基于BP神经网络的条带刚凸特征回弹预测

为了研究核燃料组件格架的条带刚凸特征的回弹量与压边力、冲压速度、凸凹模间隙、摩擦系数等冲压工艺参数之间的关系,首先,获取包含50个GA拉丁超立方抽样的数据点以及10个随机抽样的数据点的数据集,前者作为训练集、后者作为测试集。将前者输入到BP神经网络进行训练,后者验证训练模型的精度。最后,通过响应面图研究各因素之间的交互作用以及各因素的敏感程度。结果表明:BP神经网络能够有效预测刚凸回弹量与冲压工艺参数之间的关系,相对于其他因素,压边力对回弹量的影响特别明显,冲压速度对回弹量的影响不明显,但与凸凹模间隙和摩擦系数有明显的交互作用。     

储能装置运行策略及运行特性对微电网可靠性的影响

储能装置可有效平抑间歇性分布式电源的出力波动,有助于提高系统运行的可靠性。然而,储能装置运行策略和运行特性的选取对系统可靠性水平有着重要影响。搭建了计及储能运行策略和运行特性的、更为精准的储能充放电模型,提出了基于序贯蒙特卡洛模拟法的风/光/柴/储微电网可靠性评估模型,并分别针对负荷点、系统和储能提出了一系列评估指标。通过对IEEE-RBTS标准算例系统进行仿真,分析了储能装置的运行策略、容量、最大充放电功率对微电网可靠性水平的影响,并评估了切负荷策略中位置权重系数对负荷点及系统可靠性水平的影响,评估结果可为微电网的优化规划提供有益参考。     

考虑随机特性的独立微网储能裕度计算方法

依据含储能设备的独立微网的功能和运行特性,提出微网储能裕度的概念及考虑随机特性的微网储能裕度的计算方法,以便对微网负荷承载能力进行量化评估,并使微网在满足系统可靠性要求的同时合理调节储能设备的充放电。针对风电、光电及负荷的随机特性,在日前预测的基础上,建立考虑预测误差的风电、光伏出力和负荷的概率模型。提出一种基于场景生成和削减技术、储能设备充放电优化技术和弦截法的考虑随机特性的储能裕度计算方法。以某微网为例,验证了所提方法的有效性和准确性,并对蓄电池容量、出力限制和初始荷电状态等可能影响储能裕度的因素进行了分析。     

计及互动响应的电力系统不确定性综合安全评估

随着当前智能电网建设的推进,电网逐渐具有了灵活互动的特征。大量可再生能源以及主动负荷的接入给电网注入了更多的随机性和互动性,进而也给系统的安全评估带来了挑战。针对这一问题,提出一种新的不确定性安全评估方法,首先根据Hyper-box和Hyper-ellipse空间理论建立电力系统综合安全指标,然后在该指标下将基于拉丁超立方采样的概率潮流与系统安全评估相结合,针对系统不同的运行状态采用拉丁超立方采样方法对系统综合安全指标进行计算;最后,对计算的指标值进行统计和区间数排序得到系统的综合安全评估信息。仿真结果表明,所提算法可计及安全指标的概率性分布,能够反映考虑了可再生能源波动性和主动负荷互动响应后的系统安全水平,为主动负荷进行良性互动提供了一种有效的评估指标。     

近似模型聚合在机床立柱优化设计中的应用

建立了某重型机床立柱部件优化问题的数学模型,采用拉丁超立方试验设计生成50个隐式约束的有限元仿真数据样本。使用样本集分别建立Kriging模型、径向基神经网络和二阶多项式响应面模型,通过近似模型聚合方法来替代隐式约束的有限元计算。结合全局优化算法,得到一组可行的立柱尺寸参数组合,使机床立柱的质量减轻,并满足工作载荷下的挠度、应力和油压约束条件。研究表明:近似模型聚合方法很适合解决高仿真代价的隐式约束问题。     

Individual-based multi-objective optimal structured treatment interruption for HIV infection

In this paper, based on the measurable quantities from an individual patient that has infection to human immunodeficiency virus (HIV) and his/her condition is near to acquired immune deficiency syndrome (AIDS), individual-based multi-objective optimal treatments have been proposed. Firstly, the most effective parameters of the patient in computing Long-term non-progressor (LTNP) equilibrium are derived using global sensitivity analysis (GSA). To accomplish GSA effectively, Latin hypercube sampling (LHS) and partial rank correlation coefficients (PRCC) are utilized to rank each of the parameters based on each state of the 5-dimensional model. Then, these results are used by Dempster–Shafer (D–S) evidence theory (DSET) to rank the most effective parameters comprehensively. Now, these effective identified parameters are estimated using extended Kalman filter (EKF), which its covariance matrices are optimized based on particle swarm optimization (PSO) algorithm. Thus, the proposed methodology gives a calibrated model corresponding to the individual patient. Based on this calibrated model, the LTNP equilibrium related to the individual patient is derived. Using the derived individual-based LTNP equilibrium optimal structured treatment interruption (STI) strategies are extracted by defining suitable multi-objective optimization problem and solving it through using non-dominated sorting genetic algorithm-II (NSGA-II). The results demonstrate that the proposed optimal treatments are able to effectively reach LTNP equilibrium with using the minimum and maximum drug usage of 3.6% and 35.1% of full drug usage treatment. Meanwhile, the different optimal treatments give the decision-makers enough flexibility to choose the suitable treatment based on existing facilities and necessities.     

Methods for uncertainty propagation in life cycle assessment

Life cycle assessment (LCA) calculates the environmental impact of a product over its entire life cycle. Uncertainty analysis is an important aspect in LCA, and is usually performed using Monte Carlo sampling. In this study, Monte Carlo sampling, Latin hypercube sampling, quasi Monte Carlo sampling, analytical uncertainty propagation and fuzzy interval arithmetic were compared based on e.g. convergence rate and output statistics. Each method was tested on three LCA case studies, which differed in size and behaviour. Uncertainty propagation in LCA using a sampling method leads to more (directly) usable information compared to fuzzy interval arithmetic or analytical uncertainty propagation. Latin hypercube and quasi Monte Carlo sampling provide more accuracy in determining the sample mean than Monte Carlo sampling and can even converge faster than Monte Carlo sampling for some of the case studies discussed in this paper.     

Reliability simulation of solar concentrator receiver

The reliability of solar concentrator is investigated using finite element (FE) modelling. An FE model of the receiver absorber is built and simulated using latin hypercube sampling. A transient thermal structural simulation is conducted, and the maximum thermal stress affecting the absorber is determined. Based on the failure criterion, the most effective parameters are determined and assigned as random variables. A stochastic simulation is performed resulting in a probability density function (PDF) of the thermal stress-life. The PDF is used to estimate the reliability of the absorber. Different designs and materials of the absorber tubes are investigated. Consequently, methods to improve the reliability of the absorber are identified.     

A numerical integration-based yield estimation method for integrated circuits

A novel integration-based yield estimation method is developed for yield optimization of integrated circuits.This method tries to integrate the joint probability density function on the acceptability region directly. To achieve this goal,the simulated performance data of unknown distribution should be converted to follow a multivariate normal distribution by using Box-Cox transformation(BCT).In order to reduce the estimation variances of the model parameters of the density function,orthogonal array-based modified Latin hypercube sampling (OA-MLHS) is presented to generate samples in the disturbance space during simulations.The principle of variance reduction of model parameters estimation through OA-MLHS together with BCT is also discussed.Two yield estimation examples,a fourth-order OTA-C filter and a three-dimensional(3D) quadratic function are used for comparison of our method with Monte Carlo based methods including Latin hypercube sampling and importance sampling under several combinations of sample sizes and yield values.Extensive simulations show that our method is superior to other methods with respect to accuracy and efficiency under all of the given cases.Therefore,our method is more suitable for parametric yield optimization.     

考虑输入变量相关性的概率潮流计算方法

风电场的大规模接入使得电力系统在进行规划设计和方式安排时需要计及风电出力的不确定性,概率潮流计算方法是在计及不确定因素的条件下分析电力系统运行状态的重要工具。针对当前所使用的概率潮流计算方法的不足,提出一种可以处理多个输入随机变量相关性的基于拉丁超立方采样（Latin hypercube sampling,LHS）的Monte Carlo模拟概率潮流计算方法,该方法同时还具有精度高和速度快等优点,并且不受输入随机变量的概率分布类型的约束。在同时考虑风电和负荷不确定性的条件下,对IEEE14和IEEE118节点系统进行仿真计算,结果验证了所提出方法的有效性和准确性。