1 files changed, 60 insertions, 0 deletions
diff --git a/boltzgen/lbm/__init__.py b/boltzgen/lbm/__init__.py
new file mode 100644
index 0000000..f80feaa
--- /dev/null
+++ b/boltzgen/lbm/__init__.py
@@ -0,0 +1,60 @@
+from sympy import *
+from sympy.codegen.ast import Assignment
+
+import utility.optimizations as optimizations
+from lbm.model.characteristics import weights, c_s
+
+
+def assign(names, definitions):
+    return list(map(lambda x: Assignment(*x), zip(names, definitions)))
+
+class LBM:
+    def __init__(self, descriptor):
+        self.descriptor = descriptor
+        self.f_next = symarray('f_next', descriptor.q)
+        self.f_curr = symarray('f_curr', descriptor.q)
+
+        if not hasattr(descriptor, 'w'):
+            self.descriptor.w = weights(descriptor.d, descriptor.c)
+
+        if not hasattr(descriptor, 'c_s'):
+            self.descriptor.c_s = c_s(descriptor.d, descriptor.c, self.descriptor.w)
+
+    def moments(self, optimize = True):
+        rho = symbols('rho')
+        u   = Matrix(symarray('u', self.descriptor.d))
+
+        exprs = [ Assignment(rho, sum(self.f_curr)) ]
+
+        for i, u_i in enumerate(u):
+            exprs.append(
+                Assignment(u_i, sum([ (c_j*self.f_curr[j])[i] for j, c_j in enumerate(self.descriptor.c) ]) / sum(self.f_curr)))
+
+        if optimize:
+            return cse(exprs, optimizations=optimizations.custom, symbols=numbered_symbols(prefix='m'))
+        else:
+            return ([], exprs)
+
+    def equilibrium(self):
+        rho = symbols('rho')
+        u   = Matrix(symarray('u', self.descriptor.d))
+
+        f_eq = []
+
+        for i, c_i in enumerate(self.descriptor.c):
+            f_eq_i = self.descriptor.w[i] * rho * ( 1
+                                                  + c_i.dot(u)    /    self.descriptor.c_s**2
+                                                  + c_i.dot(u)**2 / (2*self.descriptor.c_s**4)
+                                                  - u.dot(u)      / (2*self.descriptor.c_s**2) )
+            f_eq.append(f_eq_i)
+
+        return f_eq
+
+    def bgk(self, tau, f_eq, optimize = True):
+        exprs = [ self.f_curr[i] + 1/tau * (f_eq_i - self.f_curr[i]) for i, f_eq_i in enumerate(f_eq) ]
+
+        if optimize:
+            subexprs, f = cse(exprs, optimizations=optimizations.custom)
+            return (subexprs, assign(self.f_next, f))
+        else:
+            return ([], assign(self.f_next, exprs))