A Multi-space Interrelation Theory for Correlating Aerodynamic Data from Hypersonic Ground Testing

Prediction of aerodynamic force/heating acting on hypersonic vehicles in flight conditions with experimental data is a critical yet challenging step in developing hypersonic vehicles. A multi-space interrelation (MSI) theory and its correlation algorithms have been presented. MSI considers the flight condition as an ideal wind tunnel and then aims at detecting an inherent invariant of aerodynamic data from different wind tunnels. The invariant detection is carried out by special supervised self-learning schemes, adaptive space transformation (AST), and/or parse-matrix evolution (PME). The invariant is then used to predict the aerodynamic force/heating coefficients. The study indicates that the multi-space interrelation theory agrees well with physical phenomena. The correlation algorithm can make use of hypersonic wind-tunnel experimental data effectively, and the correlation function is capable of unifying all the experimental data in an analytical form. With the proposed theory and algorithm, one can expect to find a reliable correlation formula with high accuracy based on plenty of wind-tunnel experimental data, provided that the physical condition has not essentially changed

复现高超声速飞行条件下10°尖锥标模气动力特性试验研究

综述了应用中科院力学所的JF12长试验时间激波风洞中,在复现40km高度,飞行马赫数7的试验条件下,开展的10°尖锥标模天平测力试验研究结果

配船计划的优化模型与算法

配船计划是经济管理中遇到的一个实际问题．某公司用M艘油轮给W个位于不同海港的储气罐供应液化天然气．每艘油轮的容量已知，每个储气罐的消耗率可以预测．储气罐需要维持一定的库存水平（比如50%）才能最小化库存成本．配船计划的目标就是要在一定时期内（比如一个季度）寻找一个最优的运输方案，使得总的库存成本最低．本文首先提出配船计划的组合优化模型，然后提出两方面的改进将模型简化为仅含有线性约束的0-1 规划模型．最后，针对该0-1 规划模型，我们提出了专门的遗传算法．数值结果表明，本文提出的模型和算法对配船计划的优化是切实有效的

Triangle Evolution-A Hybrid Heuristic for Global Optimization

Abstract This paper presents a hybrid heuristic{triangle evolution (TE) for global optimization. It is a real coded evolutionary algorithm. As in di®erential evolution (DE), TE targets each individual in current population and attempts to replace it by a new better individual. However, the way of generating new individuals is di®erent. TE generates new individuals in a Nelder- Mead way, while the simplices used in TE is 1 or 2 dimensional. The proposed algorithm is very easy to use and e±cient for global optimization problems with continuous variables. Moreover, it requires only one (explicit) control parameter. Numerical results show that the new algorithm is comparable with DE for low dimensional problems but it outperforms DE for high dimensional problems

A Fast Mathematical Modeling Method for Aerodynamic-Heating Predictions

Prediction of aerodynamic heating under different flight conditions is a critical and challenging step in developing a new hypersonic vehicle. The prediction model usually involves a large number of variables, and this makes genetic programming converge too slow. This paper presents a fast mathematical modeling method, divide and conquer, for aerodynamic-heating predictions. It can use the separability feature of the target model to decompose a high-dimensional function into many low-dimensional sub-functions. The separability is detected by a special algorithm, bi-correlation test (BiCT), and the sub-functions could be determined by general genetic programming (GP) algorithms one by one. Thus the computational cost will be increased almost linearly with the increase of function dimension. This can help to break the curse of dimensionality and greatly improved the convergence speed to get the underlying target models from a set of sample data

符号回归问题的解析矩阵进化算法

在许多工程应用中,人们希望通过积累下来的已知数据对某些感兴趣的指标进行预测与分析.比如在飞行器设计问题中,已知某些工况下(不同的飞行高度、攻角和来流马赫数等)飞行器的驻点温度数据,需要根据这些数据研究其他工况下的驻点温度情况.根据经验公式建立线性或非线性模型是一种常用的预测与分析方法.然而,当经验公式缺乏或经验公式包含错误,或者所研究的系统已经发生了变化时,这种方法就失去了效用.此时,数据驱动模

An Integrated Evolutionary Algorithm for Expensive Global Optimization

We propose an integrated algorithm named low dimensional simplex evolution extension (LDSEE) for expensive global optimization in which only a very limited number of function evaluations is allowed. The new algorithm accelerates an existing global optimization, low dimensional simplex evolution (LDSE), by using radial basis function (RBF) interpolation and tabu search. Different from other expensive global optimization methods, LDSEE integrates the RBF interpolation and tabu search with the LDSE algorithm rather than just calling existing global optimization algorithms as subroutines. As a result, it can keep a good balance between the model approximation and the global search. Meanwhile it is self-contained. It does not rely on other GO algorithms and is very easy to use. Numerical results show that it is a competitive alternative for expensive global optimization