Parallel evaluation of properties for fits/training #289
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With the extremely performant implementations in Vapor pressuresn = 10000000
fit_params = ["m", "sigma", "epsilon_k"]
parameters = np.array([[1.5,3.4,230.,2.3]]*n)
temperature = np.expand_dims(np.linspace(250., 400.0, n),1)
%time ParameterFit.vapor_pressure(Model.PcSaftNonAssoc, fit_params, parameters, temperature)Liquid densitiesn = 10000000
fit_params = ["m", "sigma", "epsilon_k"]
parameters = np.array([[1.5,3.4,230.,2.3]]*n)
temperature = np.linspace(250., 400.0, n)
pressure = np.array([1e5]*n)
input = np.stack((temperature, pressure),axis=1)
%time ParameterFit.liquid_density(Model.PcSaftNonAssoc, fit_params, parameters, input)Bubble pointsn = 1000000
fit_params = ["k_ij"]
parameters = np.array([[1.5,3.4,230.,2.3,2.3,3.5,245.,1.4,0.01]]*n)
temperature = np.linspace(200., 406.0, n)
molefracs = np.array([.5]*n)
pressure = np.array([1e5]*n)
input = np.stack((temperature, molefracs, pressure),axis=1)
%time ParameterFit.bubble_point_pressure(Model.PcSaftNonAssoc, fit_params, parameters, input)A million bubble point calculations in under 15 seconds 🥳 Bubble points with cross-associationn = 100000
fit_params = ["k_ij"]
parameters = np.array([[1.5,3.4,230.,2.3,0.01,1200.,1.0,2.0,2.3,3.5,245.,1.4,0.01,0.005,500.,1.0,1.0]]*n)
temperature = np.linspace(200., 388.0, n)
molefracs = np.array([.5]*n)
pressure = np.array([1e5]*n)
input = np.stack((temperature, molefracs, pressure),axis=1)
%time ParameterFit.bubble_point_pressure(Model.PcSaftFull, fit_params, parameters, input)As expected significantly slower but still one million in under one minute |
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g-bauer
approved these changes
Oct 13, 2025
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example
return values are the vapor pressure in Pa, the gradients of the vapor pressure with respect to
fit_paramsand the success status of every input.Went to 10 million vapor pressures to catch a glimpse of the CPU usage in
top. Efficiency as defined as total CPU time divided by number of cores and wall time is 88.4% which also seems high.liquid density