A high-fidelity aerodynamic shape optimization framework based on the Reynolds-averaged Navier-Stokes equations is applied to the optimization of a boundary-layer ingesting S-duct designed for embedded engines on a hi...
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A high-fidelity aerodynamic shape optimization framework based on the Reynolds-averaged Navier-Stokes equations is applied to the optimization of a boundary-layer ingesting S-duct designed for embedded engines on a high-subsonic, unmanned flight vehicle. The optimizations initially target a cruise operating condition and are further extended to single-point and multipoint optimizations considering descent and climb. Two different composite objective functions are used. The first combines distortion and swirl at the fan interface plane as well as total pressure recovery, with user-defined weights for each objective, whereas the second involves pressure recovery, fan blade load variation, and fan blade incidence variation. Pareto fronts show the tradeoffs between objectives. The results indicate that compared to the baseline geometry, a simultaneous improvement in all objectives contained in the composite objective function can be obtained, depending on the priorities of each objective pre-assigned by the user. It was also found that if swirl can be ignored, then fan-face distortion can be greatly reduced while simultaneously reducing total pressure loss in the S-duct. Similarly, fan blade load variation and fan blade incidence variation can be significantly reduced while reducing total pressure loss. Finally, the multipoint optimization results show that a single S-duct geometry can perform well during cruise, climb, and descent conditions.
In-flight alignment is the basis of projectile strapdown inertial navigation system (SINS) accurate navigation. In-flight alignment of guided projectile SINS is faced with "high" complexity, such as satellit...
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In-flight alignment is the basis of projectile strapdown inertial navigation system (SINS) accurate navigation. In-flight alignment of guided projectile SINS is faced with "high" complexity, such as satellite interference and random wind disturbance, and "high" dynamics, such as high velocity (>= 500 m/s), high spin (>= 20 r/s), and high overload (>= 10 000 g). Thus, this article proposes a relaying fast in-flight alignment method based on adaptive multiconstraints, which is mainly divided into the optimization stage and filtering stage. First, the initial attitude angles are taken as the optimization object to establish the optimization model, and the K matrix is taken as the state variable to establish the filtering model. Then, the internal relaying mode in the optimization stage and the external relaying mode from the optimization stage to the filtering stage are designed. The rapidity of the optimization algorithm is exerted via adaptive multiconstraint mode to speed up the alignment process. Taking the filtering stage as the main step, the high precision of the filtering algorithm is brought into play to improve alignment accuracy. Simulation and experimental results show that the alignment accuracy and alignment time of this method are better than those of traditional methods.
This study aims to execute machine learning methods to predict the mechanical properties containing TS and CS of HPC. They are essential parameters for the durability, workability, and efficiency of concrete structure...
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This study aims to execute machine learning methods to predict the mechanical properties containing TS and CS of HPC. They are essential parameters for the durability, workability, and efficiency of concrete structures in civil engineering. In this regard, obtaining the estimation of the mechanical properties of HPC is complex energy and time-consuming. Due to this, an observed database was compiled, including 168 datasets for CS and 120 for TS. This database trained and validated two machine learning models: SVR and RT. The models combine the prediction outputs from the meta-heuristic algorithms to build hybrid and ensemble-hybrid models, which include dwarf mongoose optimization, PPSO, and moth flame optimization. According to the observed outputs, the ensemble models have great potential to be a recourse to deal with the overfitting problem of civil engineering, thus leading to the development of more supportable and less polluting concrete structures. This research significantly improves the efficiency and accuracy of predicting vital mechanical properties in high-performance concrete by integrating machine learning and metaheuristic algorithms, offering promising avenues for enhanced concrete structure design and development.
Under varying operational conditions, the contact and relative movement of a polymer and metal result in surface wear, accompanied by the emission of noise. The relationship between friction noise and wear is inherent...
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Under varying operational conditions, the contact and relative movement of a polymer and metal result in surface wear, accompanied by the emission of noise. The relationship between friction noise and wear is inherently complex and nonlinear. In light of these tribological characteristics, this paper introduces the implementation of a random forest algorithm and generalized regression neural network algorithm to establish a mathematical model for predicting the wear rate based on friction noise. To enhance the accuracy of wear rate regression, this study incorporates L2 norm feature selection and the sparrow search algorithm, which are tailored toward the friction characteristics. These techniques optimize the machine learning-based friction model, ultimately improving the regression accuracy of the wear rate.
This paper proposes a novel control method applied to the trajectory tracking of the wheeled mobile robot. This method can solve the tracking difficulty caused by the non-holonomic constraint and the under-actuated pr...
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This paper proposes a novel control method applied to the trajectory tracking of the wheeled mobile robot. This method can solve the tracking difficulty caused by the non-holonomic constraint and the under-actuated properties. First, according to the kinematic and dynamic tracking error models, the desired velocities for trajectory tracking purposes are obtained. Second, the control method, consisting of an enhanced backstepping controller with fewer gains and an optimization algorithm, is designed. The actual trajectory of the mobile robot is exactly converged and kept at the predefined reference trajectory by the operation of this method. Next, this method with globally uniformly asymptotically stability is theoretically analyzed. Finally, simulation comparisons and physical experiments are conducted in different scenarios. The tracking performance is evaluated by three metrics, namely convergence speed, tracking accuracy and robustness, thus verifying the effectiveness of the novel control method. An optimal enhanced backstepping tracking control method is proposed for wheeled mobile robot. The control method has fast convergence speed, high tracking accuracy and robustness. The actual trajectory converges rapidly and stably remains on the reference trajectory under the action of the control method. image
This paper investigates the influence of tiltrotor blade twist on whirl-flutter stability boundaries. Preliminary evaluations indicate that the whirl-flutter speed can be increased if the blade twist slope is reduced....
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This paper investigates the influence of tiltrotor blade twist on whirl-flutter stability boundaries. Preliminary evaluations indicate that the whirl-flutter speed can be increased if the blade twist slope is reduced. This positive effect results from the shift in the overall thrust toward the blade tip, increasing the flapwise bending moment at the blade root and the trim coning angle. This, in turn, generates a positive pitch-lag coupling, increasing the whirl-flutter speed. However, the shift of high sectional thrust forces toward the blade tip sections returns a higher induced drag, showing the tendency to increase the power required. The paper shows that, by using blade twist laws based on piecewise linear functions and adding the wing airfoil thickness as a second design parameter, it is possible to identify aircraft configurations that improve the whirl-flutter stability boundaries without penalizing the power required in airplane and helicopter mode flight. This is possible because the blade twist and the wing airfoil thickness have an impact on both power required and whirl-flutter speed, so a simple optimization algorithm can identify good tradeoffs. A detailed tiltrotor model representative of the Bell XV-15 is used to display the effectiveness of the proposed approach. The examples show that increases up to 21% on the whirl-flutter speed are achievable without penalties in the aircraft power required and with the additional benefit of a benign impact on rotor pitch link loads.
Motion primitives enable fast planning for complex and dynamic environments. Adversarial environments pose a particularly challenging and unpredictable scenario. Motion-primitive-based planners have the potential to p...
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Motion primitives enable fast planning for complex and dynamic environments. Adversarial environments pose a particularly challenging and unpredictable scenario. Motion-primitive-based planners have the potential to provide benefit in these types of environments. The key challenge is to design a library of maneuvers that effectively capture the necessary capabilities of the vehicle. This work presents a primitive-based game tree search to solve adversarial games in continuous state and action spaces and applies a reinforcement learning framework to autonomously generate effective primitives for the given task. The results demonstrate the ability of the learning framework to produce maneuvers necessary for competing against adversaries. Furthermore, we propose a method for learning a model to estimate the state-dependent value of each motion primitives and demonstrate how to incorporate this model to increase planning performance under time constraints. Additionally, we compare our primitive-based algorithm against forward simulated methods from existing literature and highlight the benefits of motion primitives.
In this paper, we consider a nonlinear switched dynamical system (NSDS) with unknown system parameters in the context of uncertain experimental data. This system is employed to model the continuous fermentation for th...
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In this paper, we consider a nonlinear switched dynamical system (NSDS) with unknown system parameters in the context of uncertain experimental data. This system is employed to model the continuous fermentation for the production of 1,3-propanediol through glycerol bioconversion. The uncertain experimental data points are regarded as stochastic variables and only their first-order moment information of the probability distributions is available. The target of this paper is to optimize these system parameters under the environment of uncertain experimental data. Taking these factors into account, we propose a distributionally robust system identification (DRSI) problem (i.e., a bi-level system identification problem) governed by the NSDS. The objective functional comprises two level objectives: (i) the inner-level objective aims to maximize the expectation of the relative error between the solution of the NSDS and the uncertain experimental data with respect to their probability distributions at approximately stable time;and (ii) the outer-level objective is to minimize the expectation with respect to these system parameters. The DRSI problem is equivalently transformed into a single-level system identification (SLSI) problem with non-smooth term through the application of the duality theory in the probability space. A smoothing technique is employed to approximate the non-smooth term in the SLSI problem. Subsequently, an error analysis of the employed smoothing technique is derived. The gradients of the objective functional in the SLSI with respect to these system parameters are obtained. An optimization algorithm is designed to solve the SLSI problem. Finally, the paper concludes with simulation results.
Deep learning model is a multi-layered network structure, and the network parameters that evaluate the final performance of the model must be trained by a deep learning optimizer. In comparison to the mainstream optim...
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Deep learning model is a multi-layered network structure, and the network parameters that evaluate the final performance of the model must be trained by a deep learning optimizer. In comparison to the mainstream optimizers that utilize integer-order derivatives reflecting only local information, fractional-order derivatives optimizers, which can capture global information, are gradually gaining attention. However, relying solely on the long-term estimated gradients computed from fractional-order derivatives while disregarding the influence of recent gradients on the optimization process can sometimes lead to issues such as local optima and slower optimization speeds. In this paper, we design an adaptive learning rate optimizer called AdaGL based on the Grunwald-Letnikov (G-L) fractional-order derivative. It changes the direction and step size of parameter updating dynamically according to the long-term and short-term gradients information, addressing the problem of falling into local minima or saddle points. To be specific, by utilizing the global memory of fractional-order calculus, we replace the gradient of parameter update with G-L fractional-order approximated gradient, making better use of the long-term curvature information in the past. Furthermore, considering that the recent gradient information often impacts the optimization phase significantly, we propose a step size control coefficient to adjust the learning rate in real-time. To compare the performance of the proposed AdaGL with the current advanced optimizers, we conduct several different deep learning tasks, including image classification on CNNs, node classification and graph classification on GNNs, image generation on GANs, and language modeling on LSTM. Extensive experimental results demonstrate that AdaGL achieves stable and fast convergence, excellent accuracy, and good generalization performance.
Solid propellant grain reverse design aims to discover optimal grain geometries by shape optimization methods to match the desired solid motor performance curves. To maximize the performance matching degree and the pr...
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Solid propellant grain reverse design aims to discover optimal grain geometries by shape optimization methods to match the desired solid motor performance curves. To maximize the performance matching degree and the propellant loading fraction simultaneously, this study develops a multi-objective evolutionary neural network for the grain reverse design, where the burning surface regression calculation is efficiently employed using the fast-sweeping method. Then, grain shape feature extraction and pattern analysis are achieved through image singular value decomposition and self-organizing mapping, respectively. Finally, the design case of a dual-thrust motor and a Mars ascent vehicle show that the method can well balance the performance-matching degree and propellant loading fraction. Moreover, without any training data set, it can generate dozens of grain shape patterns, highlighting their diversity and providing new ideas for solid rocket motor designers. Our method can offer a new pathway for the research field of solid rocket motor design.
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