Groundwater contaminant source identification based on QS-ILUES

When groundwater pollution occurs,to come up with an efficient remediation plan,it is particularly important to collect information of contaminant source(location and source strength)and hydraulic conductivity field of the site accurately and quickly.However,the information can not be obtained by direct observation,and can only be derived from limited measurement data.Data assimilation of observations such as head and concentration is often used to estimate parameters of contaminant source.As for hydraulic conductivity field,especially for complex non-Gaussian field,it can be directly estimated by geostatistics method based on limited hard data,while the accuracy is often not high.Better estimation of hydraulic conductivity can be achieved by solving inverse groundwater problem.Therefore,in this study,the multi-point geostatistics method Quick Sampling(QS)is proposed and introduced for the first time and combined with the iterative local updating ensemble smoother(ILUES)to develop a new data assimilation framework QS-ILUES.It helps to solve the contaminant source parameters and non-Gaussian hydraulic conductivity field simultaneously by assimilating hydraulic head and pollutant concentration data.While the pilot points are utilized to reduce the dimension of hydraulic conductivity field,the influence of pilot points’layout and the ensemble size of ILUES algorithm on the inverse simulation results are further explored.

A fault-tolerant triple-redundant voice coil motor for direct drive valves:Design, optimization, and experiment

A direct drive actuator （DDA） with direct drive valves （DDVs） as the control device is an ideal solution for a flight actuation system. This paper presents a novel triple-redundant voice coil motor （TRVCM） used for redundant DDVs. The TRVCM features electrical/mechanical hybrid triple-redundancy by securing three stators along with three moving coils in the same frame. A permanent magnet （PM） Halbach array is employed in each redundant VCM to simplify the system structure. A back-to-back design between neighborly redundancies is adopted to decouple the magnetic flux linkage. The particle swarm optimization （PSO） method is implemented to optimize design parameters based on the analytical magnetic circuit model. The optimization objective function is defined as the acceleration capacity of the motor to achieve high dynamic performance. The optimal geometric parameters are verified with 3D magnetic field finite element analysis （FEA）. A research prototype has been developed for experimental purpose. The experimental results of magnetic field density and force output show that the proposed TRVCM has great potential of applications in DDA systems.