Simulation Experimental Results Research Articles

TEMPERATURE FIELD OPTIMIZATION AND TRIBOLOGICAL PERFORMANCE FOR BATCH PREPARATION OF DIAMOND-COATED MECHANICAL SEAL RINGS BY HFCVD METHOD

Diamond coatings are deposited on the end surfaces of mechanical seal rings by the hot filament chemical vapor deposition (HFCVD) method. To improve the homogeneity of batch preparation of HFCVD diamond-coated seal rings, the temperature distributions on the end surfaces of seal rings are simulated and optimized using the finite volume method (FVM). The influence mechanism of various setup parameters on coating deposition is demonstrated systematically with the orthogonal experimental method, including the filament diameter d, the filament temperature [Formula: see text], the separation between two adjacent filaments D and the filament height H. The optimal HFCVD setting parameters for batch preparation of diamond-coated seal rings are proposed based on the simulation results of orthogonal experiments. Afterwards, mass deposition experiments of seal rings are conducted using the optimal setting parameters, and the diamond coatings deposited on the end surfaces of seal rings are characterized. Furthermore, mechanical seal operation bench tests are carried out to evaluate the abrasion resistance of diamond-coated seal rings. The results indicate that the optimized setting parameters can produce a homogeneous temperature field, and diamond coatings with uniform thickness and excellent wear resistance can be obtained in the batch preparation of diamond-coated seal rings.

Robotic Arm Trajectory Planning Based on Improved Slime Mould Algorithm

The application of robotic arms in the industrial field is continuously becoming greater and greater. The impact force generated by a robotic arm in a gripping operation leads to vibration and wear. To address this problem, this paper proposes a trajectory planning method based on the improved Slime Mould Algorithm. An interpolation curve under the joint coordinate system is constructed by using seven non-uniform B-spline functions, with time and impact force as the optimization objectives and angular velocity, angular acceleration, and angular acceleration as the constraints. The original algorithm introduces Bernoulli chaotic mapping to increase the diversity of the population, adaptively adjusts the feedback factor, improves the crossover operator to accelerate the global convergence, and combines the original algorithm with an improved artificial bee colony search strategy guided by the global optimal solution, adding a quadratic interpolation method to increase the diversity of the population and to accelerate the global convergence speed. Combined with the improved artificial swarm search strategy guided by the global optimal solution, the quadratic interpolation method is added to enhance the local utilization ability. The simulation and real-machine experimental results show that the improved algorithm shortens the movement time of the robotic arm, reduces the joint impacts, minimizes the vibration and wear, and prolongs the service life of the robotic arm.

Distributed Inference Models and Algorithms for Heterogeneous Edge Systems Using Deep Learning

Computations performed by using convolutional layers in deep learning require significant resources; thus, their scope of applicability is limited. When deep neural network models are employed in an edge-computing system, the limited computational power and storage resources of edge devices can degrade inference performance, require a considerable amount of computation time, and result in increased energy consumption. To address these issues, this study presents a convolutional-layer partitioning model, based on the fused tile partitioning (FTP) algorithm, for enhancing the distributed inference capabilities of edge devices. First, a resource-adaptive workload-partitioning optimization model is designed to promote load balancing across heterogeneous edge systems. Next, the FTP algorithm is improved, leading to a new layer-fused partitioning method that is used to solve the optimization model. The results of simulation experiments show that the proposed convolutional-layer partitioning method effectively improves the inference performance of edge devices. When five edge devices are used, the speed of the proposed method becomes 1.65–3.48 times those of existing algorithms.

Advancement of the Dragon Heart 7-Series for Pediatric Patients With Heart Failure.

Safe and effective pediatric blood pumps continue to lag far behind those developed for adults. To address this growing unmet clinical need, we are developing a hybrid, continuous-flow, magnetically levitated, pediatric total artificial heart (TAH). Our hybrid TAH design, the Dragon Heart (DH), integrates both an axial flow and centrifugal flow blood pump within a single, compact housing. The axial pump is embedded in the central hub region of the centrifugal pump, and both pumps rotate around a common central axis, while maintaining separate fluid domains. In this work, we concentrated our design and development effort on the centrifugal blood pump by performing computational modeling. An iterative process was employed to improve the DH design. The pressure generation, scalar stress levels, and fluid forces exerted on the magnetically levitated impellers were computationally estimated. A shaft driven centrifugal prototype was also manufactured and tested using a hydraulic flow loop circulating a water-glycerol blood analog. Pressure and flow performance of the pump prototype was measured for a given rotational speed for comparison to computational predictions. Our design achieved the target pump pressures of 60-140 mm Hg for flow rates of 1-5 L/min, and strong agreement in pressure rise was demonstrated between the experimental data and simulation results (less than 10% deviation on average). Fluid stress levels were, however, found to exceed thresholds in the outflow region of the pump, and fluid residence times were less than 600 ms. The findings of this work demonstrate that the more compact, next-gen DH's centrifugal pump design is able to achieve pressure-capacity requirements. Next steps will require a focused strategy to reduce hemolytic potential and to integrate magnetic suspension components for full rotor levitation.

Coupling mechanisms of plasmon resonance and Bi3+ emission in YAG: Bi, Ce, Yb epitaxial films at low temperatures

This paper is devoted to the investigation of the plasmonic effect of metal nanoparticles (NPs) formed on the surface of the YAG: Bi, Ce, Yb phosphors in a temperature range between 4 and 300 K. Combination of a thin conversion layer with silver plasmonic nanostructures leads to increase of sensitizer absorption and emission efficiency. Enhancement of Bi3+ luminescence in YAG epitaxial films with Ag NPs was observed upon cooling the samples below 200 K. High enhancement factors were associated with closely matching the maximum of plasmon extinction and Bi3+ emission bands. The maximum value of enhancement factor near 170% at 4 K was obtained. It is shown that temperature decrease causes an increase in the EM field intensity around the NPs, the probability of spontaneous recombination, the penetration depth of the localized surface plasmon resonances (LSPR) into the substrate, and the adjustment of the position of the LSPR. Simultaneous action of all these factors leads to Bi3+ emission intensity enhancement. Comparative analysis of the Finite-Difference Time-Domain (FDTD) simulation data vs. experimental results of the temperature behavior of plasmon absorption spectra, luminescence spectra of Bi3+ ions, and their decay kinetics confirms the correctness of the proposed mechanisms.

Experimental Determination of the Equivalent Moment of Inertia and Stresses of Aluminium Profiles with Thermal Breaks

This paper presents the results of experimental tests and computer simulations on the stiffness of composite aluminium mullions used in unitised façades. The elements analysed were subjected to bending in order to simulate the actual operating conditions of aluminium façades subjected to significant wind pressure or suction loads. The basic mechanical and physical properties of the materials from which the analysed type of aluminium façade is made (Aluminium EN AW-6060 in the T66 temper and polyamide PA66 25GF), the test method, and the results obtained are described. As a result of the tests, equivalent moments of inertia of the composite profile (aluminium profile with the thermal break) were determined, which are strongly dependent on the strength of the connection between the individual elements, the asymmetry of the cross-section, and the properties of the thermal break. Strain measurements carried out using FBG (Fiber Bragg Grating) strain sensors installed in the profiles under tests allowed for determining the actual stress values of the aluminium profiles under consideration. The results obtained were compared to theoretical (numerical) values, indicating discrepancies at higher load values. The methodology presented in this article is to be used to monitor the deformation of the aluminium façade mullions of HRB (High-Rise Buildings).

High-Speed Target HRRP Reconstruction Based on Fast Mean-Field Sparse Bayesian Unrolled Network

The rapid and accurate reconstruction of the high-resolution range profiles (HRRPs) of high-speed targets from incomplete wideband radar echoes is a critical component in space target recognition tasks (STRTs). However, state-of-the-art HRRP reconstruction algorithms based on sparse Bayesian learning (SBL) are computationally expensive and require the manual selection of prior scale parameters. To address these challenges, this paper proposes a model-driven deep network based on fast mean-field SBL (FMFSBL-Net) for the HRRP reconstruction of high-speed targets under missing data conditions. Specifically, we integrate a precise velocity compensation and HRRP reconstruction into the mean-field SBL framework, which introduces a unified SBL objective function and a mean-field variational family to avoid matrix inversion operations. To reduce the performance loss caused by mismatched prior scale parameters, we unfold the limited FMFSBL iterative process into a deep network, learning the optimal global prior scale parameters through training. Additionally, we introduce a sparsity-enhanced loss function to improve the quality and noise robustness of HRRPs. In addition, simulation and measurement experimental results show that the proposed FMFSBL-Net has a superior reconstruction performance and computational efficiency compared to FMFSBL and existing state-of-the-art SBL framework type algorithms.

Outliers rejection for robust camera pose estimation using graduated non‐convexity

AbstractCamera pose estimation plays a crucial role in computer vision, which is widely used in augmented reality, robotics and autonomous driving. However, previous studies have neglected the presence of outliers in measurements, so that even a small percentage of outliers will significantly degrade precision. In order to deal with outliers, this paper proposes using a graduated non‐convexity (GNC) method to suppress outliers in robust camera pose estimation, which serves as the core of GNCPnP. The authors first reformulate the camera pose estimation problem using a non‐convex cost, which is less affected by outliers. Then, to apply a non‐minimum solver to solve the reformulated problem, the authors use the Black‐Rangarajan duality theory to transform it. Finally, to address the dependence of non‐convex optimisation on initial values, the GNC method was customised according to the truncated least squares cost. The results of simulation and real experiments show that GNCPnP can effectively handle the interference of outliers and achieve higher accuracy compared to existing state‐of‐the‐art algorithms. In particular, the camera pose estimation accuracy of GNCPnP in the case of a low percentage of outliers is almost comparable to that of the state‐of‐the‐art algorithm in the case of no outliers.

Research on CFRP Defects Recognition and Localization Based on Metamaterial Sensors

In the paper, the concept of symmetry is utilized to detect internal defects in Carbon fiber reinforced polymer (CFRP), that is, the reconstruction and localization methods for internal defects in CFRP are symmetrical. CFRP is widely used in industrial, biological and other fields. When there are defects inside the composite materials, its dielectric constant, magnetic permeability, etc. change. Therefore, metamaterial sensors are widely used in non-destructive testing of CFRP Defects. This paper proposes a defect identification and location method based on principal component analysis (PCA) and support vector machine (SVM). The trained model is used to classify the dimensionally reduced data, and the reconstructed defect binary image is obtained. Simulation and physical experiment results show that the method used in this article can effectively identify and locate defects in carbon fiber composite materials.

Predictor-based neural network control for unmanned aerial vehicles with input quantization: design and application

In this article, I design a predictor-based neural network (NN) controller for unmanned aerial vehicles (UAVs) with input quantization to address the trajectory tracking problem in the presence of time-varying disturbances caused by aerodynamics and external environment. The NN with a state predictor (SP) is employed in the controller design to improve transient performance without high-frequency oscillations and address the problem of instability caused by the time-varying disturbances. Additionally, the prediction errors from the SP are used to update the learning rate of the NN, resulting in smoother and faster learning responses. Furthermore, a hysteresis quantizer is employed to discretize signals and reduce the transmission burden on digital hardware, which can enhance the suitability of the system for practical implementation. Based on the Lyapunov method, the closed-loop system of the UAV achieves input-to-state stability (ISS). Finally, to validate and assess the performance and effectiveness of our proposed control method, I present and analyze both simulation results and experimental results from real-world applications.

The Effectiveness of Local Exhaust Ventilation (LEV) System in Welding Training Facilities using Validation Computational Fluid Dynamics (CFD) Simulation

The Local Exhaust Ventilation (LEV) is the most common type of engineering control equipment used to control employees' exposure to chemicals that are hazardous to their health. Before a contaminant disperses into the workroom environment, LEV systems operate on the principle of capturing it at or near its source. The welding guideline stated that the suggested minimum hood velocity is 100 ft/min, the recommended velocity along ducts for vapors, gases, and smoke is 1000 ft/min, and 2000 ft/min. The research objective is to identify the effectiveness of the LEV system using validation computational fluid dynamics (CFD) simulation. The data collected by experimental design during the pre-testing phase of the LEV system is quantitative and obtained through a fieldwork survey and document analysis. Findings found that LEV systems are effective to be used and meet all the minimum requirements set by the guideline. In CFD simulation, upon validation, the average absolute error obtained from the case study is 8.4%. There is good agreement between actual experimental and CFD simulation results, and the acceptable validity of CFD simulation is less than 10%. Therefore, simple CFD modeling is a tool to simulate air velocity in the LEV system, saving labor costs and time consumption during the earliest stage of LEV design development before actual construction. This study's outcome can serve as a benchmark or guideline for training facilities equipped with the LEV system to prioritize safety and health.

Inspection Path Planning of Mobile Communication Base Stations Based on Automatic UAV Nest

With the development of 5G mobile networks, the number of mobile communication base stations in China has increased rapidly, and regular inspection and maintenance are required. Traditional manual inspection has some shortcomings and limitations. In recent years, the development of drones and drone nest technology has made drone inspection feasible. Since the automation level of traditional manually controlled drone inspection is low, this paper proposes a base station inspection scheme based on drone nests. By establishing an inspection model and using the simulated annealing algorithm to solve the TSP problem, the drone inspection path is planned to improve the inspection efficiency and the unmanned and automated inspection level. In this paper, the simulation experimental results show that the algorithm can effectively obtain the global optimal solution in a relatively short time, realize the path planning of drone automatic inspection of base stations, and effectively improve the inspection efficiency of drones.

Hybrid Multi-Strategy Improved Butterfly Optimization Algorithm

To address the issues of poor population diversity, low accuracy, and susceptibility to local optima in the Butterfly Optimization Algorithm (BOA), an Improved Butterfly Optimization Algorithm with multiple strategies (IBOA) is proposed. The algorithm employs SPM mapping and reverse learning methods to initialize the population, enhancing its diversity; utilizes Lévy flight and trigonometric search strategies to update individual positions during global and local search phases, respectively, expanding the search scope of the algorithm and preventing it from falling into local optima; and finally, it introduces a simulated annealing mechanism to accept worse solutions with a certain probability, enriching the diversity of solutions during the optimization process. Simulation experimental results comparing the IBOA with Particle Swarm Optimization, BOA, and three other improved BOA algorithms on ten benchmark functions demonstrate that the IBOA has improved convergence speed and search accuracy.

Conceptual Approach to Permanent Magnet Synchronous Motor Turn-to-Turn Short Circuit and Uniform Demagnetization Fault Diagnosis

Permanent magnet synchronous motors (PMSMs) play a crucial role in industrial production, and in response to the problem of PMSM turn-to-turn short-circuit and demagnetization faults affecting production safety, this paper proposes a PMSM turn-to-turn short-circuit and demagnetization fault diagnostic method based on a convolutional neural network and bidirectional long and short-term memory neural network (CNN-BiLSTM). Firstly, analyzing the PMSM turn-to-turn short-circuit and demagnetization faults, one takes the PMSM stator current as the fault signal and optimizes the variational modal decomposition (VMD) by using the Gray Wolf Optimization (GWO) algorithm in order to achieve efficient noise reduction processing of the stator current signal and improve the fault feature content in the stator current signal. Finally, the fault diagnostics are classified by using the CNN-BiLSTM, which collects advanced optimization algorithms and deep learning networks. The effectiveness of the method is verified by simulation experiment results. This scheme has high practical value and broad application prospects in the field of PMSM turn-to-turn short circuit and demagnetization fault diagnosis.

Methylophiopogonanone A Inhibits Ferroptosis in H9c2 Cells: An Experimental and Molecular Simulation Study.

In this study, homoisoflavone methylophiopogonanone A (MOA) was investigated for its inhibitory effect on ferroptosis of H9c2 cells using a set of cellular assays, such as BODIPY-probed and H2DCFDA-probed flow cytometry analyses, cell counting kit-8 analysis (CCK-8), and lactate dehydrogenase (LDH) release analysis. All these cellular assays adopted Fer-1 as the positive control. Subsequently, MOA and Fer-1 were subjected to two antioxidant assays, i.e., 2-phenyl-4,4,5,5-tetramethylimidazoline-1-oxyl 3-oxide radical (PTIO•)-scavenging and 2,2'-azinobis(3-ethylbenzo-thiazoline-6-sulfonic acid radical (ABTS•+)-scavenging. Finally, MOA, along with Fer-1, were systematically analyzed for molecular docking and dynamics simulations using a set of software tools. The experimental results revealed that MOA could inhibit ferroptosis of H9c2 cells but did not effectively scavenge PTIO• and ABTS•+ free radicals. Two molecular simulation methods or algorithms suggested that MOA possessed similar binding affinity and binding free energy (∆Gbind) to Fer-1. Visual analyses indicated various hydrophobic interactions between MOA and one of the seven enzymes, including superoxide dismutase (SOD), dihydroorotate dehydrogenase (DHODH), ferroportin1 (FPN), ferroptosis suppressor protein 1 (FSP1), glutathione peroxidase 4 (GPX4), nicotinamide adenine dinucleotide phosphate (NADPH), and solute carrier family 7 member 11 (SLC7A11). Based on these experimental and molecular simulation results, it is concluded that MOA, a homoisoflavonoid with meta-di-OHs, can inhibit ferroptosis in H9c2 cells. Its inhibitory effect is mainly attributed to the regulation of enzymes rather than direct free radical scavenging. The regulation of enzymes primarily depends on hydrophobic interactions rather than H-bond formation. During the process, flexibility around position 9 allows MOA to adjust to the enzyme binding site. All these findings provide foundational information for developing MOA and its derivatives as potential drugs for myocardial diseases.

Control of Parallel Quadruped Robots Based on Adaptive Dynamic Programming Control

With the rapid development of robotics technology, quadruped robots have shown significant potential in navigating complex terrains due to their excellent stability and adaptability. This paper proposes an adaptive dynamic programming control method based on policy iteration, aimed at improving the motion performance and autonomous adaptability of parallel quadruped robots in unknown environments. First, the study establishes a kinematic model of the robot and performs inverse kinematics calculations to determine the angular functions for each joint of the robot’s legs. To improve the robot’s mobility on challenging terrains, we design an optimal tracking controller based on Generalized Policy Iteration (GPI). This approach reduces the model’s dependency on strict requirements and is applied to the control of quadruped robots. Finally, kinematic simulations are conducted based on pre-planned robot gaits. In addition, experiments are then conducted based on the simulation results. The results of simulation experiments indicate that the quadruped robot, under the adaptive optimal control algorithm, can achieve smooth walking on complex terrains, verifying the rationality and effectiveness of the parallel quadruped robot in handling such conditions. The experimental results further demonstrate that this strategy significantly improves the stability and robustness of the robot across various terrains.

TD-STrans: Tri-domain sparse-view CT reconstruction based on sparse transformer.

Sparse-view computed tomography (CT) speeds up scanning and reduces radiation exposure in medical diagnosis. However, when the projection views are severely under-sampled, deep learning-based reconstruction methods often suffer from over-smoothing of the reconstructed images due to the lack of high-frequency information. To address this issue, we introduce frequency domain information into the popular projection-image domain reconstruction, proposing a Tri-Domain sparse-view CT reconstruction model based on Sparse Transformer (TD-STrans). TD-STrans integrates three essential modules: the projection recovery module completes the sparse-view projection, the Fourier domain filling module mitigates artifacts and over-smoothing by filling in missing high-frequency details; the image refinement module further enhances and preserves image details. Additionally, a multi-domain joint loss function is designed to simultaneously enhance the reconstruction quality in the projection domain, image domain, and frequency domain, thereby further improving the preservation of image details. The results of simulation experiments on the lymph node dataset and real experiments on the walnut dataset consistently demonstrate the effectiveness of TD-STrans in artifact removal, suppression of over-smoothing, and preservation of structural fidelity. The reconstruction results of TD-STrans indicate that sparse transformer across multiple domains can alleviate over-smoothing and detail loss caused by reduced views, offering a novel solution for ultra-sparse-view CT imaging.

A heterogeneous transfer learning method for fault prediction of railway track circuit

Prediction and identification of faults in track circuits are crucial for improving the safety and efficiency of railway transportation. However, due to the absence of real data, the task of track circuit fault prediction through deep learning methods facing significant challenges. This paper proposed a novel heterogeneous transfer learning network structure for track circuit deep learning fault prediction. The proposed transfer learning network can reduce the reliance on track circuit data in the process of deep learning models training by utilizing public datasets from other similar tasks. In this paper, an index describing the data distribution is used to demonstrate the transferability between heterogeneous data firstly. Then a heterogeneous transfer learning network structure is proposed to help the deep learning model training on the track circuit fault prediction task. Finally, the effect of transfer learning is comprehensively examined. The simulation experimental results show that the proposed heterogeneous transfer learning network structure can transfer useful knowledge in other similar fields for tasks in track circuit fault prediction, and the resulting model can distinguish between nine different classes with a high accuracy level over 99% on the test dataset while reducing the amount of required training data to 10% of the traditional training methods.

Deformation failure mechanism and stability-control technology of deep layered clastic-rock roadway under dynamic disturbance

The deep layered clastic-rock roadways (LCRRs) of the gold mines in southwestern Guizhou, China, are adversely affected by dynamic disturbances such as blasting and mechanical operations, yielding poor roadway stability and unsatisfactory control effectiveness. This study investigates the deformation failure mechanism of a deep LCRR under dynamic disturbance and proposes and validates an optimised roadway stability-control scheme. Through on-site investigation and theoretical analysis, the main roadway stability-control factors are determined; that is, the mudstone-layer thickness, roadway buried depth, and dynamic-load amplitude. Similar simulation experiments reveal the deformation failure characteristics and strain-field evolution laws of similar models. Numerical analysis shows that increasing buried depth most severely affects the two sides of the roadway, whereas the dynamic-load amplitude determines the disturbance duration. Moreover, with increasing mudstone-layer thickness, the overall impact of the dynamic disturbance on the surrounding rock first intensifies and then diminishes. The results of the similar simulation experiment and numerical analyses are highly consistent, indicating a danger zone on the roadway roof. The roadway deformation and failure mechanisms are revealed, and a more rational technical solution for roadway stability control is proposed and implemented on-site. Analysis and monitoring results reveal a relative reduction in roadway surface displacement exceeding 30 %, and improvement of the overall roadway stability. The findings constitute a valuable reference and guide for roadway stability control under such geological conditions.

Mechanism of shear-enhanced rotating-disk microfiltration for separation of microbe/protein bio-suspension

Shear-enhanced dynamic filtration is a promising approach for separating fermented biological products. In this study, we aimed to investigate the separation of bovine serum albumin (BSA) and polymethyl methacrylate (PMMA) particles in a binary suspension using rotating-disk microfiltration. Type-R and Type-RB rotating-disk configurations were designed and compared using experimental data and simulated results. The effects of operating conditions, including rotational speed and filtration pressure, on filtration flux, resistance, and protein rejection, were analyzed. Because the filter membrane rejected all the PMMA particles, the primary source of filtration resistance in the PMMA/BSA suspension was the fouling cake. Although BSA rejection increased with higher filtration pressure, it decreased as the rotational speed increased. Computational fluid dynamics simulations were used to model the flow velocity and shear stress distributions within the rotating-disk dynamic microfiltration system. The results showed that Type-RB achieved a 55 % increase in flux compared to Type-R, owing to a significant reduction in cake resistance by approximately 46 %. A proportional relationship between the mean cake mass and the ratio of qs/τw under varying operating conditions was established using a force balance model. These findings suggest that elevated shear stress significantly enhances filtration performance, with Type-RB demonstrating superior effectiveness for bio-suspension separation.