factors that affect sedimentation efficiency. In particular, two case studies with different inlet and outlet pipes arrangements are investigated under different steady inflow conditions and bed boundary conditions. A method is employed and hooked to FLUENT for accurate simulations of particle settling behavior in stormwater ponds. The method considers critical bed shear stress as a threshold and evaluates local bed shear stress with this value to determine the particle deposition behavior. It is demonstrated that this model is an efficient 3D hydrodynamic flow and sediment transport numeric model for low sediment-laden flows, thus providing engineers and scientists with a useful tool for studying sediment deposition with a variety of sediment sizes, inflow conditions, and geometry arrangements. In order to gain more insight into the fundamental flow and sediment interaction mechanics of sediment transport, an Eulerian two-phase model embeded in FLUENT is implemented in an open channel with loose bed based on two-phase mass and momentum equations. These equations are used in conjunction with the constitutive relations that are obtained by applying kinetic theory. Different from traditional sediment transport models, this model uses the two-phase theory, and thus, has no need to invoke any empirical sediment transport formulas. In this application, predictions for turbulent fluctuations for the fluid phase are obtained using a modified k – ϵ turbulence model with a supplement of extra terms which take into account the interphase turbulent momentum transfer. Predictions for turbulent quantities for the solid phase are obtained using Tchen-theory correlations for the discrete particles under homogeneous and steady turbulent flows. Besides simulation of sediment transport, the model also provides some ideas for simulating scour and bed deformation. The results presented in this study demonstrate that the model is efficient and quite accurate in dealing with sediment transport and scour simulation with loose bed

A conformal test of linear models via permutation-augmented regressions

Permutation tests are widely recognized as robust alternatives to tests based on the normal theory. Random permutation tests have been frequently employed to assess the significance of variables in linear models. Despite their widespread use, existing random permutation tests lack finite-sample and assumption-free guarantees for controlling type I error in partial correlation tests. To address this standing challenge, we develop a conformal test through permutation-augmented regressions, which we refer to as PALMRT. PALMRT not only achieves power competitive with conventional methods but also provides reliable control of type I errors at no more than $2\alpha$ given any targeted level $\alpha$, for arbitrary fixed-designs and error distributions. We confirmed this through extensive simulations. Compared to the cyclic permutation test (CPT), which also offers theoretical guarantees, PALMRT does not significantly compromise power or set stringent requirements on the sample size, making it suitable for diverse biomedical applications. We further illustrate their differences in a long-Covid study where PALMRT validated key findings previously identified using the t-test, while CPT suffered from a drastic loss of power. We endorse PALMRT as a robust and practical hypothesis test in scientific research for its superior error control, power preservation, and simplicity

An Authenticated Routing Protocol for Wireless Ad Hoc Network Based on Small World Model

Compared with traditional cellular networks, wireless ad hoc networks do not have trusted entities such as routers, since every node in the network is expected to participate in the routing function. Therefore, routing protocols need to be specifically designed for wireless ad hoc networks. In this work, we propose an authenticated routing protocol based on small world model (ARSW). With the idea originating from the small world theory, the operation of the protocol we proposed is simple and flexible. Our simulation results show the proposed ARSW not only increases packet delivery ratio, but also reduces packet delivery delay. In particularly, Using authentication theory, the proposed ARSW improves communication security