涡轮冷却叶片参数化造型与网格自动生成

为提高涡轮叶片的数值计算效率,开发出基于参数控制的涡轮叶片模型软件。可实现叶片模型重构、计算域分区、结构化网格生成过程的自动完成和参数化控制,使得叶片的造型和网格生成过程简单化;对生成网格进行数值模拟计算,计算结果表明:生成网格在满足叶片数值计算精度的前提下,工作量大大减少,网格生成效率极大提高;在模型调整时,只需更改相应参数即可快速更新模型,缩短模型的生成周期,提高设计工作的可重用性。     

载人运输飞船流体回路试验研究

详细介绍了载人运输飞船热控船流体回路主要技术方案,包括系统组成与流程设计、工作模式设计、主要技术状态等,并列出了流体回路全部试验工况及相关试验结果,试验结果表明流体回路具有良好的温度调控能力和适应能力。重点对流体回路辐射器、冷凝干燥器、冷板、换热器等设备换热能力及低温情况下工质体积补偿、低压情况下工质溶气释出导致泵压头波动等问题进行了分析。最后根据分析和试验结果对系统提出了优化改进建议。     

非线性晃动问题的ALE边界元方法

利用ＡＬＥ（任意的Ｌａｇｒａｎｇｅ－Ｅｕｌｅｒ）边界元方法数值求解了具有自由液面的非线性晃动问题，即受外力激励下流体的非线性振动问题。把ＡＬＥ有限元方法的思想应用到边界元方法中，得到了ＡＬＥ边界元方法。对于自由液面的非线性动力边界条件，应用Ｇａｌｅｒｋｉｎ加权方法进行了有限元数值离散。为了增加求解精度，对动力边界条件提出了增加误差修正项的数值求解方法。对时间变量采用Ｎｅｗｍａｒｋ方法进行离散。推导了系统非线性方程的预测－多次校正法迭代格式。进行了算例分析与比较，得到了令人比较满意的结果。     

多介质流动改进型Level-Set重新初始化方法

根据Level-Set函数在网格点处的值,预估出间断面的位置,并对间断面附近网格点上的LS值作相应调整,使其保持距离函数的性质,能够避免由于重新初始化造成间断面位置移动的问题。数值计算采用高精度WENO格式,对于多个可压缩流问题进行了数值模拟,表明了该方法的有效性。     

Effect of Coriolis and centrifugal forces on flow and heat transfer at high rotation number and high density ratio in non orthogonally internal cooling channel

Numerical predictions of three-dimensional flow and heat transfer are performed for a two-pass square channel with 45° staggered ribs in non-orthogonally mode-rotation using the second moment closure model. At Reynolds number of 25,000, the rotation numbers studied were 0,0.24, 0.35 and 1.00. The density ratios were 0.13, 0.23 and 0.50. The results show that at high buoyancy parameter and high rotation number with a low density ratio, the flow in the first passage is governed by the secondary flow induced by the rotation whereas the secondary flow induced by the skewed ribs was almost distorted. As a result the heat transfer rate is enhanced on both co-trailing and co-leading sides compared to low and medium rotation number. In contrast, for the second passage, the rotation slightly reduces the heat transfer rate on co-leading side at high rotation number with a low density ratio and degrades it significantly on both co-trailing and co-leading sides at high buoyancy parameter compared to the stationary, low and medium rotation numbers. The numerical results are in fair agreement with available experimental data in the bend region and the second passage, while in the first passage were overestimated at low and medium rotation numbers.     

固体火箭发动机真实流体输运系数的自动模拟

为缩短固体火箭发动机的研究周期、降低研究成本和揭示工作机理,需要对其工作过程进行三维数值仿真,但数值计算中许多物性参数如输运系数的确定存在困难,为此主要研究了适合于固体火箭发动机内高温高压下的多组分混合物输运系数的计算方法,并通过高级计算机语言管理数据库等方法实现自动计算,可准确快速地计算发动机内流过程中输运系数的变化,为真实模拟发动机的工作过程提供正确的物性参数。     

评涡轮流量计Thompson-Grey的理论模型

运用流体动力学分析,指出了涡轮流量计的Thompson—Grey理论模型中存在的各种缺陷。计算结果表明,改进后的模型与实际情况的吻合程度有了很大提高,涡轮流量计仪表系数的理论估计值与实际值之间的偏差由原来的±(4％～6％)下降至±1.5％以内。     

多辐射器航天器热控流体回路布局的(火积)耗散分析

为了优化多辐射器航天器热控流体回路布局，提高流体回路的散热效率，降低流体回路温度，本文基于(火积)理论，分别对多个辐射器串联和并联的流体回路布局的散热进行了分析。结果表明，排散相同热量时，流体回路的流体与管路壁面之间的温差均匀性越好，流体回路散热过程(火积)耗散越小，系统散热过程越优。进一步，对于2个辐射器的情况，分别对辐射器设置了不同的空间辐射加热热流，对辐射器的流体回路布局方式进行了比较。结果表明，辐射器与流体回路串联时，系统散热性能要优于两者并联，系统的流体温度水平最低，结果与(火积)理论分析的预测完全一致。研究结论对多辐射器的航天器热控流体回路设计具有指导意义。     

On developing data-driven turbulence model for DG solution of RANS

High-order Discontinuous Galerkin(DG) methods have been receiving more and more attentions in the area of Computational Fluid Dynamics(CFD) because of their high-accuracy property. However, it is still a challenge to obtain converged solution rapidly when solving the Reynolds Averaged Navier–Stokes(RANS) equations since the turbulence models significantly increase the nonlinearity of discretization system. The overall goal of this research is to develop an Artificial Neural Networks(ANNs) model with low complexity acting as an algebraic turbulence model to estimate the turbulence eddy viscosity for RANS. The ANN turbulence model is off-line trained using the training data generated by the widely used Spalart–Allmaras(SA) turbulence model before the Optimal Brain Surgeon(OBS) is employed to determine the relevancy of input features.Using the selected relevant features, a fully connected ANN model is constructed. The performance of the developed ANN model is numerically tested in the framework of DG for RANS, where the‘‘DG+ANN" method provides robust and steady convergence compared to the ‘‘DG+SA" method. The results demonstrate the promising potential to develop a general turbulence model based on artificial intelligence in the future given the training data covering a large rang of flow conditions.