Solving single-frequency phase ambiguity using parameter weights fitting and constrained equation ambiguity resolution methods

Based on the structural characteristics of the double-differenced normal equation, a new method was proposed to resolve the ambiguity float solution through a selection of parameter weights to construct an appropriate regularized matrix, and a singular decomposition method was used to generate regularization parameters. Numerical test results suggest that the regularized ambiguity float solution is more stable and reliable than the least-squares float solution. The mean square error matrix of the new method possesses a lower correlation than the variancecovariance matrix of the least-squares estimation. The size of the ambiguity search space is reduced and the search efficiency is improved. The success rate of the integer ambiguity searching process is improved significantly when the ambiguity resolution by using constraint equation method is used to determine the correct ambiguity integervector. The ambiguity resolution by using constraint equation method requires an initial input of the ambiguity float solution candidates which are obtained from the LAMBDA method in the new method. In addition, the observation time required to fix reliable integer ambiguities can he significantly reduced.

Parameter identification for a damage phase field model using a physics-informed neural network

This work applies concepts of artificial neural networks to identify the parameters of a mathematical model based on phase fields for damage and fracture.Damage mechanics is the part of the continuum mechanics that models the effects of micro-defect formation using state variables at the macroscopic level.The equations that define the model are derived from fundamental laws of physics and provide important relationships among state variables.Simulations using the model considered in this work produce good qualitative and quantitative results,but many parameters must be adjusted to reproduce certain material behavior.The identification of model parameters is considered by solving an inverse problem that uses pseudo-experimental data to find the best values that fit the data.We apply physics informed neural network and combine some classical estimation methods to identify the material parameters that appear in the damage equation of the model.Our strategy consists of a neural network that acts as an approximating function of the damage evolution with output regularized using the residue of the differential equation.Three stages of optimization seek the best possible values for the neural network and the material parameters.The training alternates between the fitting of only the pseudo-experimental data or the total loss that includes the regularizing terms.We test the robustness of the method to noisy data and its generalization capabilities using a simple physical case for the damage model.This procedure deals better with noisy data in comparison with a more standard PDE-constrained optimization method,and it also provides good approximations of the material parameters and the evolution of damage.

A Statistical Model for Phase Difference Spectrum of Ground-Motion and Its Application in Generating Non-Stationary Seismic Waves

The intensity non-stationarity is one of the most important features of earthquake records.Modeling of this feature is significant to the generation of artificial earthquake waves.Based on the theory of phase difference spectrum,an intensity non-stationary envelope function with log-normal form is proposed.Through a tremendous amount of earthquake records downloaded on Kik-net,a parameter fitting procedure using the genetic algorithm is conducted to obtain the value of model parameters under different magnitudes,epicenter distances and site conditions.A numerical example is presented to describe the procedure of generating fully non-stationary ground motions via spectral representation,and the mean EPSD(evolutionary power spectral density)of simulated waves is proved to agree well with the target EPSD.The results show that the proposed model is capable of describing the intensity non-stationary features of ground motions,and it can be used in structural anti-seismic analysis and ground motion simulation.

Dynamic determination reserves of the underground gas storage

One of the key problems in the use of underground gas storage is frequent leakage. It can lead to the actual gas storage amount being less than that accounted for. Combining numerical simulation and parameter auto fit, this paper ascertains the dynamic variation of the pressure in the storage reservoir, adjusts the actual injecting and producing gas to fit the accounted pressure with the tested pressure, obtains the gas leakage of the storage, and then determines the difference between accounted amount and leakage amount. The result is the actual reserves of the storage. The simulation result shows that the method presented can provide a theoretic foundation for estimating the leakage amount, thereby ensuring the actual reserves, searching the leakage route, and reducing leakage by adjusting the storage method.

Discussion on Atmospheric Pollutant Source Seeking Model with Surface Soil Sample

Through the analysis on the migratory diffusion process of atmospheric pollutants,we proposed to seek atmospheric pollutant source with surface soil sample of data.Based on Gaussian plume model and deposition model,atmospheric pollutants distribution model was deduced,with which a schema matching source seeking model was established.The model was used to seek the pollutant source by using the arsenic data in the surface soil sample of a city.

Comparative research on the influence of varied Al component on the active layer of AlGaN photocathode

To theoretically research the influence of a varied Al component on the active layer of AlGaN photocathodes,the first principle based on density functional theory is used to calculate the formation energy and band structure of AlxGa（1-x）N with x at 0,0.125,0.25,0.325,and 0.5.The calculation results show that the formation energy declines along with the Al component rise,while the band gap is increasing with Al component increasing.AlxGa（1-x）N with x at 0,0.125,0.25,0.325,and 0.5 are direct band gap semiconductors,and their absorption coefficient curves have the same variation tendency.For further study,we designed two kinds of reflection-mode AlGaN photocathode samples.Sample 1 has an AlxGa（1-x）N active layer with varied Al component ranging from0.5 to 0 and decreasing from the bulk to the surface,while sample 2 has an AlxGa（1-x）N active layer with the fixed Al component of 0.25.Using the multi-information measurement system,we measured the spectral response of the activated samples at room temperature.Their photocathode parameters were obtained by fitting quantum efficiency curves.Results show that sample 1 has a better spectral response than sample 2 at the range of short-wavelength.This work provides a reference for the structure design of the AlGaN photocathode.

MIA analysis of FPGA BPMs and beam optics at APS

Model independent analysis, which was developed for high precision and fast beam dynamics analysis, is a promising diagnostic tool for modern accelerators. We implemented a series of methods to analyze the turn-by-turn BPM data. Green＇s functions corresponding to the local transfer matrix elements R12 or R34 are extracted from BPM data and fitted with the model lattice using least-square fitting. Here, we report experimental results obtained from analyzing the transverse motion of a beam in the storage ring at the Advanced Photon Source. BPM gains and uncoupled optics parameters are successfully determined. Quadrupole strengths are adjusted for fitting but can not be uniquely determined in general due to an insufficient number of BPMs.