Speed Operation Research Articles

Analysis of Mechanical Behavior of Symmetric Prefabricated Bodies and Metal Connecting Components During Hoisting and Overturning in Assembly Structures

Prefabricated assembly structures play a pivotal role in modern building construction and underground transit developments, offering benefits such as ease of installation, rapid construction, and environmental sustainability. These prefabricated assembly structures are always symmetric and particularly prevalent in projects like subway station construction, where symmetry prefabricated blocks are commonly used. The hoisting and overturning of these blocks are crucial stages in the construction sequence. Given the substantial weight (tens of tons) and size (several meters) of these prefabricated elements, the materials and structural integrity of the metal components, including bolts and steel rods, must meet strict standards during these phases. To ensure stability during overturning and safety throughout hoisting, this paper utilizes a finite element model to analyze the hoisting and overturning of three prefabricated blocks used in subway station assembly. This paper investigates the mechanical behavior of embedded components, such as lifting lugs, steel liners, and hoisting steel rods, during these processes, analyzing their stress and strain. The selection methods of different steel bars (diameter, hollow, solid, etc.) in the hoisting process were obtained, and the operation speed in the hoisting and overturning process was determined, which guided the selection of the hoisting position when the common overturning action was known. The results offer valuable guidelines for the placement and spacing of lifting lugs, as well as the optimal hoisting speed, thereby informing the selection of embedded lifting lugs and the design of operational protocols in actual assembly construction.

Potential Effects of Statistical complexity measure in improving the fault diagnosis performance under speed fluctuations

Abstract Many research studies are performed in extracting non-stationary signal properties such as instantaneous frequency (IF) and envelope to diagnose machinery faults under time-varying speed conditions. But accurate IF extraction is difficult from such complex fluctuating signals to identify the faults efficiently. Therefore, this paper proposes the Statistical Complexity Measure (SCM) as a feature that evaluates the randomness and complexity without any additional requirement, providing an outstanding result in multi-component fault detection under speed fluctuations. Primarily, the influence of SCM is proved by ranking the features with the help of the Random Forest (RF) algorithm. The fault classification of individual signal characteristics confirms its ranking order as well. Then, it is targeted towards the efficacy of the signal fusion approach in multicomponent fault diagnosis. Focusing on the essence of SCM, it is merged with the other three signal properties based on the ranking technique to investigate which one of the feature compositions is more advantageous. The machine learning classifier, SVM-RBF, achieves 94.7%, which proves the potential effects of SCM features in the industrial rotational machinery component under variable speed operating conditions.

Discrete Element-Based Design of a High-Speed Rotary Tiller for Saline-Alkali Land and Verification of Optimal Tillage Parameters

Aiming at the saline soil in Binhai New Area, which is solid and sclerotic, and addressing the problem of poor quality and low efficiency of traditional rotary tillage, this research designed a high-speed rotary tiller that can realize the high-speed rotation of knife rollers to cut. The average operating speed is higher than that of the ordinary rotary tiller. We analyzed the rotary tiller operating conditions and rotary tiller knife cutting process and conducted a movement trajectory theoretical analysis to determine the rotary tiller’s high-speed operating speed relationship. The working process of a high-speed rotary tiller was simulated using EDEM software. The experimental indicators included the soil-crushing rate and surface smoothness after tilling. The experimental factors included the forward speed of the machine, the rotational speed of the blade roller, and the tilling depth. An orthogonal experiment was performed to establish regression equations for the soil-crushing rate and surface smoothness. Using Design-Expert analysis software, we obtained the following optimal combination of parameters: a knife roller speed at 310 r/min, tillage depth of 13.2 cm, and machine forward speed of 4.8 km/h. At this time, the simulation values of the soil fragmentation rate and surface flatness were 90.6% and 18.2 mm, respectively. When determining the optimal knife roller speed of 310 r/min, a transient structural simulation under the mesh bevel gear transient was conducted. The simulation analysis showed that the maximum equivalent stress value was 584.57 MPa, which was smaller than the permissible stress of 695.8 MPa, meeting the bevel gear meshing strength requirements. Under the optimal combination determined by a field comparison test, the results show that the values of the high-speed rotary tiller operation after the soil-breaking rate, tillage depth, the tillage depth stability coefficient, and vegetation cover were 89.3%, 14.2 cm, 92.8%, and 90.3%. The land surface flatness was 16.4 mm, which is superior to the ordinary rotary tiller operation effects, meeting the agronomic requirements for pre-sowing land preparation for peanuts in the saline land of Binhai New Area.

Prediction and Analysis of Expressway Tunnels Crash Based on M-CNN and SHAP Techniques

The prediction and analysis of traffic crashes in expressway tunnels plays a pivotal role in enhancing tunnel safety. This study introduces the modified Convolutional Neural Network (M-CNN) for tunnel traffic crash prediction. The Synthetic Minority Oversampling Technique (SMOTE) is utilized to address the issue of imbalanced crash data. Based on the prediction results, this study identifies sections of high risk in tunnels and utilizes SHapley Additive Explanations (SHAP) to enhance the interpretability of M-CNN. The results demonstrate that the prediction accuracy of M-CNN is significantly higher at 74.62%, surpassing the baseline models including Convolutional Neural Network (CNN), back propagation neural network (BPNN), random forest (RF), long short-term memory (LSTM) and support vector machine (SVM). Moreover, zone 1, zone 2, zone 6, and zone 7 are identified as tunnel risk zones. In addition, driver’s operation, tunnel grade and vehicle speed have the greatest impact on rear-end crash, sideswipe crash, hit-guardrail crash respectively. This study also reveals intricate interaction effects between the variables and (Skidding resistance index) SRI exhibits a negative correlation with crash risk. The research findings have significant implications for the future implementation of machine learning models in crash studies, with practical applications for reducing crash rates.

A novel dielectric elastomer with low modulus and fast response

Abstract Dielectric elastomers (DEs) are highly valued in massive fields of actuators and sensors due to their unique advantages of large actuation strain, fast response speed, high energy density and excellent elasticity. The challenge of balancing the elastic modulus, actuation strain and response speed of DE actuators to stably enhance the actuation performance remains a major issue. In this work, a novel DE was prepared by employing polyurethane acrylate (CN9021) as a crosslinker, n-butyl acrylate as a base monomer and 2-ethylhexyl acrylate (2-EHA) as a functional flexible monomer via a UV curing method. The swelling test indicates a reduction of crosslinking density with the increment of 2-EHA concentration in EHA films. As a consequence, the elastic modulus displays a notable decline while the dielectric constant slightly rises, leading to an enhancement of the actuation sensitivity. More specifically, the elastic modulus of our fabricated EHA-1 is only one-third of the commercial VHB-4910. The developed DEs achieve an actuation strain of nearly 150% with low viscoelasticity and mechanical loss resulting in high response speed and broad operating frequency range to the input dynamic voltages. All these actuation performances are superior to VHB-4910. This work provides a promising strategy for developing DEs with balanced performance and superior actuation characteristics.

Modeling of Tank Vehicle Rollover Risk Assessment on Curved–Slope Combination Sections for Sustainable Transportation Safety

Tank vehicles are highly prone to rollover accidents, especially on curved–slope combination sections, which can cause hazardous chemical spills, endangering the environment, public safety, and human health. Therefore, it is crucial to conduct research aimed at reducing the risk of such incidents. Method: The rollover risk of tank vehicles under various loading conditions while traveling on curved–slope combination sections was investigated using driver–vehicle–road dynamics simulation. A multiple linear regression model was then developed to further quantify the impact of key factors on the rollover risk. Results: The results revealed that the road curve radius, vehicle operating speed, and liquid cargo fill level have the greatest impact on a tank vehicle’s rollover risk, and higher fill levels, higher speeds, and steeper downhill slopes all amplify the impact of curve radius on the rollover risk. In some cases, adhering to the road’s speed limit alone was insufficient to ensure the safe passage of the tank vehicle through curves. Conclusions: This study introduced, for the first time, a rollover risk assessment model for tank vehicles operating on curved–slope combination sections. The findings reveal effective methods to improve the transportation safety of tank vehicles. Practical Applications: The findings of this study can assist transportation agencies in selecting routes with lower rollover risks for tank vehicles with different configurations, as well as guide the development of loading standards and curve speed limits. This will effectively reduce rollover accidents of tank vehicles and support sustainable, safer transportation practices.

Optimization of the convergent-divergent nozzle tip and reducing the acoustic energy

One of the practical components that can be found in aircraft engines, the Delaval nozzle, works to improve the aircraft's performance in terms of its stability and its capacity to manoeuvre at different speeds of operation. This research concentrates on the acoustic behaviors shown by this nozzle during the transition from low rate to sonic speed. The modification of the nozzle tip at various degrees of inclination on the transition velocity is the procedure used to minimize the amount of acoustic energy emitted. The acoustic behavior is analyzed based on the FW-H acoustics model utilized, and the optimization problem is solved using the practical eddy simulation approach in CFD. The acoustic Power, measured in dB, is estimated at various angles on the nozzle tip, and the performance of the Delaval nozzle is used to show how the redesigned nozzle tip affects the nozzle's acoustic behavior.

Enhancing High-Speed Railway Timetable Resilience: A Two-Level Spatiotemporal Network Model Focused on Disturbance Absorption

Abstract Enhancing the resilience of train timetables can effectively improve their resistance and recovery capabilities against operational disturbances, thereby ensuring the stable operation of the railway system and the quality of passenger transportation services. This paper defined timetable resilience as three parts: disturbance absorptive capacity, resistance capacity, and post-disturbance recovery capacity. These capacities were quantitatively evaluated using the buffer time of trains, the number of train delays at departures and arrivals, and the duration of delay state. The evaluation metrics of the three resilience capacities and the total travel time of trains were used as the objective to establish a spatiotemporal network model. Utilizing actual operational data from the Beijing–Shanghai high-speed railway in China, the model was validated through a case study. Sensitivity analysis of the model's key parameters was conducted under two experimental scenarios: routine operational disturbances and speed restrictions on specific sections. Results showed that our model can effectively reduce the delay time and the number of delays in both the routine operational disturbance scenario and the 300 km/h speed restriction scenario with a frequent disturbance value of 2 min. Moreover, the model's performance with 3-min frequent disturbance outperformed that with 2-min frequent disturbance in speed restriction at 250 km/h and 200 km/h, yielding a 30% improvement.

Online monitoring of water quality in industrial wastewater treatment process based on near-infrared spectroscopy.

Water quality monitoring is one of the critical aspects of industrial wastewater treatment, which is important for checking the treatment effect, optimizing the treatment technology and ensuring that the water quality meets the standard. Chemical oxygen demand (COD) is a key indicator for monitoring water quality, which reflects the degree of organic matter pollution in water bodies. However, the current methods for determining COD values have drawbacks such as slow speed and complicated operation, which hardly meet the demand of online monitoring. This article firstly proposed a novel quantitative analysis method based on NIR spectroscopy and multi-preprocessing stacking, successfully enabling real-time online monitoring of COD values during industrial wastewater treatment. First, the existing swarm intelligence algorithm was enhanced to optimize the hyperparameters of various base models. Next, multiple spectral preprocessing techniques were innovatively combined with a stacking strategy to construct multi-preprocessing stacking models, enabling comprehensive extraction of effective spectral information. Finally, various combinations of base models were evaluated, leading to the selection of the multi-preprocessing stacking model with optimal performance. The results indicate that the model achieves excellent predictive performance and strong generalization ability. For equalization tank samples, the R2 and RMSE values were 0.8640 and 326.6845 mg/L, respectively. For secondary settling tank samples, the R2 and RMSE values were 0.8798 and 15.1917 mg/L, respectively. Compared to traditional single and stacking models, the RMSE was reduced by at least 12.75 % and 5.11 %, respectively. In addition, the method has a relatively low modeling cost and offers interpretability. This study presents an efficient and straightforward solution for the online monitoring of COD values in industrial wastewater treatment, laying a solid technical foundation for the efficient management of industrial wastewater and the protection of water resources and the ecological environment.

High Gain, Low Voltage Solar-Blind Deep UV Photodetector Based on Ga2O3/(AlxGa1-x)2O3/GaN nBp Heterojunction.

In this study an (AlxGa1-x)2O3 barrier layer is inserted between β-Ga2O3 and GaN in a p-GaN/n-Ga2O3 diode photodetector, causing the dark current to decrease considerably, and device performance to improve significantly. The β-Ga2O3/β-(AlxGa1-x)2O3/GaN n-type/Barrier/p-type photodetector achieves a photocurrent gain of 1246, responsivity of 237 A W-1, and specific detectivity of 5.23 × 1015cm Hz1/2W-1 under a bias of -20V. With the irradiation of 250nm solar-blind ultraviolet light, the photocurrent exhibits a dramatic nonlinear increase beyond a bias of ≈-4V, attributed to interband electron tunneling. The onset of interband tunneling at a relatively low bias is due to the strong internal electric field formed by self-trapped holes (STHs) in Ga2O3. This study also proposes an effective way to suppress persistent photoconductivity and significantly increase the device operation speed in photodetectors fabricated from Ga2O3 through the light-induced neutralization of STHs.

3D Vertical Ferroelectric Capacitors with Excellent Scalability.

Three-dimensional vertically stacked memory is more cost-effective than two-dimensional stacked memory. Vertically stacked memory using ferroelectric materials has great potential not only in high-density memory but also in neuromorphic fields because it secures low voltage and fast operation speed. This paper presents the implementation of a ferroelectric capacitor comprising a vertical two-layer stacked structure composed of a titanium nitride (TiN)/aluminum-doped hafnium oxide/TiN configuration. To enhance the ferroelectric properties influenced by the active area, we propose a structure in which multiple small holes share a common pillar electrode. Comprehensive analyses using transmission electron microscopy and energy-dispersive X-ray spectroscopy were conducted to confirm the chemical composition and physical structure of the device. This newly engineered architecture demonstrated promising characteristics, including a sufficient remnant polarization, small device-to-device variations, high endurance, and excellent retention in 3D vertical structures. Moreover, this structure can be applied to one-transistor n-capacitor ferroelectric random access memory with a vertical transistor.

A CNN-based self-supervised learning framework for small-sample near-infrared spectroscopy classification.

Near-infrared (NIR) spectroscopy, with its advantages of non-destructive analysis, simple operation, and fast detection speed, has been widely applied in various fields. However, the effectiveness of current spectral analysis techniques still relies on complex preprocessing and feature selection of spectral data. While data-driven deep learning can automatically extract features from raw spectral data, it typically requires large amounts of labeled data for training, limiting its application in spectral analysis. To address this issue, we propose a self-supervised learning (SSL) framework based on convolutional neural networks (CNN) to enhance spectral analysis performance with small sample sizes. The method comprises two learning stages: pre-training and fine-tuning. In the pre-training stage, a large amount of pseudo-labeled data is used to learn intrinsic spectral features, followed by fine-tuning with a smaller set of labeled data to complete the final model training. Applied to our own collected dataset of three tea varieties, the proposed model achieved a classification accuracy of 99.12%. Additionally, experiments on three public datasets demonstrated that the SSL model significantly outperforms traditional machine learning methods, achieving accuracies of 97.83%, 98.14%, and 99.89%, respectively. Comparative experiments further confirmed the effectiveness of the pre-training stage, with the highest accuracy improvement, reaching 10.41%. These results highlight the potential of the proposed method for handling small sample spectral data, providing a viable solution for improved spectral analysis.

Enhanced Operation Speed and Thermal Stability of a 150 nm-Thick SbTe Film by Y Doping for Optoelectronic Hybrid Phase-Change Memory

Enhanced Operation Speed and Thermal Stability of a 150 nm-Thick SbTe Film by Y Doping for Optoelectronic Hybrid Phase-Change Memory

Qualitative and quantitative nondestructive determination of cyanide in water and distilled spirits by Raman integrating sphere.

Cyanide often forms as a byproduct during the fermentation process of distilled spirits, and excessive amounts can cause damage to health. Cyanide poisoning is also common in alcoholic beverages and water. Therefore, the cyanide content measurement in water and distilled spirits is essential. Here, a Raman integrating sphere technique for the in situ detection of cyanide has been established, and a lower limit of detection of 1.56 mg L-1 in water and ethanol was achieved, owing to the efficient collection of Raman signals and the efficient use of laser power. The Raman peak of cyanide is located at 2080 cm-1. The detection range was from 1.56 to 25 mg L-1 cyanide in water, ethanol, and liquor with high linearity (R2 = 0.9986, 0.9986, and 0.9908) and precision (%RSD = 5.53%, 6.92%, and 14.12%). This instrument provides an alternative method for detecting cyanide, which can not only be used for accurate qualitative and quantitative detection of cyanide but also for food safety detection and physical evidence analysis of cyanide in beverage poisoning. Key to our method is that it offers advantages such as no need for sample pretreatment, simplicity and speed of operation, non-destructive analysis, minimal sample consumption (only 3 milliliters are required for a single measurement), and the ability to preserve the analyzed sample as evidence.

Design of Multilayered XOR Gate Using Quantum Dot Cellular Automata

The rapid advancement of microelectronics technology necessitates the development of high-speed, low-power devices. Quantum Cellular Automaton (QCA) is emerging as a promising technology enabling logic circuits with exceptional operating speeds. The XOR gate is crucial in nanocomputing applications, such as nano-processors and nano-communication units, and requires innovative design approaches. This paper presents a novel design of an XOR gate using a multilayered layout methodology within the QCA framework. The circuit’s design and validation are performed using QCADesigner 2.0.3, while energy dissipation analysis is conducted with QCADesigner-E, resulting in an energy dissipation of 15 meV. Comprehensive parameter analysis, including cost functions, highlights the superiority of the proposed design. A comparative analysis with similar existing multilayered designs shows a 55% improvement in QCA-specific quantum cost. Notably, the proposed design eliminates the need for internal nodes within the layout, enhancing the methodology’s applicability to higher-order and more complex circuits.

Data-driven framework with theoretical modelling to evaluate fuel savings through air lubrication system

This study introduces a data-driven and theoretical framework to evaluate the Air Lubrication System (ALS) in maritime operations, analyzing its impact on fuel consumption under varied conditions. The operational data from three 325k series ships is used to examine ALS performance over operational scenarios, including different drafts and speeds. The Partial Cavity Drag Reduction (PCDR) model consistently outperformed the Air Layer Drag Reduction (ALDR) model, providing an average fuel consumption savings of 10–15%. Notably, ALS demonstrated enhanced effectiveness at operational speeds above the critical threshold speed of about 9 knots, defined as the minimum speed at which ALS begins to yield net positive energy savings. Additionally, increased draft conditions significantly influenced ALS efficiency; higher drafts increased the compressor power required, reducing the net savings, with net power savings of up to about 4000 kW (approximately 25% reduction in power consumption). This comprehensive evaluation highlights the pivotal role of operational speed and draft in optimizing ALS performance. The integration of data-driven analytics with theoretical insights significantly strengthens the evaluation process, offering a powerful approach that substantiates the ALS's potential to reduce fuel consumption and meet stringent environmental standards, thus providing crucial operational insights for maritime operators.

Comparative Studies on Micro-Fins Geometry for Fin Efficiency and Effectiveness

The emergence of high-speed internet connectivity has enabled the development of very large-scale, integrated semiconductor chips and processors. High-speed data processors emit heat energy proportional to their operation speeds. Micro-fins have broad applications in medical devices and for cooling solar and fuel cells. Herein, natural convective heat transfer studies were conducted on copper- and aluminium-coated micro-fins. Rectangular, triangular, and parabolic specimens were fabricated (dimensions: 45×45×6mm3, spacing: 5 mm). The rectangular copper-coated micro-fin exhibited better performance than triangular micro-fins. Among all specimens, the parabolic copper- and aluminium-coated ones exhibited the best fin efficiency and effectiveness. The parabolic aluminium-coated micro-fin showed fin efficiency and fin effectiveness of 98.80% and 2.0, respectively, after one hour of heating. For the same heating duration, the parabolic copper-coated micro-fin showed the highest fin efficiency of 99.02%; its fin effectiveness was 2.60. The copper-coated micro-fin with the same geometry showed fin efficiency and effectiveness of 99.02 and 2.493, respectively, for four hours of heating.

Numerical study of a small-dimensional volume roller pump

One of the solutions to reduce capital and operating costs for oil production can be the development of energy-intensive equipment with increased reliability characteristics. The work is devoted to the study of the design of a volumetric multistage roller rotary pump. Experimentally observing the behavior of fluid inside a pump during operation is expensive, but predictive tools based on computational fluid dynamics, have emerged as a viable alternative approach to pump design and optimization. The article presents the results of a numerical study of a roller pump in the Ansys Fluent software package using a dynamically tunable mesh by the overset mesh method. A method for studying the working volume of chambers separated by roller partitions is proposed. Analysis of the pump operation showed the characteristic pulsation of the values of the main parameters. For a pump with an operating speed of 3000 rpm, a chamber diameter of 40 mm and a productivity at a given speed of 15 m3/day, an increase in amplitude is observed at the initial moment of time and after about 6 seconds from the beginning of the simulation a quasi-stationary mode appears. The flow rate at the outlet is 1 mm/s, at the inlet 0.5 mm/s, the torque values on the pump shaft are in the range from -0.02 N·mm to 0.14 N·mm, the shaft power is from ‒ 2 N·mm/s to 12 N·mm/s, shaft rotation speed from 70 to 130 rps. All pump parameters correlate with the fluid flow rate at the pump inlet. Keywords: fuel and energy complex; oil and gas industry; well equipment; volumetric rotor pump; computational fluid dynamics; numerical simulation; moving mesh.

Tractor-free agriculture is an intersectoral end-to-end technology of agriculture

The article presents a new technological direction – «tractor-free agriculture». Its advantages are proved and the attributes that translate this technological direction into the category of intersectoral end-to-end technology are determined. The paradigm of «tractor-free farming» declares the rejection of the use of classic tractors of traction and traction-energy concepts and defines the transition to mobile energy modules-transformers of the energy concept. The principles of the energy concept determine the maximum possible use of engine power for useful movement and execution of work processes, which ensures high efficiency of «tractor-free farming». The use of sets of mobile power transformer modules of electric drive type, complete with technological modules, instead of tractors, ensures maximum technical and technological equipment of agricultural industries for all conditions and various technological operations. The authors have proved that «tractor-free farming» is the most effective in the field of industrial horticulture and nursery farming, characterized by a wide range of conditions and a variety of technological operations performed. The article provides examples of basic configurations and technical appearance of «tractor-free farming» products for the industrial horticulture and nursery industry. The intersectoral nature of the new technological direction makes it possible to effectively use products of «tractor–free agriculture» in other sectors of agriculture – vegetable growing, breeding and seed production, as well as in municipal and urban logistics. An example of the practical implementation of individual fragments of «tractor-free farming» products in industrial gardening can be an existing model demonstrator of a garden e-Drone, with the pilot name «Russian Shuttle», created at the INTECH Engineering Center of the Michurinsk State Agrarian University. This product with a load capacity of up to 1000 kg and an operating speed from 0 to 30 km/h is designed for harvesting and transporting fruits. A distinctive feature of the Russian Shuttle garden drone is the use of 2 mass–produced electric drive bridges with a capacity of 1.2 kW, which significantly reduces its cost (by 10 times) compared to existing foreign self-propelled garden platforms.

Optimization of a disturbance-invariant automatic control system

The possibility of optimizing the automatic control system invariant to perturbations by expanding the structure of the perturbation control coefficient is shown. The use of the algebraic-integral-differential structure and their individual components is analyzed, and the prevailing influence of each of them on the speed of operation, the nature of the system's behavior, and its fixed error is established. Numerical modeling confirmed the effectiveness of using such a structure of the controller for the impact of disturbances to ensure speed and quality of control under arbitrary external disturbances.