Novel Methodologies for Preventing Crack Propagation in Steel Gas Pipelines Considering the Temperature Effect

Using the software ANSYS-19.2/Explicit Dynamics,this study performedfinite-element modeling of the large-diameter steel pipeline cross-section for the Beineu-Bozoy-Shymkent gas pipeline with a non-through straight crack,strengthened by steel wire wrapping.The effects of the thread tensile force of the steel winding in the form of single rings at the crack edges and the wires with different winding diameters and pitches were also studied.The results showed that the strengthening was preferably executed at a minimum value of the thread tensile force,which was 6.4%more effective than that at its maximum value.The analysis of the influence of the winding dia-meters showed that the equivalent stresses increased by 32%from the beginning of the crack growth until the wire broke.The increment in winding diameter decelerated the disclosure of the edge crack and reduced its length by 8.2%.The analysis of the influence of the winding pitch showed that decreasing the distance between the winding turns also led to a 33.6%reduction in the length of the straight crack and a 7.9%reduction in the maximum stres-ses on the strengthened pipeline cross-section.The analysis of the temperature effect on the pipeline material,within a range from-40℃ to+50℃,resulted in a crack length change of up to 5.8%.As the temperature dropped,the crack length decreased.Within such a temperature range,the maximum stresses were observed along the cen-tral area of the crack,which were equal to 413 MPa at+50℃ and 440 MPa at-40℃.The results also showed that the presence of the steel winding in the pipeline significantly reduced the length of crack propagation up to 8.4 times,depending on the temperature effect and design parameters of prestressing.This work integrated the existing methods for crack localization along steel gas pipelines.

A Power Battery Fault Diagnosis Method Based on Long-Short Term Memory-Back Propagation

The lithium battery is an essential component of electric cars;prompt and accurate problem detection is vital in guaranteeing electric cars’safe and dependable functioning and addressing the limitations of Back Propagation(BP)neural networks in terms of vanishing gradients and inability to effectively capture dependencies in time series,and the limitations of Long-Short Term Memory(LSTM)neural network models in terms of risk of overfitting.A method based on LSTM-BP is put forward for power battery fault diagnosis to improve the accuracy of lithium battery fault diagnosis.First,a lithium battery model is constructed based on the second-order RC equivalent circuit and the electro-thermal coupling model,and various lithium battery failures are simulated to examine the fault characteristics.Then,the lithium battery charging and discharging experiments collect,clean,and process the battery data.By constructing a neural network LSTM-BP model,we verified the superiority and accuracy of the LSTM-BP neural network model by comparing the LSTM model and BP model vertically and by comparing the Recurrent Neural Network(RNN)model,the Gated Recurrent Unit(GRU)model,and the Residual Neural Network(ResNet)model of a more advanced architecture horizontally.Finally,the lithium battery fault diagnosis process is summarized through the threshold quantitative criteria,and different faults are diagnosed and analyzed.Theresults show that the LSTM-BP neural network not only overcomes the limitations of the LSTMneural network and BP neural network but also improves the ability to process sequence data and reduces the risk of overfitting.

PCA-BP神经网络模型在拖拉机发动机故障诊断中的应用

拖拉机发动机故障诊断是指通过对拖拉机发动机的运行状态、传感器数据等信息进行分析和处理,识别出发动机故障的类型和位置,及时准确地诊断拖拉机发动机故障,对于提高农机装备的使用效率和经济效益具有重要的意义。为此,基于主成分分析(PCA)算法对拖拉机发动机的传感器数据进行降维处理,并使用BP神经网络对降维后的数据进行分类识别,以实现拖拉机发动机故障的诊断。试验结果表明:PCA-BP神经网络模型可以准确地诊断拖拉机发动机的多种故障,相比于传统的BP神经网络模型,具有更高的准确率和更好的泛化能力,表明PCA-BP神经网络模型在拖拉机发动机故障诊断中具有较大的应用前景。

特征融合与BP神经网络结合的刀具磨损预测

通过监测刀具磨损情况,能够有效应对生产加工中的意外状况。为了精确监测刀具的磨损状态,提出了一种多传感器特征融合及BP神经网络结合的刀具磨损预测方法。首先对工业加工中采集到的切削力、振动、声发射信号进行小波阈值去噪,然后在时域、频域和时频域内分析并提取特征,再将融合后的各类传感器特征使用Pearson相关系数和主成分分析(PCA)实现数据降维,最后将降维后的融合特征输入搭建好的BP神经网络,通过非线性仿真分析,从而实现刀具磨损量的预测。案例验证表明:与单一传感器预测相比,提出的多传感器特征融合的刀具磨损预测方法误差最小,且决定系数R2达到0.993。

改进SSA优化BP神经网络的变压器故障诊断

变压器故障类型的准确诊断对保障电网的安全与稳定至关重要。针对BP神经网络与麻雀搜索算法(SSA)存在收敛缓慢和易陷入局部极值导致无法准确诊断的问题,提出将改进的麻雀搜索算法(ISSA)优化BP神经网络应用于变压器故障诊断。首先,引入非线性惯性权重和纵横交叉策略,从而提高算法的收敛速度和全局寻优能力;其次,将ISSA与传统SSA在收敛函数上进行对比分析,得到ISSA算法在迭代12次后以52%的准确率收敛,而SSA算法迭代23次后才达到25%的准确率,证明了ISSA在收敛速度和精度方面有明显提高;最后,将ISSA-BP、SSA-BP和BP诊断模型进行对比。实验结果表明,ISSA-BP模型准确率达到了97%,比SSA-BP、BP神经网络模型分别提高了4%和11%,可以认为提出的算法模型在变压器故障诊断领域具有更高的精度与良好的发展前景。

鸡生长性状相关SH3RF2、LncFAM及IGF2BP1基因SV变异的聚合效应分析

试验旨在对生长性状大效应基因SH3RF2、LncFAM及IGF2BP1的3个突变位点进行聚合效应分析,选出最优组合基因型。试验以鸡7个群体(固始-安卡F2代资源种群、AA肉鸡、哈伯德肉鸡、科宝肉鸡、817肉鸡、广西金陵麻鸡和卢氏绿壳蛋鸡)中的2228个个体为研究对象,检测三个基因位点的基因型变异,利用F2资源群和广西金陵麻鸡研究鸡SH3RF2、LncFAM及IGF2BP1基因的遗传效应,对三个基因组合位点的聚合效应进行分析。结果显示:在分析群体中共有54种可能的组合基因型,通过基因型频率和生长性状关联分析发现,在7个群体中,等位基因Sh-I、Lnc-I和Ig-L1的频率均明显高于Sh-D、Lnc-D、Ig-L2及Ig-W;在F2资源群体和广西金陵麻鸡中,IIIIL1L1基因型在三个基因聚合效应中具有较高的基因频率和较高的体重。结果表明,在提高生长性状方面,IIIIL1L1基因型既能提高体重,又有较高的基因型频率,可以作为选择推荐联合标记。

BP神经网络回归预测模型的改进

为了优化BP神经网络,提出了一种优化BP神经网络的流程。首先,判断各影响因素之间的自相关性,如果各影响因素满足自相关评价指标,则可以使用BP神经网络进行回归训练;其次,改变BP神经网络的隐藏节点数、学习效率、训练误差和训练次数等影响因素;最后,加入遗传算法或者粒子群算法与BP神经网络组成混合算法,以提高BP神经网络的训练精度。

基于BP神经网络的机制砂混凝土抗压强度预测

机制砂混凝土强度影响因素复杂,收集国内外权威文献试验数据建立了162组机制砂抗压强度的数据库,利用BP神经网络对机制砂混凝土抗压强度进行预测。采用多层反向传播算法对人工神经网络模型进行训练并预测,发现BP神经网络模型具有良好的预测和泛化能力,模型的预测值与实测值高度吻合;基于BP神经网络模型分析了石粉含量对机制砂混凝土不同强度等级的影响,发现石粉含量约10%时达到最大值,预测值与实际值的误差在8%以内。深度学习方法可有效提高机制砂混凝土配合比设计的试验效率,降低材料和时间成本。

BP神经网络在离心压缩机叶轮优化中的应用

为了提高离心式压缩机叶轮设计效率并降低计算资源消耗,针对遗传算法优化中计算量大、效率低的问题,提出基于改进粒子群优化算法(IPSO)优化BP神经网络的方法。通过少量计算流体动力学(CFD)仿真样本,训练BP神经网络建立效率与叶轮参数的映射关系,结合IPSO优化其参数,同时利用遗传算法(GA)确定叶轮的最佳性能参数。研究表明,改进的IPSO算法通过增强粒子群的动态适应性和全局搜索能力,提高了BP神经网络的预测精度和优化效率。优化后的叶轮等熵效率提高1.34%,多变效率提高1.04%,流量增加10.4%。该方法显著提升了离心式压缩机叶轮的设计效率和性能,为复杂流体机械的优化设计提供了新思路。

GA-BP模型在HSS模型参数取值中的应用

小应变硬化土(HSS)模型可以有效反映土的压缩硬化特性和小应变特性,非常适合黄土基坑的数值模拟计算。但是,HSS模型包含了11个硬化土(HS)模型参数和2个小应变参数,而这2个小应变参数往往需要采用试验方法确定,获取过程复杂。为了探讨小应变参数的预测方法,采用经过遗传算法优化的BP神经网络模型,即GA-BP神经网络模型,首先根据预设的小应变参数水平经过数值模拟计算得到49组位移数据,然后将得到的数据用于GA-BP神经网络的训练,待GA-BP神经网络的预测误差达到要求之后,再使用实际的位移数据反演得到小应变参数,最后基于预测得到的小应变参数进行数值模拟。结果显示,GA-BP神经网络模型预测的小应变参数在基坑围护结构最大水平位移和地表最大沉降计算方面表现良好,可以应用于实际工程。

Mechanism of internal thermal runaway propagation in blade batteries

Blade batteries are extensively used in electric vehicles,but unavoidable thermal runaway is an inherent threat to their safe use.This study experimentally investigated the mechanism underlying thermal runaway propagation within a blade battery by using a nail to trigger thermal runaway and thermocouples to track its propagation inside a cell.The results showed that the internal thermal runaway could propagate for up to 272 s,which is comparable to that of a traditional battery module.The velocity of the thermal runaway propagation fluctuated between 1 and 8 mm s^(-1),depending on both the electrolyte content and high-temperature gas diffusion.In the early stages of thermal runaway,the electrolyte participated in the reaction,which intensified the thermal runaway and accelerated its propagation.As the battery temperature increased,the electrolyte evaporated,which attenuated the acceleration effect.Gas diffusion affected thermal runaway propagation through both heat transfer and mass transfer.The experimental results indicated that gas diffusion accelerated the velocity of thermal runaway propagation by 36.84%.We used a 1D mathematical model and confirmed that convective heat transfer induced by gas diffusion increased the velocity of thermal runaway propagation by 5.46%-17.06%.Finally,the temperature rate curve was analyzed,and a three-stage mechanism for internal thermal runaway propagation was proposed.In Stage I,convective heat transfer from electrolyte evaporation locally increased the temperature to 100℃.In Stage II,solid heat transfer locally increases the temperature to trigger thermal runaway.In StageⅢ,thermal runaway sharply increases the local temperature.The proposed mechanism sheds light on the internal thermal runaway propagation of blade batteries and offers valuable insights into safety considerations for future design.

Modelling of debris-flow susceptibility and propagation: a case study from Northwest Himalaya

The geological and geographical position of the Northwest Himalayas makes it a vulnerable area for mass movements particularly landslides and debris flows. Mass movements have had a substantial impact on the study area which is extending along Karakorum Highway(KKH) from Besham to Chilas. Intense seismicity, deep gorges, steep terrain and extreme climatic events trigger multiple mountain hazards along the KKH, among which debris flow is recognized as the most destructive geohazard. This study aims to prepare a field-based debris flow inventory map at a regional scale along a 200 km stretch from Besham to Chilas. A total of 117 debris flows were identified in the field, and subsequently, a point-based debris-flow inventory and catchment delineation were performed through Arc GIS analysis. Regional scale debris flow susceptibility and propagation maps were prepared using Weighted Overlay Method(WOM) and Flow-R technique sequentially. Predisposing factors include slope, slope aspect, elevation, Topographic Roughness Index(TRI), Topographic Wetness Index(TWI), stream buffer, distance to faults, lithology rainfall, curvature, and collapsed material layer. The dataset was randomly divided into training data(75%) and validation data(25%). Results were validated through the Receiver Operator Characteristics(ROC) curve. Results show that Area Under the Curve(AUC) using WOM model is 79.2%. Flow-R propagation of debris flow shows that the 13.15%, 22.94%, and 63.91% areas are very high, high, and low susceptible to debris flow respectively. The propagation predicated by Flow-R validates the naturally occurring debris flow propagation as observed in the field surveys. The output of this research will provide valuable input to the decision makers for the site selection, designing of the prevention system, and for the protection of current infrastructure.

Numerical modeling of fracture propagation of supercritical CO_(2)compound fracturing

The exploitation of shale gas is promising due to depletion of the conventional energy and intensification of the greenhouse effect.In this paper,we proposed a heat-fluid-solid coupling damage model of supercritical CO_(2)(SC-CO_(2))compound fracturing which is expected to be an efficient and environmentally friendly way to develop shale gas.The coupling model is solved by the finite element method,and the results are in good agreement with the analytical solutions and fracturing experiments.Based on this model,the fracture propagation characteristics at the two stages of compound fracturing are studied and the influence of pressurization rate,in situ stress,bedding angle,and other factors are considered.The results show that at the SC-CO_(2)fracturing stage,a lower pressurization rate is conducive to formation of the branches around main fractures,while a higher pressurization rate inhibits formation of the branches around main fractures and promotes formation of the main fractures.Both bedding and in situ stress play a dominant role in the fracture propagation.When the in situ stress ratio(δ_(x)/δ_(y))is 1,the presence of bedding can reduce the initiation pressure and failure pressure.Nevertheless,it will cause the fracture to propagate along the bedding direction,reducing the fracture complexity.In rocks without bedding,hydraulic fracturing has the lengthening and widening effects for SC-CO_(2)induced fracture.In shale,fractures induced at the hydraulic fracturing stage are more likely to be dominated by in situ stresses and have a shorter reorientation radius.Therefore,fracture branches propagating along the maximum principal stress direction may be generated around the main fractures induced by SC-CO_(2)at the hydraulic fracturing stage.When the branches converge with the main fractures,fracture zones are easily formed,and thus the fracture complexity and damage area can be significantly increased.The results are instructive for the design and application of SC-CO_(2)compound fracturing.

Effect of Blasting Stress Wave on Dynamic Crack Propagation

Stress waves affect the stress field at the crack tip and dominate the dynamic crack propagation.Therefore,evaluating the influence of blasting stress waves on the crack propagation behavior and the mechanical characteristics of crack propagation is of great significance for engineering blasting.In this study,ANSYS/LS-DYNA was used for blasting numerical simulation,in which the propagation characteristics of blasting stress waves and stress field distribution at the crack tip were closely observed.Moreover,ABAQUS was applied for simulating the crack propagation path and calculating dynamic stress intensity factors(DSIFs).The universal function was calculated by the fractalmethod.The results show that:the compressive wave causes the crack to close and the reflected tensile wave drives the crack to initiate and propagate,and failure mode is mainly tensile failure.The crack propagation velocity varies with time,which increases at first and then decreases,and the crack arrest occurs due to the attenuation of stress waves and dissipation of the blasting energy.In addition,crack arrest toughness is smaller than the crack initiation toughness,applied pressure waveforms(such as the peak pressure,duration,waveforms,wavelengths and loading rates)have a great influence on DSIFs.It is conducive to our deep understanding or the study of blasting stress waves dominated fracture,suggesting a broad reference for the further development of rock blasting in engineering practice.

Heat transfer enhanced inorganic phase change material compositing carbon nanotubes for battery thermal management and thermal runaway propagation mitigation

Developing technologies that can be applied simultaneously in battery thermal management(BTM)and thermal runaway(TR)mitigation is significant to improving the safety of lithium-ion battery systems.Inorganic phase change material(PCM)with nonflammability has the potential to achieve this dual function.This study proposed an encapsulated inorganic phase change material(EPCM)with a heat transfer enhancement for battery systems,where Na_(2)HPO_(4)·12H_(2)O was used as the core PCM encapsulated by silica and the additive of carbon nanotube(CNT)was applied to enhance the thermal conductivity.The microstructure and thermal properties of the EPCM/CNT were analyzed by a series of characterization tests.Two different incorporating methods of CNT were compared and the proper CNT adding amount was also studied.After preparation,the battery thermal management performance and TR propagation mitigation effects of EPCM/CNT were further investigated on the battery modules.The experimental results of thermal management tests showed that EPCM/CNT not only slowed down the temperature rising of the module but also improved the temperature uniformity during normal operation.The peak battery temperature decreased from 76℃to 61.2℃at 2 C discharge rate and the temperature difference was controlled below 3℃.Moreover,the results of TR propagation tests demonstrated that nonflammable EPCM/CNT with good heat absorption could work as a TR barrier,which exhibited effective mitigation on TR and TR propagation.The trigger time of three cells was successfully delayed by 129,474 and 551 s,respectively and the propagation intervals were greatly extended as well.

Numerical analysis of hydraulic fracture propagation in deep shale reservoir with different injection strategies

Deep shale reservoirs are characterized by elevated breakdown pressures,diminished fracture complexity,and reduced modified volumes compared to medium and shallow reservoirs.Therefore,it is urgent to investigate particular injection strategies that can optimize breakdown pressure and fracturing efficiency to address the increasing demands for deep shale reservoir stimulation.In this study,the efficiency of various stimulation strategies,including multi-cluster simultaneous fracturing,modified alternating fracturing,alternating shut-in fracturing,and cyclic alternating fracturing,was evaluated.Subsequently,the sensitivity of factors such as the cycle index,shut-in time,cluster spacing,and horizontal permeability was investigated.Additionally,the flow distribution effect within the wellbore was discussed.The results indicate that relative to multi-cluster simultaneous fracturing,modified alternating fracturing exhibits reduced susceptibility to the stress shadow effect,which results in earlier breakdown,extended hydraulic fracture lengths,and more consistent propagation despite an increase in breakdown pressure.The alternating shut-in fracturing benefits the increase of fracture length,which is closely related to the shut-in time.Furthermore,cyclic alternating fracturing markedly lowers breakdown pressure and contributes to uniform fracture propagation,in which the cycle count plays an important role.Modified alternating fracturing demonstrates insensitivity to variations in cluster spacing,whereas horizontal permeability is a critical factor affecting fracture length.The wellbore effect restrains the accumulation of pressure and flow near the perforation,delaying the initiation of hydraulic fractures.The simulation results can provide valuable numerical insights for optimizing injection strategies for deep shale hydraulic fracturing.

Impact Analysis of Microscopic Defect Types on the Macroscopic Crack Propagation in Sintered Silver Nanoparticles

Sintered silver nanoparticles(AgNPs)arewidely used in high-power electronics due to their exceptional properties.However,the material reliability is significantly affected by various microscopic defects.In this work,the three primary micro-defect types at potential stress concentrations in sintered AgNPs are identified,categorized,and quantified.Molecular dynamics(MD)simulations are employed to observe the failure evolution of different microscopic defects.The dominant mechanisms responsible for this evolution are dislocation nucleation and dislocation motion.At the same time,this paper clarifies the quantitative relationship between the tensile strain amount and the failure mechanism transitions of the three defect types by defining key strain points.The impact of defect types on the failure process is also discussed.Furthermore,traction-separation curves extracted from microscopic defect evolutions serve as a bridge to connect the macro-scale model.The validity of the crack propagation model is confirmed through tensile tests.Finally,we thoroughly analyze how micro-defect types influence macro-crack propagation and attempt to find supporting evidence from the MD model.Our findings provide a multi-perspective reference for the reliability analysis of sintered AgNPs.

Si掺杂对Ga_(2)O_(3)/BP异质结光电性能调控的第一性原理研究

近年来,氧化镓(Ga_(2)O_(3))是新一代超宽禁带(4.9 eV)半导体,基于优越的热稳定和化学稳定性、高击穿场强和可见光透过率等优点,在日盲紫外探测器和透明光电器件领域中引起了广泛的关注.但是,Ga_(2)O_(3)基光电器件存在迁移率较低而限制其应用的现象,而构建合适的Ga_(2)O_(3)异质结是改善光电探测器的光电性能的有效手段之一.基于第一性原理构建β-Ga_(2)O_(3)/BP异质结模型,研究了氧空位(Vo)和Si掺杂对β-Ga_(2)O_(3)/BP异质结光电性质的调控以及相关机理.结果显示:β-Ga_(2)O_(3)/BP异质结结构有效降低β-Ga_(2)O_(3)功函数,提高灵敏度,Si掺杂降低结合能,增强稳定性;β-Ga_(2)O_(3)/BP异质结有效减小带隙,Si掺杂以及氧空位的存在,进一步减小带隙,增强光导电性,并且Si掺杂引起了光电导各向异性.此结果对改善Ga_(2)O_(3)基异质结光电性能提供理论参考.

Robust autofocusing propagation in turbulence

Turbulence in complex environments such as the atmosphere and biological media has always been a great challenge to the application of beam propagation in optical communication, optical trapping and manipulation. To overcome this challenge, this study comprehensively investigates the robust propagation of traditional Gaussian and autofocusing beams in turbulent environments. In order to select stable beams that exhibit high intensity and high field gradient at the focal position in complex environments, Kolmogorov turbulence theory is used to simulate the propagation of beams in atmospheric turbulence based on the multi-phase screen method. We systematically analyze the intensity fluctuations, the variation of the coherence factor and the change in the scintillation index with propagation distance. The analysis reveals that the intensity fluctuations of autofocusing beams are significantly smaller than those of Gaussian beams, and the coherence of autofocusing beams is better than that of Gaussian beams under turbulence. Moreover, autofocusing beams exhibit less oscillation than Gaussian beams, indicating that autofocusing beams propagate in complex environments with less distortion and intensity fluctuation. Overall, this work clearly demonstrates that autofocusing beams exhibit higher stability in propagation compared with Gaussian beams, showing great promise for applications such as optical trapping and manipulation in complex environments.

基于图像处理和BP神经网络的森林防火无人机系统

对无人机设计方案、图像处理和火焰分割算法的技术原理进行了介绍,并利用BP神经网络对图像中的火焰面积变化率和火焰尖角等特征进行识别,实现了对森林火灾的快速监测。实验结果表明:系统的准确率为98.5%,比普通神经网络的84.5%更高;耗时仅22 s,比普通神经网络159 s缩短很多。这表明,BP神经网络是更可靠且更有效率的火灾识别方案。