运载火箭动力学建模中液体推进剂模拟技术

针对以往火箭结构动力学分析过程中,横向采用梁模型、纵向采用弹簧-质量模型无法反映捆绑火箭纵向与横向、纵向与扭转模态之间耦合效应的问题,提出了基于梁模型的推进剂模拟方法,建立了以附加质量形式表示的液体推进剂耦合质量矩阵.将此方法用于串联式贮箱结构的动力学建模和模态分析,实现了基于梁模型的火箭纵向、横向和扭转一体化建模及动力学特性分析,为研究捆绑火箭纵向-横向、纵向-扭转模态之间的耦合问题奠定了基础.     

大型捆绑火箭姿态动力学模型研究

针对大型捆绑运载火箭所呈现出的低频密频模态、纵-横-扭强耦合、复杂局部变形和发动机低频谐振以及多贮箱液体晃动的特点,推导建立了工程实用的姿态动力学模型.基于牛顿-欧拉法建立了刚体质心和绕质心运动方程、液体晃动方程以及发动机振动方程,基于有限元方法建立了火箭弹性振动方程.模型充分反映了发动机摆动、液体晃动等活动质量相对箭体运动时对刚体运动和弹性振动的影响,充分反映了刚-晃-弹以及发动机低频谐振之间的耦合,同时保留了工程上所熟悉的形式.基于坐标变换思想提出了弹性旋转矩阵方法求解复杂弹性变形后外力的大小和作用点,推导过程中严格按照定义求解,避免了错项和漏项,推导原理简明、概念清晰,过程规范,易于设计人员理解.基于ADAMS的虚拟样机仿真结果验证了本文推导模型的正确性.     

基于软件数据耦合精细建模的采区稳定性分析研究

针对地下矿山采区稳定性数值模拟分析过程中存在的三维矿床模型建立以及单元网格划分等难点问题,提出利用三维矿山软件进行精细建模与数值模拟软件耦合的模式,简化复杂矿山地质条件下模型前处理过程的思路。利用3DMINE对地下矿山三维矿床模型进行精细建模,通过MIDAS GTS软件对精细建模得到的三维矿床模型进行单元网格的划分与材料组的分配,快速、准确地为采区稳定性数值模拟分析提供具有较高几何相似性的模型,采用具有较强计算能力的计算软件FLAC3D进行计算。这种耦合精细建模方式不仅简化了矿山采区稳定性数值模拟的前处理过程,提高了地下矿山采区稳定性模拟计算的准确性,同时也将不同精度要求的三维矿床模型用于数值模拟运算,拓宽了三维矿山模型的应用领域。     

大型液体运载火箭POGO动力学模型研究

本文基于键合图理论的建模思想提出新的液体火箭跷振（POGO）稳定性分析状态变量模型,该模型的系数矩阵线性且非奇异,与以往模型相比更有利于实现模块化建模和时域仿真分析.在此基础上通过模型研究给出补燃发动机燃气系统的动力学模型,使该分析模型可以对采用补燃循环发动机的火箭进行POGO稳定性分析,是对目前POGO理论分析模型的一个重要完善.     

大速差射流装置对固体粉末冲压发动机燃烧性能的影响分析

采用颗粒轨道模型对固体粉末冲压发动机的两相流场进行了三维数值模拟,研究了大速差射流稳燃装置对发动机掺混燃烧性能的影响.计算结果表明,大速差射流稳燃装置提高了粉末燃料与空气的掺混程度,同时增大了粉末燃料在发动机中的停留时间,大幅度提高了粉末冲压发动机的燃烧效率,发动机燃烧效率达到89.3%.     

运载火箭动力学建模及振型斜率预示技术

针对姿态控制系统设计关心的速率陀螺安装位置局部振型和振型斜率问题,提出了在传统等效梁模型基础上对局部结构详细建模方法,进行了全箭动特性分析及振型和振型斜率提取,揭示了速率陀螺安装位置局部振型和振型斜率沿圆周方向的分布规律以及口盖附近刚度变化对局部振型和振型斜率的影响.试验结果表明该方法获得的全箭动特性结果合理.     

水利水电工程地质三维统一建模方法研究

针对水利水电工程地质的三维建模与分析问题, 提出了面向水利水电工程地质建模的混合数据结构; 以面向对象的分类思想, 基于混合数据结构实现了地形类、地层类、断层类、界限类4类地质对象的拟合构造与几何建模, 并提供可供选择的建模机制, 为解决倒转褶皱、复杂断层等建模难点发展了新的方法; 集成地质对象和人工对象模型, 实现了水利水电工程地质三维统一模型的构建. 基于三维统一模型可进行一系列水利水电工程地质分析应用, 包括岩体质量可视化分级、三维模型任意剖切分析、大坝和地下工程地质分析等, 为分析复杂地质条件下水利水电工程勘测、设计与施工中的地质问题提供了理论基础和技术手段.     

超声速气流中稳焰凹腔吹熄极限分析与建模

吹熄极限的研究对于超燃冲压发动机燃烧室中稳焰凹腔和燃料喷注方案的设计具有重要学术意义和工程应用价值．针对凹腔上游喷注燃料的火焰稳定过程进行了研究,从理论上分析了超声速气流中凹腔稳定燃烧的贫燃与富燃吹熄机制,基于剪切层稳燃模式,进一步考虑流场的三维结构,并结合横向射流穿透与混合模型,在有效当量比的计算、凹腔的卷吸过程以及燃料射流与凹腔剪切层／回流区的质量交换等方面改进了已有模型,重新定义了与吹熄过程密切相关的Damokhler数和有效当量比,并以两者关系为准则建立了描述富燃和贫燃吹熄极限的数学模型,进而通过实验数据验证了模型的有效性．     

液体运载火箭的一种迭代制导方法

针对液体运载火箭的基本特点和对迭代制导的要求，从火箭简化运动方程及其最优控制解出发，以火箭推力方向的3分量为控制变量，导出了一种迭代制导方法，并通过数字仿真证明了其有效性．     

六点支承航空发动机双转子系统动力学离散模型

针对六点支承形式的航空发动机双转子系统,建立了复杂离散动力学模型,对该模型进行结构降维,得到了两种简化动力学模型.首先采用有限元法建立了较为精确的多轮盘复杂离散动力学模型,然后基于质心集中方法对该模型进行了简化,分别得到了四轮盘双转子简化动力学模型和三轮盘双转子简化动力学模型,并将其与复杂离散动力学模型进行了临界转速的对比.结果表明,简化得到的四轮盘双转子简化模型,前三阶相应临界转速误差分别在5%,22%,10%之内,可以很好地保有原系统的动力学特性;简化得到的三轮盘双转子简化模型,在柔性支承下,前三阶相应临界转速误差在15%之内,可以较好地保有原系统的动力学特性.最后,给出了3种模型不平衡响应的对比分析图.这是一种从结构上将实际多轮盘双转子简化成少轮盘双转子的方法,降低了双转子系统的自由度,有利于在考虑到故障、支承非线性等因素后的理论计算分析.     

Forecasting daily foreign exchange rates using genetically optimized neural networks

Forecasting currency exchange rates is an important financial problem that has received much attention especially because of its intrinsic difficulty and practical applications. The statistical distribution of foreign exchange rates and their linear unpredictability are recurrent themes in the literature of international finance. Failure of various structural econometric models and models based on linear time series techniques to deliver superior forecasts to the simplest of all models, the simple random walk model, have prompted researchers to use various non‐linear techniques. A number of non‐linear time series models have been proposed in the recent past for obtaining accurate prediction results, in an attempt to ameliorate the performance of simple random walk models. In this paper, we use a hybrid artificial intelligence method, based on neural network and genetic algorithm for modelling daily foreign exchange rates. A detailed comparison of the proposed method with non‐linear statistical models is also performed. The results indicate superior performance of the proposed method as compared to the traditional non‐linear time series techniques and also fixed‐geometry neural network models. Copyright © 2002 John Wiley & Sons, Ltd.     

两相燃烧的大涡模拟

两相燃烧大涡模拟研究正在取得迅速进展.和雷诺平均模拟相比,大涡模拟可以给出流动和火焰的瞬态结构,并且很多情况下可以给出比雷诺平均模拟更准确的统计结果.湍流燃烧大涡模拟的一个关键问题是亚网格模型,特别是燃烧模型.不同研究者提出了或者应用了不同的燃烧模型,但是有的模拟结果缺乏仔细的实验验证,有的统计结果和实验不符合.提出了二阶矩(SOM)燃烧模型,分别成功地用于气体湍流燃烧和液雾燃烧的大涡模拟,显示其优于其他模型.并对近年来气体燃烧、液雾燃烧、煤粉燃烧和油滴燃烧大涡模拟的研究给出了简要的综述.     

美国登月火箭项目进展

研究美国登月火箭的项目管理有助于我国探月工程的规划。2004年美国公布重返月球计划。为实现这一目标,美国国家航空航天局决定以航天飞机推进系统为基础研制新型运载火箭。新火箭于2006年被命名为“战神”,包括“战神”-1乘员运载火箭和“战神”-5货物运载火箭两种型号。目前,“战神”-1火箭已完成系统需求评审,进入设计评审阶段。火箭第一级、上面级、上面级发动机以及电子设备的研制合同都已于2007年授出。预计2009年5月进行首次试验飞行,2015年3月进行首次载人飞行。而“战神”-5火箭还处于早期研究阶段。由于预算调整和航天项目的复杂性,“战神”火箭的研制已经有所拖延。虽然大型航天集团和国家航空航天局努力争取更多预算,但“战神”火箭未来的研制不排除进一步拖延的可能。     

离散型两相流动的大涡模拟

大涡模拟(Large-eddy simulation,LES)的研究正在取得迅速进展.和雷诺平均模拟(Reynolds-averaged Navier-Stokes modeling,RANS modeling)相比,LES可以给出流动和火焰的瞬态结构,并且在不少情况下可以给出比雷诺平均模拟更准确的统计平均结果.本文作者及其同事从2002年开始,用大涡模拟研究了气泡-液体流动的瞬态结构.后来从2005年至今陆续研究了气体-颗粒两相流动的大涡模拟、气体绕过单个颗粒流动的大涡模拟、以及有蒸发和燃烧的油滴周围的流动的大涡模拟.本文对作者及其同事近期进行的上述离散型两相流动的大涡模拟研究给出了简要的综述,包括控制方程、亚网格模型、数值方法、主要的模拟结果及其实验验证.     

Models and the dynamics of theory-building in physics. Part II—Case studies

In Part I, it was argued that models are best explained by considering the strategies from which they issue. A distinction was proposed between two classes of modeling that contribute to theory-building: H-modeling and L-modeling. Case studies are presented in this Part II to illustrate the characteristic features of these modeling strategies; examples are drawn from classical statistical mechanics and quantum physics.     

改进的模糊神经网络算法在车型识别中的应用

FBP和FCNN网络是模式识别中应用最为广泛的两种神经网络,本文将这两种网络应用于车型识别,分别建立了车型识别模型。利用混沌对初值的极端敏感依赖提出了FCNN网络算法,通过对车型图像数据库进行仿真实验,对比分析它们各自的识别率和泛化能力等性能指标,证明了FCNN网络算法的有效性。     

Model robustness as a confirmatory virtue: The case of climate science

I propose a distinct type of robustness, which I suggest can support a confirmatory role in scientific reasoning, contrary to the usual philosophical claims. In model robustness, repeated production of the empirically successful model prediction or retrodiction against a background of independently-supported and varying model constructions, within a group of models containing a shared causal factor, may suggest how confident we can be in the causal factor and predictions/retrodictions, especially once supported by a variety of evidence framework. I present climate models of greenhouse gas global warming of the 20th Century as an example, and emphasize climate scientists' discussions of robust models and causal aspects. The account is intended as applicable to a broad array of sciences that use complex modeling techniques.     

Technological forecasting with nonlinear models

The S-shaped growth curves such as Gompertz, logistic, normal and Weibuli are widely used for forecasting technological substitutions. A family of data-based transformed (DBT) models, which are linear in the regression parameters, including the above-mentioned four models as special cases has been shown to be quite useful for short-term forecasts. This paper explores modeling the technology penetration data directly with assumed S-shaped growth curves. The resulting models, which are nonlinear in the regression parameters, also incorporate proper dependence structure and power transformation. It appears that the nonlinear modeling is a viable alternative to the DBT and other conventional forecasting models in forecasting technological substitutions. Hence, an appropriate strategy is to consider the nonlinear modeling approaches as possible alternatives and use the data at hand to select, via pseudo-cross-validation, the best model for forecasting purposes.     

Model uncertainty and policy choice: A plea for integrated subjectivism

A question at the intersection of scientific modeling and public choice is how to deal with uncertainty about model predictions. This “high-level” uncertainty is necessarily value-laden, and thus must be treated as irreducibly subjective. Nevertheless, formal methods of uncertainty analysis should still be employed for the purpose of clarifying policy debates. I argue that such debates are best informed by models which integrate objective features (which model the world) with subjective ones (modeling the policy-maker). This integrated subjectivism is illustrated with a case study from the literature on monetary policy. The paper concludes with some morals for the use of models in determining climate policy.     

Forecasting volatility in commodity markets

This paper uses recent advances in time-series modeling to derive long-horizon forecasts of commodity price volatility which incorporate investors' expectations of volatility. Our results are promising. We compare several different forecasts of commodity price volatility, which we divide into three categories: (1) forecasts using only expectations derived from options prices; (2) forecasts using only time-series modeling; and (3) forecasts which combine market expectations and time-series methods. The forecasts in (1) and (2) are used extensively in the literature, while those in (3) are new in this paper. On comparing these different forecasts, we find that our proposed forecasts from category (3) outperform both market expectations forecasts and time-series forecasts. This result holds both in and out of sample for virtually all commodities considered.