Speaker
Description
Cryogenic sensors are widely adopted in cryogenic and semi-cryogenic rocket engines to measure the temperature values. These sensors are in-house developed resistance sensors and are calibrated in between 4.2 K to 300 K to ensure the desired redundancy, accuracy, and repeatability before their authentic use in space missions. With our in-house wet cryogenic calibration facility, these sensors are calibrated and post-processed for plotting graphs of resistance versus temperature in appropriate ranges and developing fitting equations. In this paper, an attempt has been made to use artificial intelligence techniques to predict the resistances as a function of temperatures for the first time. The raw data of a typical cryogenic sensor has been collected from the experimental investigation and categorized into three categories: about 70% of data is used for training, 15% data is used for testing, and the remaining 15% is used for validation purposes. Various types of membership functions and training algorithms have been selected during training the neural network and the effect of each of them on the predicted value has been compared. It is noticed that the accuracy of the prediction of resistance as a function of temperature is better than that of the polynomial equation.
| Submitters Country | India |
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