Ultrahigh-brightness 50 MeV electron beam generation from laser wakefield acceleration in a weakly nonlinear regime

We propose an efficient scheme to produce ultrahigh-brightness tens of MeV electron beams by designing a density-tailored plasma to induce a wakefield in the weakly nonlinear regime with a moderate laser energy of 120 mJ.In this scheme,the second bucket of the wakefield can have a much lower phase velocity at the steep plasma density down-ramp than the first bucket and can be exploited to implement longitudinal electron injection at a lower laser intensity,leading to the generation of bright electron beams with ultralow emittance together with low energy spread.Three-dimensional particle-in-cell simulations are carried out and demonstrate that high-quality electron beams with a peak energy of 50 MeV,ultralow emittance of28 nm rad,energy spread of 1%,charge of 4.4 pC,and short duration less than 5 fs can be obtained within a 1-mm-long tailored plasma density,resulting in an ultrahigh six-dimensional brightness B6D,n of2×1017 A/m2/0.1%.By changing the density parameters,tunable bright electron beams with peak energies ranging from 5 to 70 MeV,a small emittance of B0.1 mm mrad,and a low energy spread at a few-percent level can be obtained.These bright MeV-class electron beams have a variety of potential applications,for example,as ultrafast electron probes for diffraction and imaging,in laboratory astrophysics,in coherent radiation source generation,and as injectors for GeV particle accelerators.

Large Power Transformer Fault Diagnosis and Prognostic Based on DBNC and D-S Evidence Theory

Power transformer is a core equipment of power system, which undertakes the important functions of power transmission and transformation, and its safe and stable operation has great significance to the normal operation of the whole power system. Due to the complex structure of the transformer, the use of single information for condition-based maintenance (CBM) has certain limitations, with the help of advanced sensor monitoring and information fusion technology, multi-source information is applied to the prognostic and health management (PHM) of power transformer, which is an important way to realize the CBM of power transformer. This paper presents a method which combine deep belief network classifier (DBNC) and D-S evidence theory, and it is applied to the PHM of the large power transformer. The experimental results show that the proposed method has a high correct rate of fault diagnosis for the power transformer with a large number of multi-source data.

Reliability-based design optimization of offshore wind turbine support structures using RBF surrogate model

An efficient reliability-based design optimization method for the support structures of monopile offshore wind turbines is proposed herein.First,parametric finite element analysis(FEA)models of the support structure are established by considering stochastic variables.Subsequently,a surrogate model is constructed using a radial basis function(RBF)neural network to replace the time-consuming FEA.The uncertainties of loads,material properties,key sizes of structural components,and soil properties are considered.The uncertainty of soil properties is characterized by the variabilities of the unit weight,friction angle,and elastic modulus of soil.Structure reliability is determined via Monte Carlo simulation,and five limit states are considered,i.e.,structural stresses,tower top displacements,mudline rotation,buckling,and natural frequency.Based on the RBF surrogate model and particle swarm optimization algorithm,an optimal design is established to minimize the volume.Results show that the proposed method can yield an optimal design that satisfies the target reliability and that the constructed RBF surrogate model significantly improves the optimization efficiency.Furthermore,the uncertainty of soil parameters significantly affects the optimization results,and increasing the monopile diameter is a cost-effective approach to cope with the uncertainty of soil parameters.

Dispersion effects on performance of free-electron laser based on laser wakefield accelerator

In this study, we investigate a new simple scheme using a planar undulator(PU) together with a properly dispersed electron beam(e beam) with a large energy spread(～1%) to enhance the free-electron laser(FEL) gain. For a dispersed e beam in a PU, the resonant condition is satisfied for the center electrons, while the frequency detuning increases for the off-center electrons, inhibiting the growth of the radiation. The PU can act as a filter for selecting the electrons near the beam center to achieve the radiation. Although only the center electrons contribute, the radiation can be enhanced significantly owing to the high-peak current of the beam. Theoretical analysis and simulation results indicate that this method can be used for the improvement of the radiation performance, which has great significance for short-wavelength FEL applications.

Long-distance characterization of high-quality laser-wakefield-accelerated electron beams

Beam quality degradation during the transition from a laser wakefield accelerator to the vacuum is one of the reasons that cause the beam transport distortion, which hinders the way to compact free-electron-lasers. Here,we performed transition simulation to initialize the beam parameters for beam optics transport. This initialization was crucial in matching the experimental results and the designed evolution of the beamline. We experimentally characterized properties of high-quality laser-wakefield-accelerated electron beams, such as transverse beam profile, divergence, and directionality after long-distance transport. By installing magnetic quadrupole lenses with tailored strength gradients, we successfully collimated the electron beams with tunable energies from 200 to 600 MeV.