Global aerodynamic design optimization based on data dimensionality reduction

In aerodynamic optimization, global optimization methods such as genetic algorithms are preferred in many cases because of their advantage on reaching global optimum. However, for complex problems in which large number of design variables are needed, the computational cost becomes prohibitive, and thus original global optimization strategies are required. To address this need, data dimensionality reduction method is combined with global optimization methods, thus forming a new global optimization system, aiming to improve the efficiency of conventional global optimization. The new optimization system involves applying Proper Orthogonal Decomposition (POD) in dimensionality reduction of design space while maintaining the generality of original design space. Besides, an acceleration approach for samples calculation in surrogate modeling is applied to reduce the computational time while providing sufficient accuracy. The optimizations of a transonic airfoil RAE2822 and the transonic wing ONERA M6 are performed to demonstrate the effectiveness of the proposed new optimization system. In both cases, we manage to reduce the number of design variables from 20 to 10 and from 42 to 20 respectively. The new design optimization system converges faster and it takes 1/3 of the total time of traditional optimization to converge to a better design, thus significantly reducing the overall optimization time and improving the efficiency of conventional global design optimization method.     

Aerodynamic optimization design for high pressure turbines based on the adjoint approach

A first study on the continuous adjoint formulation for aerodynamic optimization design of high pressure turbines based on S2surface governed by the Euler equations with source terms is presented.The objective function is defined as an integral function along the boundaries,and the adjoint equations and the boundary conditions are derived by introducing the adjoint variable vectors.The gradient expression of the objective function then includes only the terms related to physical shape variations.The numerical solution of the adjoint equation is conducted by a finitedifference method with the Jameson spatial scheme employing the first and the third order dissipative fluxes.A gradient-based aerodynamic optimization system is established by integrating the blade stagger angles,the stacking lines and the passage perturbation parameterization with the quasi-Newton method of Broyden–Fletcher–Goldfarb–Shanno(BFGS).The application of the continuous adjoint method is validated through a single stage high pressure turbine optimization case.The adiabatic efficiency increases from 0.8875 to 0.8931,whilst the mass flow rate and the pressure ratio remain almost unchanged.The optimization design is shown to reduce the passage vortex loss as well as the mixing loss due to the cooling air injection.     

变形飞行器建模及控制方法研究综述

针对变形飞行器的发展现状、气动建模、动力学建模以及控制方法研究进行了综述。首先阐述了变形飞行器的定义及变形需求,再从四个方面梳理了变形飞行器的技术优势。针对不同的机翼变形方式,对飞行器各种变形技术的特点及发展现状进行了详细的分析与总结;将变形飞行器与固定外形飞行器的气动机理进行了对比分析,并对变形飞行器的动力学建模方法进行了梳理,总结了各种方法的特点;结合变形飞行器自身特性分析了飞行控制的特点及难点,并详细阐述了各类变形飞行器的控制研究进展。最后总结分析了变形飞行器的发展趋势：深化基础理论与技术研究,从材料、结构及控制等领域全方面朝着智能化方向发展,最终实现变形飞行器的工程化、自主化、智能化应用。     

An artificial neural network approach for aerodynamic performance retention in airframe noise reduction design of a 3D swept wing model

With the progress of high-bypass turbofan and the innovation of silencing nacelle in engine noise reduction, airframe noise has now become another important sound source besides the engine noise. Thus, reducing airframe noise makes a great contribution to the overall noise reduction of a civil aircraft. However, reducing airframe noise often leads to aerodynamic perfor-mance loss in the meantime. In this case, an approach based on artificial neural network is intro-duced. An established database serves as a basis and the training sample of a back propagation (BP) artificial neural network, which uses confidence coefficient reasoning method for optimization later on. Then the most satisfactory configuration is selected for validating computations through the trained BP network. On the basis of the artificial neural network approach, an optimization pro-cess of slat cove filler (SCF) for high lift devices (HLD) on the Trap Wing is presented. Aerody-namic performance of both the baseline and optimized configurations is investigated through unsteady detached eddy simulations (DES), and a hybrid method, which combines unsteady DES method with acoustic analogy theory, is employed to validate the noise reduction effect. The numerical results indicate not merely a significant airframe noise reduction effect but also excel-lent aerodynamic performance retention simultaneously.     

A combined turbulent combustion model in studying a lean flameout process

A modification of the Zimont turbulent combustion model is presented; the modification makes it possible to describe processes in diffusion and homogeneous flame fronts as well as to take into account the influence of different factors that cause flame destabilization. The model is based on the equation for a reaction completeness extent (completeness of combustion). The model realization to simulate the premixed combustion is described and the modification of the turbulent combustion model is applied to simulate a process of flameout downstream of the flameholder.     

弯曲通道模拟离心条件下的主燃级和预燃级联焰试验

冲压转子发动机是冲压发动机和燃气涡轮发动机的有机结合,其结构简单。冲压转子发动机燃烧室由于其内部受到强离心力场的作用,燃烧室火焰的稳定燃烧以及传播都会受其影响。为了得到离心力场对分级燃烧中两级联焰的影响规律,本文进行了试验研究工作。针对燃烧室在强离心条件下的燃烧特点,采用弯曲通道模拟气流的离心效应。在弯曲试验段上对预燃级和主燃级进行了联焰研究,试验在常温常压条件下进行,进口气流速度范围为10~70m/s。试验中考察了离心力、燃油分级比例以及主燃级燃油喷射初始角度对联焰的影响,试验结果初步验证了燃烧方案的可行性,为冲压转子发动机燃烧室的研究奠定了基础。     

箭体上的凸起物对火箭气动特性的影响

综合大量实验研究结果,本文给出了火箭箭体上的环形凸起物和纵向凸起物对火箭气动特性的影响。其中最主要的是改变滚转力矩的大小和方向。而这正是姿态控制系统设计的重要依据之一。因此,给出凸起物的设计原则,对火箭气动设计有实际意义。     

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.     

Assessment on critical technologies for conceptual design of blended-wing-body civil aircraft

Civil aviation faces great challenges because of its robust projected future growth and potential adverse environmental effects. The classical Tube-And-Wing(TAW) configuration following the Cayley's design principles has been optimized to the architecture's limit, which can hardly satisfy the further requirements on green aviation. By past decades' investigations the BlendedWing-Body(BWB) concept has emerged as a potential solution, which can simultaneously fulfill metrics of noise, emission and fuel burn. The purpose of the present work is to analyze the developments of critical technologies for BWB conceptual design from a historical perspective of technology progress. It was found that the high aerodynamic efficiency of BWB aircraft can be well scaled by the mean aerodynamic chord and wetted aspect ratio, and should be realized with the trade-offs among stability and control and low-speed performance. The structure concepts of non-cylinder pressurized cabin are of high risks on weight prediction and weight penalty. A static stability criterion is recommended and further clear and adequate criteria are required by the evaluations of flying and handling qualities. The difficulties of propulsion and airframe integration are analyzed. The energy to revenue work ratios of well-developed BWB configurations are compared,which are 31.5% and 40% better than that of TAW, using state-of-art engine technology and future engine technology, respectively. Finally, further study aspects are advocated.     

Application of a PCA-DBN-based surrogate model to robust aerodynamic design optimization

An efficient method employing a Principal Component Analysis (PCA)-Deep Belief Network (DBN)-based surrogate model is developed for robust aerodynamic design optimization in this study. In order to reduce the number of design variables for aerodynamic optimizations, the PCA technique is implemented to the geometric parameters obtained by parameterization method. For the purpose of predicting aerodynamic parameters, the DBN model is established with the reduced design variables as input and the aerodynamic parameters as output, and it is trained using the k-step contrastive divergence algorithm. The established PCA-DBN-based surrogate model is validated through predicting lift-to-drag ratios of a set of airfoils, and the results indicate that the PCA-DBN-based surrogate model is reliable and obtains more accurate predictions than three other surrogate models. Then the efficient optimization method is established by embedding the PCA-DBN-based surrogate model into an improved Particle Swarm Optimization (PSO) framework, and applied to the robust aerodynamic design optimizations of Natural Laminar Flow (NLF) airfoil and transonic wing. The optimization results indicate that the PCA-DBN-based surrogate model works very well as a prediction model in the robust optimization processes of both NLF airfoil and transonic wing. By employing the PCA-DBN-based surrogate model, the developed efficient method improves the optimization efficiency obviously.