Journal of Mechanical Engineering, Automation and Control Systems
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Application of modal analysis to multi-objective optimization of gear box
Get PDFThe high stiffness and low mass gearbox structure design requirements were met through multi-objective optimization based on finite element modal analysis. The correlation between structural parameters and optimization objectives was identified, providing guidance for optimal design. A parametric finite element model of the box cover was established to calculate the first-order modal shape and equivalent stiffness. By using mass and equivalent stiffness as optimization objectives, a discrete data set was constructed with key dimensions of the box cover as design variables, from which a surrogate model was obtained. Standard response surface and full second-order polynomial fitted response surface function were applied after error verification. Different optimization algorithms were employed to search for the optimal value, resulting in obtaining design variables meeting the optimization objectives. The results indicated a reduction in optimized mass by approximately 6.8 %, with an error of less than 1 %
Modeling of unsteady-state creep of asphalt concrete
Get PDFThe article experimentally investigated unsteady-state creep of a hot fine-grained dense asphalt concrete under uniaxial tension at temperatures of 22-24 °C. 61 samples of the asphalt concrete in the form of a rectangular beam with dimensions of 50×50×150 mm were tested to complete failure at seven different stresses (from 0.084 MPa to 0.3053 MPa) in a special installation with a heat chamber. Based on the test results, unsteady-state creep curves were constructed, which were normalized and approximated with high accuracy by a power function. Reliable dependences of the limiting time of hardening, the limiting strain of hardening, and the hardening rate on stress have been established
An improved wavelet threshold function denoising method based on IGA optimization
Get PDFIn single crystal diamond tool grinding, cutting-edge quality is affected by various parameters, with tool vibration playing a crucial role. Due to environmental factors and the complexity of the process, vibration signals are often noisy and non-stationary. This study proposes a denoising method that optimizes the wavelet threshold function using an Improved Genetic Algorithm (IGA). The method introduces a configurable value α in the wavelet threshold function, which is optimized with IGA to improve denoising. MATLAB R2018b simulations show that this approach achieves better denoising, a higher signal-to-noise ratio, and lower mean square error
On the decisional problem based on matrix power function defined over non-commutative group
Get PDFIn this paper, we perform statistical analysis for the decisional problem which is fundamental for the security of the key exchange protocol based on matrix power function. We have proven previously that the considered decisional problem is NP-complete and hence our proposal could potentially be quantum-safe. However, we did not explore the dependence of the complexity of the considered problem on the security parameters. Here we show that for small matrices certain information could be gained from the distribution of the entries of the public key matrices. On the other hand, we show that as the size of the matrices grows, the public key matrices are indistinguishable from truly random matrices
Secure metric dimension of new classes of graphs
Get PDFThe metric representation of a vertex v of a graph G is a finite vector representing distances of v with respect to vertices of some ordered subset S⊆V (G). If no suitable subset of S provides separate representations for each vertex of V(G), then the set S is referred to as a minimal resolving set. The metric dimension of G is the cardinality of the smallest (with respect to its cardinality) minimal resolving set. A resolving set S is secure if for any v∈V–S, there exists x∈S such that (S–{x})∪{v} is a resolving set. For various classes of graphs, the value of the secure resolving number is determined and defined. The secure metric dimension of the graph classes is being studied in this work. The results show that different graph families have different metric dimensions
Optimization and experimental validation of the air intake holes of the lithium-ion battery pack
Get PDFEnergy storage systems enable the storage of energy and provide access to carbon-neutral, environmentally friendly energy whenever or wherever it is needed. Lithium-ion batteries are currently the most preferred type among various battery technologies and are widely used in energy storage systems. Some of the features that make lithium-ion batteries advantageous include high energy density, long life, low maintenance requirements, and high operating voltage. The growing demand for energy throughout the day increases the need for batteries with high storage capacity. However, the increased capacity also leads to heating issues in lithium-ion batteries. The heating problem in lithium-ion batteries can result in nonhomogeneous temperature distribution, shortened lifespan, thermal runaway, increased internal resistance, and performance loss. Therefore, an effective thermal management system is essential for cooling lithium-ion batteries. This study aims to provide insight into the forced air cooling of prismatic 280 Ah LiFePo4 batteries, which have limited information in the literature and are more prone to overheating compared to lower-capacity batteries. In this study, five different battery pack case designs, each with different sizes and numbers of air intake holes, were determined and modelled using the SolidWorks program. Within the battery pack cases, 16 280 Ah lithium-ion batteries are placed, and an axial fan is used to cool these batteries. Initially, computational fluid dynamics analyses of the five different designs were performed in the SolidWorks Flow Simulation program. An experiment was then conducted on the design that provided the most efficient thermal management to validate the numerical results. The selected design, fulfilling the purpose of homogeneous temperature distribution and having the minimum temperature difference between batteries, was designated as Design 5. It exhibited a 62 % improvement in cooling performance with a 0.25 °C temperature difference, indicating successful temperature homogeneity between batteries. During a two-hour experiment with a 140 A discharge current, temperature measurements were taken from the surfaces of the batteries using thermocouples. Finally, the maximum error rate between experimental and numerical studies was determined to be 1.47 %, indicating successful validation of the numerical study. The air intake hole optimization, a novel design approach, prevents temperature distribution inhomogeneity caused by the distance of the batteries to the fan and offers an effective way to cool down high-capacity 280 Ah batteries
Adoption of metal additive manufacturing in nnpc limited: current state and challenges
Get PDFMetal additive manufacturing has emerged as a promising technology with vast potential in the oil and gas industry. The Nigerian National Petroleum Company (NNPC) Limited recognizes the significance of this technology and has initiated efforts to adopt metal additive manufacturing within its operations. This paper aims to provide an overview of the current state of metal additive manufacturing in the NNPC and highlight the challenges faced during its adoption process. The study goes on further to suggest strategies and future directions to ensure successful company-wide and industry-wide adoption and acceptance
Lubrication optimization of high-speed train drive gearbox
Get PDFThe gears in the high-speed heavy-duty gearbox of the high-speed train are typical high-speed heavy-duty gears. Combined with the transmission principle and structural characteristics of the high-speed train drive gearbox, to ensure adequate lubrication of meshing gears and bearings, an optimization of the lubricating oil flow inside the gearbox was conducted. The oil and gas two-phase flow model inside the gearbox adopts the VOF model, and the turbulence model adopts the standard κ-ε model. Fluent is used for simulation calculation. The results show that the exhaust port position of the gearbox has little effect on the flow of lubricating oil inside the gearbox; the overall pressure distribution inside the gearbox is relatively uniform, with higher pressure only at the meshing gears; the distribution of lubricating oil inside the gearbox is related to the rotation of the gears, and the flow velocity of lubricating oil is mainly affected by the rotation of the gears, with the maximum flow velocity appearing around the gears; the flow of lubricating oil inside the gearbox meets the lubrication requirements of the gearbox. These results provide support for the lubrication design, flow channel structure improvement, and effectiveness evaluation of high-speed train transmission gearboxes
Experimental study on the influence of different curing methods on the performance of concrete
Get PDFCuring concrete is an effective method to ensure concrete’s mechanical and durability performance. This article experimentally investigates the impact of various curing methods (air curing, sprinkler curing, geotextile curing, and composite geotextile curing) on the compressive strength of concrete at 7, 14, and 28 days, as well as the carbonation depth and chloride ion diffusion coefficient at 28, 56, and 90 days. The effects of different curing methods on concrete performance are compared. The experimental results demonstrate that sprinkler, geotextile, and composite geotextile curing at 7 and 14 days effectively enhance concrete’s mechanical and durability performance. Compared to air curing concrete at 28 days, sprinkler, geotextile, and composite geotextile curing reduced by 17.75 %, 25.11 %, and 31.51 %, respectively, but the average absolute deviation is reducing. From 28 to 90 days, air curing concrete’s chloride ion diffusion coefficient decreases by 8.5 %. For concrete specimens under sprinkler curing, geotextile curing, and composite geotextile curing, the chloride ion diffusion coefficient decreases by 20.4 %, 8.3 %, and 6.0 %, respectively. Beyond 28 days, the durability performance of concrete under composite geotextile curing, including carbonation depth and chloride ion diffusion coefficient, tends to stabilize. The optimal curing period of 28 days is determined based on comprehensive mechanical and durability performance. Composite geotextile curing retains moisture on the concrete surface, slows evaporation, reduces watering frequency and labour costs, and promotes long-term concrete performance development. Carbonation tests and durability performance, such as chloride ion diffusion coefficient, are more sensitive to concrete curing effects. Single indicators like mechanical or durability performance cannot comprehensively evaluate concrete’s long-term performance. Concrete quality should be comprehensively evaluated by considering strength, carbonation depth, chloride ion diffusion coefficient, and other indicators
Multi-source partial discharge pattern recognition in GIS based on Grabcut-MCNN
Get PDFPartial discharge (PD) surveillance constitutes a pivotal methodology for diagnosing insulation failures in electrical equipment. Enhancing comprehensively the precision of identifying PD anomalies in Gas Insulated Switchgear (GIS) is of paramount significance for ensuring the steady functioning of power grids. This study introduces a novel framework that integrates Phase-Resolved PD Graph Segmentation (PRPD-Grabcut) with a tailored MobileNets-based Convolutional Neural Network (MCNN) to classify GIS-related PD issues. Leveraging image segmentation via PRPD-Grabcut, crucial features are extracted from PRPD diagrams, which then facilitate the construction of the MCNN model. This model employs depth-wise separable convolutions alongside inverted residual architectures to tackle the vanishing gradient dilemma inherent in Deep Convolutional Neural Networks (DCNNs) during GIS PD pattern discernment. Upon the model's subsequent training and validation, empirical evidence illustrates that the PRPD-Grabcut-MCNN hybrid significantly alleviates the computational load and storage requisites of the model, concurrently enhancing the recognition precision and expediting the training process of the neural network. Relative to diverse established lightweight neural network architectures, MCNN manifests superior performance in terms of recognition accuracy, reduced cross-entropy loss, and expedited training duration