Direct Digital Frequency Control Based on the Phase Step Change Characteristic between Signals

We present a new digital phase lock technology to achieve the frequency control and transformation through high precision multi-cycle group synchronization between signals without the frequency transformation circuit. In the case of digital sampling, the passing zero point of the phase of the controlled signal has the phase step characteristic, the phase step of the passing zero point is monotonic continuous with high resolution in the phase lock process, and using the border effect of digital fuzzy area, the gate can synchronize with the two signals, the quantization error is reduced. This technique is quite different from the existing methods of frequency transformation and frequency synthesis, the phase change characteristic between the periodic signals with different nominal is used. The phase change has the periodic phenomenon, and it has the high resolution step value. With the application of the physical law, the noise is reduced because of simplifying frequency transformation circuits, and the phase is locked with high precision. The regular phase change between frequency signals is only used for frequency measurement, and the change has evident randomness, but this randomness is greatly reduced in frequency control, and the certainty of the process result is clear. The experiment shows that the short term frequency stability can reach 10-12/s orders of magnitude.

A step parameters prediction model based on transfer process neural network for exhaust gas temperature estimation after washing aero-engines

The prediction of Exhaust Gas Temperature Margin(EGTM)after washing aeroengines can provide a theoretical basis for airlines not only to evaluate the energy-saving effect and emission reduction,but also to formulate reasonable maintenance plans.However,the EGTM encounters step changes after washing aeroengines,while,in the traditional models,a persistence tendency exists between the prediction results and the previous data,resulting in low accuracy in prediction.In order to solve the problem,this paper develops a step parameters prediction model based on Transfer Process Neural Networks(TPNN).Especially,“step parameters”represent the parameters that can reflect EGTM step changes.They are analyzed in this study,and thus the model concentrates on the prediction of step changes rather than the extension of data trends.Transfer learning is used to handle the problem that few cleaning records result in few step changes for model learning.In comparison with Long Short-Term Memory(LSTM)and Kernel Extreme Learning Machine(KELM)models,the effectiveness of the proposed method is verified on CFM56-5B engine data.

An Upwind Mixed Volume Element-Fractional Step Method on a Changing Mesh for Compressible Contamination Treatment from Nuclear Waste

In this paper the authors discuss the numerical simulation problem of threedimensional compressible contamination treatment from nuclear waste.The mathematical model is defined by an initial-boundary nonlinear convection-diffusion system of four partial differential equations:a parabolic equation for the pressure,two convection-diffusion equations for the concentrations of brine and radionuclide and a heat conduction equation for the temperature.The pressure appears within the concentration equations and heat conduction equation,and the Darcy velocity controls the concentrations and the temperature.The pressure is solved by the conservative mixed volume element method,and the order of the accuracy is improved by the Darcy velocity.The concentration of brine and temperature are computed by the upwind mixed volume element method on a changing mesh,where the diffusion is discretized by a mixed volume element and the convection is treated by an upwind scheme.The composite method can solve the convection-dominated diffusion problems well because it eliminates numerical dispersion and nonphysical oscillation and has high order computational accuracy.The mixed volume element has the local conservation of mass and energy,and it can obtain the brine and temperature and their adjoint vector functions simultaneously.The conservation nature plays an important role in numerical simulation of underground fluid.The concentrations of radionuclide factors are solved by the method of upwind fractional step difference and the computational work is decreased by decomposing a three-dimensional problem into three successive one-dimensional problems and using the method of speedup.By the theory and technique of a priori estimates of differential equations,we derive an optimal order result in L^(2) norm.Numerical examples are given to show the effectiveness and practicability and the composite method is testified as a powerful tool to solve the well-known actual problem.