RKDG to shallow water equations

1.Governing Equations

\[\frac{\partial U}{\partial t} + \frac{\partial F}{\partial x} = 0
\]
\[U = \begin{bmatrix} h \cr q \end{bmatrix} \quad
F = \begin{bmatrix} q \cr gh^2/2 + q^2/h \end{bmatrix}\]

2.Discrete with DGM

\[\begin{equation} U_h = \sum{l_j U_j} \quad F_h(U) = \sum{l_j F(U_j)} \end{equation}
\]
\[\begin{equation}\int_{\Omega} l_i l_j \frac{\partial U_j}{\partial t} dx+
\int_{\Omega} l_i \frac{\partial l_j}{\partial x} F(U_j) dx= 0 \end{equation}\]
\[\begin{equation} \int_{\Omega} l_i l_j \frac{\partial U_j}{\partial t} dx +
\int_{\Omega} l_i \frac{\partial l_j}{\partial x} F(U_j) dx+
\oint_{\partial \Omega} l_i l_j (F^* - F)\cdot \vec{n} ds = 0 \end{equation}\]
\[\begin{equation} JM \frac{\partial U}{\partial t} + JMD_x F(U) + J_E M_E (F^* - F)\cdot \vec{n} = 0 \end{equation}
\]

ODE:

\[\begin{equation} \frac{\partial U}{\partial t} = -\frac{\partial r}{\partial x}D_r F(U) + \frac{J_E}{J}M^{-1} M_E (F^* - F)\cdot \vec{n}=L(U(t)) \end{equation}
\]
\[\begin{equation} rhs = -\frac{\partial r}{\partial x}D_r F(U) + \frac{J_E}{J}M^{-1} M_E (F - F^*)\cdot \vec{n}\end{equation}
\]

It is important to point out that at dry cells no flux is flow inside the elemnt. Therefor, for dry cells

\[\begin{equation} rhs = \frac{J_E}{J}M^{-1} M_E (F - F^*)\cdot \vec{n}\end{equation}
\]

3.Numerical Flux

3.1.HLL flux function

Formulations are given as

\[F^{HLL} = \left\{ \begin{matrix}
F^- \cr
\frac{S_R F^- - S_L F^+ + S_L S_R(U^+ - U^-)}{S_R S_L} \cr
F^+ \end{matrix} \right.
\begin{matrix}
S_L \geq 0 \cr
S_L < 0 < S_R \cr
S_R \leq 0
\end{matrix}\]

Wave Speed is suggested by Fraccarollo and Toro (1995)

\[S_L = min(u^- - \sqrt{gh^-}, u^* - c^*)
\]
\[S_R = min(u^+ + \sqrt{gh^+}, u^* + c^*)
\]

\(u^*\) and \(c^*\) is defined by

\[u^* = \frac{1}{2}(u^- + u^+) + \sqrt{gh^-} - \sqrt{gh^+}
\]
\[c^* = \frac{1}{2}(\sqrt{gh^-} + \sqrt{gh^+}) + \frac{1}{4}(u^- - u^+)
\]

for wet-dry interface, the wave speed is giving as

  1. left-hand dry bed
\[\begin{equation}
S_L = u^+ - 2\sqrt{g h^+} \quad S_R = u^+ + \sqrt{g h^+}
\end{equation}\]
  1. right-hand dry bed
\[\begin{equation}
S_L = u^- - \sqrt{g h^-} \quad S_R = u^- + 2\sqrt{g h^-}
\end{equation}\]
  1. both sides are dry
\[\begin{equation}
S_L = 0 \quad S_R = 0
\end{equation}\]

Noticing. 1

For flux terms, the discharge \(q^2\) is divided by water depth \(h\)

\[F = \begin{bmatrix} q \cr gh^2/2 + q^2/h \end{bmatrix}
\]

so a threadhold of water depth \(h_{flux}\) ( \(10^{-3}\)m ) is add into flux function SWEFlux.m. When \(h\) is less than \(h_{flux}\), the \(q^2/h\) is approximated to 0 as there is no flow at this node.

Noticing. 2

When defining the dry beds, another threadhold of water depth \(h_{dry}\) is used. It is convenient to deine \(h_{dry}\) equals to \(h_{flux}\).

3.2.Rotational invariance

\[T = \begin{bmatrix} 1 & 0 \cr
0 & n_x\end{bmatrix} \quad
T^{-1} = \begin{bmatrix} 1 & 0 \cr
0 & n_x\end{bmatrix}\]
\[\mathbf{F} \cdot \mathbf{n} = \mathbf{F} \cdot n_x = T^{-1}\mathbf{F}(TU)
\]

defining \(Q = TU\), the numerical flux \(\hat{\mathbf{F}}\) can be obtained through the evaluation of numerical flux \(\mathbf{F}\) by

\[\hat{\mathbf{F}} \cdot n = T^{-1}\mathbf{F}^{HLL}(Q)
\]

4.Limiter

Note: discontinuity detector from Krivodonova (2003) is not working

For better numerical stability, minmod limiter is used in limiting the discharge and elevation.

Check testing/Limiter1D/doc for more details about minmod limiter.

5. Positive preserving limiter

For the thin layer approach, a small depth ( \(h_{positive} = 10^{-3} m\)) and zeros velocity are prescribed for dry nodes.

The first step is to define wet elements. After each time step, the whole domain is calculated; If the any depth of nodes in \(\Omega_i\) is greater than \(h_{positive}\), then the element is defined as wet element, otherwise the water height of all nodes are remain unchanged.

The second step is to modify wet cells; If the depth of any nodes is less than \(h_{positive}\), then the flow rate is reset to zero and the new water depth is constructed as

\[\begin{equation}
\mathrm{M}\Pi_h h_i(x) = \theta_1 \left( h_i(x) - \bar{h}_i \right) + \bar{h}_i
\end{equation}\]

where

\[\begin{equation}
\theta_1 = min \left\{ \frac{\bar{h}_i - \xi }{\bar{h}_i - h_{min}}, 1 \right\}, \quad h_{min} = min\{ h_i (x_i) \}
\end{equation}\]

It is necessary to fulfill the restriction that the mean depth \(\bar{h}_i\) is greater than \(\xi\), i.e. \(10^{-4}\)m. In the function PositiveOperator, if the mean depth of element is less than \(\xi\), all nodes will add a small depth \(\xi - \bar{h}_i\) to re-fulfill the restriction.

At last, all values of water height at nodes with negative \(h_i(x_j) <0\) will be modified to zero and the discharge of dry nodes ( \(h_i \le h_{positive}\) ) will be reseted to zero.

6. Wet/Dry reconstruction

No special treatment is introduced in the model at the moment.

5.Numerical Test

5.1.Wet dam break

Model Setting value
channel length 1000m
dam position 500m
upstream depth 10m
downstream depth 2m
element num 400
Final Time 20s

5.2.Dry dam break

Model Setting value
channel length 1000m
dam position 500m
upstream depth 10m
downstream depth 0m
element num 400
Final Time 20s

5.3.Parabolic bowl

Model Setting value
channel length 2000m
\(h_0\) 10m
\(a\) 600m
\(B\) 5m/s
\(T\) 269s

Exact solution

\[\begin{equation}
Z(x,t) = \frac{-B^2 \mathrm{cos}(2wt) - B^2 - 4Bw \mathrm{cos}(wt)x}{4g}
\end{equation}\]
  1. \(t = T/2\)

  2. \(t = 3T/4\)

  3. \(t = T\)

1D RKDG to shallow water equations的更多相关文章

  1. 论文翻译:2018_Source localization using deep neural networks in a shallow water environment

    论文地址:https://asa.scitation.org/doi/abs/10.1121/1.5036725 深度神经网络在浅水环境中的源定位 摘要: 深度神经网络(DNNs)在表征复杂的非线性关 ...

  2. BJ2 斜率限制器

    BJ2 斜率限制器 本文介绍斜率限制器取自于 Anastasiou 与 Chan (1997)[1]研究,其所利用的斜率限制器也是 Barth 与 Jespersen 限制器的一种修正形式,并且包含一 ...

  3. 体积与边精确积分DGM方法

    Triangular DGM 1. Basis functions decomposing the domain \(\Omega\) into \(N_e\) conforming non-over ...

  4. TRANSFORM YOUR HABITS

    TRANSFORM YOUR HABITS3rd EditionNote from James Clear:I wrote Transform Your Habits to create a free ...

  5. TPO 02 - The Origins of Cetaceans

    TPO 02 - The Origins of Cetaceans It should be obvious that cetaceans[n. 鲸目动物]-whales, porpoises [n. ...

  6. 洛谷P3070 [USACO13JAN]岛游记Island Travels

    P3070 [USACO13JAN]岛游记Island Travels 题目描述 Farmer John has taken the cows to a vacation out on the oce ...

  7. BZOJ_3049_[Usaco2013 Jan]Island Travels _状压DP+BFS

    BZOJ_3049_[Usaco2013 Jan]Island Travels _状压DP+BFS Description Farmer John has taken the cows to a va ...

  8. viva correction statements

    * List of amendments| No. | Location     | Amendments                                                ...

  9. [Usaco2013 Jan]Island Travels

    Description Farmer John has taken the cows to a vacation out on the ocean! The cows are living on N ...

随机推荐

  1. 【UE4 C++ 基础知识】<6> 容器——TMap

    概述 TMap主要由两个类型定义(一个键类型和一个值类型),以关联对的形式存储在映射中. 将数据存储为键值对(TPair<KeyType, ValueType>),只将键用于存储和获取 映 ...

  2. iNeuOS工业互联网操作系统,智慧用电测控应用案例

    目       录 1.      概述... 2 2.      系统部署结构... 2 3.      用电测控终端... 3 4.      系统应用介绍... 6 1.   概述 通过物联网技 ...

  3. C/C++编程笔记:浪漫流星雨表白装b程序

    作为一个未来可能会成为一个专业程序员的小伙们,不知道你们现在学到哪里了,学了点东西之后有没有想在你女朋友面前装个大大的b呢,今天小编就给你一个机会来研究一下下边的代码吧,保证大写的N,当然大佬是排除在 ...

  4. 零基础入门Linux有什么好的学习方法吗?(超详细)

    本节旨在介绍对于初学者如何学习 Linux 的建议,在这里不具体分析Linux的学习节点只分析对于零基础的伙伴的学习方法.那么如果你已经确定对 Linux 产生了兴趣,那么接下来我们介绍一下学习 Li ...

  5. HCNP Routing&Switching之BGP路由属性和优选规则

    前文我们了解了BGP防环机制和路由聚合相关话题,回顾请参考https://www.cnblogs.com/qiuhom-1874/p/15458110.html:今天我们来聊一聊BGP路由属性和选路规 ...

  6. 数字在排序数组中出现的次数 牛客网 剑指Offer

    数字在排序数组中出现的次数 牛客网 剑指Offer 题目描述 统计一个数字在排序数组中出现的次数. class Solution: def GetNumberOfK(self, data, k): i ...

  7. binary-tree-preorder-traversal leetcode C++

    Given a binary tree, return the preorder traversal of its nodes' values. For example: Given binary t ...

  8. BQ40Z50固件怎么升级?告诉你BQ系列芯片内部结构和升级方法

    一 BQ芯片初步认识 包括BQ40Z50在内,BQ系列电池管理芯片看起来是一个芯片,其实芯片里面封装了两个die.一个是MCU部分负责计算和控制,其采用的是bqBMP内核的16位处理器:另外一个die ...

  9. git与pycharm的使用详解(git+gitlab+pycham)

    前言 当自动化框架搭建出来后,需要多个人来使用框架,写自动化用例. 在这个阶段,我们不可能将写好的代码打包发给其他人,这样很麻烦,多人协作一点也不灵活. 这时候,就提现出了git的价值 一.下载安装 ...

  10. SpringCloud概念

    SpringCloud概述 1.SpringCloud是什么? 官方解释:  官网: https://spring.io/projects/spring-cloud/  SpringCloud是一系列 ...