ACHPCorrelations.py 文件源码

def LMPressureGradientAvg(x_min,x_max,Ref,G,D,Tbubble,Tdew,C=None,satTransport=None):
    """
    Returns the average pressure gradient between qualities of x_min and x_max.

    To obtain the pressure gradient for a given value of x, pass it in as x_min and x_max

    Required parameters:
    * x_min : The minimum quality for the range [-]
    * x_max : The maximum quality for the range [-]
    * Ref : String with the refrigerant name
    * G : Mass flux [kg/m^2/s]
    * D : Diameter of tube [m]
    * Tbubble : Bubblepoint temperature of refrigerant [K]
    * Tdew : Dewpoint temperature of refrigerant [K]

    Optional parameters:
    * C : The coefficient in the pressure drop
    * satTransport : A dictionary with the keys 'mu_f','mu_g,'v_f','v_g' for the saturation properties.  So they can be calculated once and passed in for a slight improvement in efficiency
    """
    def LMFunc(x):
        dpdz,alpha=LockhartMartinelli(Ref,G,D,x,Tbubble,Tdew,C,satTransport)
        return dpdz

    ## Use Simpson's Rule to calculate the average pressure gradient
    ## Can't use adapative quadrature since function is not sufficiently smooth
    ## Not clear why not sufficiently smooth at x>0.9
    if x_min==x_max:
        return LMFunc(x_min)
    else:
        #Calculate the tranport properties once
        satTransport={}
        satTransport['v_f']=1/PropsSI('D','T',Tbubble,'Q',0.0,Ref)
        satTransport['v_g']=1/PropsSI('D','T',Tdew,'Q',1.0,Ref)
        satTransport['mu_f']=PropsSI('V','T',Tbubble,'Q',0.0,Ref)
        satTransport['mu_g']=PropsSI('V','T',Tdew,'Q',1.0,Ref)

        xx=np.linspace(x_min,x_max,30)
        DP=np.zeros_like(xx)
        for i in range(len(xx)):
            DP[i]=LMFunc(xx[i])
        return -simps(DP,xx)/(x_max-x_min)