Harmonic Resonance Analysis of Shale Gas Distribution Network with Phased Load

Based on the background of achieving carbon peaking and carbon neutrality, the development and application of new high-power compressors, electric grid drilling RIGS and electric fracturing pump system provide new equipment support for the electric, green and intelligent development of shale gas fields in China. However, the harmonic pollution of shale gas grid becomes more serious due to the converter and frequency conversion device in the system, which easily causes harmonic resonance problem. Therefore, the harmonic resonance of shale gas grid is comprehensively analyzed and treated. Firstly, the working mechanism of compressor, electric drilling RIGS of the harmonic impedance model of electric fracturing pump system is established. Secondly, the main research methods of harmonic resonance analysis are introduced, and the basic principle of modal analysis is explained. Modal analysis method was used to analyze. Finally, harmonic resonance is suppressed. The results show that there may be multiple resonant frequency points in the distribution network changes, but these changes are relatively clear;if the original resonant frequency point of the resonant loop does not exist, the resonant frequency point disappears. The optimal configuration strategy of passive filter can effectively suppress harmonic resonance of distribution network in shale gas field.

Modal Frequency Prediction of Chladni Patterns Using Machine Learning

The introduction of machine learning (ML) in the research domain is a new era technique. The machine learning algorithm is developed for frequency predication of patterns that are formed on the Chladni plate and focused on the application of machine learning algorithms in image processing. In the Chladni plate, nodes and antinodes are demonstrated at various excited frequencies. Sand on the plate creates specific patterns when it is excited by vibrations from a mechanical oscillator. In the experimental setup, a rectangular aluminum plate of 16 cm x 16 cm and 0.61 mm thickness was placed over the mechanical oscillator, which was driven by a sine wave signal generator. 14 Chladni patterns are obtained on a Chladni plate and validation is done with modal analysis in Ansys. For machine learning, a large number of data sets are required, as captured around 200 photos of each modal frequency and around 3000 photos with a camera of all 14 Chladni patterns for supervised learning. The current model is written in Python language and model has one convolution layer. The main modules used in this are Tensor Flow Keras, NumPy, CV2 and Maxpooling. The fed reference data is taken for 14 frequencies between 330 Hz to 3910 Hz. In the model, all the images are converted to grayscale and canny edge detected. All patterns of frequencies have an almost 80% - 99% correlation with test sample experimental data. This approach is to form a directory of Chladni patterns for future reference purpose in real-life application. A machine learning algorithm can predict the resonant frequency based on the patterns formed on the Chladni plate.

Broadband energy harvesting based on one-to-one internal resonance

We design an electromechanical transducer harvesting system with one-to-one internal resonance that can emerge a broader spectrum vibrations. The novel harvester is composed of a Duffing electrical circuit coupled to a mobile rod, and the coupling between both components is realized via the electromagnetic force. Approximate analytical solutions of the electromechanical system are carried out by introducing the multiple scales analysis, also the nonlinear modulation equation for one-to-one internal resonance is obtained. The character of broadband harvesting performance are analyzed, the two peaks and one jump phenomenon bending to the right for variation of control parameters are observed. It is shown that an advanced bandwidth over a corresponding linear model that does not possess a modal energy interchange.

Brain Tumor Segmentation in Multimodal MRI Using U-Net Layered Structure

The brain tumour is the mass where some tissues become old or damaged,but they do not die or not leave their space.Mainly brain tumour masses occur due to malignant masses.These tissues must die so that new tissues are allowed to be born and take their place.Tumour segmentation is a complex and time-taking problem due to the tumour’s size,shape,and appearance variation.Manually finding such masses in the brain by analyzing Magnetic Resonance Images(MRI)is a crucial task for experts and radiologists.Radiologists could not work for large volume images simultaneously,and many errors occurred due to overwhelming image analysis.The main objective of this research study is the segmentation of tumors in brain MRI images with the help of digital image processing and deep learning approaches.This research study proposed an automatic model for tumor segmentation in MRI images.The proposed model has a few significant steps,which first apply the pre-processing method for the whole dataset to convert Neuroimaging Informatics Technology Initiative(NIFTI)volumes into the 3D NumPy array.In the second step,the proposed model adopts U-Net deep learning segmentation algorithm with an improved layered structure and sets the updated parameters.In the third step,the proposed model uses state-of-the-art Medical Image Computing and Computer-Assisted Intervention(MICCAI)BRATS 2018 dataset withMRI modalities such as T1,T1Gd,T2,and Fluidattenuated inversion recovery(FLAIR).Tumour types in MRI images are classified according to the tumour masses.Labelling of these masses carried by state-of-the-art approaches such that the first is enhancing tumour(label 4),edema(label 2),necrotic and non-enhancing tumour core(label 1),and the remaining region is label 0 such that edema(whole tumour),necrosis and active.The proposed model is evaluated and gets the Dice Coefficient(DSC)value for High-grade glioma(HGG)volumes for their test set-a,test set-b,and test set-c 0.9795, 0.9855 and 0.9793, respectively. DSC value for the Low-gradeglioma (LGG) volumes for the test set is 0.9950, which shows the proposedmodel has achieved significant results in segmenting the tumour in MRI usingdeep learning approaches. The proposed model is fully automatic that canimplement in clinics where human experts consumemaximumtime to identifythe tumorous region of the brain MRI. The proposed model can help in a wayit can proceed rapidly by treating the tumor segmentation in MRI.

基于模态分析的多逆变器系统串并联谐振特性分析

基于模态分析的多逆变器系统谐振分析方法能够获得系统准确的谐振定位,但在谐振类型辨识方面存在不足。该文提出一种基于模态分析方法和参与因子计算的多逆变器系统谐振辨识方法。考虑线路阻抗,建立计及电容电流有源阻尼的逆变器诺顿等效模型。当逆变器组合变化时,对比串、并联谐振模态分析结果的差异。通过对系统谐振类型的划分,获取不同类型谐振的特征信息。针对不同类型谐振选择特定的模态分析方法,借助参与因子表现实现谐振类型辨识。最后,搭建仿真电路模型和实验平台,通过仿真和实验验证所得结论的正确性。

结构动力学应用中若干基本概念的研究

结构动力特性被广泛应用于结构振动控制、结构可靠性设计和健康监测,由此涉及到一些动力学基本概念的理解问题尚待深入探讨。采用一简支梁横向振动算例以及分别通过模态位移法和模态加速度法对多自由度系统的响应振幅进行分析计算,探讨结构位移响应与第一阶固有频率的关系、结构各阶位移模态的贡献与模态应变能的关系。基于模态正交性,通过分析多自由度振动系统的位移响应,论述了系统共振的必要条件,即在保证系统振动频率(某阶固有频率)等于激励频率的同时,其位移响应还应呈现出相应模态的形态;实现多自由度系统纯模态共振,可用于精确识别结构的模态参数。

针灸不同穴位组治疗中风后假性球麻痹吞咽功能障碍疗效研究

目的:分析针灸不同穴位组对中风后假性球麻痹吞咽功能障碍的治疗效果。方法:将90例中风后假性球麻痹吞咽功能障碍患者分为常规组、项针组、舌针组,每组30例,治疗14 d。比较各组治疗14 d后的疗效,治疗前、治疗14 d后营养指标、梗死灶多模态磁共振参数、吞咽功能相关指标、症状改善情况。结果:治疗14 d后,舌针组总有效率高于项针组、常规组(P<0.05)。治疗14 d后,各组血清白蛋白(ALB)、前白蛋白(PAD)、血红蛋白(Hb)、N-乙酰天门冬氨酸(NAA)/肌酸(Cr)、N-乙酰天门冬氨酸与(胆碱+肌酸)比值、韦氏成人智力量表评分高于治疗前,且舌诊组最高,差异有统计学意义(均P<0.05)。各组Cho/Cr、标准吞咽功能评价量表(SAA)、洼田饮水试验评分、洼田饮水试验前后脉冲血氧饱和度下降值、美国国立卫生研究院卒中量表、汉密尔顿焦虑量表、汉密尔顿抑郁量表评分低于治疗前,且舌诊组最低,差异有统计学意义(均P<0.05)。结论:与常规治疗或项针治疗相比,舌针治疗中风后假性球麻痹吞咽功能障碍能更有效地改善患者营养状态、梗死灶多模态磁共振参数、吞咽功能及临床症状。

双振头超声固结振动装置设计及实验

为了提高传统超声固结振动装置的输出振幅,达到金属箔材稳定固结的要求,提出了一种单侧双换能器结构的双振头超声固结振动装置。基于谐振系统设计理论完成了尺寸参数设计,对该装置进行模态分析及优化,得到最佳纵振频率为20 247 Hz;基于等效四端网络法对双振头超声固结振动装置进行建模仿真,最大输出振幅能达到36μm;最后,制作实验样机进行振幅测试实验,探究输出振幅与激励电压之间的关系。实验结果表明:输出振幅与激励电压线性相关,该装置的最大输出振幅为34.4μm,与仿真结果的误差率小于5%。验证了结构的合理性,有效提高了装置的输出振幅,促进了超声固结技术在相关领域的应用。

多模态多参数磁共振成像在脑胶质瘤Ki-67表达中的研究

目的探讨多模态磁共振成像技术及其定量和定性参数预测胶质瘤Ki-67表达水平的价值。材料与方法选取330例胶质瘤患者,其中异柠檬酸脱氢酶(isocitrate dehydrogenase,IDH)野生型201例,IDH突变型129例。获得常规MRI特征、表观扩散系数(apparentdiffusion coefficient,ADC)、动态对比增强(dynamic contrast-enhanced,DCE)MRI定性及定量参数时间-强度曲线(time-intensity curve,TIC)、转运常数(volume transfer constant,K^(trans))、渗出速率常数(the rate constant,K_(ep))、血管外细胞外容积分数(fractional volume of the extravascular-extracellular,V_(e))、血浆分数(plasma fraction,V_(p))及磁共振波谱(magnetic resonance spectroscopy,MRS)代谢产物比值,对胶质瘤患者进行logistic回归,以确定与Ki-67表达水平相关因素。用受试者工作特征曲线下面积(area under the curve,AUC)评价预测模型的性能。结果在胶质瘤患者分析中,K^(trans)(OR=1.012,P<0.001)、ADC(OR=0.998,P<0.05)、强化程度(OR=3.317,P<0.05)是Ki-67表达水平的独立预测因素,AUC为0.893。结论K^(trans)、ADC和强化程度可能是预测胶质瘤Ki-67表达水平的有效参数。

基于自适应步长的电力系统谐波谐振快速分析方法

随着电力系统的快速发展,其规模越来越大、节点数量越来越多,频率扫描法和模态分析法作为目前应用最为广泛的两种谐波谐振分析方法,计算效率亟须提升。在传统方法的基础上,文中分别提出基于自适应步长的频率扫描法和基于自适应步长的模态分析法。首先,通过设置相应判据自适应改变扫描步长,使扫描区间能够快速到达谐振频率点附近,大幅减少了计算量;然后,在谐振频率点附近使用二次插值法确定谐振频率,保证了谐振频率计算精度;最后,以IEEE 14节点、IEEE 39节点和IEEE 118节点系统为例进行分析。结果表明,与传统方法相比,所提两种方法在保证谐振频率精度的同时可大幅提高运算速度,验证了所提方法的准确性和高效性。

小水电尾水水轮机转轮优化研究

【目的】为解决实际小水电站工程中尾水水轮机性能较差、运行效率较低的问题,缓解转轮叶片空化现象,【方法】设计了转轮桨叶倾斜角度分别为30°和45°的两种水轮机优化方案,并基于ANSYS仿真计算软件,在设计工况下对两种转轮设计方案进行了数值计算,同时对流场流态与模态分布计算结果进行了对比,根据两方案计算结果的整体流线分布、速度分布、压力分布及转轮区域涡量云图等开展流态分析,并进行干湿模态下的振型对比分析。【结果】结果显示:在设计工况下,转轮桨叶倾斜角度为45°时整体水流流态更加平稳、转轮区域压力分布更为均匀、旋涡更少,且预应力湿模态前五阶共振频率中最接近振动频率的数值仍存在40%的差异,工作环境中未产生共振现象,表明其整体性能较优。【结论】结果表明:数值模拟方法合理,模态分析与业界学者研究结果一致,所得数据具有可靠性。通过对转轮进行设计优化可提高水轮机抗空化、抗振等运行性能,对小水电站尾水水轮机优化设计提供有益参考,亦为类似工程问题改造提供了有效理论支撑。

某地铁转向架构架端部开裂机理及分析

针对某B型地铁在正常运营过程中发生转向架构架端部开裂问题,文中采用运营模态分析和线路试验开展断裂机理研究。首先文中基于PolyMAX方法识别出构架在实际运营中的工作模态,其中构架端部存在频率为222.9 Hz,阻尼比为0.72%的纵向摆动固有模态;然后通过分析轴箱、构架端部加速度和动应力时频特性以及对典型区间轨道调查发现,车辆以60 km/h的运营速度通过含有波长为80 mm钢轨波磨的弹性短轨枕区段时,构架端部与轨道固有频率重合,从而导致结构共振引发疲劳破坏;在相同工况条件下,构架端部安装加强筋改进后的结构,其振动加速度和动应力均比原方案减小90%左右,同时对改进前后构架端部进行寿命评估,结果表明安装加强筋后的构架端部损伤值大幅降低,满足转向架的使用寿命要求。

试验模态分析方法及其在航空飞行器中的应用综述

试验模态分析是通过试验测定结构固有振动特性的方法,其分析结果主要用于结构动力学模型修正,并为结构动力学分析、气动弹性分析和振动控制设计提供数据支撑。本文系统梳理了国内外有关试验模态分析方法的研究进展,从经典试验模态分析方法和现代试验模态分析方法两方面展开,总结了经典的相位共振法和相位分离法两大主流方法的基本原理,对近年来发展的试验模态分析方法进行了简单介绍,归纳了不同模态分析方法的特点。最后指出了试验模态分析方法现今存在的问题与未来可能的发展趋势。

Residue Based Open-Loop Modal Analysis Method for Detecting LFMR of PMSG-WFs Penetrated Power Systems

This paper proposes a residue based open-loop modal analysis method to detect low frequency modal resonance(LFMR),including asymmetric low frequency modal attraction(ALFMA)and asymmetric low frequency modal repulsion(ALFMR),of permanent magnetic synchronous generator based wind farms(PMSG-WFs)penetrated power systems.The formation of ALFMA and ALFMR caused by two open-loop low frequency oscillation(LFO)modes moving close and apart is analyzed in detail.Via predicting the trajectories of closed-loop LFO modes based on calculation of residue of open-loop LFO modes,both ALFMA and ALFMR can be detected.The proposed method can select LFO modes which move to the right half complex plane as control parameters vary.Simulation studies are carried out on a three-machine power system and a four-machine 11-bus power system to verify the properties of the proposed method.

基于多能量场景谐振域构建的风机控制器参数优化方案

文中提出了一种基于谐振域(RA)的风机控制器参数优化方案,用于刻画在多能量场景下风机与多机系统的模式谐振,并通过优化风机的控制器参数降低潜在的模式谐振风险。首先,建立计及能量场景变化的系统小信号模型。其次,提出确定复平面上可能发生模式谐振区域的RA概念。然后,设计衡量谐振强度以及谐振对模式振荡阻尼影响的谐振强度指标(RSI)。为了避免潜在的模式谐振风险,基于RA和RSI对风机控制器参数进行优化。通过IEEE 39节点模型,验证了所提优化方案可在多能量场景下捕捉模式谐振发生区域,并通过风机控制器参数优化有效避免系统潜在的模式谐振。

基于MRI影像和临床参数特征融合的深度学习模型预测术前肝细胞癌的细胞角蛋白19状态

目的探索并建立深度学习模型,验证MRI影像深度学习特征结合临床显著性特征在术前预测肝细胞癌(HCC)的细胞角蛋白19(CK19)状态上的可行性。方法收集116例已证实CK19状态的HCC患者数据进行回顾性实验。基于增强MRI影像的肝胆期(HBP)和扩散加权成像(DWI)序列,以及统计学分析筛选的与CK19状态显著相关的临床参数特征,建立了单序列多尺度特征融合模型(MSFF-IResnet)和多尺度多模态特征融合模型(MMFF-IResnet)。通过模型间的分类性能对比评估,突出深度学习模型对于术前预测HCC的CK19状态的有效性。结果多变量分析显示,升高的NLR值(P=0.029)和不完整的肿瘤包膜(P=0.028)是CK19表达的独立预测因子。多尺度特征融合和多模态特征融合方法改进后的深度学习模型均取得了优于传统机器学习模型和基线模型的分类结果,且最终的MMFF-IResnet表现出最佳的分类性能,其AUC为84.2%、准确度为80.6%,敏感度为80.1%,特异度为81.2%。结论本研究建立的基于MRI影像和临床参数的多尺度和多模态特征融合模型成功预测了HCC的CK19状态,验证了深度学习方法结合MRI影像和临床参数在术前预测CK19状态上的可行性。

钢悬链线立管全尺寸共振弯曲疲劳试验系统与模态分析

钢悬链线立管(steel catenary riser,SCR)作为支撑海上平台和其他关键设备的重要组成部分,在极端的海洋环境中长时间服役运行时,其结构完整性和安全性至关重要。为确保SCR立管在恶劣环境下的安全运行,对立管进行共振弯曲疲劳试验研究成为必要的评估标准,同时也为后续立管疲劳寿命预测和失效分析提供基础。针对两种特定型号的SCR立管,推导并求解不同约束条件下的共振系统的固有频率与振型函数,分析管道长度、管道壁厚、偏心块和配重块质量对共振系统固有频率的影响;在此基础上开展不同特定工况下的全尺寸共振弯曲疲劳试验,同时对SCR立管在全尺寸共振弯曲疲劳试验系统中不同跨距及内压的振动响应特性进行了模拟。结合理论分析、有限元计算和共振弯曲疲劳试验结果,发现管道长度和两端的配重约束是影响共振系统1阶固有频率的重要参数,特别是在管道长度较短和壁厚较薄的情况下,影响更为显著。此外,注水加压和跨距增加会降低共振系统的固有频率,支撑跨距越短,系统的固有频率越大。分析所得结果为SCR立管的共振弯曲疲劳试验系统结构设计和试验方案优化提供了重要的参考依据。

Connections Between Induction Generator Effect and Open-loop Modal Proximity to Cause Sub-synchronous Oscillations in Series-compensated Power System

The induction generator effect(IGE)and the openloop modal proximity(OLMP)are two different reasons why subsynchronous oscillations(SSOs)in a series-compensated power system(SCPS)may occur.The IGE attributes the growing SSOs to negative resistance,while the OLMP explains the SSO mechanism from the standpoint of modal conditions.In this paper,we investigate the connections between the IGE and the OLMP through equivalent RLC circuit and open-loop modal analysis.Our investigation is conducted for two types of seriescompensated power systems where either a synchronous generator or a DFIG is connected at the sending end.The investigation reveals the conditions,in which the IGE and the OLMP may jointly cause the growing SSOs,i.e.,both the IGE and the OLMP can explain why the growing SSOs occur.Furthermore,the investigation indicates that the IGE and the OLMP may be totally irrelevant and lead to growing SSOs separately.This implies that it is possible that in a SCPS,the growing SSOs are only due to the IGE,and the OLMP is non-existent,and vice versa.Hence,when the growing SSOs occurs in a SCPS,examination based on both the IGE and the OLMP should be carefully conducted in order to find if the oscillatory instability is due to the IGE,or the OLMP,or both of them.

弱电网下计及背景谐波的多并网逆变器阻抗重塑谐振抑制方法

弱电网下多逆变器并网系统的谐振问题一直广受关注,当计及背景谐波时,逆变器的电网电压前馈环节引入正反馈通路,将进一步恶化系统的电能质量。鉴于此,提出了一种弱电网下计及背景谐波的多并网逆变器阻抗重塑谐振抑制方法。通过对逆变器的控制环节进行导纳划分,建立基于三分解导纳的多逆变器并网等效模型,并利用模态分析法得到逆变器数量和电网侧阻抗变化时系统的谐振特性。计及电网电压前馈和电容电流反馈环节,对加权电流控制进行改进,并通过公共耦合点并联虚拟导纳对逆变器进行阻抗重塑,以实现对弱电网下系统谐振的抑制。仿真结果表明,所提方法既能极大地减小背景谐波对逆变器输出电流的影响,又能有效地抑制弱电网下多逆变器并网系统的谐振。

机车牵引电机转子模态试验及仿真优化研究

牵引电机转子作为电机旋转的核心,转子的固有频率应避开实际的工作频率、工作转速。基于软件的有限元分析法是目前在设计阶段应用最广泛的固有频率计算方法,而影响计算结果准确性包括材料参数、各部件间的实际接触状态等。通过LMS公司的采集系统进行实际模态测试,并采用Ansys的有限元分析法,考虑导条和端环、导条和铁心、铁心和转轴实际接触状态计算转子的固有频率。根据实际模态测试结果,以验证Ansys计算结果的实效性,为进一步提高牵引电机转子模态仿真精度提供有益参考,也为进一步改进转子结构,保证转子在实际运行中不会发生共振提供依据。