Towing characteristics of large-scale composite bucket foundation for offshore wind turbines

In order to study the towing dynamic properties of the large-scale composite bucket foundation the hydrodynamic software MOSES is used to simulate the dynamic motion of the foundation towed to the construction site.The MOSES model with the prototype size is established as the water draft of 5 and 6 m under the environmental conditions on site.The related factors such as towing force displacement towing accelerations in six degrees of freedom of the bucket foundation and air pressures inside the bucket are analyzed in detail.In addition the towing point and wave conditions are set as the critical factors to simulate the limit conditions of the stable dynamic characteristics.The results show that the large-scale composite bucket foundation with reasonable subdivisions inside the bucket has the satisfying floating stability.During the towing process the air pressures inside the bucket obviously change little and it is found that the towing point at the waterline is the most optimal choice.The characteristics of the foundation with the self-floating towing technique are competitive for saving lots of cost with few of the expensive types of equipment required during the towing transportation.

Vision-based Dynamics Monitoring(VDM)for Diagnosing the Variations of Wind Turbine Tower Foundation Conditions

A slight uneven settlement of the foundation may cause the wind turbine to shake,tilt,or even collapse,so it is increasingly necessary to realize remote condition monitoring of the foundations.At present,the wind turbine foundation monitoring system is incomplete.The current monitoring research of the tower foundation is mainly of contact measurements,using acceleration sensors and static-level sensors for monitoring multiple reference points.Such monitoring methods will face some disadvantages,such as the complexity of monitoring deployment,the cost of manpower,and the load effect on the tower structure.To solve above issues,this paper aims to investigate wind turbine tower foundation variation dynamic monitoring based on machine vision.Machine vision monitoring is a kind of noncontact measurement,which helps to realize comprehensive diagnosis of early foundation uneven settlement and loose faults.The FEA model is firstly investigated as the theoretical foundation to investigate the dynamics of the tower foundation.Second,the Gaussian-based vibration detection is adopted by tracking the tower edge points.Finally,a tower structure with distributed foundation support is tested.The modal parameters obtained from the visual measurement are compared with those from the accelerometer,proving the vision method can effectively monitor the issues with tower foundation changes.

Design of Large-Scale Prestressing Bucket Foundation for Offshore Wind Turbines

The key in the force transmission between the tower and the foundation for offshore wind turbines is to transfer the large moment and horizontal loads. The finite element model of a large-scale prestressing bucket founda- tion for offshore wind turbines is set up and the structural characteristics of the arc transition structure of the founda- tion are analyzed for 40-60 channels(20-30 rows) arranged with prestressing steel strand under the same ultimate load and boundary conditions. The mechanical characteristics of the key parts of the foundation structures are illus- trated by the peak of the principal tensile stress, the peak of the principal compressive stress and the distribution areas where the principal tensile stress is larger than 2.00 MPa. It can be concluded that the maximum principal tensile stress of the arc transition decreases with the increasing number of channels, and the amplitude does not change signifi- cantly; the maximum principal compressive stress increases with the increasing number of channels and the amplitude changes significantly; however, for the distribution areas where the principal tensile stress is larger than 2.00 MPa, with different channel numbers, the phenomenon is not obvious. Furthermore, the principal tensile stress at the top of the foundation beams fluctuantly increases with the increasing number of channels and for the top cover of the bucket, the principal tensile stress decreases with the increasing number of channels.

One-Step-Installation of Offshore Wind Turbine on Large-Scale Bucket-Top-Bearing Bucket Foundation

In 2010,the first offshore wind turbine with integrated installation was established in Qidong sea area of Jiangsu Province,China,which led to the implementation phase of one-step-installation technique based on the design and construction of large-scale bucket-top-bearing (LSBTB) bucket foundation.The critical technique of LSBTB bucket foundation included self-floating towing,penetration with adjustment of horizontal levelness,removability and one-step-installation.The process of one-step-installation included the prefabrication of LSBTB bucket foundation in onshore construction base,installation and debugging of wind power,overall water transportation of foundation and wind power system,and installation of foundation and offshore wind turbine on the appointed sea area.The cost of one-step-installation technique was about 5 000 Yuan/kW,which was 30%-50% lower than that of the existing technique.The prefabrication of LSBTB bucket foundation took about two months.During the one-step-installation process,the installation and debugging of wind power and overall water transportation need about one to two days in sea area within 35 m depth.After the proposed technique is industrialized,the cost will be further reduced,and the installation capacity is expected to be up to 500 wind turbines per year.

Dynamic earth pressure on rigid retaining walls induced by a neighboring machine foundation,by the meshless local Petrov-Galerkin method

Dynamic earth pressure induced by machine foundations on a neighboring retaining wall is analyzed with emphasis on factors which control the intensity and location of the design forces. The meshless local Petrov-Galerkin (MLPG) method is used to analyze the problem for a variety of retaining wall and machine foundation geometries. The soil medium is assumed to be homogeneous and visco-elastic. The machine foundation is idealized as a harmonic sinusoidal dynamic force often encountered in practice. A number of analyses have been made to reveal the effect of the loading frequency, the location and size of the foundation and the soil shear wave velocity on the distribution and magnitude of the dynamic earth pressure. Results indicate that there is a critical frequency and a critical location for which the passive pressure takes the maxima in the entire duration of the dynamic load.

A Multilevel Design Method of Large-scale Machine System Oriented Network Environment

The design of large-scale machine system is a very complex problem.These design problems usually have a lot of design variables and constraints so that they are difficult to be solved rapidly and efficiently by using conventional methods.In this paper,a new multilevel design method oriented network environment is proposed,which maps the design problem of large-scale machine system into a hypergraph with degree of linking strength (DLS) between vertices.By decomposition of hypergraph,this method can divide the complex design problem into some small and simple subproblems that can be solved concurrently in a network.

Active Vibration Control of Rotating Machines with Active Machine Foot Mounts on Soft Foundations Based on a 3D-Model

The paper presents a simplified 3D-model for active vibration control of rotating machines with active machine foot mounts on soft foundations, considering static and moment unbalance. After the model is mathematical described in the time domain, it is transferred into the Fourier domain, where the frequencies response functions regarding bearing housing vibrations, foundation vibrations and actuator forces are derived. Afterwards, the mathematical coherences are described in the Laplace domain and a worst case procedure is presented to analyze the vibration stability. For special controller structures in combination with certain feedback strategies, a calculation method is shown, where the controller parameters can be directly implemented into the stiffness matrix, damping matrix and mass matrix. Additionally a numerical example is presented, where the vibration stability and the frequency response functions are analyzed.

Series Design of Large-Scale NC Machine Tool

Product system design is a mature concept in western developed countries. It has been applied in war industry during the last century. However,up until now,functional combination is still the main method for product system de-sign in China. Therefore,in terms of a concept of product generation and product interaction we are in a weak position compared with the requirements of global markets. Today,the idea of serial product design has attracted much attention in the design field and the definition of product generation as well as its parameters has already become the standard in serial product designs. Although the design of a large-scale NC machine tool is complicated,it can be further optimized by the precise exercise of object design by placing the concept of platform establishment firmly into serial product de-sign. The essence of a serial product design has been demonstrated by the design process of a large-scale NC machine tool.

A NEW MODELING METHOD FOR MACHINE-FOUNDATION COUPLING SYSTEM AND ITS APPLICATION TO THE CONTROL OF POWER FLOW

A new concept, namely, the equivalent mobility matrix of coupling subsystem is proposed, and the corresponding threesubsystem coupling progressive approach is explored. With the new efficient approach presented, the complexity in dealing with a more complicated dynamic coupling system is greatly reduced. The new modeling method is then combined with the theory of power flow to investigate the dynamics of the overall non rigid isolation system from the viewpoint of energy. The interaction between the resilient machine of its main modes and the resonant behavior of the flexible foundation on power flow transmission is studied. Taking a machine tool mounted on a multi story working plant as an example, the dynamic characteristics of the machine foundation coupling system are analyzed, and their effects on power flow transmission are revealed under various service frequency bands. Some advisable control strategies and the design principle for machinery mounted on flexible structure are proposed.

Integrating BIM and machine learning to predict carbon emissions under foundation materialization stage:Case study of China’s 35 public buildings

For the significant energy consumption and environmental impact,it is crucial to identify the carbon emission characteristics of building foundations construction during the design phase.This study would like to establish a process-based carbon evaluating model,by adopting Building Information Modeling(BIM),and calculated the materialization-stage carbon emissions of building foundations without basement space in China,and identifying factors influencing the emissions through correlation analysis.These five factors include the building function type,building structure type,foundation area,foundation treatment method,and foundation depth.Additionally,this study develops several machine learning-based predictive models,including Decision Tree,Random Forest,XGBoost,and Neural Network.Among these models,XGBoost demonstrates a relatively higher degree of accuracy and minimal errors,can achieve the RMSE of 206.62 and R2 of 0.88 based on testing group feedback.The study reveals a substantial variability carbon emissions per building’s floor area of foundations,ranging from 100 to 2000 kgCO_(2)e/m^(2),demonstrating the potential for optimizing carbon emissions during the design phase of buildings.Besides,materials contribute significantly to total carbon emissions,accounting for 78%e97%,suggesting a significant opportunity for using BIM technology in the design phase to optimize carbon reduction efforts.

A Distributed Framework for Large-scale Protein-protein Interaction Data Analysis and Prediction Using MapReduce

Protein-protein interactions are of great significance for human to understand the functional mechanisms of proteins.With the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them efficiently.To address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using MapReduce.To do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and prediction.Respective solutions are then devised to overcome these limitations.In particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of proteins.After that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and accuracy.Experimental results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

Large-scale functional connectivity predicts cognitive impairment related to type 2 diabetes mellitus

BACKGROUND Large-scale functional connectivity(LSFC)patterns in the brain have unique intrinsic characteristics.Abnormal LSFC patterns have been found in patients with dementia,as well as in those with mild cognitive impairment(MCI),and these patterns predicted their cognitive performance.It has been reported that patients with type 2 diabetes mellitus(T2DM)may develop MCI that could progress to dementia.We investigated whether we could adopt LSFC patterns as discriminative features to predict the cognitive function of patients with T2DM,using connectome-based predictive modeling(CPM)and a support vector machine.AIM To investigate the utility of LSFC for predicting cognitive impairment related to T2DM more accurately and reliably.METHODS Resting-state functional magnetic resonance images were derived from 42 patients with T2DM and 24 healthy controls.Cognitive function was assessed using the Montreal Cognitive Assessment(MoCA).Patients with T2DM were divided into two groups,according to the presence(T2DM-C;n=16)or absence(T2DM-NC;n=26)of MCI.Brain regions were marked using Harvard Oxford(HOA-112),automated anatomical labeling(AAL-116),and 264-region functional(Power-264)atlases.LSFC biomarkers for predicting MoCA scores were identified using a new CPM technique.Subsequently,we used a support vector machine based on LSFC patterns for among-group differentiation.The area under the receiver operating characteristic curve determined the appearance of the classification.RESULTS CPM could predict the MoCA scores in patients with T2DM(Pearson’s correlation coefficient between predicted and actual MoCA scores,r=0.32,P=0.0066[HOA-112 atlas];r=0.32,P=0.0078[AAL-116 atlas];r=0.42,P=0.0038[Power-264 atlas]),indicating that LSFC patterns represent cognition-level measures in these patients.Positive(anti-correlated)LSFC networks based on the Power-264 atlas showed the best predictive performance;moreover,we observed new brain regions of interest associated with T2DM-related cognition.The area under the receiver operating characteristic curve values(T2DM-NC group vs.T2DM-C group)were 0.65-0.70,with LSFC matrices based on HOA-112 and Power-264 atlases having the highest value(0.70).Most discriminative and attractive LSFCs were related to the default mode network,limbic system,and basal ganglia.CONCLUSION LSFC provides neuroimaging-based information that may be useful in detecting MCI early and accurately in patients with T2DM.

LS-SVM and Monte Carlo methods based reliability analysis for settlement of soft clayey foundation

A method which adopts the combination of least squares support vector machine（LS-SVM） and Monte Carlo（MC） simulation is used to calculate the foundation settlement reliability.When using LS-SVM,choosing the training dataset and the values for LS-SVM parameters is the key.In a representative sense,the orthogonal experimental design with four factors and five levels is used to choose the inputs of the training dataset,and the outputs are calculated by using fast Lagrangian analysis continua（FLAC）.The decimal ant colony algorithm（DACA） is also used to determine the parameters.Calculation results show that the values of the two parameters,and δ2 have great effect on the performance of LS-SVM.After the training of LS-SVM,the inputs are sampled according to the probabilistic distribution,and the outputs are predicted with the trained LS-SVM,thus the reliability analysis can be performed by the MC method.A program compiled by Matlab is employed to calculate its reliability.Results show that the method of combining LS-SVM and MC simulation is applicable to the reliability analysis of soft foundation settlement.

An Overview of Stochastic Quasi-Newton Methods for Large-Scale Machine Learning

Numerous intriguing optimization problems arise as a result of the advancement of machine learning.The stochastic first-ordermethod is the predominant choicefor those problems due to its high efficiency.However,the negative effects of noisy gradient estimates and high nonlinearity of the loss function result in a slow convergence rate.Second-order algorithms have their typical advantages in dealing with highly nonlinear and ill-conditioning problems.This paper provides a review on recent developments in stochastic variants of quasi-Newton methods,which construct the Hessian approximations using only gradient information.We concentrate on BFGS-based methods in stochastic settings and highlight the algorithmic improvements that enable the algorithm to work in various scenarios.Future research on stochastic quasi-Newton methods should focus on enhancing its applicability,lowering the computational and storage costs,and improving the convergence rate.

A large-scale screening of metal-organic frameworks for iodine capture combining molecular simulation and machine learning

We performed large-scale molecular simulation to screen and identify metal-organic framework materials for gaseous iodine capture,as part of our ongoing effort in addressing management and handling issues of various radionuclides in the grand scheme of spent nuclear fuel reprocessing.Starting from the computation-ready experimental(CoRE)metal-organic frameworks(MOFs)database,grand canonical Monte Carlo simulation was employed to predict the iodine uptake values of the MOFs.A ranking list of MOFs based on their iodine uptake capabilities was generated,with the Top 10 candidates identified and their respective adsorption sites visualized.Subsequently,machine learning was used to establish structure-property relationships to correlate MOFs’various structural and chemical features with their corresponding performances in iodine capture,yielding interpretable common features and design rules for viable MOF adsorbents.The research strategy and framework of the present study could aid the development of high-performing MOF adsorbents for capture and recovery of radioactive iodine,and moreover,other volatile environmentally hazardous species.

盾构机始发井深基坑力学和变形特性离心模型试验研究

为探究盾构机始发井深基坑开挖过程中周边土体的应力、变形和土压力分布规律,通过对工程原型进行概化,在典型土层条件下,采用停机-开挖-支护的模拟方式开展了方形基坑和圆形基坑两种方案共2组离心模型试验,从地连墙的弯矩和其后土体的水平位移、基坑外侧土压力分布以及地表沉降等角度,对比分析了两种盾构机始发井深基坑开挖过程的工程特性。结果表明:地面沉降随基坑开挖深度增大逐渐增加,形成沉降槽状;地连墙土压力变化呈非线性,随着基坑开挖不断深入的,离地表较近处的土压力逐渐变大,而深处的土压力则逐渐减小;在第5步开挖时2组模型地下连续墙水平位移均达到最大值,圆形基坑是方形基坑的1.4倍,同时地连墙的弯矩也达到最大值,圆形基坑比方形基坑小320 kN·m。研究成果可为深埋隧道深基坑的优化设计和开挖提供指导。

基于改进最小二乘支持向量机组合模型的深基坑沉降变形预测

为了提高深基坑沉降变形预测精度,及时为深基坑支护施工提供指导,提出一种改进最小二乘支持向量机组合模型;通过引入自适应噪声完备集合经验模态分解方法分解原始深基坑沉降变形数据,并结合粒子群优化算法和遗传算法对最小二乘支持向量机进行参数寻优,对分解的数据分别训练、预测后再叠加,得到最终预测结果;应用所提出模型对济南市某深基坑的累积沉降量进行预测,同时与其他模型对比,验证所提出模型的实用性和优越性。结果表明:所提出模型预测深基坑累积沉降量的平均相对误差为0.035%,均方误差为0.0809 mm^(2),均方根误差为0.2838 mm,所提出模型的准确性远优于其他模型的;自适应噪声完备集合经验模态分解方法的引入更有利于在深基坑沉降变形预测方面发挥最小二乘支持向量机的优势。

融合结构知识的蛋白质预训练模型进展

自然语言和图像处理领域引发的人工智能革命给蛋白质计算领域带来了新的思路和研究范式.其中一个重大的进展是从海量蛋白质序列通过自监督学习得到预训练的蛋白质语言模型.这类预训练模型编码了蛋白质的序列、进化、结构乃至功能等多种信息,可方便地迁移至多种下游任务,并展现了强大的泛化能力.在此基础上,人们正进一步发展融合更多种类数据的多模态预训练模型.考虑到蛋白质结构是决定其功能的主要因素,融合了结构信息的蛋白质预训练模型可更好地支持下游多种任务,本文对这一方向的研究工作进行了介绍和总结.此外,还简介了融合先验知识的蛋白质预训练模型、RNA语言模型、蛋白质设计等方面的工作,讨论了这些领域目前的现状、困难及可能的解决方案.

智能合约自动生成方法在基坑工程中的应用

随着建筑4.0时代的到来,基坑自动化监测已成为工程质量管理的重点。智能合约作为一种可自动执行的程序代码,在保证数据真实性、提高日常监测效率方面有很大潜力。然而,其编写的复杂性和基坑工程的不可预测性成为其应用的主要障碍。因此,提出一种针对基坑工程的智能合约自动生成方法。该方法借助有限状态机思想,将基坑工程监测流程和规则转换为有限状态机模型。这种模型有助于明确和完善深基坑施工过程中传感器数据的上链与监测业务,自动生成智能合约代码,从而实现监测数据的自动化处理和预警。同时,区块链技术的特点可保证监测数据的真实性和不可篡改性,从而提高基坑工程施工质量和安全性。该方法在基坑质量安全管理的背景下,成功生成了可用的智能合约,并使用Remix IDE实现,验证了针对基坑业务的自动生成智能合约的正确性与可行性。

基于SSA-ELM算法的基坑地表沉降预测

针对传统的极限学习机算法(ELM)在进行深基坑的地表沉降预测时易陷入局部极小、网络结构中参数选取不准确及预测精度不佳等问题,提出了一种基于麻雀搜索算法(SSA)优化极限学习机算法的基坑地表沉降预测模型。根据麻雀搜索算法收敛速度快、寻优能力与稳定性较强等特点,对极限学习机算法中的连接权值与阈值进行优化,并将优化后的模型应用于基坑的地表沉降预测。将麻雀搜索算法优化后的极限学习机算法(SSA-ELM)与ELM、 GA-ELM、 PSO-ELM算法进行预测精度对比,结果表明:SSA-ELM算法的预测精度高于ELM、 GA-ELM、 PSO-ELM算法,且其稳定性更强,在基坑的地表沉降预测方面效果更好,实现了提高预测精度的目的,具有一定的可行性和实用性。