Modelling ignition probability with pairwise mixing-reaction model for flame particle tracking

Reduced order models for ignition analysis can offer insights into ignition processes and facilitate the combustor optimization. In this study, a Pairwise Mixing-Reaction (PMR) model is formulated to model the interaction between the flame particle and the surrounding cell mixture during Lagrangian flame particle tracking. Specifically, the model accounts for the two-way coupling of mass and energy between the flame particle and the surrounding shell layer by modelling the corresponding turbulent mixing, chemical reaction and evaporation process if present. The state of a flame particle, e.g., burnt, hot gas or extinguished, is determined based on particle temperature. This model can properly describe the ignition process with a spark kernel being initiated in a nonflammable region, which is of practical importance in certain turbine engines and has not been rigorously accounted for by the existing models based on the estimation of local Karlovitz number. The model is integrated into an ignition probability analysis platform and is demonstrated for a methane/air bluff-body flame with the flow and fuel/air mixing characteristics being extracted from a non-reacting simulation. The results show that for the spark location being at the extreme fuel-lean outer shear layer of the recirculation zone, PMR can yield ignition events with a significant number of active flame particles. The mechanisms for the survival of the initial flame particles and the entrainment of the survived flame particles into the recirculation zone are analyzed. The results also show that the ignition probability map from PMR agrees well with the experimental observation: a high ignition probability in the shear layer of the recirculation zone near the mean stoichiometric surface, and low ignition probabilities inside the recirculation zone and the top stagnation region of the recirculation zone. The parametric study shows that the predicted shape of the ignition progress factor and ignition probability is in general insensitive to the model parameters and the model is adequate for quantifying the regions with high ignition probabilities.     

Cutting path-dependent machinability of SiCp/Al composite under multi-step ultra-precision diamond cutting

Particle-tool interactions, which govern the synergetic deformation of SiC particle reinforced Al matrix composites under mechanical machining, strongly depend on the geometry of particle position residing on cutting path. In the present work, we investigate the influence of cutting path on the machinability of a SiCp/Al composite in multi-step ultra-precision diamond cutting by combining finite element simulations with experimental observations and characterization. Be consistent with experimentally characterized microstructures, the simulated SiCp/Al composite is considered to be composed of randomly distributed polygonally-shaped SiC particles with a volume fraction of 25vol%. A multi-step cutting strategy with depths of cut ranging from 2 to 10 μm is adopted to achieve an ultimate depth of cut of 10 μm. Intrinsic material parameters and extrinsic cutting conditions utilized in finite element simulations of SiCp/Al cutting are consistent with those used in corresponding experiments. Simulation results reveal different particle-tool interactions and failure modes of SiC particles, as well as their correlations with machining force evolution, residual stress distribution and machined surface topography. A detailed comparison between numerical simulation results and experimental data of multi-step diamond cutting of SiCp/Al composite reveals a substantial impact of the number of cutting steps on particle-tool interactions and machined surface quality. These findings provide guidelines for achieving high surface finish of SiCp/Al composites by ultra-precision diamond cutting.     

Inverse identification of constitutive parameters of Ti2AlNb intermetallic alloys based on cooperative particle swarm optimization

Ti₂AlNb intermetallic alloy is a relatively newly developed high-temperature-resistant structural material, which is expected to replace nickel-based super alloys for thermally and mechanically stressed components in aeronautic and automotive engines due to its excellent mechanical properties and high strength retention at elevated temperature. The aim of this work is to present a fast and reliable methodology of inverse identification of constitutive model parameters directly from cutting experiments. FE-machining simulations implemented with a modified Johnson-Cook (TANH) constitutive model are performed to establish the robust link between observables and constitutive parameters. A series of orthogonal cutting experiments with varied cutting parameters is carried out to allow an exact comparison to the 2D FE-simulations. A cooperative particle swarm optimization algorithm is developed and implemented into the Matlab programs to identify the enormous constitutive parameters. Results show that the simulation observables (i.e., cutting forces, chip morphologies, cutting temperature) implemented with the identified optimal material constants have high consistency with those obtained from experiments, which illustrates that the FE-machining models using the identified parameters obtained from the proposed methodology could be predicted in a close agreement to the experiments. Considering the wide range of the applied unknown parameters number, the proposed inverse methodology of identifying constitutive equations shows excellent prospect, and it can be used for other newly developed metal materials.     

B／Al单向铺层复合板的力学性能及断裂行为的研究

研究了热压扩散结合工艺对Ｂ／Ａｌ复合材料的强度的影响,并对试样拉伸断口及断裂径进行了研究,分析表明,试样断口有三种断裂模型,断裂路径同热压工艺有密切的关系。     

Radiative properties of alumina/aluminum particles and influence on radiative heat transfer in solid rocket motor

The thermal radiation of micron-sized condensed phase particles plays a dominant role during the heat transfer process in aluminized Solid Rocket Motors（SRMs）. Open research mainly focuses on the radiative properties of alumina particles while the study considering the presence of aluminum is lacking. In addition, the thermal radiation inside the SRM with consideration of the participating particles is seldom studied. In this work, the multiscale method of predicting the thermal environment insi...     

基于卷积盲分离的强干扰下通信信号分离算法

针对目前通信辐射源个体识别算法在实际试验中由于各类干扰信号和多径衰落导致识别率较低的问题,提出一种用于识别算法前端的信号分离算法,可有效地减少其他电磁信号对于识别算法输入信号的影响,从而提高在复杂电磁环境中通信辐射源个体识别的正确识别率。该算法将灾变策略和搜索状态的自适应引入量子粒子群算法,通过对混合信号的联合对角化从截获的观测信号中提取出目标通信辐射源的有用信号。为了更加系统、直观地衡量算法的分离效果,提出分离熵来量化算法的整体性能。仿真结果表明,该分离算法可以把目标通信辐射源的有用信号从复杂电磁环境中提取出来,从而提高通信辐射源个体识别在复杂电磁环境中的正确识别率,具有较好的可行性和有效性。     

发射系下SINS/GPS/CNS组合导航系统联邦粒子滤波算法

传统的扩展卡尔曼滤波(Extended Kalman filter, EKF)算法应用于未来高超、空天飞行器的组合导航系统时,因其模型线性化展开会导致模型不准确,从而引起导航精度下降;采用蒙特卡洛方法来实现递推贝叶斯估计问题的粒子滤波(Particle filter,PF)算法能有效避免引入线性化误差,具有一定的优势。据此,针对高超、空天飞行器在发射过程中通常需要直接获得发射惯性系下的高精度导航参数的需求,提高发射惯性系下弹载组合导航系统滤波算法的精确性就尤为重要,PF滤波算法无需对非线性系统进行线性化展开即可直接实现对非线性系统的状态误差估计。为此,本文将PF滤波算法引入空天飞行器SINS/GPS/CNS多信息融合组合导航系统,设计了发射系下基于联邦滤波器的PF滤波算法,实现了对组合导航系统状态参数的直接建模估计。算法仿真结果表明,相较于发射系下SINS/GPS/CNS组合导航系统联邦EKF滤波算法,PF滤波算法有效提高了组合导航系统滤波精度。     

基于LADRC的无人机高精度定高控制

针对复杂气流扰动对无人机（UAV）航迹高度控制的影响,对存在复杂气流扰动下的定高控制策略、控制结构和控制器参数优化展开研究,实现高精度高度控制。基于线性自抗扰控制（LADRC）确定总体控制架构,设计扩张状态观测器（ESO）观测估计纵向高度通道和速度通道中存在的总扰动,在控制中引入扰动补偿,减小扰动对系统输出造成的影响。对UAV在飞行过程中存在的大气紊流扰动或离散突风等风干扰分析其功率谱密度,构造考虑风扰动对高度影响、时域响应特性和稳定裕度的综合目标函数,通过粒子群优化算法得到具有高精度、高抗干扰性能的控制器参数,优化中考虑风干扰的功率谱密度分布,减小了控制器参数设计的保守性。通过与常规比例-积分-微分（PID）控制器控制效果进行对比,说明基于线性自抗扰控制器的纵向高度控制的优异性能。      

粉末GH4169 合金中的原始颗粒边界问题

探讨了粉末GH4169高温合金中的原始颗粒边界的形成机理、其对合金组织性能的影响以及消除措施等。结果表明:粉末GH4169合金中原始颗粒边界组织主要由MC碳化物构成,而在粉末成型前进行预热处理可以有效抑制原始颗粒边界组织的生成,提高合金综合性能。     

单颗磨粒磨削仿真研究进展

概述了传统磨削仿真的基本方法及发展过程,总结了磨粒模型和工件模型的研究现状,具体分析了有限元法、光滑流体粒子动力学法、分子动力学法以及综合仿真方法等应用于单颗磨粒磨削材料的去除机理、成屑机理、工件表面质量以及磨粒磨损等仿真中的研究现状,最后阐述了各类仿真方法的局限性,并提出了单颗磨粒磨削仿真进一步的发展前景。