Replacing enthalpy with an ideal-gas temperature/density estimate as neural-network input improves prediction of real-fluid T, ρ, ψ in supercritical combustion surrogates by factors up to 14.5.
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Thermodynamics-Informed Input Reparameterization for Neural Prediction of Real-Fluid Thermodynamic Properties in Supercritical Combustion
Replacing enthalpy with an ideal-gas temperature/density estimate as neural-network input improves prediction of real-fluid T, ρ, ψ in supercritical combustion surrogates by factors up to 14.5.