Dynamic Performance Analysis of the Tuned Heave Plate System for Semi-Submersible Platform

Logistical supply is costly for the deepwater oil and gas exploitation, thereby it is necessary to develop a novel power supply solution to improve the offshore structure’s self-holding capacity. The two-body point absorbers, as a renewable energy device, have achieved a rapid development. Heave plate is used to constrain the truss’ s motion in the two-body point absorber, and the floater moves along the truss up and down. This two-body point absorber can be considered to be an essentially mass-spring-damper system. And it is well known that the heave plates have been widely used in the Spar platform to suppress the heave motions. So if the two-body point absorber can be modified to combine with offshore floating structures, this system can not only offer electric power to support operations or daily lives for the platform, but also control the large motions in the vertical plane. Following this concept, a novel tuned heave plate(THP) system is proposed for the conventional semi-submersible platform. In order to investigate the dynamic performances of the single THP, two experiments are conducted in this paper. First, the hydrodynamic coefficients of the heave plates are studied, and then the THP experiments are carried out to analyze its dynamic performance. It can be concluded that this THP is feasible and achieves the design objective.

Scalable and quantitative contention generation for performance evaluation on OLTP databases

Massive scale of transactions with critical requirements become popular for emerging businesses,especially in E-commerce.One of the most representative applications is the promotional event running on Alibaba's platform on some special dates,widely expected by global customers.Although we have achieved significant progress in improving the scalability of transactional database systems(OLTP),the presence of contention operations in workloads is still one of the fundamental obstacles to performance improving.The reason is that the overhead of managing conflict transactions with concurrency control mechanisms is proportional to the amount of contentions.As a consequence,generating contented workloads is urgent to evaluate performance of modern OLTP database systems.Though we have kinds of standard benchmarks which provide some ways in simulating contentions,e.g.,skew distribution control of transactions,they can not control the generation of contention quantitatively;even worse,the simulation effectiveness of these methods is affected by the scale of data.So in this paper we design a scalable quantitative contention generation method with fine contention granularity control.We conduct a comprehensive set of experiments on popular opensourced DBMSs compared with the latest contention simulation method to demonstrate the effectiveness of our generation work.

Development of database and mathematical models for predicting engine performance parameters using biodiesel

A database for complete and quick knowledge of biodiesel was developed in Microsoft access linked with interactive interfaced of Visual Basic which contains various information such as biodiesel properties,engine performance parameters and emission characteristics.The comparisons of fuel properties among the biodiesel,its blends and engine performance parameters are one of the most attractive features of the developed database.Based on the data collected from the developed database,the analysis of variance(ANOVA)was carried out to know the effect of fuel properties on engine performance parameters.The fuel properties such as a change in calorific value and viscosity with respect to diesel were found to be significant at 5%level with brake power(BP)and brake specific fuel consumption(BSFC).However,flash point and density were found to be insignificant.Therefore,mathematical models were developed for(i)change in BP and(ii)BSFC based on the significant fuel properties.The model efficiency for BP and BSFC were found to be 92%and 91%respectively.The RMSE values were also calculated from the predicted value and found to be 1.85 and 5.45 for BP and BSFC respectively.From the above statistical facts,it is revealed that the developed mathematical models can be used to predict the change in BP and BSFC.Further,it is also expected that the developed database shall be expedient for researchers and engineers to locate various information related to biodiesel.

Simultaneous optimization of structure together with attached tuned mass dampers considering dynamic performance

Tuned Mass Dampers(TMDs)are often attached to a main structure to reduce vibration,and the TMDs’positions are important to affect the structural dynamic performance.However,the TMDs’positions and the material layout of the structure act on each other.This paper suggests a design optimization method by combining the topology optimization of the main structure and the layout of the attached TMDs under harmonic excitations.The main structure with the attached TMDs are modeled by the continuum FEA method to consider the change of TMDs’locations.Then they are optimized simultaneously by introducing a multi-level optimization frame,which includes the structural topology optimization and the optimal tuning of TMDs.The locations and damping parameters of TMDs are optimized in every step of the SIMP-based topology optimization of the main structure,so as to fully consider the interactions between each other to improve the dynamic performance.Numerical examples of cantilever structures are studied,and the results show that when the main structure and TMDs are optimized simultaneously,the modal strain energy is more concentrated compared with that obtained by the non-simultaneous optimization approach.Therefore,the dynamic compliance of the target mode is dramatically reduced.

The Principle of Introducing Halogen Ions Into β-FeOOH: Controlling Electronic Structure and Electrochemical Performance

Coordination tuning electronic structure of host materials is a quite effective strategy for activating and improving the intrinsic properties.Herein,halogen anion(X-)-incorporated β-FeOOH(β-FeOOH(X),X=F-,Cl-,and Br-) was investigated with a spontaneous adsorption process,which realized a great improvement of supercapacitor performances by adjusting the coordination geometry.Experiments coupled with theoretical calculations demonstrated that the change of Fe-O bond length and structural distortion of β-FeOOH,which is rooted in halogen ions embedment,led to the relatively narrow band gap.Because of the strong electronegativity of X-,the Fe element in β-FeOOH(X)s presented the unexpected high valence state(3+δ),which is facilitating to adsorb S032-species.Consequently,the designed β-FeOOH(X)s exhibited the good electric conductivity and enhanced the contact between electrode and electrolyte.When used as a negative electrode,the β-FeOOH(F) showed the excellent specific capacity of 391.9 F g-1 at 1 A g-1 current density,almost tenfold improvement compared with initial β-FeOOH,with the superior rate capacity and cyclic stability.This combinational design principle of electronic structure and electrochemical performances provides a promising way to develop advanced electrode materials for supercapacitor.

Impact of seismic excitation characteristics on the efficiency of tuned liquid column dampers

Various types of passive control systems have been used to suppress the seismic response of structures in recent years. Among these systems, Tuned Liquid Column Dampers （TLCDs） dissipate the input earthquake energy by combining the effects of the movement of the liquid mass in the container, the restoring force on the liquid due to the gravity loads and the damping due to the liquid movement through orifices. In this study, the effects of seismic excitation characteristics such as frequency content and soil condition on the seismic performance of TLCDs are investigated using nonlinear time-history analyses. In this regard, among the past earthquake ground motion records of Iran, 16 records with different parameters were selected. In the structural model developed, the attached TLCD is simulated as a Tuned Mass Damper （TMD） having the same vibration period and damping ratio as the original TLCD. The numerical results show that the seismic excitation characteristics have a substantial role on the displacement reduction capability of TLCDs and they should be considered accordingly in the design of TLCDs.

Analysis and Neural Networks Modeling of Web Server Performances Using MySQL and PostgreSQL

The purpose of this study is to analyze and then model, using neural network models, the performance of the Web server in order to improve them. In our experiments, the parameters taken into account are the number of instances of clients simultaneously requesting the same Web page that contains the same SQL queries, the number of tables queried by the SQL, the number of records to be displayed on the requested Web pages, and the type of used database server. This work demonstrates the influences of these parameters on the results of Web server performance analyzes. For the MySQL database server, it has been observed that the mean response time of the Web server tends to become increasingly slow as the number of client connection occurrences as well as the number of records to display increases. For the PostgreSQL database server, the mean response time of the Web server does not change much, although there is an increase in the number of clients and/or size of information to be displayed on Web pages. Although it has been observed that the mean response time of the Web server is generally a little faster for the MySQL database server, it has been noted that this mean response time of the Web server is more stable for PostgreSQL database server.

Automated Performance Tuning of Data Management Systems with Materializations and Indices

Automated performance tuning of data management systems offer various benefits such as improved performance, declined administration costs, and reduced workloads to database administrators (DBAs). Currently, DBAs tune the performance of database systems with a little help from the database servers. In this paper, we propose a new technique for automated performance tuning of data management systems. Firstly, we show how to use the periods of low workload time for performance improvements in the periods of high workload time. We demonstrate that extensions of a database system with materialised views and indices when a workload is low may contribute to better performance for a successive period of high workload. The paper proposes several online algorithms for continuous processing of estimated database workloads and for the discovery of the best plan for materialised view and index database extensions and of elimination of the extensions that are no longer needed. We present the results of experiments that show how the proposed automated performance tuning technique improves the overall performance of a data management system.

Optimal design theories of tuned mass dampers with nonlinear viscous damping

Optimal design theory for linear tuned mass dampers （TMD） has been thoroughly investigated, but is still under development for nonlinear TMDs. In this paper, optimization procedures in the time domain are proposed for design of a TMD with nonlinear viscous damping. A dynamic analysis of a structure implemented with a nonlinear TMD is conducted first. Optimum design parameters for the nonlinear TMD are searched using an optimization method to minimize the performance index. The feasibility of the proposed optimization method is illustrated numerically by using the Taipei 101 structure implemented with TMD. The sensitivity analysis shows that the performance index is less sensitive to the damping coefficient than to the frequency ratio. Time history analysis is conducted using the Taipei 101 structure implemented with different TMDs under wind excitation. For both linear and nonlinear TMDs, the comfort requirements for building occupants are satisfied as long as the TMD is properly designed. It was found that as the damping exponent increases, the relative displacement of the TMD decreases but the damping force increases.

Parallelization and performance tuning of molecular dynamics code with OpenMP

An OpenMP approach was proposed to parallelize the sequential molecular dynamics(MD) code on shared memory machines. When a code is converted from the sequential form to the parallel form, data dependence is a main problem. A traditional sequential molecular dynamics code is anatomized to find the data dependence segments in it, and the two different methods, i.e., recover method and backward mapping method were used to eliminate those data dependencies in order to realize the parallelization of this sequential MD code. The performance of the parallelized MD code was analyzed by using some performance analysis tools. The results of the test show that the computing size of this code increases sharply form 1 million atoms before parallelization to 20 million atoms after parallelization, and the wall clock during computing is reduced largely. Some hot-spots in this code are found and optimized by improved algorithm. The efficiency of parallel computing is 30% higher than that of before, and the calculation time is saved and larger scale calculation problems are solved.

An Approach to Developing a Performance Test Based on the Tradeoffs from SW Architectures

In a performance test, the standards for assessing its test results are not sufficiently determined due to the lack of a well-structured test developing methods which are found in a functionality test. By extending the established workflow structure, this approach will concentrate on tradeoffs within T-workflow and further develop tests based on T-workflow. The monitoring and tuning point have also been investigated to understand the validity and performance of software. Finally through a case study, it has been shown that better assessment of software performance can be obtained with the suggested tests developed based on T-workflow and by locating its monitoring point and tuning point.

SHA: QoS-Aware Software and Hardware Auto-Tuning for Database Systems

While databases are widely-used in commercial user-facing services that have stringent quality-of-service(QoS)requirement,it is crucial to ensure their good performance and minimize the hardware usage at the same time.Our investigation shows that the optimal DBMS(database management system)software configuration varies for different user request patterns(i.e.,workloads)and hardware configurations.It is challenging to identify the optimal software and hardware configurations for a database workload,because DBMSs have hundreds of tunable knobs,the effect of tuning a knob depends on other knobs,and the dependency relationship changes under different hardware configurations.In this paper,we propose SHA,a software and hardware auto-tuning system for DBMSs.SHA is comprised of a scaling-based performance predictor,a reinforcement learning(RL)based software tuner,and a QoS-aware resource reallocator.The performance predictor predicts its optimal performance with different hardware configurations and identifies the minimum amount of resources for satisfying its performance requirement.The software tuner fine-tunes the DBMS software knobs to optimize the performance of the workload.The resource reallocator assigns the saved resources to other applications to improve resource utilization without incurring QoS violation of the database workload.Experimental results show that SHA improves the performance of database workloads by 9.9%on average compared with a state-of-the-art solution when the hardware configuration is fixed,and improves 43.2%of resource utilization while ensuring the QoS.

基于机器学习的数据库系统参数优化方法综述

参数优化是影响数据库性能和适应性的关键技术,合理的参数配置对于保障数据库系统的高效运行至关重要,但由于参数较多且参数间具有强关联性,传统参数优化方法难以在高维连续的参数空间中寻找最优配置,机器学习的发展为解决这一难题带来新的机遇。通过总结和分析相关工作,将已有工作按照发展时间和特性分为专家决策、静态规则、启发式算法、传统机器学习方法和深度强化学习方法。对数据库参数优化问题进行定义,并说明启发式算法在参数优化问题上的局限性。介绍基于传统机器学习的参数优化方法,包括随机森林、支持向量机、决策树等,描述机器学习方法解决参数优化问题的一般流程并给出一般实现。由于需要大量带标注的数据,传统机器学习模型在适应性和调优能力等方面存在不足。侧重介绍深度强化学习模型的工作原理,定义参数优化问题与深度强化学习模型的映射关系,比较基于深度强化学习的相关工作对数据库性能提升、模型训练时间和涉及的技术,描述基于深度神经网络构建和训练智能体的具体流程。最后,总结已有工作的特点,对当前机器学习在数据库参数优化方面的研究热点和发展方向进行展望,指出多粒度调优、自适应算法和自运维是未来的研究趋势。

炼油化工装置PID性能评测与优化系统的设计与实现

根据对装置对象定义分区与KPI,并通过管理端和控制端2个工作平台进行集中、分散部署的方案设计思想,研究开发炼化装置PID性能评测与优化系统,为企业重点生产装置建立PID回路多尺度性能监测与预警、PID控制故障诊断与预知性维护、PID参数自动调优以及PID回路档案等功能应用。该系统能够实时监控PID性能KPI指标值,并智能推荐最优PID参数。试点装置应用效果的标定测试结果表明,PID系统的实际应用效果达到了技术设计指标,模型和算法的适用性、软件的成熟度得到了验证,具备在炼化装置中规模化扩展应用的条件,具有很好的推广应用价值。

数据库索引调优技术综述

索引调优是数据库调优的重要组成部分,一直受到广泛关注.由于索引调优问题的理论复杂性和大数据时代的到来,通过DBA手动调优的方案已经无法满足现代数据库的发展需求,调优方案逐渐开始向自动化、智能化的方向发展.随着机器学习技术的发展,越来越多的索引选择方案开始引入机器学习技术,并取得了一定的研究成果.将索引调优问题的解决方案归结为一种基于搜索的调优范式,归纳了其研究内容,阐述了其面临的挑战,对调优范式内的索引配置空间的生成、索引配置的评价以及索引配置的枚举与搜索3方面的研究成果进行了归纳、总结和对比.对动态工作负载下的索引选择问题(index selection problem,ISP)所面临的新挑战进行了分析,并基于在线反馈控制回路框架对其解决方案进行梳理.讨论了索引调优工具的发展与现状,通过对现有研究的分析论述,为后来研究者提供参考和研究思路,并对索引选择方案的未来进行了展望.

数据库一体机提升医院信息系统性能的探索与实践

目的医院信息系统的数据交互与信息量的快速增长,给系统的运维和服务性能带来了重大挑战。为了解决软件的性能瓶颈、提升医院信息系统的响应性能,某医院探索性引入了Exadata数据库一体机。方法通过硬件参数选型、高可用测试以及数据迁移等实现了数据库一体机的上线,通过对比上线前后数据库的I/O操作时间、SQL语句执行时间和业务系统模块加载时长评估效益。结果上线一体机后I/O操作的整体等待时长下降83.47%、相同语句的执行时间降幅超22%、业务系统加载时间平均下降43.74%。结论数据库一体机能够有效降低查询时间,加快业务模块的运行速度,能够帮助提升医院信息系统的性能。

面向大数据场景的系统性能优化实践

在现有大规模分布式环境中,大数据应用的性能与计算效率仍有较大的提升空间。然而,在大规模环境中进行性能分析与优化需要大量领域专家。针对大数据应用中的性能优化问题,提出了一个通用的低效查询语句检测与优化流程,总结了4类显著影响大数据应用性能的低效行为,并针对每一类低效行为,提出了具体的优化策略。最后,通过实验评估验证了提出的优化方案在实际大规模集群中的有效性。

面向机器学习的数据库参数调优技术综述

大数据时代,数据规模庞大、应用场景复杂,为满足苛刻的性能要求,面对庞杂的数据库参数,给出高质量的参数调优结果挑战巨大。传统启发式调优算法或者人工干预方法很难普适地满足各类调优需求。机器学习因其强大的学习和泛化能力被广泛应用于各种复杂场景,包括数据库参数调优。经过充分调研,给出学习式数据库参数调优技术的演化路线、依据路线,按照研究内容-存在问题-解决问题的思路,叙述传统参数调优技术、基于BO模型的参数调优技术以及基于RL的参数调优技术,展望未来的研究方向和所面临的挑战,希望为这一领域科研工作者提供有价值的参考。

随机激励下带滞变阻尼的调谐质量阻尼器减振性能研究

近年来,阻尼力与位移呈线性关系的新型调谐质量阻尼器(Tuned mass damper, TMD)相继出现,其参数分析优化原理大部分属于动力数值分析缺乏理论参数优化指导。该文对白噪声作用下具有上述特性的TMD,即滞变阻尼调谐质量阻尼器(Hysteretic damping tuned mass damper, HD-TMD)进行减振优化研究。总结了HD-TMD的力学机理并推导出相应的结构-HD-TMD系统运动方程;提出适用于HD-TMD的H_(2)优化和性能平衡设计,并通过数值拟合技术得到了最优参数公式和基于容忍度的性能平衡设计流程;以真实可用的变摩擦摆式调谐质量阻尼器(VFP-TMD)为HD-TMD的实际算例,检验所提出的优化方法对风振激励的减振性能影响。结果表明:H_(2)优化和性能平衡设计下的HD-TMD可以提供略优于传统黏滞阻尼TMD的控制效果。H_(2)优化的最优参数能使HD-TMD发挥最大的潜能从而实现最好的减振率,但面临VFP-TMD行程过大导致的摆动非线性问题。性能平衡设计下的最优参数可以控制VFP-TMD行程在线性范围内,同时发挥出良好且稳定的减振控制效果实现双赢。与最优参数公式结果相比,性能平衡设计的峰值减振率和均方差峰值减振率仅损失3.19%和0.74%。

C/SiC复合材料数据库及机器学习性能预测平台的设计与开发

以MySQL Workbench 8.0为数据库平台,采用三层浏览器/服务器架构,利用PyCharm 2022开发了一套基于Web界面和机器学习技术的C/SiC复合材料数据库系统,该系统包括材料数据库和机器学习模型2部分,具有对材料组分结构、制备工艺、试验性能等材料研发各阶段的信息查询、添加、修改、删除、搜索以及支持信息管理、设计分析视图、案例推理辅助设计、用户与系统管理等功能。基于神经网络回归算法训练机器学习模型,利用材料微观结构参数预测材料力学性能,对不同微观结构参数的影响权重进行评价,并部署在系统平台中;通过Web用户界面调用机器学习模型,对预留的验证集数据进行拉伸模量和弯曲模量预测。结果表明:机器学习模型预测材料力学性能的精度达到95%左右,训练出来的预测模型具有良好的精度与泛化能力。