Recent Patents on Mechanical Engineering - Volume 19, Issue 1, 2026

In autonomous driving systems, the planning module serves as the link between environment perception and vehicle control, directly influencing the safety and efficiency of autonomous driving. Despite the existence of numerous patents and publications related to trajectory planning, there is still room for improvement in the economic efficiency of trajectory planning.

Given the limitations of the existing path planning algorithms in terms of search efficiency and path length, this study introduces an innovative and improved strategy in the horizontal dimension. Based on the cost function of the distance between sampling points, this strategy aims to improve the search efficiency of the dynamic planning algorithm and reduce the search path length. Furthermore, the smoothness of the path is optimized to suit the actual driving conditions by applying a quadratic programming algorithm. An energy consumption model for pure electric vehicles is established in the vertical dimension, effectively constraining energy use during speed dynamic planning to reduce consumption while driving. Finally, the smoothness of speed planning is improved using a quadratic programming algorithm.

The results of simulation experiments show that compared with traditional methods, the proposed algorithm achieves a substantial improvement in path length reduction of 5.8%, average curvature reduction of 31.6%, and average energy consumption reduction of 2.04% in static and dynamic obstacle avoidance environments.

The results show that the improved dynamic planning algorithm proposed in this study is significantly optimized in terms of mean path length, mean curvature, and energy consumption. Moreover, the proposed algorithm can meet the requirements of energy efficiency of vehicle driving.

This study addresses the challenges UAVs face during aerial operations, particularly concerning external interference and slow localization response. The primary objective is to propose an algorithm that integrates admittance control and non-singular fast terminal sliding mode control, verifying its effectiveness through simulation experiments while exploring its potential for patent application.

Due to their versatility and efficiency, UAVs are increasingly utilized in various aerial operations. However, they are susceptible to external disturbances, which may affect their stability and accuracy during tasks such as contact operations. Additionally, inherent delays in localization response speed may impact their performance in dynamic environments. Addressing these issues is essential for improving the reliability and robustness of UAV-based systems.

To achieve the objectives, the kinematics and dynamics of a hexacopter aerial carrier robotic arm system were initially modeled. Subsequently, an external admittance controller was designed to mitigate disturbances encountered during contact operations, achieving smooth control of the robotic arm end-effector by adjusting the desired position to enhance system stability and disturbance rejection. Additionally, to prevent performance degradation stemming from controller saturation, an internal position control mechanism utilizing a non-singular fast terminal sliding mode control algorithm was implemented. This approach enhances system robustness and convergence speed, ensuring accurate positioning.

To validate the effectiveness and feasibility of the proposed control algorithm, numerical simulations were conducted. The outer loop's admittance control exhibited a smoother control process, particularly during sudden stiffness changes when the actuator contacts the environment. The inner loop, employing Non-Singular Fast Terminal Sliding Mode Control (NFTSMC), improved joint angle tracking speed by 41%-58% compared to PID control, and by 20%-50% compared to traditional Sliding Mode Control (SMC). This algorithm demonstrated faster convergence rates and smoother transitions, significantly reducing steady-state errors in contact force while exhibiting robustness to environmental parameters. The findings indicate that the algorithm effectively addresses the issues of external interference and sluggish localization response encountered by UAVs during aerial operations.

The algorithm based on admittance control and non-singular fast terminal sliding mode control demonstrates superior performance compared to traditional sliding mode control and PID control in mitigating external disturbances and enhancing the precision of UAV aerial operations. This ensures the resilience to disturbances and the speed of localization response of the rotary-wing flying robotic arm system during cleaning processes, thus enhancing its reliability and robustness in dynamic environments.

In phase change thermal management systems, the development of magnetic phase change materials offers the possibility of effectively integrating passive and active heat control technologies. The low dispersibility of traditional heat transfer additives, the high interfacial thermal resistance with phase change matrices, and the restricted magnetic response characteristics are some of the current problems that must be resolved.

To overcome these challenges, this study employed a co-precipitation method to composite magnetic nanoparticles Fe3O4 with graphene oxide (GO). The active sites on GO were functionalized with alkyl groups to prepare Fe3O4-modified graphene oxide (Fe3O4-MGO)/paraffin magnetic composite phase change materials. The morphology, structure, chemical composition, and thermal properties of the resulting magnetic composite phase change materials were tested and characterized.

The results indicated that Fe3O4-MGO exhibits good dispersibility in paraffin, which can enhance the thermal conductivity of the phase change material. The thermal conductivity of the composite phase change material with a Fe3O4-MGO mass fraction of 2.0% was measured to be 0.461 W/m·K, representing a 47.3% increase compared to pure paraffin. Additionally, Fe3O4-MGO demonstrated a certain phase change capability, with a phase change enthalpy reaching 70.35 kJ/kg.

The findings of this study are expected to provide technical support for innovative applications of magnetic-controlled phase change thermal management. The emergence of magnetic phase change materials holds the promise of achieving efficient integration of passive and active heat control technologies within phase change thermal management systems. However, several issues still need to be addressed in patents and related research.

As one of the essential pieces of chemical equipment, a reactor provides the necessary reaction space and conditions for the materials involved in the reaction during the stirring process, there has been an increase in patents related to reactors. However, under typical operating conditions, issues such as uneven gas distribution, suboptimal gas-liquid mixing, and low product yield often arise in gas-liquid phase reactors.

To address the issues prevalent in current stirred reactors, a new design for a stirred reactor equipped with a double-suction turbine agitator was developed.

In this paper, a stirred reactor equipped with a double-suction turbine agitator was designed, and its three-dimensional modeling was conducted using SolidWorks. Computational Fluid Dynamics (CFD) simulations, based on the Euler-Euler two-phase approach with the RNG turbulence model, were performed to assess variables such as stirring speed, installation height, blade diameter and agitator inner diameter. The dispersion characteristics and flow field behaviors of the gas-liquid two-phase under varying conditions were comparatively analyzed. Optimizations were conducted across various parameters to enhance the gas mixing efficiency in the liquid phase.

The results show that a diameter of 370 mm for the double-suction turbine agitator, an installation height of 640 mm, a blade diameter of 500 mm, and an inner hole diameter of 200 mm yield optimal gas-liquid two-phase mixing performance. This configuration results in a broad and uniform gas distribution within the reactor, maintaining a desired high level of gas holdup at specific positions.

The double suction turbine agitator is a type of radial agitator. During operation, it induces significant centrifugal forces in the liquid, exerts a robust shear effect, and enhances the mixing of the gas-liquid phases, thereby increasing the production efficiency of the product.

Based on the author’s invention patent literature, this article designs a parameterized simulation model of a multi-layer jet weft insertion system by profiles reed guidance for a 3D loom on the PTC Creo9.0 platform, including the main supersonic nozzle, supersonic auxiliary nozzle (relay nozzle), and multi-slot profiles reed components.

Based on the existing basic theory, experimental results, and empirical data of turbulent jet, the parameters of the multi-layer jet weft insertion system, as well as the structural parameters and the relative positions of the main, auxiliary nozzle and profile reed components, such as the center distance of each layer's main nozzle, auxiliary nozzle spacing, auxiliary nozzle installation angle, spray direction angle, and spray angle, have been determined preliminarily.

Further, the basic flow field parameters, such as the supply pressure of the main and auxiliary nozzles and the shape of multi-layer profile reeds, have been determined optimally on the Virtual Prototype Collaborative Simulation Platform (PTC Creo9.0/ANSYS Workbench/Fluent 2024R1), and the rationality of the structural design also been verified; on the premise of ensuring that the airflow velocity of each layer's profiles groove meets the requirements of weft insertion, the design and simulation calculation is repeatedly modified, and the optimal structural design parameters of the multi-layer weft insertion system and nozzle is finally determined.

To explore the feasibility of multi-layer jet weft insertion, the design of three-dimensional multi-layer jet weft insertion looms has laid a theoretical foundation, laying a good foundation for the emergence and industrial manufacturing of three-dimensional multi-layer jet weft insertion looms, and providing an important reference for the innovative design of three-dimensional multi-layer water jet weft insertion looms.

In the large-scale steel industry, significant power load variability, especially during processes like steel smelting, poses challenges to power system safety. Although there is an abundance of research and patents related to load forecasting, studies and patents specifically addressing large industrial load forecasting are sparse. Hence, accurate ultra-short-term load forecasting becomes particularly crucial.

This study proposes an innovative method for ultra-short-term load forecasting to improve prediction accuracy during peak periods and mitigate risks in high-load conditions.

We introduce an LSTM-XGBoost model enhanced by a random forest network and an improved grey wolf optimization algorithm (IGWO) for feature selection and parameter optimization, respectively.

Compared to other advanced models, our method demonstrates superior performance across key indicators such as MAPE (1.93%), RMSE (220.81), and R² coefficient (0.99), and the prediction error is lower during both peak and off-peak periods. For instance, the proposed model achieved a MAPE improvement of over 25% compared to traditional models. Validation with data from multiple time periods confirms the model's accuracy and robustness.

The proposed forecasting method effectively tackles load fluctuations in the steel industry, supporting safe and economical power system operations. Future research will aim to further improve peak identification accuracy and enable continuous adaptive learning.