Lifting Model Research Articles

Digital twin method for intelligent lifting of cable structures under multi-collaborative mode

In order to ensure the high efficiency and quality consistency of cable structure in each construction stage, this study proposes a digital twin method for intelligent lifting of cable structure under multi-collaborative mode. Firstly, a construction and operation methods of the digital twin logic model are formed. In the logical model, a relationship between the transmission and iteration of construction information in the collaborative process is defined. In order to realize the adaptability of working conditions, the coupling relationship between construction step and structural elevation is established. A cable structure lifting analysis model driven by an improved long short-term memory neural network is proposed, which effectively solves the problem of poor convergence of the traditional finite element method. An automatic time window selection method is formed for the neural network. An adaptive moment estimation method is used for neural network parameter optimization, which improves the efficiency and accuracy of the analysis. Taking the lifting process of a cable truss structure test model as an example, a digital twin collaborative construction platform is established to verify the effectiveness and feasibility of the research method. The results show that the digital twin logic model realizes the process collaboration, scene collaboration and data collaboration, and significantly improves the stability of construction quality and the linkage of construction process. Through the improved long short-term memory neural network, the coupling relationship between the construction step and the structural elevation is established, which accurately guides the whole construction process.

Load Balancing Algorithm of API Gateway Based on Microservice Architecture for a Smart City

Abstract As the entrance of the computer systems, the API gateway is an indispensable part of the microservice architecture. To realize the load balancing of API gateway, this paper studies the load balancing algorithm of the API gateway based on the microservice architecture. In doing this, we analyze the microservice architecture level from the data layer, the basic layer, and other levels, take the container cloud as the carrier of the microservice architecture, combine it with the client and API gateway, and design the API gateway based on the microservice architecture. We then judge whether the microservice identifier in the request source of the API gateway client is included in the API gateway routing table and determine the service cluster to which the microservice belongs according to the microservice identifier. After retrieving the qualified backend microservice container list according to the service cluster information, it adopts a load balancing algorithm based on dynamic weight, takes central processing unit (CPU) utilization and memory utilization as parameters to evaluate the resource load of microservers, uses an extreme gradient lifting model to predict CPU utilization and memory utilization, calculates the weight of microservers based on the prediction results, selects the microserver with the highest weight value to make API gateway service requests, and initiates API gateway service calls to specific backend microservice containers, thereby completing the load balancing of the API gateway. The experimental results show that the average load balancing degree of the algorithm is about 95 %, the average network resource utilization rate is as high as 89 %, and the algorithm execution time is short.

Анализ сил в математической модели ножничного подъемника

The aim of study is to present a method and tools for analyzing forces in the mathematical model of a scissor lift. The object of the study is the HeshbonLift51G scissor lift. A mathematical model of the scissor lift is developed to determine the restraining force, deformations, and stresses. Graphs depicting the relationship between the equivalent moment and rotational speed with respect to the angle are identified. The maximum rotation angle reaches 70° when the reaction force becomes maximum in the supporting link. MATLAB and ANSYS programs were used to analyze the lift and its key parameters. The results of the static analysis show that with a uniform increase in load of 400N, the equivalent maximum stress increases by 3.1162 MPa, and the equivalent mean stress increases by 0.06 MPa. This indicates an increase in stresses in the lift components with an increase in load, potentially affecting the strength and durability of the system

A comparative study on kinetics and dynamics of two dump truck lifting mechanisms using MATLAB simscape

In this paper, two lifting mechanism models with opposing placements, which use the same hydraulic hoist model and have the same angle of 50°, have been developed. The mechanical and hydraulic simulation models are established using MATLAB Simscape to analyze their kinetics and dynamics in the lifting and holding stages. The simulation findings are compared to the analytical calculation results in the steady state, and both methods show good agreement. In the early lifting stage, Model 1 produces greater force and discharges goods in the container faster than Model 2. Meanwhile, Model 2 reaches a higher force and ejects goods from the container cleaner than its counterpart at the end lifting stage. The established simulation models can consider the effects of dynamic loads due to inertial moments and forces generated during the system operation. It is crucial in studying, designing, and optimizing the structure of hydraulic-mechanical systems.

Preliminary Research on Outdoor Thermal Comfort Evaluation in Severe Cold Regions by Machine Learning

The thermal comfort evaluation of the urban environment arouses widespread concern among scholars, and research in this field is mostly based on thermal comfort evaluation indexes such as PMV, PET, SET, UTCI, etc. These thermal comfort index evaluation models are complex in the calculation process and poor in operability, which makes it difficult for people who lack a relevant knowledge background to understand, calculate, and apply them. The purpose of this study is to provide a simple, efficient, and easy-to-operate outdoor thermal comfort evaluation model for severe cold areas in China using a machine learning method. In this study, the physical environment parameters are obtained by field measurement, and individual information is obtained by a field questionnaire survey. The applicability of four machine learning models in outdoor thermal comfort evaluation is studied. A total of 320 questionnaires are collected. The results show that the correlation coefficients between predicted values and voting values of the extreme gradient lifting model, gradient lifting model, random forest model, and neural network model are 0.9313, 0.7148, 0.9115, and 0.5325, respectively. Further analysis of the extreme gradient model with the highest correlation coefficient shows that individual factors (such as residence time, distance between hometown and residence, clothing, age, height, and weight) and environmental factors (such as air humidity (RH), wind speed (v), air temperature (Ta), and black bulb temperature (Tg)) have different influences on thermal comfort evaluation. In summary, using a machine learning method to evaluate outdoor thermal comfort is simpler, more direct, and more efficient, and it can make up for the lack of consideration of complex individual factors in the evaluation method of thermal comfort index. The results have reference value and application value for the research of outdoor thermal comfort evaluation in severe cold areas of China.

Three-Dimensional Aeromechanical Analysis of Lift Offset Coaxial Rotors: A Helios Test Case

Three-dimensional (3-D) solid finite element analysis (FEA) is used to model and study coaxial helicopter rotors. The 3-D FEA is coupled with a lifting line aerodynamic model with free wake to capture rotor-rotor interactions. Two open-access models are developed: one is the Metaltail (a hingeless coaxial rotorcraft), and the other is the coaxial rotor built from articulated UH-60A-like rotors. The former is the main focus of this work and is developed as an example case for the U.S. Army/DoD rotorcraft simulation software CREATETM–AV Helios, while the latter is merely for a regression test. The analysis is performed on a low-speed transition flight for which qualitative data are available for the Sikorsky S-97 Raider aircraft for comparison. Predictions of performance, airloads, vibratory loads, and 3-D stresses are discussed. In the absence of available geometry or property data of the S-97, this study is primarily meant to serve as a capability demonstration of 3-D rotor structural dynamic modeling for such rotors.

Spatial correlation-based machine learning framework for evaluating shale gas production potential: A case study in southern Sichuan Basin, China

Assessment of production potential and prediction of sweet spots in unexploited shale gas wells are crucial technologies for achieving a high success rate in drilling. Most existing methods are only applicable for yield forecasting of shale gas wells with known geological and drilling/completion factors after production. In addition, the shale gas reservoir in the Longmaxi Formation in the southern Sichuan Basin of China is characterized by low porosity, low permeability, and diverse gas reservoir states. Thus, forecasting production from unexploited wells and predicting the exact location of the sweet spot under complex geological conditions have become challenging topic for both academia and industry. According to the first law of geography, shale gas wells that are closer in space have more similar geologic features. Based on this foundation, a machine-learning prediction model based on spatial correlation (SC-MLPM) is proposed for evaluating potential production of shale gas wells in the Changning play located in Longmaxi Formation in the southern Sichuan basin, China. Specifically, an improved K-nearest neighbor algorithm (SC-KNN) based on the spatial correlation characteristics of the exploited wells is designed to estimate the relevant geological factors of the unexploited wells in the same area. In addition, an extreme gradient lifting model based on Huber loss function (Hu-XGB) is developed to predict the potential production of unexploited shale gas wells, and then the shale gas sweet spot area in the research area is predicted quickly and accurately. The experimental results on 88 shale gas wells from Changning play show that the mean relative error of the prediction results of the proposed method is 9.8%. Furthermore, the results also determine that the sweet spots of shale gas wells in the research area are mainly developed in the northwest and northeast directions, and gradually become worse in the southeast direction. Eventually, experimental results for the Fox Creek play in Canada show that the proposed model obtains better predictive performance as the number of gas wells increases, and also verifies the generalizability. This work provides a basis for the efficient development of the research area.

Intelligent Dynamic Prediction Model of Pump Pressure Lift for a Petroleum Pipeline System with Nonisothermal Batch Transportation

Intelligent Dynamic Prediction Model of Pump Pressure Lift for a Petroleum Pipeline System with Nonisothermal Batch Transportation

Reduced order modelling of thickness effects on a cantilevered hydrofoil in homogeneous isotropic turbulence

The response of a cantilevered hydrofoil excited by honeycomb-generated turbulence is studied with a reduced order analytical model that considers the thickness effects of NACA sections. This is achieved by extending a gust response model of the unsteady lift to include realistic 2D sections instead of using a flat plate assumption. The general chordwise thickness profiles are included in the model using the generalized Joukowski transformation. The problem is cast into a constrained optimization problem which is solved with the use of the Lagrange multiplier method. The frequency response function (FRF) of NACA sections with the same maximum thickness to chord ratio but with different leading-edge thickness profiles are calculated to investigate the effect of the hydrofoil leading edge thickness on the high frequency response. This FRF is combined with the Uncorrelated Wall Plane Wave (UWPW) technique to simulate the pressure jump amplitude on the hydrofoil due to the turbulence interaction which excites the hydrofoil in heaving and pitching motion according to Theodorsen’s hydroelastic theory. Finaly, the structural velocity spectra are compared with available experimental results of the turbulence ingestion and the effect of the different leading edge thickness profiles on the vibration response is demonstrated for various NACA sections.

3D Human Pose Estimation: A Survey

This comprehensive review article explores the latest research advancements in the realm of estimating 3D human pose. Traditional methods such as PSM, SVM are discussed. Besides, this review also talks about deep learning-based approaches, including direct approaches, 2D-to-3D lifting and volumetric model approach for single person, top-down approaches and bottom-up approaches for multi-person pose estimation. The analysis covers the strengths and challenges of various methods, encompassing issues such as model generalization, occlusion robustness, and computational efficiency. Current research issues are identified, and future directions are proposed. By summarizing and evaluating existing methods, this paper aims to provide valuable insights for researchers in both academia and industry, driving the evolution of 3D human pose estimation for better practical applications.

Parameter-Optimal-Gain-Arguable Iterative Learning Control for Linear Time-Invariant Systems with Quantized Error

In this paper, a parameter optimal gain-arguable iterative learning control algorithm is proposed for a class of linear discrete-time systems with quantized error. Based on the lifting model description for ILC systems, the iteration time-variable derivative learning gain in the algorithm is optimized by resolving a minimization problem regarding the tracking error energy and the learning effort amplified by a weighting factor. Further, the tracking error can be monotonically convergent to zero when the condition is guaranteed and the rate of convergence can be adjusted by scaling the weighting factor of an optimization problem. This algorithm is more innovative when compared with the existing iterative learning control algorithm for quantization systems. The innovations of this algorithm are as follows: (i) this optimization-based strategy for selecting learning gains can improve the active learning ability of the control mechanism and avoid the passivity of existing selective learning gains; (ii) the algorithm of POGAILC with data quantization can improve the convergence performance of tracking errors and reduce the negative effects of data quantization on the control performance of the logarithmic quantizer; and (iii) we provide a rigorous algorithm convergence analysis by deriving the existence of the unique solution for the optimal learning-gain vector under a singular and nonsingular tracking-error diagonalized matrix. Finally, numerical simulations are used to demonstrate the effectiveness of the algorithm.

Study on the Comprehensive Optimization of Quantum Radar Stealth Based on the Waverider Warhead

Quantum radar is a novel detection method that combines radar and quantum technologies. It exceeds the detection threshold and poses a threat to conventional stealth targets. This work aims to derive the expression of the quantum radar cross-section of a new complex target. The calculation formula of QRCS was derived after introducing the relative photon parameters and vector dot product. Subsequently, a comprehensive optimization model of quantum stealth and lift–drag ratio based on a genetic algorithm was proposed for the waverider warhead. During the optimization process, we proposed an optimization method with the objective function of the QRCS pioneering design value and achieved better outcomes than the optimization method using the average value in terms of QRCS performance and lift–drag ratio in the important azimuths of the waverider. By changing the design variables of the waverider warhead and using this new optimization method, the QRCS of the waverider in the forward and lateral angles were minimized, remarkably improving the aerodynamic performance of the waverider. Similarly, the optimization results show that the proposed design value optimization method is feasible.

Aerodynamic analysis of a downwind offshore floating wind turbine with rotor uptilt angles in platform pitching motion

In this study, the effect of the rotor uptilt angle on the aerodynamic performance of a downwind floating wind turbine (FWT) is investigated using the free vortex wake (FVW) method. The Weissinger-L blade lifting model and a three-step, third-order predictor-corrector difference approximation of wake solution are applied in the FVW model. To consider the platform pitching angle and angular velocity, the blade inflow conditions and the positions of the first three nodes of vortex filaments are modified in the implemented model. The full-scale FWT simulation results, focusing on platform pitching motion, are validated by comparing them with results obtained using other established numerical methods, ensuring the accuracy and reliability of the presented model. Furthermore, the simulation of the wake field is also validated by comparison between calculation and experiment. In this setting, the comparison of the aerodynamic performance between upwind and downwind FWTs shows that the downwind FWT with a rotor uptilt angle has slightly higher power and thrust. The results indicate that the rotor uptilt angle has a significant effect on blade aerodynamic loads, with the highest impact observed at maximum pitching velocity and angle. In this context, at the position with maximum pitching velocity, a larger rotor uptilt angle results in a larger angle of attack and increased aerodynamic load, while at the maximum pitching angle position, a larger rotor uptilt angle leads to a smaller angle of attack and decreased aerodynamic load.

Research on anti-rollover warning control of heavy dump truck lifting based on sliding mode-robust control

In order to improve the rollover stability of heavy dump trucks during lifting and unloading under special circumstances and prevent heavy dump trucks from overturning, a sliding mode-robust control based on anti-rollover early warning control method for heavy dump trucks lifting and unloading was proposed. A four-degree-freedom heavy dump truck lifting and unloading roll dynamic model was established, and the accuracy of the model was verified in the MATLAB/Simulink simulation environment. The controller was designed by combining sliding mode robust control based on HJI theory and active suspension control. The ratio of the gravity carried by the wheel with the lighter bearing capacity to the total gravity of the heavy dump truck was used as the evaluation index for rollover warning. Under two different simulation conditions, compared with conventional sliding mode control and no control, the rollover evaluation index and roll angles were analyzed under three control methods. The results showed that the anti-rollover early warning control method for lifting and unloading of heavy dump trucks proposed in this paper had better anti-rollover capability and better stability.

The study of the separation and deposition of dredging soil slurry under physical disturbance.

Most existing research uses experimental designs for testing, which cannot efficiently analyse the migration and sorting rules of particles in the disturbed slurry. Therefore, based on the fluidized bed flow film theory, a slurry flow film structure system is established according to the disturbance state of the fluid. On this basis, the particle size and distribution law of the disturbing force formed by slurry disturbance are analyzed, and the calculation model of single particle lift in the flowing film is also analyzed. On this basis, using Markov probability model, the probability of particle lifting and sorting between layers is theoretically deduced. Then, according to the particle ratio of the original mud, the settlement gradation of the particles in the disturbance is analyzed. It can also predict the separation degree of particle in natural turbulence, fluidized beds, and sludge mechanical dewatering. Finally, according to the particle flow software PFC (Particle Flow Code), the main influencing parameters (disturbing force and gradation) were verified and analyzed. The results show that the particle flow simulation results are in good agreement with the calculation results. The model of slurry membrane separation proposed in this paper can provide a basis for studying the mechanism of slurry disturbance separation and particle deposition.

A Dynamic Plunger Lift Model for Shale Gas Wells

At present, the optimization of the plunger mechanism is shale gas wells is mostly based on empirical methods, which lack a relevant rationale and often are not able to deal with the quick variations experienced by the production parameters of shale gas wells in comparison to conventional gas wells. In order to mitigate this issue, in the present work, a model is proposed to loosely couple the dynamics of gas inflow into shale gas wells with the dynamics of the liquid inflow. Starting from the flow law that accounts for the four stages of movement of the plunger, a dynamic model of the plunger lift based on the real wellbore trajectory is introduced. The model is then tested against 5 example wells, and it is shown that the accuracy level is higher than 90%. The well ‘switch’, optimized on the basis of simulations based on such a model, is tested through on-site experiments. It is shown that, compared with the original switch configuration, the average production of the sample well can be increased by about 15%.

View-Invariant Center-of-Pressure Metrics Estimation With Monocular RGB Camera

Center of pressure (CoP) metrics, including CoP path length and sway area, have been used as gold standard measurements of postural and balance control in biomechanical studies. A recent study of computer-vision-based CoP metrics estimation from 3D body landmark sequences offers a more portable and comprehensive solution than conventional force plate methods to obtain these important metrics for real-time evaluation of balance control. However, obtaining accurate 3D body landmarks requires a calibrated motion capture system or on-body markers, which involves lengthy data collection and processing time and limits their implementation in home and clinical environments. Existing methods that instead use 2D body landmarks fail to adapt to different camera positions. To overcome these challenges, we propose a view-invariant deep learning framework for video-level CoP metrics estimation, including CoP path length and sway area, using pose dimension lifting and graph convolutional network (GCN). This work is the first step toward obtaining gold-standard CoP metrics with an accessible, monocular RGB camera. We propose to use a dimension lifting convolutional neural network (CNN) to obtain view-invariant 3D body landmark features from 2D body landmarks. We also propose a two-stream regression model using GCN and discrete cosine transform (DCT) for a robust CoP metrics estimation. To facilitate the line of research, we release a novel multi-view body landmark dataset containing 2D body landmarks of a wide variety of action patterns from four different camera views with synchronized CoP labels and corresponding 3D body landmarks, which enables cross-view evaluation with different camera angles. We subsequently validate the proposed method through a cross-dataset training by training the dimension lifting model on an existing balance dataset and evaluating the CoP metrics estimation on the multi-view body landmark dataset. The experiments validate that our framework achieves state-of-the-art accuracy for both CoP path length and CoP sway area using a monocular RGB camera input for unseen views.

Comparison of aerodynamic models for horizontal axis wind turbine blades accounting for curved tip shapes

AbstractCurved tip extensions are among the rotor innovation concepts that can contribute to the higher performance and lower cost of horizontal axis wind turbines. One of the key drivers to exploit their advantages is the use of accurate and efficient computational aerodynamic models during the design stage. The present work gives an overview of the performance of different state‐of‐the‐art models. The following tools were employed, in descending order of complexity: (i) a blade‐resolved Navier Stokes solver, (ii) a lifting line model, (iii) a vortex‐based method coupling a near‐wake model with a far‐wake model, and (iv) two implementations of the widely used blade element momentum method (BEM), with and without radial induction. The predictions of the codes were compared when simulating the baseline geometry of a reference wind turbine and different tip extension designs with relatively large sweep angle and/or dihedral angle. Four load cases were selected for this comparison, to cover several aspects of the aerodynamic modeling: steady power curve, pitch step, extreme operating gust impact, and standstill in deep stall. The present study highlighted the limitations of the BEM‐based formulations to capture the trends attributed to the introduction of curvature at the tip. This was true even when using the radial induction submodel. The rest of the computational methods showed relatively good agreement in most of the studied load cases. An exception to this was the standstill configuration, as the blade‐resolved Navier‐Stokes solver was the only code able to capture the highly unsteady effects of deep stall.

Approval of a computational method for determining thermal loads during accidents accompanied by fireballs

Introduction. Large fires are quite often accompanied by the formation of fireballs (OSH), which create significant thermal loads. As a result of the fire coverage of a container containing an overheated liquid, a physical explosion occurs, which releases a significant amount of fuel forming a fireball. This phenomenon is dangerous because, with a short lifetime, it is capable of causing thermal injuries over considerable distances. The existing methods of predicting the consequences of a fireball, set out in various domestic regulatory documents and foreign literature, do not fully reflect the possible values of thermal loads. In this regard, a computational method for determining thermal loads was developed, taking into account the movement of the fireball.Goals and objectives. The purpose of this study is to test the developed computational methodology for determining thermal loads, which takes into account the kinematic characteristics of the fireball. The following tasks were solved in the study:● check the operability of the fireball lifting model;● to conduct a comparative analysis of thermal loads according to the developed methodology with the results of calculation of domestic and foreign methods;● conduct a computational experiment on the effect of air mobility (wind effect of 7 m/s) on thermal loads.Research methods. To check the operability of the OSH lifting model, footage of the formation of a fireball was used. According to the shooting frames, the position of the fireball in space and its kinematic characteristics were tracked. Using kinematic parameters, thermal loads were determined. To assess the adequacy of calculations of thermal loads, a comparative analysis of the results of calculations of the developed computational methodology with the results of calculations using existing domestic and foreign methods was used. To substantiate the application of the developed computational methodology for determining thermal loads, taking into account the kinematic parameters of the fireball, a computational experiment was conducted using the MATLAB software and computing complex.Results and their discussion.The results of the calculation of kinematic parameters are satisfactorily correlated with the results of mathematical modeling. The obtained values of thermal loads according to the developed computational methodology are in satisfactory agreement with the results of calculations according to existing domestic and foreign methods. Based on the calculations performed in the article, it is shown that a change in gas dynamic flows (wind demolition) leads to a significant change in the damaging factors of fireballs that are formed during fires in emergency situations.Conclusions. The results of the research made it possible to assess the adequacy of the performance of the improved computational methodology for determining thermal loads in accidents accompanied by fireballs, as well as to justify the relevance of the application of the developed method.

Prediction of Highway Blocking Loss Based on Ensemble Learning Fusion Model

Road blocking events refer to road traffic blocking caused by landslides, debris flow, snow disasters, rolling stones and other factors. To predict road blocking events, the limit gradient lifting model (XGBoost), random forest regression model (RF regression) and support-vector regression model (SVR) are used as the prediction meta-models, and then the meta-models are fused by a logical regression algorithm to construct a road blocking loss prediction fusion model based on ensemble learning. The actual road blocking event data are used to train the model. Using the same blocking location and similar blocking loss characteristics between adjacent points to fill in the missing value and conducting one-hot encoding for other short character sets with obvious category characteristics such as letters, numbers, and Chinese characters overcomes the problems of inherent data loss, error and time logic disorder in the blocking event data set. The test results show that the R2 score based on the stacking fusion model reaches 0.91, which is 18% higher than RF and 11% and 5.8% higher than SVR and XGBoost, respectively, and the RMSE and MAE values are 0.1707 and 0.0341, respectively. Therefore, the proposed road blocking data preprocessing method and road blocking loss prediction fusion model can be used to predict the amount of blocking event loss.