doc/notebooks/06_tutorial_modifying_method_smagorinsky.ipynb

 %% Cell type:code id: tags:
 ``` python
 from lbmpy.session import *
 from lbmpy.relaxationrates import *
 ```
 %% Cell type:markdown id: tags:
 # Tutorial 06: Modifying a LBM method: Smagorinsky model
 In this demo, we show how to modify a lattice Boltzmann method. As example we are going to add a simple turbulence model, by introducing a rule that locally computes the relaxation parameter dependent on the local strain rate tensor. The Smagorinsky model is implemented directly in *lbmpy* as well, however here we take the manual approach to demonstrate how a LB method can be changed in *lbmpy*.
 ## 1) Theoretical background
 Since we have *sympy* available, we want to start out with the basic model equations and derive the concrete equations ourselves. This approach is less error prone, since the calculations are done by the computer algebra system, and oftentimes this approach is also more general and easier to understand.
 ### a) Smagorinsky model
 The basic idea of the Smagorinsky turbulence model is to safe compute time, by not resolving the smallest eddies of the flow on the grid, but model them by an artifical dissipation term.
 The energy dissipation of small scale vortices is taken into account by introducing a "turbulent viscosity". This additional viscosity depends on local flow properties, namely the local shear rates. The larger the local shear rates the higher the turbulent viscosity and the more artifical dissipation is added.
 The total viscosity is
 $$\nu_{total} = \nu_0 + \underbrace{(C_S \Delta)^2 |S|}_{\nu_{t}}$$
 where $\nu_0$ is the normal viscosity, $C_S$ is the Smagorinsky constant, not to be confused with the speed of sound! Typical values of the Smagorinsky constant are between 0.1 - 0.2. The filter length $\Delta$ is chosen as 1 in lattice coordinates.
 The quantity $|S|$ is computed from the local strain rate tensor $S$ that is given by
 $$S_{ij} = \frac{1}{2} \left( \partial_i u_j + \partial_j u_i \right)$$
 and
 $$|S| = \sqrt{2 S_{ij} S_{ij}}$$
 ### b) LBM implementation of Smagorinsky model
 To add the Smagorinsky model to a LB scheme one has to first compute the strain rate tensor $S_{ij}$ in each cell, and compute the turbulent viscosity $\nu_t$ from it. Then the local relaxation rate has to be adapted to match the total viscosity $\nu_{total}$ instead of the standard viscosity $\nu_0$.
 A fortunate property of LB methods is, that the strain rate tensor can be computed locally from the non-equilibrium part of the distribution function. This is somewhat surprising, since the strain rate tensor contains first order derivatives. The strain rate tensor can be obtained by
 $$S_{ij} = - \frac{3 \omega_s}{2 \rho_{(0)}} \Pi_{ij}^{(neq)}$$
 where $\omega_s$ is the relaxation rate that determines the viscosity, $\rho_{(0)}$ is $\rho$ in compressible models and $1$ for incompressible schemes.
 $\Pi_{ij}^{(neq)}$ is the second order moment tensor of the non-equilibrium part of the distribution functions $f^{(neq)} = f - f^{(eq)}$ and can be computed as
 $$\Pi_{ij}^{(neq)} = \sum_q c_{qi} c_{qj} \; f_q^{(neq)}$$
 %% Cell type:markdown id: tags:
 We first have to find a closed form for $S_{ij}$ since in the formula above, it depends on $\omega$, which should be adapated according to $S_{ij}$.
 So we compute $\omega$ and insert it into the formula for $S$:
 %% Cell type:code id: tags:
 ``` python
 τ_0, ρ, ω, ω_total, ω_0 = sp.symbols("tau_0 rho omega omega_total omega_0", positive=True, real=True)
 ν_0, C_S, S, Π = sp.symbols("nu_0, C_S, |S|, Pi", positive=True, real=True)
 Seq = sp.Eq(S, 3 * ω / 2 * Π)
 Seq
 ```
 %% Output
     $\displaystyle |S| = \frac{3 \Pi \omega}{2}$
           3⋅Π⋅ω
     |S| = ─────
             2
 %% Cell type:markdown id: tags:
 Note that we left of the minus, since we took the absolute value of both tensor. The absolute value is defined as above, with the factor of two inside the square root. The $\rho_{(0)}$ has been left out, remembering that $\Pi^{(neq)}$ has to be divided by $\rho$ in case of compressible models|.
 Next, we compute $\omega$ from the total viscosity as given by the Smagorinsky equation:
 %% Cell type:code id: tags:
 ``` python
 relaxation_rate_from_lattice_viscosity(ν_0 + C_S ** 2 * S)
 ```
 %% Output
     $\displaystyle \frac{2}{6 C_{S}^{2} |S| + 6 \nu_{0} + 1}$
               2
     ─────────────────────
          2
     6⋅C_S ⋅|S| + 6⋅ν₀ + 1
 %% Cell type:markdown id: tags:
 and insert it into the equation for $|S|$
 %% Cell type:code id: tags:
 ``` python
 Seq2 = Seq.subs(ω, relaxation_rate_from_lattice_viscosity(ν_0 + C_S **2 * S ))
 Seq2
 ```
 %% Output
     $\displaystyle |S| = \frac{3 \Pi}{6 C_{S}^{2} |S| + 6 \nu_{0} + 1}$
                    3⋅Π
     |S| = ─────────────────────
                2
           6⋅C_S ⋅|S| + 6⋅ν₀ + 1
 %% Cell type:markdown id: tags:
 This equation contains only known quantities, such that we can solve it for $|S|$.
 Additionally we substitute the lattice viscosity $\nu_0$ by the original relaxation time $\tau_0$. The resulting equations get simpler using relaxation times instead of rates.
 %% Cell type:code id: tags:
 ``` python
 solveRes = sp.solve(Seq2, S)
 assert len(solveRes) == 1
 SVal = solveRes[0]
 SVal = SVal.subs(ν_0, lattice_viscosity_from_relaxation_rate(1 / τ_0)).expand()
 SVal
 ```
 %% Output
     $\displaystyle - \frac{\tau_{0}}{6 C_{S}^{2}} + \frac{\sqrt{72 C_{S}^{2} \Pi + 4 \tau_{0}^{2}}}{12 C_{S}^{2}}$
                   ___________________
                  ╱       2         2
         τ₀     ╲╱  72⋅C_S ⋅Π + 4⋅τ₀
     - ────── + ──────────────────────
            2                2
       6⋅C_S           12⋅C_S
 %% Cell type:markdown id: tags:
 Knowning $|S|$ we can compute the total relaxation time using
 $$\nu_{total} = \nu_0 +C_S^2 |S|$$
 %% Cell type:code id: tags:
 ``` python
 τ_val = 1 / (relaxation_rate_from_lattice_viscosity(lattice_viscosity_from_relaxation_rate(1/τ_0) + C_S**2 * SVal)).cancel()
 τ_val
 ```
 %% Output
     $\displaystyle \frac{\tau_{0}}{2} + \frac{\sqrt{18 C_{S}^{2} \Pi + \tau_{0}^{2}}}{2}$
             _________________
            ╱       2       2
     τ₀   ╲╱  18⋅C_S ⋅Π + τ₀
     ── + ────────────────────
     2             2
 %% Cell type:markdown id: tags:
 To compute $\Pi^{(neq)}$ we use the following functions:
 %% Cell type:code id: tags:
 ``` python
 def second_order_moment_tensor(function_values, stencil):
     assert len(function_values) == len(stencil)
     dim = len(stencil[0])
     return sp.Matrix(dim, dim, lambda i, j: sum(c[i] * c[j] * f for f, c in zip(function_values, stencil)))
 def frobenius_norm(matrix, factor=1):
     return sp.sqrt(sum(i*i for i in matrix) * factor)
 ```
 %% Cell type:markdown id: tags:
 In the next cell we construct equations that take an standard relaxation rate $\omega_0$ and compute a new relaxation rate $\omega_{total}$ according to the Smagorinksy model, using `τ_val` computed above
 %% Cell type:code id: tags:
 ``` python
 def smagorinsky_equations(ω_0, ω_total, method):
     f_neq = sp.Matrix(method.pre_collision_pdf_symbols) - method.get_equilibrium_terms()
     return [sp.Eq(τ_0, 1 / ω_0),
             sp.Eq(Π, frobenius_norm(second_order_moment_tensor(f_neq, method.stencil), factor=2)),
             sp.Eq(ω_total, 1 / τ_val)]
 smagorinsky_equations(ω_0, ω_total, create_lb_method())
 ```
 %% Output
     $\displaystyle \left[ \tau_{0} = \frac{1}{\omega_{0}}, \  \Pi = \sqrt{4 \left(- f_{5} + f_{6} + f_{7} - f_{8} - u_{0} u_{1}\right)^{2} + 2 \left(- \frac{\delta_{\rho}}{3} + f_{1} + f_{2} + f_{5} + f_{6} + f_{7} + f_{8} - u_{1}^{2}\right)^{2} + 2 \left(- \frac{\delta_{\rho}}{3} + f_{3} + f_{4} + f_{5} + f_{6} + f_{7} + f_{8} - u_{0}^{2}\right)^{2}}, \  \omega_{total} = \frac{1}{\frac{\tau_{0}}{2} + \frac{\sqrt{18 C_{S}^{2} \Pi + \tau_{0}^{2}}}{2}}\right]$
     ⎡                  ___________________________________________________________
     ⎢                 ╱
     ⎢     1          ╱                                2     ⎛  δᵨ
     ⎢τ₀ = ──, Π =   ╱   4⋅(-f₅ + f₆ + f₇ - f₈ - u₀⋅u₁)  + 2⋅⎜- ── + f₁ + f₂ + f₅ +
     ⎢     ω₀      ╲╱                                        ⎝  3
     ⎢
     ⎢
     ⎢
     ⎣
     
     ______________________________________________________________________
                         2                                               2
                       2⎞      ⎛  δᵨ                                   2⎞
      f₆ + f₇ + f₈ - u₁ ⎟  + 2⋅⎜- ── + f₃ + f₄ + f₅ + f₆ + f₇ + f₈ - u₀ ⎟  , ωₜₒₜₐₗ
                        ⎠      ⎝  3                                     ⎠
     
     
     
     
     
                                 ⎤
                                 ⎥
                    1            ⎥
      = ─────────────────────────⎥
                _________________⎥
               ╱       2       2 ⎥
        τ₀   ╲╱  18⋅C_S ⋅Π + τ₀  ⎥
        ── + ────────────────────⎥
        2             2          ⎦
 %% Cell type:markdown id: tags:
 ## 2) Application: Channel flow
 Next we modify a *lbmpy* scenario to use the Smagorinsky model.
 We create a MRT method, where we fix all relaxation rates except the relaxation rate that controls the viscosity.
 %% Cell type:code id: tags:
 ``` python
-lbm_conifg = LBMConfig(stencil=Stencil.D2Q9, method=Method.MRT, force=(1e-6, 0),
+lbm_config = LBMConfig(stencil=Stencil.D2Q9, method=Method.MRT, force=(1e-6, 0),
                        force_model=ForceModel.LUO, relaxation_rates=[ω, 1.9, 1.9, 1.9])
-method = create_lb_method(lbm_config=lbm_conifg)
+method = create_lb_method(lbm_config=lbm_config)
 method
 ```
 %% Output
     <lbmpy.methods.momentbased.momentbasedmethod.MomentBasedLbMethod at 0x7f745d5c3b20>
 %% Cell type:markdown id: tags:
 Only the collision rule has to be changed. Thus we first construct the collision rule, add the Smagorinsky equations and create a normal scenario from the modified collision rule. To avoid that the macroscopic quantity symbols in the Smagorinsky equations fall prey to optimization, we must disable simplification:
 %% Cell type:code id: tags:
 ``` python
 optimization = {'simplification' : False}
 collision_rule = create_lb_collision_rule(lb_method=method, optimization=optimization)
 collision_rule = collision_rule.new_with_substitutions({ω: ω_total})
 collision_rule.subexpressions += smagorinsky_equations(ω, ω_total, method)
 collision_rule.topological_sort(sort_subexpressions=True, sort_main_assignments=False)
 collision_rule
 ```
 %% Output
     AssignmentCollection: d_6, d_5, d_2, d_3, d_0, d_1, d_7, d_4, d_8 <- f(f_2, f_4, f_7, f_5, omega_total, f_6, f_3, f_1, f_0, f_8)
 %% Cell type:markdown id: tags:
 In the next cell the collision rule is simplified by extracting common subexpressions
 %% Cell type:code id: tags:
 ``` python
 from pystencils.simp import sympy_cse
 #collision_rule = sympy_cse(collision_rule)
 ```
 %% Cell type:markdown id: tags:
 A channel scenario can be created from a modified collision rule:
 %% Cell type:code id: tags:
 ``` python
 ch = create_channel((300, 100), force=1e-6, collision_rule=collision_rule,
                     kernel_params={"C_S": 0.12, "omega": 1.999})
 ```
 %% Cell type:code id: tags:
 ``` python
 #show_code(ch.ast)
 ```
 %% Cell type:code id: tags:
 ``` python
 ch.run(5000)
 ```
 %% Cell type:code id: tags:
 ``` python
 plt.figure(dpi=200)
 plt.vector_field(ch.velocity[:, :])
 np.max(ch.velocity[:, :])
 ```
 %% Output
     $\displaystyle 0.00504266401703371$
     0.00504266401703371
 %% Cell type:markdown id: tags:
 ## Appendix: Strain rate tensor formula from Chapman Enskog
 The connection between $S_{ij}$ and $\Pi_{ij}^{(neq)}$ can be seen using a Chapman Enskog expansion. Since *lbmpy* has a module that automatically does this expansions we can have a look at it:
 %% Cell type:code id: tags:
 ``` python
 from lbmpy.chapman_enskog import ChapmanEnskogAnalysis, CeMoment
 from lbmpy.chapman_enskog.chapman_enskog import remove_higher_order_u
 compressible_model = create_lb_method(stencil=Stencil.D2Q9, compressible=True, zero_centered=False)
 incompressible_model = create_lb_method(stencil=Stencil.D2Q9, compressible=False, zero_centered=False)
 ce_compressible = ChapmanEnskogAnalysis(compressible_model)
 ce_incompressible = ChapmanEnskogAnalysis(incompressible_model)
 ```
 %% Cell type:markdown id: tags:
 The Chapman Enskog analysis yields expresssions for the moment
 $\Pi = \Pi^{(eq)} + \epsilon \Pi^{(1)} +  \epsilon^2 \Pi^{(2)} \cdots$
 and the strain rate tensor is related to $\Pi^{(1)}$. However the best approximation we have for $\Pi^{(1)}$ is
 $\Pi^{(neq)}$. For details, see the paper "Shear stress in lattice Boltzmann simulations" by Krüger, Varnik and Raabe from 2009.
 Lets look at the values of $\Pi^{(1)}$ obtained from the Chapman enskog expansion:
 %% Cell type:code id: tags:
 ``` python
 Π_1_xy = CeMoment("\\Pi", moment_tuple=(1,1), superscript=1)
 Π_1_xx = CeMoment("\\Pi", moment_tuple=(2,0), superscript=1)
 Π_1_yy = CeMoment("\\Pi", moment_tuple=(0,2), superscript=1)
 components = (Π_1_xx, Π_1_yy, Π_1_xy)
 Π_1_xy_val = ce_compressible.higher_order_moments[Π_1_xy]
 Π_1_xy_val
 ```
 %% Output
     $\displaystyle \frac{\rho u_{0}^{2} {\partial^{(1)}_{0} u_{1}} + 2 \rho u_{0} u_{1} {\partial^{(1)}_{0} u_{0}} + 2 \rho u_{0} u_{1} {\partial^{(1)}_{1} u_{1}} + \rho u_{1}^{2} {\partial^{(1)}_{1} u_{0}} - \frac{\rho {\partial^{(1)}_{1} u_{0}}}{3} - \frac{\rho {\partial^{(1)}_{0} u_{1}}}{3} + u_{0}^{2} u_{1} {\partial^{(1)}_{0} \rho} + u_{0} u_{1}^{2} {\partial^{(1)}_{1} \rho}}{\omega}$
         2                                                    2          ρ⋅D(u_0)
     ρ⋅u₀ ⋅D(u_1) + 2⋅ρ⋅u₀⋅u₁⋅D(u_0) + 2⋅ρ⋅u₀⋅u₁⋅D(u_1) + ρ⋅u₁ ⋅D(u_0) - ──────── -
                                                                            3
     ──────────────────────────────────────────────────────────────────────────────
                                                                ω
     
      ρ⋅D(u_1)     2                  2
      ──────── + u₀ ⋅u₁⋅D(rho) + u₀⋅u₁ ⋅D(rho)
         3
     ─────────────────────────────────────────
     
 %% Cell type:markdown id: tags:
 This term has lots of higher order error terms in it. We assume that $u$ is small in lattice coordinates, so if we neglect all terms in $u$ that are quadratic or higher we get:
 %% Cell type:code id: tags:
 ``` python
 remove_higher_order_u(Π_1_xy_val.expand())
 ```
 %% Output
     $\displaystyle - \frac{\rho {\partial^{(1)}_{1} u_{0}}}{3 \omega} - \frac{\rho {\partial^{(1)}_{0} u_{1}}}{3 \omega}$
       ρ⋅D(u_0)   ρ⋅D(u_1)
     - ──────── - ────────
         3⋅ω        3⋅ω
 %% Cell type:markdown id: tags:
 Putting these steps together into a function, we can display them for the different cases quickly:
 %% Cell type:code id: tags:
 ``` python
 def get_Π_1(ce_analysis, component):
     val = ce_analysis.higher_order_moments[component]
     return remove_higher_order_u(val.expand())
 ```
 %% Cell type:markdown id: tags:
 Compressible case:
 %% Cell type:code id: tags:
 ``` python
 tuple(get_Π_1(ce_compressible, Pi) for Pi in components)
 ```
 %% Output
     $\displaystyle \left( - \frac{2 \rho {\partial^{(1)}_{0} u_{0}}}{3 \omega}, \  - \frac{2 \rho {\partial^{(1)}_{1} u_{1}}}{3 \omega}, \  - \frac{\rho {\partial^{(1)}_{1} u_{0}}}{3 \omega} - \frac{\rho {\partial^{(1)}_{0} u_{1}}}{3 \omega}\right)$
     ⎛-2⋅ρ⋅D(u_0)   -2⋅ρ⋅D(u_1)     ρ⋅D(u_0)   ρ⋅D(u_1)⎞
     ⎜────────────, ────────────, - ──────── - ────────⎟
     ⎝    3⋅ω           3⋅ω           3⋅ω        3⋅ω   ⎠
 %% Cell type:markdown id: tags:
 Incompressible case:
 %% Cell type:code id: tags:
 ``` python
 tuple(get_Π_1(ce_incompressible, Pi) for Pi in components)
 ```
 %% Output
     $\displaystyle \left( \frac{2 u_{0} {\partial^{(1)}_{0} \rho}}{3 \omega} - \frac{2 {\partial^{(1)}_{0} u_{0}}}{3 \omega}, \  \frac{2 u_{1} {\partial^{(1)}_{1} \rho}}{3 \omega} - \frac{2 {\partial^{(1)}_{1} u_{1}}}{3 \omega}, \  \frac{u_{0} {\partial^{(1)}_{1} \rho}}{3 \omega} + \frac{u_{1} {\partial^{(1)}_{0} \rho}}{3 \omega} - \frac{{\partial^{(1)}_{1} u_{0}}}{3 \omega} - \frac{{\partial^{(1)}_{0} u_{1}}}{3 \omega}\right)$
     ⎛2⋅u₀⋅D(rho)   2⋅D(u_0)  2⋅u₁⋅D(rho)   2⋅D(u_1)  u₀⋅D(rho)   u₁⋅D(rho)   D(u_0
     ⎜─────────── - ────────, ─────────── - ────────, ───────── + ───────── - ─────
     ⎝    3⋅ω         3⋅ω         3⋅ω         3⋅ω        3⋅ω         3⋅ω       3⋅ω
     
     )   D(u_1)⎞
     ─ - ──────⎟
          3⋅ω  ⎠
 %% Cell type:markdown id: tags:
 In the incompressible case has some terms $\partial \rho$ which are zero, since $\rho$ is assumed constant.
 Leaving out the error terms we finally obtain:
 $$\Pi_{ij}^{(neq)} \approx \Pi_{ij}^{(1)} = -\frac{2 \rho_{(0)}}{3 \omega_s} \left( \partial_i u_j + \partial_j u_i \right)$$

doc/notebooks/demo_shallow_water_lbm.ipynb

@@ -541,7 +541,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -555,7 +555,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.2"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,

doc/sphinx/api.rst

@@ -8,13 +8,10 @@ API Reference
    enums.rst
    stencils.rst
    kernelcreation.rst
-   methods.rst
    equilibrium.rst
    moment_transforms.rst
    maxwellian_equilibrium.rst
    continuous_distribution_measures.rst
    moments.rst
    cumulants.rst
-   boundary_conditions.rst
-   forcemodels.rst
    zbibliography.rst

doc/sphinx/lbmpy.bib

@@ -110,6 +110,24 @@ issn = {0898-1221},
 doi = {10.1016/j.camwa.2015.05.001},
 }
 
+@article{geier2017,
+author = {Geier, Martin and Pasquali, Andrea and Sch{\"{o}}nherr, Martin},
+title = {Parametrization of the Cumulant Lattice Boltzmann Method for Fourth Order Accurate Diffusion Part I},
+year = {2017},
+issue_date = {November 2017},
+publisher = {Academic Press Professional, Inc.},
+address = {USA},
+volume = {348},
+number = {C},
+issn = {0021-9991},
+url = {https://doi.org/10.1016/j.jcp.2017.05.040},
+doi = {10.1016/j.jcp.2017.05.040},
+journal = {J. Comput. Phys.},
+month = {nov},
+pages = {862–888},
+numpages = {27}
+}
+
 @Article{Coreixas2019,
   title = {Comprehensive comparison of collision models in the lattice Boltzmann framework: Theoretical investigations},
   author = {Coreixas, Christophe and Chopard, Bastien and Latt, Jonas},
@@ -248,4 +266,52 @@ journal = {Communications in Computational Physics}
   publisher = {Springer Link},
 }
 
+@article{BouzidiBC,
+    author = {Bouzidi, M’hamed and Firdaouss, Mouaouia and Lallemand, Pierre},
+    title = "{Momentum transfer of a Boltzmann-lattice fluid with boundaries}",
+    journal = {Physics of Fluids},
+    year = {2001},
+    month = {11},
+    doi = {10.1063/1.1399290},
+    url = {https://doi.org/10.1063/1.1399290},
+}
+
+@Article{rozema15,
+  doi       = {10.1063/1.4928700},
+  year      = {2015},
+  month     = {aug},
+  publisher = {AIP Publishing},
+  volume    = {27},
+  number    = {8},
+  author    = {Wybe Rozema and Hyun J. Bae and Parviz Moin and Roel Verstappen},
+  title     = {Minimum-dissipation models for large-eddy simulation},
+  journal   = {Physics of Fluids}
+}
+
+@article{Han2021,
+   doi = {10.1088/1873-7005/ac1782},
+   url = {https://dx.doi.org/10.1088/1873-7005/ac1782}                    ,
+   year = {2021},
+   month = {aug},
+   publisher = {IOP Publishing},
+   volume = {53},
+   number = {4},
+   pages = {045506},
+   author = {Mengtao Han and Ryozo Ooka and Hideki Kikumoto},
+   title = {A wall function approach in lattice Boltzmann method: algorithm and validation using turbulent channel flow},
+   journal = {Fluid Dynamics Research}
+}
+
+@article{Maronga2020,
+ author = {Maronga, Bj{\"o}rn and Knigge, Christoph and Raasch, Siegfried},
+ year = {2020},
+ title = {{An Improved Surface Boundary Condition for Large-Eddy Simulations Based on Monin--Obukhov Similarity Theory: Evaluation and Consequences for Grid Convergence in Neutral and Stable Conditions}},
+ pages = {297--325},
+ volume = {174},
+ number = {2},
+ issn = {0006-8314},
+ journal = {{Boundary-layer meteorology}},
+ doi = {10.1007/s10546-019-00485-w}
+}
+
 @Comment{jabref-meta: databaseType:bibtex;}

doc/sphinx/methods.rst

-*******************************************
-Methods and Method Creation (lbmpy.methods)
-*******************************************
+********************************
+Collision models (lbmpy.methods)
+********************************
 
 This module defines the classes defining all types of lattice Boltzmann methods available in *lbmpy*,
 together with a set of factory functions used to create their instances. The factory functions are
@@ -22,21 +22,6 @@ Abstract LB Method Base Class
 .. autoclass:: lbmpy.methods.AbstractLbMethod
     :members:
 
-
-Conserved Quantity Computation
-==============================
-
-The classes of the conserved quantity computation (CQC) submodule define an LB Method's conserved quantities and
-the equations to compute them. For hydrodynamic methods, :class:`lbmpy.methods.DensityVelocityComputation` is
-the typical choice. For custom methods, however, a custom CQC class might have to be created.
-
-.. autoclass:: lbmpy.methods.AbstractConservedQuantityComputation
-    :members:
-
-.. autoclass:: lbmpy.methods.DensityVelocityComputation
-    :members:
-
-
 .. _methods_rawmomentbased:
 
 Raw Moment-based methods
@@ -163,3 +148,17 @@ Low-Level Creation Functions
 .. autofunction:: lbmpy.methods.creationfunctions.create_with_continuous_maxwellian_equilibrium
 
 .. autofunction:: lbmpy.methods.creationfunctions.create_from_equilibrium
+
+
+Conserved Quantity Computation
+==============================
+
+The classes of the conserved quantity computation (CQC) submodule define an LB Method's conserved quantities and
+the equations to compute them. For hydrodynamic methods, :class:`lbmpy.methods.DensityVelocityComputation` is
+the typical choice. For custom methods, however, a custom CQC class might have to be created.
+
+.. autoclass:: lbmpy.methods.AbstractConservedQuantityComputation
+    :members:
+
+.. autoclass:: lbmpy.methods.DensityVelocityComputation
+    :members:
\ No newline at end of file

doc/sphinx/tutorials.rst

@@ -4,6 +4,10 @@ Tutorials
 All tutorials are automatically created by Jupyter Notebooks.
 You can open the notebooks directly to play around with the code examples.
 
+=================
+Basics
+=================
+
 .. toctree::
     :maxdepth: 1
 
@@ -13,17 +17,68 @@ You can open the notebooks directly to play around with the code examples.
     /notebooks/03_tutorial_lbm_formulation.ipynb
     /notebooks/04_tutorial_cumulant_LBM.ipynb
     /notebooks/05_tutorial_nondimensionalization_and_scaling.ipynb
+
+===================
+Turbulence modeling
+===================
+
+.. toctree::
+    :maxdepth: 1
+
     /notebooks/06_tutorial_modifying_method_smagorinsky.ipynb
+
+=================
+Thermal flows
+=================
+
+.. toctree::
+    :maxdepth: 1
+
     /notebooks/07_tutorial_thermal_lbm.ipynb
+    /notebooks/demo_thermalized_lbm.ipynb
+
+=================
+Multiphase flows
+=================
+
+.. toctree::
+    :maxdepth: 1
+
     /notebooks/08_tutorial_shanchen_twophase.ipynb
     /notebooks/09_tutorial_shanchen_twocomponent.ipynb
     /notebooks/10_tutorial_conservative_allen_cahn_two_phase.ipynb
+
+========================
+Thermocapillary flows
+========================
+
+.. toctree::
+    :maxdepth: 1
+
+    /notebooks/12_Thermocapillary_flows_heated_channel.ipynb
+    /notebooks/13_Thermocapillary_flows_droplet_motion.ipynb
+
+===================
+Non Newtonian flow
+===================
+
+.. toctree::
+    :maxdepth: 1
+
     /notebooks/11_tutorial_Non_Newtonian_Flow.ipynb
+
+=================
+Diverse
+=================
+
+.. toctree::
+    :maxdepth: 1
+
     /notebooks/demo_stencils.ipynb
     /notebooks/demo_streaming_patterns.ipynb
     /notebooks/demo_create_method_from_scratch.ipynb
     /notebooks/demo_moments_cumulants_and_maxwellian_equilibrium.ipynb
     /notebooks/demo_automatic_chapman_enskog_analysis.ipynb
-    /notebooks/demo_thermalized_lbm.ipynb
+    /notebooks/demo_interpolation_boundary_conditions.ipynb
     /notebooks/demo_shallow_water_lbm.ipynb
     /notebooks/demo_theoretical_background_generic_equilibrium_construction.ipynb

lbmpy/__init__.py

-from .creationfunctions import create_lb_ast, create_lb_collision_rule, create_lb_function,\
-    create_lb_method, create_lb_update_rule, LBMConfig, LBMOptimisation
-from .enums import Stencil, Method, ForceModel, CollisionSpace
-from .lbstep import LatticeBoltzmannStep
-from .macroscopic_value_kernels import pdf_initialization_assignments, macroscopic_values_getter,\
-    compile_macroscopic_values_getter, compile_macroscopic_values_setter, create_advanced_velocity_setter_collision_rule
-from .maxwellian_equilibrium import get_weights
-from .relaxationrates import relaxation_rate_from_lattice_viscosity, lattice_viscosity_from_relaxation_rate,\
-    relaxation_rate_from_magic_number
-from .scenarios import create_lid_driven_cavity, create_fully_periodic_flow
-from .stencils import LBStencil
-
-
-__all__ = ['create_lb_ast', 'create_lb_collision_rule', 'create_lb_function', 'create_lb_method',
-           'create_lb_method_from_existing', 'create_lb_update_rule', 'LBMConfig', 'LBMOptimisation',
-           'Stencil', 'Method', 'ForceModel', 'CollisionSpace',
-           'LatticeBoltzmannStep',
-           'pdf_initialization_assignments', 'macroscopic_values_getter', 'compile_macroscopic_values_getter',
-           'compile_macroscopic_values_setter', 'create_advanced_velocity_setter_collision_rule',
-           'get_weights',
-           'relaxation_rate_from_lattice_viscosity', 'lattice_viscosity_from_relaxation_rate',
-           'relaxation_rate_from_magic_number',
-           'create_lid_driven_cavity', 'create_fully_periodic_flow',
-           'LBStencil']
-
-
-from ._version import get_versions
-__version__ = get_versions()['version']
-del get_versions

lbmpy/boundaries/__init__.py

-from lbmpy.boundaries.boundaryconditions import (
-    UBB, FixedDensity, DiffusionDirichlet, SimpleExtrapolationOutflow,
-    ExtrapolationOutflow, NeumannByCopy, NoSlip, StreamInConstant, FreeSlip)
-from lbmpy.boundaries.boundaryhandling import LatticeBoltzmannBoundaryHandling
-
-__all__ = ['NoSlip', 'FreeSlip', 'UBB', 'SimpleExtrapolationOutflow', 'ExtrapolationOutflow',
-           'FixedDensity', 'DiffusionDirichlet', 'NeumannByCopy',
-           'LatticeBoltzmannBoundaryHandling', 'StreamInConstant']

lbmpy/phasefield_allen_cahn/analytical.py

-
-def analytic_rising_speed(gravitational_acceleration, bubble_diameter, viscosity_gas):
-    r"""
-    Calculated the analytical rising speed of a bubble. This is the expected end rising speed.
-    Args:
-        gravitational_acceleration: the gravitational acceleration acting in the simulation scenario. Usually it gets
-                                    calculated based on dimensionless parameters which describe the scenario
-        bubble_diameter: the diameter of the bubble at the beginning of the simulation
-        viscosity_gas: the viscosity of the fluid inside the bubble
-    """
-    result = -(gravitational_acceleration * bubble_diameter * bubble_diameter) / (12.0 * viscosity_gas)
-    return result

lbmpy/phasefield_allen_cahn/phasefield_simplifications.py

-import sympy as sp
-
-from pystencils.simp import (
-    SimplificationStrategy, apply_to_all_assignments,
-    insert_aliases, insert_zeros, insert_constants)
-
-
-def create_phasefield_simplification_strategy(lb_method):
-    s = SimplificationStrategy()
-    expand = apply_to_all_assignments(sp.expand)
-
-    s.add(expand)
-
-    s.add(insert_zeros)
-    s.add(insert_aliases)
-    s.add(insert_constants)
-    s.add(lambda ac: ac.new_without_unused_subexpressions())
-
-    return s

lbmpy_tests/test_float_kernel.py

-import pytest
-
-import pystencils as ps
-
-from lbmpy.creationfunctions import create_lb_function, LBMConfig
-from lbmpy.enums import Method
-from lbmpy.scenarios import create_lid_driven_cavity
-
-
-@pytest.mark.parametrize('double_precision', [False, True])
-@pytest.mark.parametrize('method_enum', [Method.SRT, Method.CENTRAL_MOMENT, Method.CUMULANT])
-def test_creation(double_precision, method_enum):
-    """Simple test that makes sure that only float variables are created"""
-    lbm_config = LBMConfig(method=method_enum, relaxation_rate=1.5, compressible=True)
-    config = ps.CreateKernelConfig(data_type="float64" if double_precision else "float32",
-                                   default_number_float="float64" if double_precision else "float32")
-    func = create_lb_function(lbm_config=lbm_config, config=config)
-    code = ps.get_code_str(func)
-
-    if double_precision:
-        assert 'float' not in code
-        assert 'double' in code
-    else:
-        assert 'double' not in code
-        assert 'float' in code
-
-
-@pytest.mark.parametrize('double_precision', [False, True])
-@pytest.mark.parametrize('method_enum', [Method.SRT, Method.CENTRAL_MOMENT, Method.CUMULANT])
-def test_scenario(method_enum, double_precision):
-    lbm_config = LBMConfig(method=method_enum, relaxation_rate=1.45, compressible=True)
-    config = ps.CreateKernelConfig(data_type="float64" if double_precision else "float32",
-                                   default_number_float="float64" if double_precision else "float32")
-    sc = create_lid_driven_cavity((16, 16, 8), lbm_config=lbm_config, config=config)
-    sc.run(1)
-    code = ps.get_code_str(sc.ast)
-
-    if double_precision:
-        assert 'float' not in code
-        assert 'double' in code
-    else:
-        assert 'double' not in code
-        assert 'float' in code