Coupling Effect of Nonlinear Stiffness of Tape Spring Hinges and Flexible Deformation of Panels during Orbit Maneuvers

Many solar panels for spacecrafts are deployed by Tape Spring Hinges (TSHs) which have changeable stiffness. The stiffness of TSH is small when panels are folded, and it becomes large quickly in its deployed status. Since the solar panel is a thin sheet, flexible deformation is easily generated by orbit maneuvers. The coupling effect between the nonlinear TSHs and the flexible panels generates obvious vibration which affects the operational stability of the satellite. To investigate this coupling effect, non-deformable, linear deformable and nonlinear deformable panels were modelled by rigid body, modal order reduction method (MORM) and finite element method (FEM), respectively. The driving torque of TSH was described as a function of the rotation angle and angular velocity. The nonlinear properties of the TSH were reflected by one angle-stiffness spline multiplied by one stiffness coefficient. Dynamic responses of a satellite in deployment and orbit steering were analyzed by numerical simulations. Analysis results indicate the local deformation of panels keeps the stiffness of the TSH within a large range which accelerates the orbit maneuvers. However, much vibration is generated by the coupling effect if the luck-up status is broken up. The coupling effect affects the sequence of deployment, overshoot phenomenon and acceleration magnitude of the panels. Although the MORM is more efficient than FEM in computation, we propose FEM is better suited in the design of TSH and in studying the precise control of spacecraft with flexible solar panels and TSHs.

A General Order Reduction Method of Wideband Digital Predistortion Model Using Attention Mechanism

In wireless networks, for the common in-phase and quadrature-phase ( I / Q ) imbalance in the transmitters, the I / Q branch models of digital predistortion (DPD) need to be identified separately, to improve the linearization effects. The existing order reduction methods of the predistorter are based on the contributions of the complex basis function terms, so as not to deal with the different contributions of I / Q components of the complex basis function terms caused by the separate identification of the I / Q branch models. The separate pruning of the I / Q branch models will increase the complexity. Aiming at this issue, this paper proposes a general order reduction method based on the attention mechanism for the predistortion of the power amplifiers (PAs). This method is suitable for pruning both the traditional models and neural network-based models. In this method, the attention mechanism is used to evaluate the contributions of the real basis function terms to the predistorted output’s I / Q components through offline training, and the influence of the cross terms of the I / Q branch models is considered. The experimental results based on the comparison with other typical methods under 100 MHz Doherty PA and different I / Q imbalance levels show that this method has superior pruning performance and good linearization ability.

The Application of Neural Operator in subsurface process simulation

<p>Numerical simulations of subsurface processes are essential to the success of many geoengineering projects. These simulations often contain significant uncertainties due to imperfect knowledge of material properties and their spatial distribution, boundary conditions, and initial conditions. However, efficient implementations for the quantification of uncertainties for such simulations are big challenges in Computational Geoscience, mainly due to the curse of dimensionality. Process simulations often involve solving high-dimensional Partial Differential Equations (PDE) by using discretization methods such as Finite Difference (FD) or Finite Elements (FE) methods. Although such methods often give good approximations, they are computationally intensive and expensive and therefore infeasible in the applications such as MCMC where thousands of evaluations of the forward simulation are required. Previous work by Degen et.al. (2020) has addressed this problem by using a model order reduction method, the so-called reduced basis (RB) method. However, the method has limitations when considering complex (i.e., hyperbolic and non-linear) PDEs. In this work, we aim to employ the recently developed Fourier Neural Operator (FNO) (Li, 2020) as a tool to implement efficient approximation of PDEs in the application of Geothermal reservoir simulation. FNO involves a Fast Fourier transform to directly learn the mapping from the input function to the output function. FNO has the advantage of being independent of the resolution and complexity of the governing PDE. Our preliminary results show that FNO can provide good approximation results in solving four-dimensional PDEs and thus can be used as a tool for further probability studies of the parameters of interest.</p>