高比转速跨声离心/斜流压气机流动特点分析

  目的  压气机是燃气轮机的核心部件之一,它直接决定了燃气轮机性能的优劣。斜流压气机是介于轴流压气机和离心压气机之间的一种形式,兼具离心压气机高压比和轴流压气机流通能力强的优点。  方法  采用一套通用于轴流、离心和斜流压气机的S2流面气动设计和任意中弧线叶片造型的程序,对某斜流+轴流组合式多级压气机进行气动设计,研究确定压气机的流道形式、环量分布,并对其进行叶片造型。在设计时,结合商用数值模拟软件对该组合式压气机进行流场数值分析。  结果  结果表明,该斜流轴流组合式压气机的各项参数均满足设计指标,压气机两级总压比和绝热效率分别达到4.3和88%。  结论  tt 该斜流轴流组合式的压气机设计有如下难度：一是斜流级静子进口处马赫数较高,在斜流静子中控制气流流动的难度较大;二是为控制气流在斜流静子中的分离,斜流级静子弯度较小,导致轴流级处于较大的负攻角状态,斜流与轴流的级间匹配难度较大。     

船舶复杂轴系扭转振动建模及动力学分析

针对船舶复杂轴系结构,提出一种基于能量法的轴系扭转振动建模及动力学分析方法。该方法对船舶复杂轴系中的典型结构进行统一能量化描述,如联轴器、齿轮、负载等,并结合弹性轴的能量函数构建轴系结构的拉格朗日能量泛函;然后,利用最小二乘法和广义变分法,对轴系角位移展开项系数进行求解,从而建立适用于船舶复杂轴系结构的扭转动力学模型。通过与有限元方法计算结果相比较来验证该建模方法的准确性,并利用该方法分析连接刚度和负载转动惯量对整体轴系扭振特性的影响,以期为船舶复杂轴系设计提供一定参考。     

规则波中船舶操纵运动预报的灰箱建模研究

针对船舶操纵运动实时准确预报的需求,开展规则波中船舶操纵运动预报的灰箱建模研究。建立船舶操纵运动方程,以表征操纵运动机理。应用泰勒级数展开方法近似静水水动力,采用经验公式估算规则波中二阶定常波浪漂移力,形成规则波中船舶操纵运动预报的数学模型。采用傅里叶变换方法解决不同频率的操纵与耐波运动数据分离问题,基于操纵运动数据和深度神经网络（DNN）技术,构建静水水动力修正及二阶定常波浪漂移力模型,并将其代入操纵运动机理方程,形成融合机理与数据的规则波中船舶操纵运动预报灰箱模型。然后以ONRT为研究对象,分别应用灰箱模型和数学模型预报规则波中船舶操纵运动。结果显示,对于所有运动工况,仿真单位时间步长耗时平均约2～3 ms,灰箱模型预报结果与试验数据相比其相对精度均值达94.83%,相比数学模型预报精度平均提高了4.50%。灰箱预报模型可以作为规则波中船舶操纵运动预报的有效方法,能为真实海洋环境中船舶操纵运动的实时预报奠定基础。     

基于特征线的客车造型设计方法研究

以造型法则为基础,探讨一种以特征线为依托的设计方式,力求使客车的设计过程变得简单明了,使设计师的思路更加清晰。     

Machine learning applications in activity-travel behaviour research: a review

ABSTRACT

This paper reviews the activity-travel behaviour literature that employs Machine Learning (ML) techniques for empirical analysis and modelling. Machine Learning algorithms, which attempt to build intelligence utilizing the availability of large amounts of data, have emerged as powerful tools in the fields of pattern recognition and big data analysis. These techniques have been applied in activity-travel behaviour studies since the early ’90s when Artificial Neural Networks (ANN) were employed to model mode choice decisions. AMOS, an activity-based modelling system developed in the mid-’90s, has ANN at its core to model and predict individual responses to travel demand management measures. In the dawn of 2000, ALBATROSS, a comprehensive activity-based travel demand modelling system, was proposed by Arentze and Timmermans using Decision Trees. Since then researchers have been exploring ML techniques like Support Vector Machines (SVM), Decision Trees (DT), Neural Networks (NN), Bayes Classifiers, and more recently, Ensemble Learners to model and predict activity-travel behaviour. A large number of publications over the years and an upward trend in the number of published articles over time indicate that Machine Learning is a promising tool for activity-travel behaviour analysis and prediction. This article, first of its kind in the literature, reviews these studies and explores the trends in activity-travel behaviour research that apply ML techniques. The review finds that mode choice decisions have received wide attention in the literature on ML applications. It was observed that most of the studies identify the lack of interpretability as a serious shortcoming in ML techniques. However, very few studies have attempted to improve the interpretability of the models. Further, some studies report the importance of feature engineering in ML-based studies, but very few studies adopt feature engineering before model development. Spatiotemporal transferability of models is another issue that has received minimal attention in the literature. In the end, the paper discusses possible directions for future research in the area of activity-travel behaviour modelling using ML techniques.     

Future aspects in marine ecosystem modelling

Existing ecosystem models are briefly presented and summarised. The problem of coupling physical and biological models as well as aspects of prediction and predictability are discussed. The general perception that marine ecosystems are inherently unpredictable due to non-linearity becomes questionable if the response of climate variability in marine ecosystems is analysed. Many authors have shown correlations between climate variability and the variability of abundance or biomass of marine organisms such as phytoplankton, zooplankton, benthos or fish recruitment in different parts of the world ocean. In the northern hemisphere, certain species show a linear response to climate variability mainly during winter and spring. However, the underlying mechanisms are not well understood. Often, a phase lag can be observed between climate variability and the reaction of organisms. The identification of a plausible mediator between climate and biology is difficult, since all possible physicochemical mechanisms having a direct or indirect influence on the variability of abundance or biomass of marine organisms have to be considered as mediator.The understanding of the reason of the phase lag, which possibly implies a “biological memory”, and the identification of all possible mediators are necessary to predict the response of marine organisms to climate variability. The identification of mediators will result in an improvement of coupled models, a deeper understanding of physical–biological interaction and the improvement of predictive capability of marine ecosystem models.     

潍坊寿光港区航道工程防沙堤平面布置形式研究

采用波浪潮流泥沙数学模型试验手段,对潍坊寿光港区防沙堤建设中的平面布置形式进行研究。根据研究成果,建议选择北槽航道作为5 000 t级通海航道,航道走向63.5°~243.5°。防沙堤工程建议采用双导堤形式,其中北导堤应接岸。建议堤头可先达-4.0 m水深处,观测其堤头附近冲淤变化后,再决定是否继续向海延伸。     

The influence of different turbulence schemes on modelling primary production in a 1D coupled physical–biological model

A one-dimensional (1D) coupled physical–microbiological model has been applied to a site in the central North Sea. The impact of the choice of the turbulence closure scheme on the modelling the primary production has been investigated.The model was run with four different parameterisations of vertical mixing of heat, momentum and dissolved and suspended matters, using M2 tidal forcing and the hourly mean meteorological forcing of 1989 to reproduce the annual thermal structure and primary production. The four mixing parameterisations are: Level 2 turbulence closure scheme [Mellor, G.L., Yamada, T., 1974. A hierarchy of turbulence closure models for planetary boundary layers. J. Atmos. Sci. 31, 1791–1806; Mellor, G.L., Yamada, T., 1982. Development of a turbulence closure model for geophysical Fluid problems. Rev. Geophys. Space Phys. 20 (4) 851–875] using an explicit numerical scheme [Sharples, J., Tett, P., 1994. Modelling the effect of physical variability on the midwater chlorophyll maximum. J. Mar. Res. 52, 219–238]; a version of the Level 2.5 turbulence closure scheme [Galperin, B., Kantha, L.H., Hassid, S., Rosati, A., 1988. A quasi-equilibrium turbulent energy model for geophysical flows. J. Atmos. Sci. 45, 55–62; Ruddick, K.G., Deleersnijder, E., Luyten, P.J., Ozer, J., 1995. Haline stratification in the rhine/meuse freshwater plume: a 3D model sensitivity analysis. Cont. Shelf Res. 15 (13) 1597–1630] simplified to use an algebraic mixing length by Sharples and Simpson [Sharples, J., Simpson, J.H., 1995. Semidiurnal and longer period stability cycles in the Liverpool Bay region of freshwater influence. Cont. Shelf Res. 15, 295–313], also solved explicitly; the same simplified L2.5 scheme with an implicit numerical solution and modified vertical discretisation scheme [Annan, J.D., 1999. Numerical methods for the solution of the turbulence energy equations in the shelf seas. Int. J. Numer. Methods Fluids 29, 193–206]; and another version of the same scheme (but using a different algebraic mixing length) as described by Xing and Davies [Xing, J., Davies, A.M., 1996a. Application of turbulence energy models to the computation of tidal currents and mixing intensities in the shelf edge regions. J. Phys. Oceanogr. 26, 417–447; Xing, J., Davies, A.M., 1996b. Application of a range of turbulence models to the computation of tidal currents and mixing intensities in shelf edge regions. Cont. Shelf. Res. 16, 517–547; Xing, J., Davies, A.M., 1998. Application of a range of turbulence energy models to the computation of the internal tide. Int. J. Numer. Methods Fluids 26, 1055–1084]. Various model outputs at the sea surface and in depth profiles have been compared with data collected in 1989 as part of the North Sea Project [Huthnance, J.M., 1990. Progress on North Sea Project. NERC News, vol. 12, pp. 25–29, UK]. It is shown that the biological results are extremely sensitive to the small changes in the physical conditions, which arise due to the different turbulence schemes tested. The timing of the spring bloom and the maintenance of the midwater chlorophyll maximum all differ greatly between model runs, and the gross primary production varies by a factor of two from the highest to lowest results. The simplified Level 2.5 scheme, implemented using the numerical methods of Annan [Annan, J.D., 1999. Numerical methods for the solution of the turbulence energy equations in the shelf seas. Int. J. Numer. Methods Fluids 29, 193–206], produces results, which give the best agreement with the available data.     

船舶永磁电动机推进系统建模与仿真概述

本文简要介绍永磁电动机推进系统,以及用于永磁电动机推进系统的建模和仿真的工具,并给出永磁电动机船舶电力推进系统的Matlab/Simulink仿真实例.