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Summary: Lunar-based equipment plays a vital role in the exploration of the moon because it undertakes the tasks of moving, transporting, digging, and so on. In order to control the gait of lunar-based equipment more precisely and guarantee mobile stability, the contact mechanism between its foot and lunar soil is worthy of in-depth study. In this paper, a contact model is proposed to predict the stress, strain, and displacement both on the contact surface and in the lunar soil when the foot is under vertical load. The axial stress in the proposed contact model is verified through the experiment and its accuracy in the lunar equipment is verified through simulation. The error is in a reasonable range and the influence depth of load conforms to the experiment results. This paper provides a relatively accurate model to describe the contact force between the lunar-based equipment’s foot and the lunar soil and will promote the research of lunar exploration.

In recent years, the increasing frequency of climate change and extreme weather events has significantly elevated the risk of levee breaches, potentially triggering large-scale floods that threaten surrounding environments and public safety. Rapid and accurate measurement of river surface velocities is crucial for developing effective emergency response plans. Video image velocimetry has emerged as a powerful new approach due to its non-invasive nature, ease of operation, and low cost. This paper introduces the Dynamic Feature Point Pyramid Lucas–Kanade (DFP-P-LK) optical flow algorithm, which employs a feature point dynamic update fusion strategy. The algorithm ensures accurate feature point extraction and reliable tracking through feature point fusion detection and dynamic update mechanisms, enhancing the robustness of optical flow estimation. Based on the DFP-P-LK, we propose a river surface velocity measurement model for rapid levee breach emergency response. This model converts acquired optical flow motion to actual flow velocities using an optical flow-velocity conversion model, providing critical data support for levee breach emergency response. Experimental results show that the method achieves an average measurement error below 15% within the velocity range of 0.43 m/s to 2.06 m/s, demonstrating high practical value and reliability.

Abstract Crude oil pipelines are considered as the lifelines of energy industry. However, accidents of the pipelines can lead to severe public health and environmental concerns, in which greenhouse gas (GHG) emissions, primarily methane, are frequently overlooked. While previous studies examined fugitive emissions in normal operation of crude oil pipelines, emissions resulting from accidents were typically managed separately and were therefore not included in the emission account of oil systems. To bridge this knowledge gap, we employed a bottom-up approach to conducted the first-ever inventory of GHG emissions resulting from crude oil pipeline accidents in the United States at the state level from 1968 to 2020, and leveraged Monte Carlo simulation to estimate the associated uncertainties. Our results reveal that GHG emissions from accidents in gathering pipelines (~720,000 tCO2e) exceed those from transmission pipelines (~290,000 tCO2e), although significantly more accidents have occurred in transmission pipelines (6883 cases) than gathering pipelines (773 cases). Texas accounted for over 40% of total accident-related GHG emissions nationwide. Our study contributes to enhanced accuracy of the GHG account associated with crude oil transport and implementing the data-driven climate mitigation strategies.

Abstract Natural gas is believed to be a critical transitional energy source. However, natural gas pipelines, once failed, will contribute to a large amount of greenhouse gas (GHG) emissions, including methane from uncontrolled natural gas venting and carbon dioxide from flared natural gas. However, the GHG emissions caused by pipeline incidents are not included in the regular inventories, making the counted GHG amount deviate from the reality. This study, for the first time, establishes an inventory framework for GHG emissions including all natural gas pipeline incidents in the two of the largest gas producers and consumers in North America (United States and Canada) from 1980s to 2021. The inventory comprises GHG emissions resulting from gathering and transmission pipeline incidents in a total of 24 states or regions in the United States between 1970 and 2021, local distribution pipeline incidents in 22 states or regions between 1970 and 2021, as well as natural gas pipeline incidents in a total of 7 provinces or regions in Canada between 1979 and 2021. These datasets can improve the accuracy of regular emission inventories by covering more emission sources in the United States and Canada and provide essential information for climate-oriented pipeline integrity management.

To address the challenges of sinking, imbalance, and complex control systems faced by hexapod robots walking on lunar soil, this study develops an umbrella-shaped foot lunar exploration hexapod robot. The overall structure of the robot is designed to mimic the body structure of insects. By incorporating a four-bar linkage mechanism to replace the commonly used naked joints in traditional hexapod robots, the robot reduces the number of degrees of freedom and simplifies control complexity. Additionally, an extension mechanism is added to the robot’s foot, unfolding into an umbrella shape to provide a larger support area, effectively addressing the issue of foot sinking instability during walking. This study adopts and simplifies the Central Pattern Generator (CPG) model to generate stable periodic control signals for the robot’s legs. Precise control of the extension mechanism’s unfolding period is achieved through mapping functions. A joint simulation platform using Solid Works and Matlab is established to analyze the stability of the robot’s walking. Finally, walking experiments are conducted on the prototype, confirming the smooth walking of the lunar exploration hexapod robot. The results indicate that the designed lunar exploration hexapod robot has a reasonable structure, excellent stability in motion, and the CPG control scheme is feasible.

As a typical intelligent device, magnetorheological (MR) dampers have been widely applied in vibration control and mitigation. However, the inherent hysteresis characteristics of magnetic materials can cause significant time delays and fluctuations, affecting the controllability and damping performance of MR dampers. Most existing mathematical models have not considered the adverse effects of magnetic hysteresis characteristics, and this study aims to consider such effects in MR damper models. Based on the magnetic circuit analysis of MR dampers, the Jiles–Atherton (J-A) model is adopted to characterize the magnetic hysteresis properties. Then, a weight adaptive particle swarm optimization algorithm (PSO) is introduced to the J-A model for efficient parameter identifications of this model, in which the differential evolution and the Cauchy variation are combined to improve the diversity of the population and the ability to jump out of the local optimal solution. The results obtained from the improved J-A model are compared with the experimental data under different working conditions, and it shows that the proposed J-A model can accurately predict the damping performance of MR dampers with magnetic hysteresis characteristics.

To address the challenges posed by the loose lunar surface structure, including the adhesive nature of lunar soil, strong corrosiveness and the slow walking speed of robots using traditional tripod gaits, this paper proposes the design of a small lunar exploration hexapod robot with hollow legs, employing anti-corrosive aerospace materials throughout. Additionally, an inverted gait motion mode is introduced. Simulation analysis is conducted on the displacement, angular velocity, angular acceleration and joint torque of the robot’s body under both traditional tripod gaits and the “inverted gait” motion mode. A physical prototype of the robot is developed to validate the rationality of its structure. Our research results indicate that the designed lunar exploration hexapod robot’s body structure is reasonable, enabling it to stand and walk normally on the unstructured lunar terrain. The hollow design reduces the adhesion of lunar soil. The inverted gait motion mode expands the effective swinging range of the robot’s legs and increases the effective step length during leg swing. Additionally, it improves the robot’s movement speed, eliminates vibrations at the joints during motion and improves the robot’s stability during the support phase.

Carrier phase measurements currently play a crucial role in achieving rapid and highly accurate positioning of global navigation satellite systems (GNSS). Resolving the integer ambiguity correctly is one of the key steps in this process. To address the inefficiency and slow search problem during ambiguity solving, we propose a single-frequency GNSS integer ambiguity solving based on an adaptive genetic particle swarm optimization (AGPSO) algorithm. Initially, we solve for the floating-point solution and its corresponding covariance matrix using the carrier-phase double difference equation. Subsequently, we decorrelate it using the inverse integer Cholesky algorithm. Furthermore, we introduce an improved fitness function to enhance convergence and search performance. Finally, we combine a particle swarm optimization algorithm with adaptive weights to conduct an integer ambiguity search, where each generation selectively undergoes half-random crossover and mutation operations to facilitate escaping local optima. Comparative studies against traditional algorithms and other intelligent algorithms demonstrate that the AGPSO algorithm exhibits faster convergence rates, improved stability in integer ambiguity search results, and in practical experiments the baseline accuracy of the solution is within 0.02 m, which has some application value in the practical situation of short baselines.

The low light conditions, abundant dust, and rocky terrain on the lunar surface pose challenges for scientific research. To effectively perceive the surrounding environment, lunar rovers are equipped with binocular cameras. In this paper, with the aim of accurately detect obstacles on the lunar surface under complex conditions, an Improved Semi-Global Matching (I-SGM) algorithm for the binocular cameras is proposed. The proposed method first carries out a cost calculation based on the improved Census transform and an adaptive window based on a connected component. Then, cost aggregation is performed using cross-based cost aggregation in the AD-Census algorithm and the initial disparity of the image is calculated via the Winner-Takes-All (WTA) strategy. Finally, disparity optimization is performed using left–right consistency detection and disparity padding. Utilizing standard test image pairs provided by the Middleburry website, the results of the test reveal that the algorithm can effectively improve the matching accuracy of the SGM algorithm, while reducing the running time of the program and enhancing noise immunity. Furthermore, when applying the I-SGM algorithm to the simulated lunar environment, the results show that the I-SGM algorithm is applicable in dim conditions on the lunar surface and can better help a lunar rover to detect obstacles during its travel.

In order to improve the seismic performance of damaged reinforced concrete joints, a new retrofitting method using viscoelastic materials and steel plates is proposed. A reversal cyclic loading test was carried out on four T-shaped RC joints to investigate the reliability of this method. Seismic performance, including the strength, stiffness, ductility, and energy dissipation capacity of the original non-damaged joint and different repaired and retrofitted joints are then compared. The results show that this new method can significantly improve the strength of the retrofitted joints, and the joints can be loaded to a larger displacement value. After retrofitting, the energy dissipation of the joints increases, and the stiffness degradation decreases. The test results indicate that the retrofitting method is effective in repairing seismically damaged RC joints.

Simple and light-weighted quasi-zero stiffness (QZS) isolators can be designed based on nonlinear and negative stiffness generated by the snap-through effect of bistable structures. Traditionally, the snap-through force of the bistable structure is limited which makes the weight which can be isolated based on this mechanism very low. This paper investigates increasing loading capacity of this kind of isolator by using an optimized and varying sectional profile. Numerical models were derived for the bistable structures with variable sectional distributions. Optimized sections’ alignment of the bistable beam was derived based on the numerical model which was consequently validated by experimental results. Influences of the bistable beams with a variable section on nonlinear stiffness characteristics and performance of the isolator were at last investigated with the harmonic balance method.

The identification of wildfires is a very complex task due to their different shapes, textures, and colours. Traditional image processing methods need to manually design feature extraction algorithms based on prior knowledge, and because fires at different stages have different characteristics, manually designed feature extraction algorithms often have insufficient generalization capabilities. A convolutional neural network (CNN) can automatically extract the deeper features of an image, avoiding the complexity and blindness of the feature extraction phase. Therefore, a wildfire identification method based on an improved two-channel CNN is proposed in this paper. Firstly, in order to solve the problem of the insufficient dataset, the dataset is processed by using PCA_Jittering, transfer learning is used to train the model and then the accuracy of the model is improved by using segmented training. Secondly, in order to achieve the effective coverage of the model for fire scenes of different sizes, a two-channel CNN based on feature fusion is designed, in which the fully connected layers are replaced by a support vector machine (SVM). Finally, in order to reduce the delay time of the model, Lasso_SVM is designed to replace the SVM in the original model. The results show that the method has the advantages of high accuracy and low latency. The accuracy of wildfire identification is 98.47% and the average delay time of fire identification is 0.051 s/frame. The wildfire identification method designed in this paper improves the accuracy of identifying wildfires and reduces the delay time in identifying them.

A real-time hybrid simulation (RTHS) is a promising technique to investigate a complicated or large-scale structure by dividing it into numerical and physical substructures and conducting cyber-physical tests on it. The control system design of an RTHS is a challenging topic due to the additional feedback between the physical and numerical substructures, and the complexity of the physical control plant. This paper proposes a novel RTHS control strategy by combining the theories of adaptive control and robust control, where a reformed plant which is highly simplified compared to the physical plant can be used to design the control system without compromising the control performance. The adaptation and robustness features of the control system are realized by the bounded-gain forgetting least-squares estimator and the sliding mode controller, respectively. The control strategy is validated by investigating an RTHS benchmark problem of a nonlinear three-story steel frame The proposed control strategy could simplify the control system design and does not require a precise physical plant; thus, it is an efficient and practical option for an RTHS.

Harmful vibrations can cause many engineering problems, such as disasters of civil engineering structures, failure of precision equipment, and the disruption of some spacecraft components. In most situations, the vibrations are usually three-dimensional excitations. Therefore, research on multi-dimensional vibration control has become a world-wide hotspot in engineering. This article presents a comprehensive assessment of recent developments of multi-dimensional vibration mitigation devices and the material applied in such devices. The vibration mitigation materials mainly include the passive vibration mitigation materials and smart vibration mitigation materials. Subsequently, the vibration mitigation devices in civil engineering, mechanical engineering, and aerospace engineering are reviewed and commented, respectively. Above all, the multi-dimensional vibration mitigation devices and materials are found to be effective for reducing multi-dimensional vibrations. However, enhancing the mitigation performance of the devices in each direction, including improving the mitigation capability of materials, is still a challenging issue.

The magnetorheological (MR) fluid is a typical smart material, whose shear yield stress can be adjusted through changing the strength of external magnetic field, and the changing process only takes a few milliseconds. The MR fluid is composed of micro/nanometer ferromagnetic particles, carrier fluids, and some additives. Among them, the performance of ferromagnetic particles will mainly affect the sedimentation stability and the magnetic saturation of the MR fluid. Therefore, the ferromagnetic particles are expected to have characteristics of both low density and high magnetism. In this paper, the multi-walled carbon nanotubes (MWNTs) were adopted to coat on the carbonyl iron (CI) particles with grafting technology using ultrasonication and mechanical stirring. The coated CI particles with perfect core-shell structure were developed and the influence of the dosages of grafting agent and MWNTs were tested. And then, MR fluids with CI particles coated with MWNTs were established and the coating effect was studied through surface topography particle density, and magnetic properties of composite magnetic particles and stability tests of the prepared MR fluids. The results showed that although the magnetic saturation of the prepared MR fluids with CI particles coated with MWNTs would reduce slightly, the particles density and the adsorption force between the particles were decreased effectively, which are both advantageous to the improvement of the sedimentation stability of MR fluids.

Magnetorheological (MR) damper, a kind of intelligent vibration mitigation device, can be used to reduce the vibration or dynamic responses of structures. Its damping forces can be adjusted in real-time by updating control currents of the MR damper according to the state parameters of the structure. In this paper, a single-chip microprocessor (SCM) for MR damper coupling sensing and control is developed. The SCM has three main functions: the first one is that it can collect the acceleration responses of the structure by using the acceleration sensor in real-time; the second one is that it can use the segmented control strategy to decide the control currents of the MR damper according to the collected acceleration information; the third one is that based on the PWM technology it can realize to accurately supply power for the MR damper. The developed SCM is tested through experiment. The analysis results of the simulation and experiment show that the SCM can collect the state information of the structure in real-time and realize to choose the currents of MR damper accurately to control the damping forces of the MR damper.

The slotting method is a nondestructive detection method based on the stress release principle to evaluate in-place stress in concrete. By measuring the change in strain of the slotting area using a strain gauge, the in-place stress within the concrete member can be calculated by elastic theory. This paper proposes a multi-step slotting method that combines experimental strain measurements with numerical simulation results. For concrete specimens under unidirectional stress, the effects of compressive stress, slotting spacing, slotting length, and slotting depth on the degree of stress release were analyzed using finite element analysis, and a normalized fitting equation was proposed that can be quickly and accurately applied in engineering detection. The excellent agreement between the experimental results and the numerical simulation (fitted equation) results shows that the slotting method can facilitate the accurate evaluation of the in-place stress in concrete, and the relative error can be reduced to less than 10% when it is calculated using the optimized multi-step experimental data. [ABSTRACT FROM AUTHOR]