A Computationally Efficient Methodology for Generating Training Data for a Transient Neural Network of a Tip-Jet Reaction Drive System

Abstract

This paper presents a computationally efficient methodology for generating training data for a transient neural network model of a tip-jet reaction drive system for potential use as an onboard model in a model based control application. This methodology significantly reduces the number of training points required to capture the transient peiformance of the system. The challenge in developing an onboard model for a tip-jet reaction drive system is that the model has to operate over the whole flight envelope, to account for the different dynamics present in the system, and to adjust to system degradation or potential faults. In addition, the onboard model must execute in less time than the update interval of the controller. To address these issues, a computationally efficient training methodology and neural network surrogate model have been developed that captures the transient peiformance of the tip-jet reaction system. As the number of inputs to a neural net^'ork becomes large, the computational time needed to generate the number of training points required to accurately represent the range of operating conditions of the system may become quite large also. A challenge for the tip-jet reaction drive system is to minimize the number of neural network training points, while maintaining the high accuracy. To address this issue, a novel training methodology is presented which first trains a steadystate neural network model and uses deviations from steady-state operating conditions to define the transient portion of the training data. The combined results from both the transient and the steady-state training data can then be used to create a single transient neural network of the system. The results in this paper demonstrate that a transient neural network using this new computationally efficient training methodology has the potential to be a feasible option for use as an onboard real-time model for model based control of a tip-jet reaction drive system.

Effects of tip-jet on the performance of a ducted fan

“…To evaluate the efficiency of the fan, the figure of merit (FM: C T 3/2 /2 1/2 C Q ) versus various rotational speed is illustrated in Figure 9. It was found that the efficiencies of the fans with tip-jet were lower than that of the fan without tip-jet, indicating that an induced torque was generated by the tip-jet.…”

“…It represents the second invariant of the velocity gradient tensor. It was defined as Q ¼ 0:5 2 À S 2 À Á , where S and are, respectively, the symmetric and antisymmetric parts of the velocity gradient tensor. A positive Q value means that it could include regions with negative discriminants and exclude region with positive discriminants.…”

“…Figure 18(a) shows the ratio of the fan's thrust coefficient to the jet momentum coefficient which was expressed in equation 3under different rotational speeds. Term qm Á v j is jet momentum and term 0:5v 2 tip is the air dynamic head at the blade tip, where A is the rotor disk area. This ratio of all cases increased from 650 to 950 r/min and then dropped with the increase of the rotational speed.…”

“…The tip-jet rotor is driven by the reaction torque produced by the high-energy gas ejected out of the nozzle at the blade tip. [1][2][3] This technology can transfer the energy of a core generator to the rotor without heavy and complex mechanism components. Thus, it could be served as a propulsion unit in the vertical take-off and landing field.…”

Transient Surrogate Modeling for Thermal Management Systems

“…The first method treats time as an additional variable [4][5] [6][7] [8]. The second method treats time as an independent variable[9] [10]. There are strengths and weaknesses to both of these methods.…”