The complete dynamic stall process encompasses a series of complex developmental stages, such as flow separation, leading edge vortex shedding, and reattachment. Unlike static stall, dynamic stall exhibits hysteresis, rendering phenomenological models as complex nonlinear state-space systems, often accompanied by numerous empirical parameters, which complicates practical applications. To address this issue, the Goman–Khrabrov (G-K) dynamic stall model simplifies the state space and retains only two empirical parameters related to time delays. Our study finds that different developmental stages of dynamic stall exhibit various time delay scales. The G-K dynamic stall model, which utilizes a first-order time-invariant inertia system, forcibly unifies the time scales across different stages. Consequently, this leads to intractable nonphysical modeling errors. This paper introduces the latest revised G-K model that employs a time-varying state space system. This model not only maintains a concise form but also eliminates the nonphysical modeling errors previously mentioned. In response to the challenge of identifying empirical parameters, this paper presents a parameter identification method for both the original and revised G-K models utilizing a Physics-Informed Neural Network. The revised model was validated through dynamic stall load prediction cases for mild, moderate and deep dynamic stall on various airfoils, achieving a maximum accuracy improvement of up to 74.5%. The revised G-K model is capable of addressing a broader range and more complex practical applications.
An aeroelastic model is developed by using multivariable solid-shell elements. Geometrical nonlinearity and the elimination of corresponding locking phenomena are considered accurately in the modeling. Unsteady aerodynamic forces are addressed by solving Euler flow dynamic equations. The computational-structural-dynamics solver is loosely coupled to the computational-fluid-dynamics solver, and the full match of the computational structural dynamics and computational fluid dynamics interfaces is achieved by three-dimensional modeling. An interpolation method using finite-element shape functions is developed to transform forces from aerodynamic grids to structural grids. The aerodynamic mesh deformation is obtained by using radial basis functions combined with the transfinite interpolation method. The aeroelastic model is validated through the study of the flutter and the limit cycle oscillation of a cropped delta wing in transonic flow. Moreover, the establishment of the aeroelastic model using multivariable solid shell elements is compared with other modeling methods. Improved numerical results show that the multivariable finite-element method is an efficient and capable tool for simulating geometrical nonlinearity, and the developed three-dimensional data-exchange method also plays a key role in the nonlinear aeroelastic modeling.
Computational structural dynamics (CSD) and Computational fluid dynamics (CFD) referring to aeroelasticity are highly nonlinear problems in time domain. Euler equation is solved by finite volume method with dual-time technology to obtain nonlinear unsteady aerodynamic load; the finite element co-rotational theory is applied to model geometric nonlinear structure, and an approximate energy conservation algorithm is developed to achieve nonlinear dynamic response; combined with interface technique, which involves conservation volume transformation and second order time loosely coupled algorithm, an improved CFD/CSD coupled system is designed to solver nonlinear aeroelasticity, Then, the developed algorithm is performed on aeroelastic response of NACA 0012 airfoil, the results illustrate that the improved algorithm has superior accuracy and efficiency compared with conventional method.
With the reduction of rotor diameter and motor size, the hovering performance measurement becomes a challenge for rotary wing Nano Air Vehicles (NAVs). Conventional test benches for Micro Aerial Vehicles fail to measure some characteristics of Nano Air Vehicles. In this paper, five test benches with highly sensitive mechanisms were successively designed in order to measure the thrust and torque of nano-rotors simultaneously and respond to the change of variables rapidly with sufficient accuracy. A commercial micro brushless motor and a micro rotor were studied experimentally and computationally at a low Reynolds range from 4,000 to 19,000. Computational and experimental comparisons were carried out and the performance of the test benches was discussed. The analysis suggests that the thrust coefficients measured by each test bench vary little from each other, while the power coefficients present significant differences. Then the hovering performance of the micro rotor and power efficiency of the motor were studied. Degradation of motor efficiency and rotor figure of merit are observed with size reduction associated with NAV applications.
Coupled with Co-flow Jet (CFJ) technology, the Non-dominated Sorting Genetic Algorithm II was utilized for the multi-objective combination optimization of an optimized Co-flow Jet wing, based on National Advisory Committee for Aeronautics (NACA) 6421. A high-precision numerical simulation using the delayed detached eddy simulation model was performed on the optimized wing to investigate the three-dimensional flow separation characteristics after static stall. The stall improvement was investigated by adjusting the momentum coefficient of the injection. The results show that the optimized wing exhibits significant improvements in aerodynamic performance and corrected aerodynamic efficiency. At an angle of attack of 10°, the average lift increased by 16.25% and the drag decreased by 27.23% compared to the CFJ6421 wing, while effectively addressing the problem of low modified aerodynamic efficiency of the CFJ wing at lower angles of attack. By utilizing higher momentum and improving the boundary layer control capability, flow separation is effectively suppressed, thus achieving the goal of stall recovery of the CFJ wing.
A low-thrust guidance scheme, which is weighted combined by taking the optimum strategy of thrust allocation and the target deficits value into consideration for each orbital element, is developed. The presented guidance scheme is predictive in nature and does not rely on a stored reference trajectory or reference controls. The orbit transfer problem is converted into parametric optimization and utilizing a hybrid genetic algorithm. The minimum-time orbit transfer is considered. The influence of the Earth’s oblateness is taken into consideration in the simulation of minimum-time. A conclusion is drawn that the designed method presented here turns out to be an autonomous scheme because the information of target orbit is considered in the transfer process.
§Reentry trajectory Optimization is an important job in RLV preliminary design. A great many of optimization algorithms have been applied in solving this problem. But most of these applications just consider a single object such as minimum heat load or maximum maneuverable range. In practically, multi -objective reentry trajectory optimization is often necessary such as minimum heat load and maximum maneuverable range. These types of optimization problems have tr aditionally been solved by averaging each objective with a weighting factor, and then combine the objectives into a single scalar objective. Such reduction techniques eli minate the need for a more complex multi -objective algorithm, but introduce new parameters in the form of weighting factors. Except that，every run the algorithm can produce one optimal trajectory. NSGA -II algorithm is a good multi -objective genetic optimiz ation algorithm based on Pareto -optimal front with low computational requirements, elitist approach, parameter -less niche approach and simple constraint handling strategy. In this paper, NSGA -II algorithm is used to the RLV multi -objective reentry optimiza tion design with minimum heat load and maximum maneuverable range. The simulation result indicates that NSGA -II has good performance in reentry trajectory design and can produce all of the optimal results for different weighting factor.
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