Xiwen Li

High-viscosity non-Newtonian fluid extrusion is associated with substantial flow resistance, while the mechanism underlying ultrasonic flow enhancement remains insufficiently understood. In this study, a non-contact ultrasonic-assisted extrusion system was developed, and finite element analysis, fluid–structure interaction (FSI) CFD simulations, and extrusion experiments were combined to elucidate how the excitation position governs flow enhancement. The results demonstrate that the excitation position strongly regulates the axial spatial vibration response of the nozzle wall, thereby producing distinct displacement-gradient variations within the constriction section. Among the investigated conditions, Mid excitation generates the most favourable dynamic boundary response, promoting pressure-gradient development and fluid momentum transfer in the constriction region and consequently achieving the highest extrusion flow-rate gain. Experimental measurements exhibit enhancement trends consistent with the numerical predictions. These findings demonstrate that ultrasonic flow enhancement is governed not solely by vibration amplitude, but primarily by the spatial vibration response induced by the excitation position and its correspondence with the constriction flow region. A dynamic pathway of excitation position → wall vibration response → flow-field regulation → extrusion enhancement is thereby established, providing a basis for the optimisation of excitation position in non-contact ultrasonic-assisted extrusion of high-viscosity non-Newtonian fluids.

It is a challenge for manipulator to quickly achieve detumbling of a space instable satellite (SIS) with low joint torque. In this paper, an adaptive variable admittance control method is proposed to address the above issue. Firstly, the on-ground experimental process of capturing and detumbling the SIS is introduced. Then, steady state error of the built Cartesian pose admittance controller is analyzed. An adaptive variable admittance controller is designed and the necessary and sufficient conditions of its stability and convergence are proved. After that, multi-objective optimization function for detumbling is established and non-dominated sorting genetic algorithm II (NSGA-II) based on general regression neural network (GRNN) is developed to optimize the parameters of proposed controller. Finally, numerical simulations and the ground detumbling experiment are conducted to validate the proposed method. Simulation results show that the proposed control method can effectively reduce detumbling torque and shorten detumbling time and the controller optimized by GRNN and NSGA-II significantly improves detumbling capacity. The comparison with other adaptive compliance control methods shows that the proposed method has better performance, with the 9.42% reduction in detumbling torque and the 13.25% reduction in detumbling time. Ground experiment data shows that the manipulator operates stably adopting the adaptive variable admittance control method and can effectively reduce the angular velocity of the spin SIS.

The challenge of mixing high-viscosity materials is a common issue encountered in the manufacturing process of food materials. The advantages of the internal meshing screw mixer have led to its adoption in various manufacturing processes, but it has yet to be implemented in the food industry. The paper presents a design method for an internal meshing screw mixer based on kinematic principles. The mixer features a helical chamber created by alternating volumes formed by the stator and rotor, establishing an extension-dominated environment for mixing high-viscosity fluids. A kinematic model based on the internal cycloid principle was established, providing trajectories and equations for key points on the rotor, simulating both its rotation and revolution processes, and revealing the velocity variations at different points on the rotor. Based on the kinematic analysis results, a stator and rotor design method was developed according to relevant functional divisions. To achieve the desired motion effects, transmission and support devices were designed, and the relationship between the transmission device and the internal cycloid surface of the fixed rotor was established. The mixer’s application and mixing effectiveness in the food industry were validated using corn syrup and flour. Experimental results showed that the extensional mixer described in the paper effectively mixed high-viscosity fluids while also efficiently blending fine powders. Slurry viscosity was tested with a rheometer at different speeds with an eccentric rotor mixer. Results showed that viscosity decreased with increasing shear rate, with a more pronounced decrease at higher shear rates. The apparent viscosity trend remained consistent at different speeds, although variations were observed at lower shear rates, especially concerning speed. The non-Newtonian fluid index exhibited minimal variations at different speeds, while the consistency coefficient showed significant fluctuations. The mixing uniformity index of the slurry was used to evaluate the mixing uniformity and dispersion uniformity of this extensional mixer. At different rotational speeds, the density of the slurry changes little. The uniformity index of mixing decreases gradually with the increase of rotational speed, reaching its maximum at 15 r/min. The overall trend of the uniformity index decreases with increasing rotational speed, indicating a decrease in density uniformity. A peak appears at 45 r/min, possibly due to the maximum values of elongation rate and shear rate at this speed. As the rotational speed increases, the residence time of the material in the mixer decreases, which may be the main reason for the decrease in mixing uniformity. These findings provide valuable insights into the design and utilization of extension-dominated screw mixers within the food industry, laying a solid foundation for future research and practical applications in this field.

The space instable target simulator (SITS) is a vital actuator for ground verification of on-orbital capture technology, the motion performance of which directly affects simulation credibility. Different delays reduce the stability of SITS and ultimately lead to its divergence. In order to achieve high-fidelity simulation, the impacts of force measurement delay, the discrete control cycle, and simulator response delay on stability are analyzed first. Then, the dynamic equation and transfer function identification model of the hybrid simulator is constructed, and the necessary and sufficient conditions of its stability and convergence are obtained using the Routh criterion. After that, a novel switching compensator with variable gain is proposed to reduce the superimposed effects of the three delays, the compensation principle diagram of which was built, and its mathematical model including the energy observer and nonlinear tracking differentiator is also established. Finally, three sets of numerical simulations were conducted to validate the correctness of the stability analysis and effectiveness of the proposed compensation method. The simulation results show that all three types of delays can cause SITS to lose stability under critical stable motion states, and the delay in force measurement has the greatest impact, followed by the influence of the control cycle. Compared with the force applied to the simulated target, the velocity, and the recovery coefficient of the space instable target using fixed gain and linear gain compensation, the proposed compensator has significantly better performance.

The mixing and processing of high-viscosity materials play a pivotal role in composite material processing. In this context, the internal meshing screw mixer, rooted in volume extensional rheology, offers distinct advantages, including heightened mixing efficiency, exceptional material adaptability, and favorable thermomechanical properties. This research endeavors to advance our understanding of these qualities by presenting an in-depth exploration of internal meshing screw mixing. To facilitate this, an internal meshing screw mixing experimental apparatus was meticulously constructed, accompanied by extensive numerical simulations and experimental investigations into its heat transfer characteristics. Two distinct heat transfer modes are established: Mode 1 entails the transfer of the high temperature from the outer wall of the stator to the interior, while Mode 2 involves the transmission of the high temperature from the inner wall of the rotor to the exterior. The ensuing research yields several notable findings: 1. It is evident that higher rotational speeds lead to enhanced heat transfer efficiency across the board. However, among the three rotational speeds examined, 60 rpm emerges as the optimal parameter for achieving the highest heat transfer efficiency. Furthermore, within this parameter, the heat transfer efficiency is superior in Mode 1 compared to Mode 2. 2. As eccentricity increases, a corresponding decline in comprehensive heat transfer efficiency is observed. Moreover, the impact of eccentricity on heat transfer efficiency becomes increasingly pronounced over time. 3. A lower gap dimension contributes to higher heat transfer within the system. Nevertheless, this heightened heat transfer comes at the expense of reduced stability in the heat transfer process. 4. It is demonstrated that heat transfer in Mode 1 primarily follows a convection heat transfer mechanism, while Mode 2 predominantly exhibits diffusion-based heat transfer. The heat transfer efficiency of Mode 1 significantly surpasses that of Mode 2. This research substantiates its findings with the potential to enhance the heat transfer efficiency of internal meshing screw mixers, thereby making a valuable contribution to the field of polymer engineering and science.

Speckle removal is a crucial preliminary step for synthetic aperture radar (SAR) image processing. In recent years, the application of deep neural networks towards solving SAR image despeckling problems has yielded commendable outcomes. However, prevailing deep learning methods for SAR image despeckling rely on convolutional neural network (CNN) architectures, which inherently capture only local information within their receptive fields. Consequently, the despeckling performance can be further improved through advanced network structure design. More recently, the Transformers based methods show impressive performance in natural image denoising relying on the long-range dependency modeling capability of the self-attention mechanism. In this paper, we introduce a practical despeckling network that incorporates the global modeling capability of the Swin Transformers and the local modeling ability of the residual CNNs. Specifically, the proposed despeckling network is based on the widely-used U-Net architecture, wherein a Swin-Conv (SC) block is adopted to replace the convolutional layer in the baseline UNet. The SC block mainly comprises a Residual Convolutional (RConv) block and a Swin Transformer (SwinT) block, which are used to extract local features and long-range dependencies from images respectively. Furthermore, to deal with the spatially correlated real SAR speckle, a Pixel-shuffle Down-sampling (PD) post-processing strategy is adopted, which can significantly improve the practicality of the proposed method without additional real dataset for fine-tuning. Experimental results demonstrate that the proposed method achieves state-of-the-art performance on both synthetic and real SAR images, and outperforms CNN-based and Transformer-based methods (L=1) by an average PSNR of 0.94dB and 1.58dB, respectively.

Speckle suppression is a crucial preliminary step for synthetic aperture radar (SAR) image processing. Supervised despeckling approaches trained on synthetic datasets usually perform poorly in practice due to the unavailability of clean SAR images. Besides, the spatial correlation of speckle is rarely considered in many methods based on the fully developed speckle assumption. In this paper, we propose an unsupervised despeckling method to address these issues by combining online speckle generation and unpaired training. The method consists of two branches: the stop-gradient branch and the unpaired branch. First, the stop-gradient branch learns to generate the spatially correlated speckle. Then, the unpaired branch combines the generated speckle with the unpaired optical image to form pairs of training data for network parameter updates. More specifically, in order to generate the more realistic speckle in the stop-gradient branch, we design a speckle correction module with three SAR speckle priors: the threshold prior, the unit mean prior, and the correlation prior coupled with the weighted patch-shuffle. In the unpaired training, a hybrid loss function is employed, which takes spatial smoothness and detail protection into consideration. Afterwards, we combine the stop-gradient branch with the unpaired branch by the Siamese network to achieve alternate optimization of speckle generation and speckle removal. Finally, the optimization process in our method is analyzed theoretically. Qualitative and quantitative experiments demonstrate that the proposed method is comparable to the supervised despeckling approaches on synthetic datasets and outperforms several state-of-the-art unsupervised methods on real SAR datasets.

The frequency modulation continuous-wave (FMCW) radar has been widely concerned for its compact system structure, low hardware cost, and excellent imaging resolution. However, an FMCW radar suffer from the nonlinearity commonly, which leads to the deterioration of the imaging quality. A challenging problem is that the nonlinear reference beat signal contains multiple components with closely spaced and approximately equal amplitude, which has not been researched in the existing literature to the best of our knowledge. To deal with this trouble, a software-based correction method is proposed by utilizing the complementary ensemble empirical mode decomposition (CEEMD). First, several intrinsic model functions (IMFs) are generated by decomposing the beat signal obtained from a reference target by the CEEMD method. Then, an identification method is proposed to find the reference IMF from the IMFs by average period method, and a reference delay estimation method based on windowed fast Fourier transform is proposed. Finally, the higher-order ambiguity function (HAF) is employed to estimated the polynomial coefficients of the reference IMF, which are utilized to compensate the nonlinear beat signal by time resampling method. The effectiveness and superiority are verified by numerical simulation and real data. Compared with the state-of-the-art nonlinear correction algorithms, we obtain more outstanding radar imaging quality by adopting the proposed method.