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Helium turbines are widely used in the Closed Brayton Cycle for powergeneration and aerospace applications. The primary concerns of designing highlyloaded helium turbines include choosing between conventional andcontra-rotating designs and the guidelines for selecting design parameters. Aloss model serving as an evaluation means is the key to addressing this issue.Due to the property disparities between helium and air, turbines utilizingeither as working fluid experience distinct loss mechanisms. Consequently,directly applying gas turbine experience to the design of helium turbines leadsto inherent inaccuracies. A helium turbine loss model is developed by combiningknowledge transfer and the Neural Network method to accurately predictperformance at design and off-design points. By utilizing the loss model,design parameter selection guidelines for helium turbines are obtained. Acomparative analysis is conducted of conventional and contra-rotating heliumturbine designs. Results show that the prediction errors of the loss model arebelow 0.5% at over 90% of test samples, surpassing the accuracy achieved by thegas turbine loss model. Design parameter selection guidelines for heliumturbines differ significantly from those based on gas turbine experience. Thecontra-rotating helium turbine design exhibits advantages in size, weight, andaerodynamic performance.

Development of helium turbine loss model based on knowledge transfer with Neural Network and its application on aerodynamic design