电动汽车参与经济调度的多目标模型

电动汽车通过V2G技术可以作为电网负荷侧的备用容量,由此提出了一个多目标优化模型,将电动汽车车主成本和经济调度成本作为其目标函数,并让电动汽车通过有序充放电来作为经济调度时的备用容量.在满足各种约束条件下,采用多目标遗传算法(NSGA-Ⅱ)对模型进行求解.电动汽车的负荷特性、负荷波动、真实风能输出和机组停运状态均采用蒙特卡洛算法得到,并以一小时为时间间隔来进行仿真.由模型的求解结果可知,通过选择合适的pareto解集中的值,可以节省车主成本和经济调度的成本,并且可以实现对负荷削峰填谷的功能.     

基于QS-ML-PMHT的多目标无源协同定位方法

针对目标个数未知时无源协同定位系统低可观测目标的航迹起始及维持问题,提出一种基于拟蒙特卡罗模拟退火极大似然概率多假设的双基站多目标无源协同定位方法。首先,建立双基站无源协同定位系统数学模型。其次,提出基于极大似然概率多假设的多目标无源协同定位航迹起始算法,通过假设法确定目标个数,并首次利用拟蒙特卡罗模拟退火算法解决极大似然概率多假设中多目标的优化求解问题,以提高多目标检测跟踪性能。最后,通过滑窗法实现航迹维持。仿真结果表明,所提方法能够有效解决目标个数未知时双基站无源协同定位系统低可观测目标的航迹起始及维持问题。     

Molecular dynamics simulation of non-covalent single-walled carbon nanotube functionalization with surfactant peptides

Non-covalent functionalized single-walled carbon nanotubes (SWCNTs) with improved solubility and biocompatibility can successfully transfer drugs, DNA, RNA, and proteins into the target cells. Theoretical studies such as molecular docking and molecular dynamics simulations in fully atomistic scale were used to investigate the hydrophobic and aromatic π–π-stacking interaction of designing four novel surfactant peptides for non-covalent functionalization of SWCNTs. The results indicated that the designed peptides have binding affinity towards SWCNT with constant interactions during MD simulation times, and it can even be improved by increasing the number of tryptophan residues. The aromatic content of the peptides plays a significant role in their adsorption in SWCNT wall. The data suggest that π–π stacking interaction between the aromatic rings of tryptophan and π electrons of SWCNTs is more important than hydrophobic effects for dispersing carbon nanotubes; nevertheless SWCNTs are strongly hydrophobic in front of smooth surfaces. The usage of aromatic content of peptides for forming SWCNT/peptide complex was proved successfully, providing new insight into peptide design strategies for future nano-biomedical applications.     

Virtual screening of eighteen million compounds against dengue virus: Combined molecular docking and molecular dynamics simulations study

Dengue virus is a major issue of tropical and sub-tropical regions. Dengue virus has been the cause behind the major alarming epidemics in the history with mass causalities from the decades. Unavailability of on-shelf drugs for the prevention of further proliferation of virus inside the human body results in immense number of deaths each year. This issue necessitates the design of novel anti-dengue drug. The protease enzyme pathway is the critical target for drug design due to its significance in the replication, survival and other cellular activities of dengue virus. Therefore, approximately eighteen million compounds from the ZINC database have been virtually screened against nonstructural protein 3 (NS3). The incremental construction algorithm of Glide docking program has been used with its features high throughput virtual screening (HTVS), standard precision (SP), extra precision (XP) and in combination of Prime module, induced fit docking (IFD) approach has also been applied. Five top-ranked compounds were then selected from the IFD results with better predicted binding energies with the catalytic triad residues (His51, Asp75, and Ser135) that may act as potential inhibitors for the underlying target protease enzyme. The top-ranked compounds ZINC95518765, ZINC44921800, ZINC71917414, ZINC39500661, ZINC36681949 have shown the predicted binding energies of −7.55, −7.36, −8.04, −8.41, −9.18 kcal/mol, respectively, forming binding interactions with three catalytically important amino acids. Top-docking poses of compounds are then used in molecular dynamics (MD) simulations. In computational studies, our proposed compounds confirm promising results against all the four serotypes of dengue virus, strengthening the opportunity of these compounds to work as potential on-shelf drugs against dengue virus. Further experimentation on the proposed compounds can result in development of strong inhibitors.     

基于概率断裂力学的管道疲劳寿命分析

采用奇异单元模拟裂纹尖端应力场的奇异性,计算裂纹尖端的应力强度因子和张开应力.以概率论为基础,结合确定性疲劳断裂力学估算方法,考虑参数的不确定性和随机性,应用蒙特卡洛模拟法分析管道的疲劳寿命.结果表明：通过J积分计算得到的裂纹尖端张开应力与计算得到的管道工作应力基本相等.采用蒙特卡洛模拟法进行的一定可靠度和置信度下的疲劳寿命预测能反映评定参数的不确定性,较传统的断裂力学计算结果更安全.     

Limitations of a Mapping Algorithm with Fragmentation Mimics (MAFM) when modeling statistical data sources based on measured packet network traffic

Knowing the estimation of a statistical process’s parameters for measured network traffic is very important as it can then be further used for the statistical analyses and modeling of network traffic in simulation tools. It is for this reason that different estimation methods are proposed that allow estimations of the statistical processes of network traffic. One of them is our own histograms comparison (EMHC) based method that can be used to estimate statistical data-length process parameters from measured packet traffic. The main part of EMHC method is Mapping Algorithm with Fragmentation Mimics (MAFM).     

运动机构可靠性仿真试验系统体系结构研究

针对工程实际复杂运动机构系统成本高、研制周期长、试验量少等特点,提出基于机液协同仿真模型的运动机构可靠性仿真分析方法。从工程实际运动机构系统的运动规律和系统性能出发,给出系统的主要失效模式并定量确定主要失效模式的失效判据,通过整合运动机构系统仿真模型可靠性分析的数据来源,建立并不断修正运动机构系统可靠性仿真模型,同时综合集成三维辅助建模软件、多体分析软件、有限元软件和控制系统建模软件等商用软件并结合经典可靠性分析方法,建立一个协同的运动机构可靠性仿真试验平台集成系统,从而对运动机构系统进行可靠性仿真试验分析与评估,为今后可靠性设计提供大量的有用信息。通过起落架落震可靠性仿真试验研究,表明该系统能够实现工程实际复杂运动机构可靠性仿真试验。     

Innovative seismic design optimization with reliability constraints

Performance-Based Design (PBD) methodologies is the contemporary trend in designing better and more economic earthquake-resistant structures where the main objective is to achieve more predictable and reliable levels of safety and operability against natural hazards. On the other hand, reliability-based optimization (RBO) methods directly account for the variability of the design parameters into the formulation of the optimization problem. The objective of this work is to incorporate PBD methodologies under seismic loading into the framework of RBO in conjunction with innovative tools for treating computational intensive problems of real-world structural systems. Two types of random variables are considered: Those which influence the level of seismic demand and those that affect the structural capacity. Reliability analysis is required for the assessment of the probabilistic constraints within the RBO formulation. The Monte Carlo Simulation (MCS) method is considered as the most reliable method for estimating the probabilities of exceedance or other statistical quantities albeit with excessive, in many cases, computational cost. First or Second Order Reliability Methods (FORM, SORM) constitute alternative approaches which require an explicit limit-state function. This type of limit-state function is not available for complex problems. In this study, in order to find the most efficient methodology for performing reliability analysis in conjunction with performance-based optimum design under seismic loading, a Neural Network approximation of the limit-state function is proposed and is combined with either MCS or with FORM approaches for handling the uncertainties. These two methodologies are applied in RBO problems with sizing and topology design variables resulting in two orders of magnitude reduction of the computational effort.