Buildings.Utilities.Math.BaseClasses.OneNonLinearEquation

Information

This function should only be used in exceptional cases since it might be replaced in the future by another strategy, where the tool is responsible for the solution of the non-linear equation.

This library determines the solution of one non-linear algebraic equation "y=f(x)" in one unknown "x" in a reliable way. As input, the desired value y of the non-linear function has to be given, as well as an interval x_min, x_max that contains the solution, i.e., "f(x_min) - y" and "f(x_max) - y" must have a different sign. If possible, a smaller interval is computed by inverse quadratic interpolation (interpolating with a quadratic polynomial through the last 3 points and computing the zero). If this fails, bisection is used, which always reduces the interval by a factor of 2. The inverse quadratic interpolation method has superlinear convergence. This is roughly the same convergence rate as a globally convergent Newton method, but without the need to compute derivatives of the non-linear function. The solver function is a direct mapping of the Algol 60 procedure "zero" to Modelica, from:

Brent R.P.:

Algorithms for Minimization without derivatives. Prentice Hall, 1973, pp. 58-59.

Due to current limitations of the Modelica language (not possible to pass a function reference to a function), the construction to use this solver on a user-defined function is a bit complicated (this method is from Hans Olsson, Dynasim AB). A user has to provide a package in the following way:

  package MyNonLinearSolver
    extends OneNonLinearEquation;

    redeclare record extends Data
      // Define data to be passed to user function
      ...
    end Data;

    redeclare function extends f_nonlinear
    algorithm
       // Compute the non-linear equation: y = f(x, Data)
    end f_nonlinear;

    // Dummy definition that has to be present for current Dymola
    redeclare function extends solve
    end solve;
  end MyNonLinearSolver;

  x_zero = MyNonLinearSolver.solve(y_zero, x_min, x_max, data=data);

Extends from Modelica.Icons.Library (Icon for library).

Package Content

Name	Description
f_nonlinear	Nonlinear algebraic equation in one unknown: y = f_nonlinear(x,p,X)
solve	Solve f_nonlinear(x_zero)=y_zero; f_nonlinear(x_min) - y_zero and f_nonlinear(x_max)-y_zero must have different sign

Buildings.Utilities.Math.BaseClasses.OneNonLinearEquation.f_nonlinear

Information

Inputs

Type	Name	Default	Description
Real	x		Independent variable of function
Real	f_nonlinear_data[:]		Additional data for the function

Outputs

Type	Name	Description
Real	y	= f_nonlinear(x)

Modelica definition

replaceable partial function f_nonlinear 
  "Nonlinear algebraic equation in one unknown: y = f_nonlinear(x,p,X)"
  extends Modelica.Icons.Function;
  input Real x "Independent variable of function";
  input Real[:] f_nonlinear_data "Additional data for the function";
  output Real y "= f_nonlinear(x)";
  // annotation(derivative(zeroDerivative=y)); // this must hold for all replaced functions
end f_nonlinear;

Buildings.Utilities.Math.BaseClasses.OneNonLinearEquation.solve

Information

Inputs

Type	Name	Default	Description
Real	y_zero		Determine x_zero, such that f_nonlinear(x_zero) = y_zero
Real	x_min		Minimum value of x
Real	x_max		Maximum value of x
Real	f_nonlinear_data[:]		Additional data for function f_nonlinear
Real	x_tol	100*Modelica.Constants.eps	Relative tolerance of the result

Outputs

Type	Name	Description
Real	x_zero	f_nonlinear(x_zero) = y_zero

Modelica definition

replaceable function solve 
  "Solve f_nonlinear(x_zero)=y_zero; f_nonlinear(x_min) - y_zero and f_nonlinear(x_max)-y_zero must have different sign"
 import Modelica.Utilities.Streams.error;
  extends Modelica.Icons.Function;
  input Real y_zero "Determine x_zero, such that f_nonlinear(x_zero) = y_zero";
  input Real x_min "Minimum value of x";
  input Real x_max "Maximum value of x";
   input Real[:] f_nonlinear_data "Additional data for function f_nonlinear";
   input Real x_tol =  100*Modelica.Constants.eps 
    "Relative tolerance of the result";
   output Real x_zero "f_nonlinear(x_zero) = y_zero";
protected 
   constant Real eps = Modelica.Constants.eps "machine epsilon";
   Real a = x_min "Current best minimum interval value";
   Real b = x_max "Current best maximum interval value";
   Real c "Intermediate point a <= c <= b";
   Real d;
   Real e "b - a";
   Real m;
   Real s;
   Real p;
   Real q;
   Real r;
   Real tol;
   Real fa "= f_nonlinear(a) - y_zero";
   Real fb "= f_nonlinear(b) - y_zero";
   Real fc;
   Boolean found = false;
algorithm 
   // Check that f(x_min) and f(x_max) have different sign
   fa :=f_nonlinear(x_min, f_nonlinear_data) - y_zero;
   fb :=f_nonlinear(x_max, f_nonlinear_data) - y_zero;
   fc := fb;
   if fa > 0.0 and fb > 0.0 or 
      fa < 0.0 and fb < 0.0 then
      error("The arguments x_min and x_max to OneNonLinearEquation.solve(..)\n" +
            "do not bracket the root of the single non-linear equation:\n" +
            "  x_min  = " + String(x_min) + "\n" +
            "  x_max  = " + String(x_max) + "\n" +
            "  y_zero = " + String(y_zero) + "\n" +
            "  fa = f(x_min) - y_zero = " + String(fa) + "\n" +
            "  fb = f(x_max) - y_zero = " + String(fb) + "\n" +
            "fa and fb must have opposite sign which is not the case");
   end if;

   // Initialize variables
   c :=a;
   fc :=fa;
   e :=b - a;
   d :=e;

   // Search loop
   while not found loop
      if abs(fc) < abs(fb) then
         a :=b;
         b :=c;
         c :=a;
         fa :=fb;
         fb :=fc;
         fc :=fa;
      end if;

      tol :=2*eps*abs(b) + x_tol;
      m :=(c - b)/2;

      if abs(m) <= tol or fb == 0.0 then
         // root found (interval is small enough)
         found :=true;
         x_zero :=b;
      else
         // Determine if a bisection is needed
         if abs(e) < tol or abs(fa) <= abs(fb) then
            e :=m;
            d :=e;
         else
            s :=fb/fa;
            if a == c then
               // linear interpolation
               p :=2*m*s;
               q :=1 - s;
            else
               // inverse quadratic interpolation
               q :=fa/fc;
               r :=fb/fc;
               p :=s*(2*m*q*(q - r) - (b - a)*(r - 1));
               q :=(q - 1)*(r - 1)*(s - 1);
            end if;

            if p > 0 then
               q :=-q;
            else
               p :=-p;
            end if;

            s :=e;
            e :=d;
            if 2*p < 3*m*q-abs(tol*q) and p < abs(0.5*s*q) then
               // interpolation successful
               d :=p/q;
            else
               // use bi-section
               e :=m;
               d :=e;
            end if;
         end if;

         // Best guess value is defined as "a"
         a :=b;
         fa :=fb;
         b :=b + (if abs(d) > tol then d else if m > 0 then tol else -tol);
         fb :=f_nonlinear(b, f_nonlinear_data) - y_zero;

         if fb > 0 and fc > 0 or 
            fb < 0 and fc < 0 then
            // initialize variables
            c :=a;
            fc :=fa;
            e :=b - a;
            d :=e;
         end if;
      end if;
   end while;
end solve;