抗滑桩在加固边坡稳定性中的应用及影响分析

为保证河道边坡稳定及行洪能力,减少城市用地,研究挡墙与抗滑桩相结合的边坡复式断面作为河道断面设计的重点。采用有限元计算软件,建立挡墙与抗滑桩相结合的复式断面数值模型,分析不同桩长、桩径的抗滑桩设计与安全系数之间的变化规律。结果表明,此类边坡的潜在滑弧位置会随着抗滑桩的桩长增加而上移,计算所得安全系数随着抗滑桩桩长的增长,出现先增大后减少的现象,故抗滑桩桩径的设计应结合实际需要综合确定。本研究为类似边坡抗滑桩设计提供参考依据。     

Network level bridges maintenance planning using Multi-Attribute Utility Theory

Bridge infrastructure managers are facing multiple challenges to improve the availability and serviceability of ageing infrastructure, while the maintenance planning is constrained by budget restrictions. Many research efforts are ongoing, for the last few decades, ranging from development of bridge management system, decision support tools, optimisation models, life cycle cost analysis, etc. Since transport infrastructures are deeply embedded in society, they are not only subject to technical requirements, but are required to meet the requirements of societal and economic developments. Therefore, bridge maintenance planning should accommodate multiple performance goals which need to be quantified by various performance indicators. In this paper, an application of Multi-Attribute Utility Theory (MAUT) for bridge maintenance planning is illustrated with a case study of bridges from the Netherlands road network. MAUT seeks to optimise multiple objectives by suggesting a trade-off among them and finally assigns a ranking to the considered bridges. Moreover, utility functions of MAUT appropriately account for the involved uncertainty and risk attitude of infrastructure managers. The main contribution of this study is in presenting a proof-of-concept on how MAUT provides a systematic approach to improve the decision-making of maintenance planning by making use of available data, accommodating multiple performance goals, their uncertainty, and preferences of infrastructure managers.     

Optimal control of combined heat and power units under varying thermal loads

This paper describes a model based optimizer that allows a combined heat and power (CHP) unit to supply backup power to a Smart Grid on the one hand and minimize the cost for heat and power supply on the other hand. The model of the CHP unit is lean but nevertheless accurately represents the unit behavior, including thermal behavior of the storage as well as the aging effect of engine starts. Thanks to the small model dimension we can solve the optimal dispatch problem efficiently using dynamic programming. Two selected soft- and hard-ware in the loop tests are discussed to demonstrate the performance of the approach. A re-optimization strategy is discussed that allows reactions to wrong predictions of external influences like weather.     

Airfoil optimisation for vertical‐axis wind turbines with variable pitch

To advance the design of a multimegawatt vertical‐axis wind turbine (VAWT), application‐specific airfoils need to be developed. In this research, airfoils are tailored for a VAWT with variable pitch. A genetic algorithm is used to optimise the airfoil shape considering a balance between the aerodynamic and structural performance of airfoils. At rotor scale, the aerodynamic objective aims to create the required optimal loading while minimising losses. The structural objective focusses on maximising the bending stiffness. Three airfoils from the Pareto front are selected and analysed using the actuator cylinder model and a prescribed‐wake vortex code. The optimal pitch schedule is determined, and the loadings and power performance are studied for different tip‐speed ratios and solidities. The comparison of the optimised airfoils with similar airfoils from the first generation shows a significant improvement in performance, and this proves the necessity to properly select the airfoil shape.     

Analysis of switched quantizer based on the quadratic spline functions

In this paper, an approximation of the optimal compressor function using the quadratic spline functions with 2L?=?8 segments is described. Since the quadratic spline with 2L?=?8 segments provides better approximation of the optimal compression function than quadratic spline with 2L?=?4 segments, capitalizing on the benefits of the obtained spline approximation, quantizer designing process is firstly performed for the so assumed number of segments and the Laplacian source of a unit variance. Then, to enhance the usability of the proposed model, the switched quantization technique is applied and a beneficial analysis is derived, providing insight in the robustness of the proposed quantizer performances with respect to the mismatch in designed for and applied to variances. Reached quality has been compared to another model from the literature, and it has been shown that the proposed model outperforms the previous model by almost 1.3?dB.     

Fault diagnosis of wind turbine planetary gearbox under nonstationary conditions via adaptive optimal kernel time–frequency analysis

Planetary gearboxes play an important role in wind turbine (WT) drivetrains. WTs usually work under time-varying running conditions due to the volatile wind conditions. The planetary gearbox vibration signals in such an environment are hence highly nonstationary. Conventional spectral analysis and demodulation analysis methods are unable to identify the characteristic frequency of gear fault from such nonstationary signals. As such, this paper presents a time–frequency analysis methods to reveal the constituent frequency components of nonstationary signals and their time-varying features for WT planetary gearbox monitoring. More specifically, we exploit the adaptive optimal kernel (AOK) method for this challenging application because of its fine time–frequency resolution and cross-term free nature, as demonstrated by our simulation analysis. In this study, the AOK method has been applied to identify the time-varying characteristic frequencies of gear fault or to extract different levels of impulses induced by gear faults from lab WT experimental signals and in-situ WT signals under time-varying running conditions. We have demonstrated that the AOK is effective diagnosis of: (a) both the local damage (a single chipped tooth) and distributed faults (wear of all teeth), (b) both sun gear and planet gear faults, and (c) faults with very weak signature (e.g., the sun gear fault at the low speed stage of a WT planetary gearbox).     

A simulation‐based algorithm for optimal pricing policy under demand uncertainty

We propose a simulation‐based algorithm for computing the optimal pricing policy for a product under uncertain demand dynamics. We consider a parameterized stochastic differential equation (SDE) model for the uncertain demand dynamics of the product over the planning horizon. In particular, we consider a dynamic model that is an extension of the Bass model. The performance of our algorithm is compared to that of a myopic pricing policy and is shown to give better results. Two significant advantages with our algorithm are as follows: (a) it does not require information on the system model parameters if the SDE system state is known via either a simulation device or real data, and (b) as it works efficiently even for high‐dimensional parameters, it uses the efficient smoothed functional gradient estimator.     

Comparison of biochemical properties and thermal inactivation of membrane‐bound polyphenol oxidase from three apple cultivars (Malus domestica Borkh)

A comparative study of the properties of membrane‐bound polyphenol oxidase (mPPO) from three apple cultivars, namely Red Fuji (FJ), Granny Smith (GS) and Golden Delicious (GD), was carried out for the first time. Data indicate that mPPOs from three cultivars exhibit significantly different properties. GS mPPO had the strongest affinity to catechol, but FJ mPPO had the highest maximum velocity. Red Fuji (FJ) mPPO had the significantly higher activity than those of GD and GS mPPOs. Red Fuji (FJ) mPPO had the highest activity at pH 8.00, while GD and GS mPPOs at 4.50 and 7.50–8.00, respectively. Red Fuji (FJ) mPPO was more stable than GD and GS mPPOs over the pH range of 5.0–8.5. The optimal temperature for GS mPPO was within 70–75 °C, which is higher than those for mPPOs from FJ and GD. Thermal inactivation of the three mPPOs followed a first‐order kinetic model with different inactivation kinetic parameters.     

Performance optimization of integrated gas and power within microgrids using hybrid PSO–PS algorithm

In this paper, a hybrid algorithm consisting of particle swarm optimization and pattern search algorithm is proposed to evaluate and optimize the design and operation of microgrids (MGs) in combined gas and power networks. Key performance indicators (KPIs) are modeled and proposed to evaluate and assess MGs. The paper begins by proposing a comprehensive study to define KPIs, which are used to evaluate the following MG parameters: economical efficiency, reliability, environmental conservation, and power quality. Multi‐objective evaluation functions are then developed by building a relationship matrix of MG and KPI components. The results are then displayed as optimized power generation percentages for each technology with values for four KPI categories: cost, quality, reliability and environmental friendliness. Two case studies are examined in this paper; both the province of Ontario and Toronto regional zone under all system parameters with varying percentage of generation via gas technology. Results indicated that the optimal scenario for both Ontario and Toronto was achieved at hybrid PSO–patern search percentage generation via gas technology with improved cost KPI and other KPIs remaining approximately constant. Copyright © 2016 John Wiley & Sons, Ltd.     

A novel multi-objective optimization method: Input values effect on responses (IVER)

In this study, a novel multi-objective optimization method based on the best effect of unique input (independent variable) values on responses (dependent variables) was proposed. The proposed method was compared with optimization using Derringer & Suich function that is still the most used. The comparison was made using the response values measured in real experiments and available in the literature. The advantages of the proposed method such as not needing the polynomial model aiming to predict the response values, no parameter selection problem, being able to offer optimum range instead of single optimum value, being suitable for use with existing experimental designs and being simple and interpretable were demonstrated as a result of comparison. It was also suggested how the proposed method will be effective according to experimental designs, and application for the users' application was presented.