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This paper proposes a nonlinear space dimension reduction method named Optimized Generative Topographic Mapping (OGTM). The Generative Topographic Mapping (GTM) method relies on the training sample set to capture the manifold of objective functions, and the generation of the training sample set causes an enormous computational burden. The choice of GTM hyperparameters has a significant influence on the design results. Traditional research has generally adopted the "cut-and-try" method to determine the corresponding hyperparameters and the best design, leading to wasted computational cost. The proposed OGTM overcomes this issue by minimizing the fitting error between the low-dimensional and high-dimensional samples, and the suitable hyperparameters are directly obtained by minimizing the fitting. In addition, the paper adopts a variable-fidelity sample filtration method to extract the promising regions with fewer sample points. To test and verify the effectiveness of the proposed method, it was then compared with the PCA and EGO methods in RAE2822 airfoil and ONERA M6 wing aerodynamic designs. The results demonstrate that the proposed method could capture the effective design space and generally take less computational cost to find the ideal results in all design optimizations. [ABSTRACT FROM AUTHOR]

Surrogate models have been widely applied in the aerodynamic optimization of aircrafts, whereas the traditional individual surrogate models have the defects of low robustness and applicability. In this study, a novel ensemble surrogate model is proposed and applied in the multi-objective optimization of the airfoil. The backpropagation neural network, deep belief network, and kriging surrogate models are selected as the member surrogate models, and the Dirichlet distribution strategy is introduced to adaptively generate the weights of the member surrogate models in constructing the ensemble surrogate model. An improved multi-objective particle swarm optimization (MOPSO) framework is established by employing the α -stable distribution function to enhance the global convergence rate of the algorithm. Based on the improved MOPSO framework in which the ensemble surrogate model is embedded, the multi-objective optimization of the airfoil is conducted. The results indicate that the proposed ensemble-surrogate-model-based optimization obtains better aerodynamic performance of the airfoil under multiple operating conditions, compared to the individual-surrogate-model-based optimization. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The development of new turbomachinery designs requires numerous time-consuming and computationally intensive computational fluid dynamics (CFD) calculations. However, most of the generated high spatial resolution data remain unused at later development steps. That is also the case with automated optimization processes that use only a few integral values to determine objectives and constraints. To make further use of this vast amount of CFD data a data-driven AI model based on the Transformer architecture is developed and trained using the available CFD data. The presented method subsequently provides a fast approximation of the 3D flow for new designs. In this paper, the structure of the developed AI model is presented and the approximation quality is analyzed using a complex, state-of-the-art compressor test case. It is shown that the AI model can reproduce many characteristics of the 3D flow of new designs, and performance measures such as efficiency can be derived from these flow predictions. In addition, the complex test case revealed that greater design variation reduces the AI approximation quality which can lead to undesirable exploratory behavior in an optimization setup. Overall, the test case has shown promising results and has provided hints for further improvements to the AI model. [ABSTRACT FROM AUTHOR]

Purposes Aiming at the performance conflict between optimization parameters in prior optimization method, a hybrid optimization model based on MOEA/D is proposed, which integrates CNN and genetic algorithm into MOEA/D framework to balance the correlation and complexity between various objective functions. Methods First, the deep learning method is used as a supplement to the conventional fluid mechanics analysis method to establish a highly reliable CNN response prediction model for airfoil aerodynamic characteristics, which can be used to quickly evaluate the aerodynamic parameters of airfoil. Then, the response model and genetic operator are interpolated into the MOEA/D framework to construct a multi-objective hybrid optimization model based on MOEA/D. And the lift drag ratio and moment coefficient of a NACA high lift airfoil under cruise condition are taken as the optimization objectives for testing. Finally, through the analysis of aerodynamic performance and flow field structure of the airfoil on the Pareto front, the distribution law of different airfoil configurations on the front is studied, which further guides the designer to explore the potential basic airfoil in the airfoil selection.

Renewable energy sources are essential to address climate change, fossil fuel depletion, and stringent environmental regulations in the subsequent decades. Horizontal-axis wind turbines (HAWTs) are particularly suited to meet this demand. However, their efficiency is affected by environmental factors because they operate in open areas. Adverse weather conditions like rain reduce their aerodynamic performance. This study investigates wind turbine power prediction under rainy conditions by integrating Blade Element Momentum (BEM) theory with explainable artificial intelligence (XAI). The S809 airfoil's aerodynamic characteristics, used in NREL wind turbines, were analyzed using ANSYS FLUENT and symbolic regression under varying rain intensities. Simulations at a Reynolds number (Re) of 1 × 106 were performed using the Discrete Phase Model (DPM) and k– ω SST turbulence model, with liquid water content (LWC) values of 0 (dry), 10, 25, and 39 g/m3. The lift and drag coefficients were calculated at various angles of attack for all the conditions. The results indicated that rain led to reduced lift and increased drag. The innovative aspect of this research is the development of machine learning models predicting changes in the airfoil coefficients under rain with an R 2 value of 0.97. The proposed XAI framework models rain effects at a lower computational time, enabling efficient wind farm performance assessment in rainy conditions compared to conventional CFD simulations. It was found that a heavy rain LWC of 39 g/m3 could reduce power output by 5.7% to 7%. These findings highlight the impact of rain on aerodynamic performance and the importance of advanced predictive models for optimizing renewable energy generation. [ABSTRACT FROM AUTHOR]

The tail wing of box-launched aircraft needs to be folded in the launch box, which can easily cause malfunctions during flight deployment. This article presents a ducted tail wing aircraft that does not require folding of the tail wing. To address the nonlinear problem of lift coefficient in the ducted tail, an aerodynamic optimization method for ducted tails based on the sparrow search algorithm with back-propagation (SSA-BP) neural network approximate model and multi-objective genetic algorithm fusion is proposed, with the goal of improving the lift-to-drag ratio and linearization degree of the lift curve. The linearization degree of the optimized tail lift coefficient curve is significantly improved, and the lift-to-drag ratio is significantly improved under cruising conditions. Based on this optimization result, the shape of the tail wing and fuselage combination was optimized, and the optimal configuration of the ducted tail wing aircraft was selected, providing a reference for the design of ducted tail wing aircraft. [ABSTRACT FROM AUTHOR]

Purpose: This paper delves into the aerodynamic optimization of a single-stage axial turbine employed in aero-engines. Design/methodology/approach: An efficient integrated design optimization approach tailored for turbine blade profiles is proposed. The approach combines a novel hierarchical dynamic switching PSO (HDSPSO) algorithm with a parametric modeling technique of turbine blades and high-fidelity Computational Fluid Dynamics (CFD) simulation analysis. The proposed HDSPSO algorithm introduces significant enhancements to the original PSO in three pivotal aspects: adaptive acceleration coefficients, distance-based dynamic neighborhood, and a switchable learning mechanism. The core idea behind these improvements is to incorporate the evolutionary state, strengthen interactions within the swarm, enrich update strategies for particles, and effectively prevent premature convergence while enhancing global search capability. Findings: Mathematical experiments are conducted to compare the performance of HDSPSO with three other representative PSO variants. The results demonstrate that HDSPSO is a competitive intelligent algorithm with significant global search capabilities and rapid convergence speed. Subsequently, the HDSPSO-based integrated design optimization approach is applied to optimize the turbine blade profiles. The optimized turbine blades have a more uniform thickness distribution, an enhanced loading distribution, and a better flow condition. Importantly, these optimizations lead to a remarkable improvement in aerodynamic performance under both design and non-design working conditions. Originality/value: These findings highlight the effectiveness and advancement of the HDSPSO-based integrated design optimization approach for turbine blade profiles in enhancing the overall aerodynamic performance. Furthermore, it confirms the great prospects of the innovative HDSPSO algorithm in tackling challenging tasks in practical engineering applications. [ABSTRACT FROM AUTHOR]

This study presents an efficient surrogate-based optimization (SBO) method of the aerodynamic performance of a contra-rotating open rotor (CROR). The objective was to maximize propulsion efficiency while reaching the target thrust coefficient at the cruise condition. To reduce the sample size and improve the optimization convergence speed, an infilling criterion was proposed based on the features of the interaction between the CROR front and rear rotors. The efficient front and rear rotors of the initial samples were selected and then combined to form the infilled samples. The results show that the infilled samples were closer to the Pareto front than the initial samples. For the six optimization parameters, 20 initial sample points were used, 11 samples were infilled, and the surrogate-based optimization was completed in five iterations. In total 43 samples were calculated during the optimization. The number of overall samples is approximately seven times the number of optimization parameters. The optimization results in parameter changes compared to the baseline and improved propulsion efficiency while meeting the thrust target. The optimization process increases the torque share of the rear rotor and changes the flow state at different radial positions, leading to a more uniform total pressure distribution at the outlet position, both circumferentially and radially. [ABSTRACT FROM AUTHOR]

Improvements in aerodynamic turbomachinery design have gained attraction due to the increased demand for a more sustainable future. Several optimization approaches have been presented and employed in the realm of aerodynamic design. However, among all of them, the adjoint approach has emerged as a hot research topic for aerodynamic optimization in the field of turbomachinery. The ability of this method to efficiently compute the derivatives of objective functions for several design variables has made it a promising optimization tool. This study provides a comprehensive review of all significant studies undertaken since the turn of the 21st century when the adjoint method was employed for the aerodynamic optimization of turbomachinery applications. The application of the adjoint approach in that context is extensively discussed under various aspects, including shape optimization in both steady and unsteady flows, varied eddy viscosity, non-ideal compressible fluid-dynamics, multi-objective and multi-point optimizations, multidisciplinary optimization, coupling adjoint method with other approaches, parametrization methods, and uncertainty quantification. Finally, the review concludes by highlighting key points and outlooks on future developments. [ABSTRACT FROM AUTHOR]

The airfoil is the foundation of an aircraft, and its characteristics have a significant impact on those of the aircraft. Conventional airfoil design mainly focuses on improving aerodynamic performance, while flying wing airfoil designs should also consider layout stability and stealth performance. The design requirements for an airfoil vary with its position on the flying wing layout aircraft based on corresponding spanwise flow field characteristics. By analyzing the spanwise flow characteristics of the flying wing, partition design models for flying wing airfoils were established in this study, and a series of flying wing airfoil designs that consider aerodynamics and aerodynamic/stealth were implemented. Then, the designed airfoils were configured on a three-dimensional X-47B layout for testing and verification. The results showed that the aerodynamic design and the aerodynamic/stealth design exhibited significant improvements in terms for aerodynamic and longitudinal trimming characteristics. However, the cruise drag performance of the aerodynamic/stealth design was slightly worse than that of the aerodynamic design, although the longitudinal moment trimming characteristics were basically the same. The stealth characteristics of the aerodynamic/stealth design had significant advantages, indicating that there were weak contradictions between the aerodynamic, stealth, and trimming requirements in the design of the flying wing. [ABSTRACT FROM AUTHOR]

Amid rising global demand for sustainable energy, wind energy emerges as a crucial renewable resource, with the aerodynamic optimization of wind turbine blades playing a key role in enhancing energy efficiency. This systematic review scrutinizes recent advancements in blade aerodynamics, focusing on the integration of cutting-edge aerodynamic profiles, variable pitch and twist technologies, and innovative materials. It extensively explores the impact of Computational Fluid Dynamics (CFD) and Artificial Intelligence (AI) on blade design enhancements, illustrating their significant contributions to aerodynamic efficiency improvements. By reviewing research from the last decade, this paper provides a comprehensive overview of current trends, addresses ongoing challenges, and suggests potential future developments in wind turbine blade optimization. Aimed at researchers, engineers, and policymakers, this review serves as a crucial resource, guiding further innovations and aligning with global renewable energy objectives. Ultimately, this work seeks to facilitate technological advancements that enhance the efficiency and viability of wind energy solutions. [ABSTRACT FROM AUTHOR]

To address the challenges of multiple design variables, long evaluation times, and poor global search capability of traditional surrogate-assisted algorithms that rely on the complete substitution of accurate function evaluations in the turbine aerodynamic optimization process, a pre-screening surrogate-assisted elitist preservation genetic algorithm (pre-SAEGA) optimizer is proposed. Pre-screening strategy in the pre-SAEGA can screen samples instead of directly estimating them, thereby reducing expensive evaluations in each generation. The directly manipulated free-form deformation (DFFD) method is applied to parameterized multi-stage axial turbines, and multi-degree-of-freedom flexible control is realized. Combining the pre-SAEGA with the DFFD method, a data-driven multi-stage axial turbine optimization platform is established. A two-stage axial turbine is the research object, and 44 design variables are selected for the combined optimization design of flow path and blade rows. The results show that the isentropic efficiency and flow rate improve by 1.33 % and 1.81 % respectively, and the pressure ratio decreases by 0.47 % at the turbine design point. The presented optimization platform not only improves the aerodynamic optimization effect but also significantly reduces the number of design variables and real evaluation samples, making it suitable for solving multi-stage turbine optimization problems with multiple degrees of freedom.

This study presents an efficient surrogate-based optimization (SBO) method of the aerodynamic performance of a contra-rotating open rotor (CROR). The objective was to maximize propulsion efficiency while reaching the target thrust coefficient at the cruise condition. To reduce the sample size and improve the optimization convergence speed, an infilling criterion was proposed based on the features of the interaction between the CROR front and rear rotors. The efficient front and rear rotors of the initial samples were selected and then combined to form the infilled samples. The results show that the infilled samples were closer to the Pareto front than the initial samples. For the six optimization parameters, 20 initial sample points were used, 11 samples were infilled, and the surrogate-based optimization was completed in five iterations. In total 43 samples were calculated during the optimization. The number of overall samples is approximately seven times the number of optimization parameters. The optimization results in parameter changes compared to the baseline and improved propulsion efficiency while meeting the thrust target. The optimization process increases the torque share of the rear rotor and changes the flow state at different radial positions, leading to a more uniform total pressure distribution at the outlet position, both circumferentially and radially.

With the rapid development of artificial intelligence technology, data-driven advanced models have provided new ideas and means for airfoil aerodynamic optimization. As the advanced models update and iterate, many useful explorations and attempts have been made by researchers on the integrated application of artificial intelligence and airfoil aerodynamic optimization. In this paper, many critical aerodynamic optimization steps where data-driven advanced models are employed are reviewed. These steps include geometric parameterization, aerodynamic solving and performance evaluation, and model optimization. In this way, the improvements in the airfoil aerodynamic optimization area led by data-driven advanced models are introduced. These improvements involve more accurate global description of airfoil, faster prediction of aerodynamic performance, and more intelligent optimization modeling. Finally, the challenges and prospect of applying data-driven advanced models to aerodynamic optimization are discussed. [ABSTRACT FROM AUTHOR]

Multi-objective evolutionary algorithms (MOEA) have attracted much attention because of their good global exploration ability; however, their local search ability near the optimal value is weak, and for large-scale decision-variable optimization problems the number of populations and iterations required by MOEA are very large, so the optimization efficiency is low. Gradient optimization algorithms can overcome these difficulties well, but gradient search methods are difficult to apply to multi-objective optimization problems (MOPs). To this end, this paper introduces a stochastic weighting function based on the weighted average gradient and proposes two multi-objective stochastic gradient operators. Further, two efficient evolutionary algorithms, MOGBA and HMOEA, are developed. Their local search capability has been greatly enhanced while retaining the good global exploration capability by using different offspring update strategies for different subpopulations. Numerical experiments show that HMOEA has excellent capture ability for various Pareto formations, and it can easily solve multi-objective optimization problems with many objectives, which improves the efficiency by a factor of 5–10 compared with typical multi-objective evolutionary algorithms. HMOEA is further applied to the multi-objective aerodynamic optimization design of the RAE2822 airfoil and the ideal Pareto front is obtained, which indicates that HMOEA is an efficient optimization algorithm with potential applications in aerodynamic optimization design. [ABSTRACT FROM AUTHOR]

Because of the extremely low density and low heat capacity of the Martian atmosphere, the atmospheric pressure and atmospheric density of Mars change greatly with temperature, which has strong uncertainty. The deterministic aerodynamic design of the rotor for a single atmospheric density may lead to a significant increase in the power consumption of the rotor at off⁃design points, affecting the normal use of the Mars helicopter. In order to avoid the above problems, this paper first studies of Martian atmospheric environment and establishes the probability distribution model of Martian atmospheric density. For the Mars hexacopter with greater application potential, the viscous vortex particle method is then used to establish a high⁃confidence refined rotor aerodynamic model in the Martian atmosphere, and the robust design optimization of the rotor aerodynamic shape is carried out by non⁃dominated sorting genetic algorithm Ⅱ. The designed rotor aerodynamic shape scheme maintains good aerodynamic performance in the face of Mars atmospheric density changes, and has stronger robustness, which is conducive to the Mars hexacopter to perform longer and longer Mars exploration missions in different atmospheric environments. [ABSTRACT FROM AUTHOR]

The rotor–stator interaction noise is a major source of fan noise. Especially for low-speed fan stages, the tonal component is typically a dominant noise source. A challenge is to reduce this tonal noise, as it is typically perceived as unpleasant. Therefore, in this paper, we analytically, numerically and experimentally investigate an acoustic effect to lower the tonal noise excitation. Our study on an existing low-speed fan indicates a reduction in tonal interaction noise of more than 9 dB at the source if the excited acoustic modes propagate parallel to the stator leading edge angle. Moreover, a design-to-low-noise approach is demonstrated in order to apply this effect to two new fan stages with fewer stator than rotor blades. The acoustic design of both fans is determined by an appropriate choice of the rotor and stator blade numbers in order to align the modal propagation angle with the stator stagger angle. The blade geometries are obtained from aerodynamic optimization. Both fans provide similar aerodynamic but opposing acoustic radiation characteristics compared to the baseline fan and a significant tonal noise reduction resulting from the impact of the modal propagation angle on noise excitation. To ensure that this effect can also be applied to other low-speed fans, a design rule is derived and validated. [ABSTRACT FROM AUTHOR]

High-pressure ratio centrifugal compressors' diffusers face challenges from high-velocity, non-uniform flow at the impeller outlet, decreasing efficiency and stall margin. To address this, this paper presents a novel vaned diffuser passage design method that successfully improved the compressor's performance. An optimization method using axisymmetric hub contours and NURBS curves was applied to modify the diffuser's design. After optimization, centrifugal compressor peak efficiency increased by 0.78%, and stall margin expanded from 12.8% to 20.4%. Analysis at the peak efficiency point showed loss reduction mainly from decreased recirculation and mixing losses in the diffuser's vaneless and semi-vaneless spaces. Furthermore, correlation analysis and Mach number distribution revealed that flow behavior at the diffuser's leading edge significantly influences efficiency. Consequently, design principles emphasize satisfying specific Mach number distribution rules at the diffuser's leading edge under certain inflow conditions for optimal performance. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Southeast University / Dongnan Daxue Xuebao is the property of Journal of Southeast University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The development of new turbomachinery designs requires numerous time-consuming and computationally intensive computational fluid dynamics (CFD) calculations. However, most of the generated high spatial resolution data remain unused at later development steps. That is also the case with automated optimization processes that use only a few integral values to determine objectives and constraints. To make further use of this vast amount of CFD data a data-driven AI model based on the Transformer architecture is developed and trained using the available CFD data. The presented method subsequently provides a fast approximation of the 3D flow for new designs. In this paper, the structure of the developed AI model is presented and the approximation quality is analyzed using a complex, state-of-the-art compressor test case. It is shown that the AI model can reproduce many characteristics of the 3D flow of new designs, and performance measures such as efficiency can be derived from these flow predictions. In addition, the complex test case revealed that greater design variation reduces the AI approximation quality which can lead to undesirable exploratory behavior in an optimization setup. Overall, the test case has shown promising results and has provided hints for further improvements to the AI model.

The optimization of aircraft is a typical multidisciplinary and multi-objective problem. To solve this problem, the difficulty lies not only in the high cost of discipline performance evaluation but also in the complex coupling relationship between different disciplines. To improve the optimization efficiency, a new optimization method is proposed, including two new algorithms: conditional generative adversarial nets with vector similarity (VS-CGAN) and distributed single-step deep reinforcement learning with transfer learning (TL-DSDRL). For low-cost disciplines, VS-CGAN learns the relationship between variables and objectives through presampling to compress the variable domains. The cosine function is used to evaluate the similarity between the random noise and generated variables to avoid mode collapse. For high-cost disciplines, TL-DSDRL improves optimization efficiency through pretraining. The newly designed reward function and multi-agent cooperation mechanism enhance the multi-objective search ability of reinforcement learning. [ABSTRACT FROM AUTHOR]

A flapping-wing flow control method based on a prepositive elliptical wing is proposed in this study. A tiny elliptical wing is designed at a specific position around the leading edge of the flapping wing to suppress and improve flow separation by the airfoil and achieve the purpose of increasing lift and reducing drag. Furthermore, the particle swarm optimization algorithm is used to optimize the aerodynamic design of the prepositive elliptical wing–flapping wing composite configuration under attached flow and separated flow. The long axis length, short axis length, deflection angle, and positional relationship with the main wing of the prepositive wing are taken as the design variables, and the maximum time-averaged lift-to-drag ratio in a flapping cycle is taken as the optimization objective to obtain the optimal configuration under attached flow and separated flow. The simulation and optimization results show that the increment of the time-averaged lift-to-drag ratio of the prepositive elliptical wing–flapping wing optimal configuration under attached flow is 1.2 times that of the original airfoil, while the increment of the time-averaged lift-to-drag ratio of the optimal configuration under separated flow is approximately 0.5, and the prepositive wing completely inhibits the separation of the flow on the top surface of the flapping wing. In addition, the influence of the parameters is analyzed, and a general design for the prepositive elliptical wing–flapping wing configuration is given. [ABSTRACT FROM AUTHOR]

The application of the variable camber technology has promising results in improving the lift-to-drag performance during the cruise phase, particularly under multi-lift conditions. This improvement is crucial for enhancing the economic benefits of the entire flight. A smooth and continuous flow separation function was developed to constrain the buffeting performance. An optimization model for cruise performances under multi-lift conditions of wing cross sections was constructed through combination of this function with the variable camber technology and an artificial neural network surrogate model. The deep deterministic policy gradient (DDPG) method was used to optimize this model, resulting in a cruise average lift-to-drag ratio improvement of 6.8% under buffeting constraints. This improvement surpasses the results obtained by other optimization algorithms, such as the particle swarm optimization (PSO) and the improved gray wolf optimization (GWO). The results of the generation and analysis of 2 conical swept wings with the unoptimized and optimized airfoils, show the contribution of the 2D variable camber airfoil optimization to 3D wings. [ABSTRACT FROM AUTHOR]

The wing aerodynamic shape optimization is a typical high-dimensional problem with numerous independent design variables. Researching methods to reduce the dimensionality of optimization from the perspective of aerodynamic characteristics is necessary. One traditional aerodynamic-based approach decouples the wing's camber and thickness according to the thin airfoil theory, but it has limitations due to unclear application scope and effectiveness. This paper proposes an improved approach that determines the values of certain thickness variables based on a data-driven aerodynamic characteristics model before optimization, which considers longitudinal stability. By reducing the number of design variables, the dimensionality of optimization is decreased effectively. The derivation of the improved approach is accomplished through the design of experiments, parametric modeling, computational fluid dynamics, and sensitivity analysis. The effectiveness of the improved approach is validated by applying it to the aerodynamic optimization of an ONERA-M6 wing in subsonic flow based on the surrogate-based optimization algorithm. The results demonstrate that the improved approach significantly accelerates the optimization process while maintaining global effectiveness. [ABSTRACT FROM AUTHOR]

The tail wing of box-launched aircraft needs to be folded in the launch box, which can easily cause malfunctions during flight deployment. This article presents a ducted tail wing aircraft that does not require folding of the tail wing. To address the nonlinear problem of lift coefficient in the ducted tail, an aerodynamic optimization method for ducted tails based on the sparrow search algorithm with back-propagation (SSA-BP) neural network approximate model and multi-objective genetic algorithm fusion is proposed, with the goal of improving the lift-to-drag ratio and linearization degree of the lift curve. The linearization degree of the optimized tail lift coefficient curve is significantly improved, and the lift-to-drag ratio is significantly improved under cruising conditions. Based on this optimization result, the shape of the tail wing and fuselage combination was optimized, and the optimal configuration of the ducted tail wing aircraft was selected, providing a reference for the design of ducted tail wing aircraft.

The airfoil is the foundation of an aircraft, and its characteristics have a significant impact on those of the aircraft. Conventional airfoil design mainly focuses on improving aerodynamic performance, while flying wing airfoil designs should also consider layout stability and stealth performance. The design requirements for an airfoil vary with its position on the flying wing layout aircraft based on corresponding spanwise flow field characteristics. By analyzing the spanwise flow characteristics of the flying wing, partition design models for flying wing airfoils were established in this study, and a series of flying wing airfoil designs that consider aerodynamics and aerodynamic/stealth were implemented. Then, the designed airfoils were configured on a three-dimensional X-47B layout for testing and verification. The results showed that the aerodynamic design and the aerodynamic/stealth design exhibited significant improvements in terms for aerodynamic and longitudinal trimming characteristics. However, the cruise drag performance of the aerodynamic/stealth design was slightly worse than that of the aerodynamic design, although the longitudinal moment trimming characteristics were basically the same. The stealth characteristics of the aerodynamic/stealth design had significant advantages, indicating that there were weak contradictions between the aerodynamic, stealth, and trimming requirements in the design of the flying wing.

Amid rising global demand for sustainable energy, wind energy emerges as a crucial renewable resource, with the aerodynamic optimization of wind turbine blades playing a key role in enhancing energy efficiency. This systematic review scrutinizes recent advancements in blade aerodynamics, focusing on the integration of cutting-edge aerodynamic profiles, variable pitch and twist technologies, and innovative materials. It extensively explores the impact of Computational Fluid Dynamics (CFD) and Artificial Intelligence (AI) on blade design enhancements, illustrating their significant contributions to aerodynamic efficiency improvements. By reviewing research from the last decade, this paper provides a comprehensive overview of current trends, addresses ongoing challenges, and suggests potential future developments in wind turbine blade optimization. Aimed at researchers, engineers, and policymakers, this review serves as a crucial resource, guiding further innovations and aligning with global renewable energy objectives. Ultimately, this work seeks to facilitate technological advancements that enhance the efficiency and viability of wind energy solutions.

Conventional wing aerodynamic optimization processes can be time-consuming and imprecise due to the complexity of versatile flight missions. Plenty of existing literature has considered two-dimensional infinite airfoil optimization, while three-dimensional finite wing optimizations are subject to limited study because of high computational costs. Here we create an adaptive optimization methodology built upon digitized wing shape deformation and deep learning algorithms, which enable the rapid formulation of finite wing designs for specific aerodynamic performance demands under different cruise conditions. This methodology unfolds in three stages: radial basis function interpolated wing generation, collection of inputs from computational fluid dynamics simulations, and deep neural network that constructs the surrogate model for the optimal wing configuration. It has been demonstrated that the proposed methodology can significantly reduce the computational cost of numerical simulations. It also has the potential to optimize various aerial vehicles undergoing different mission environments, loading conditions, and safety requirements.

In this paper, a three-dimensional aerodynamics optimization system is built and applied to optimize a rotor blade to balance the conflicts between stall margin, total pressure ratio, adiabatic efficiency, and mass flow rate for the high-loading and transonic-flow fan. A novel flow diagnostic method based on vorticity dynamics theory is utilized to analyze the reasons for the improvement in aerodynamic performance in the optimized transonic fan. In the established aerodynamic optimization method, use the blade profile camber line curvature and its leading edge metal angle as the optimization variables, which are optimized by modifying the coordinates of their control points and introducing a genetic algorithm. Finally, the vorticity dynamics parameters, such as the boundary vorticity flux (BVF), azimuthal vorticity and skin-friction lines are used to diagnose the key flow features in the optimized and baseline fan passage. The results indicate that, by controlling skillfully the blade camber line curvature in the optimization improves the aerodynamic performance of the fan stage, increasing the total pressure ratio by 1.90% while increasing the mass flow rate and adiabatic efficiency by 5.82% and 4.45%, respectively. The formulas from the vorticity dynamics diagnosis method indicate a close link between the aerodynamic performance and vorticity dynamic parameters for the axial fan/compressor passage flow, and that both azimuthal vorticity and boundary vorticity flux have significant influence on fan stage performance. Moreover, the boundary layer separation flow on the rotor blade surface is accompanied by a spike of entropy and static pressure, and their derivative/gradient also suffer drastic changes under the effect of shock waves. Detailed flow information can be obtained about the on-wall with high accuracy based on the vorticity dynamics diagnosis method, which provides researchers with a novel method for the turbomachinery aerodynamic design and analysis in the aero-engine engineering development field. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence technology, data-driven advanced models have provided new ideas and means for airfoil aerodynamic optimization. As the advanced models update and iterate, many useful explorations and attempts have been made by researchers on the integrated application of artificial intelligence and airfoil aerodynamic optimization. In this paper, many critical aerodynamic optimization steps where data-driven advanced models are employed are reviewed. These steps include geometric parameterization, aerodynamic solving and performance evaluation, and model optimization. In this way, the improvements in the airfoil aerodynamic optimization area led by data-driven advanced models are introduced. These improvements involve more accurate global description of airfoil, faster prediction of aerodynamic performance, and more intelligent optimization modeling. Finally, the challenges and prospect of applying data-driven advanced models to aerodynamic optimization are discussed.

Multi-objective evolutionary algorithms (MOEA) have attracted much attention because of their good global exploration ability; however, their local search ability near the optimal value is weak, and for large-scale decision-variable optimization problems the number of populations and iterations required by MOEA are very large, so the optimization efficiency is low. Gradient optimization algorithms can overcome these difficulties well, but gradient search methods are difficult to apply to multi-objective optimization problems (MOPs). To this end, this paper introduces a stochastic weighting function based on the weighted average gradient and proposes two multi-objective stochastic gradient operators. Further, two efficient evolutionary algorithms, MOGBA and HMOEA, are developed. Their local search capability has been greatly enhanced while retaining the good global exploration capability by using different offspring update strategies for different subpopulations. Numerical experiments show that HMOEA has excellent capture ability for various Pareto formations, and it can easily solve multi-objective optimization problems with many objectives, which improves the efficiency by a factor of 5–10 compared with typical multi-objective evolutionary algorithms. HMOEA is further applied to the multi-objective aerodynamic optimization design of the RAE2822 airfoil and the ideal Pareto front is obtained, which indicates that HMOEA is an efficient optimization algorithm with potential applications in aerodynamic optimization design.

High-pressure ratio centrifugal compressors’ diffusers face challenges from high-velocity, non-uniform flow at the impeller outlet, decreasing efficiency and stall margin. To address this, this paper presents a novel vaned diffuser passage design method that successfully improved the compressor’s performance. An optimization method using axisymmetric hub contours and NURBS curves was applied to modify the diffuser’s design. After optimization, centrifugal compressor peak efficiency increased by 0.78%, and stall margin expanded from 12.8% to 20.4%. Analysis at the peak efficiency point showed loss reduction mainly from decreased recirculation and mixing losses in the diffuser’s vaneless and semi-vaneless spaces. Furthermore, correlation analysis and Mach number distribution revealed that flow behavior at the diffuser’s leading edge significantly influences efficiency. Consequently, design principles emphasize satisfying specific Mach number distribution rules at the diffuser’s leading edge under certain inflow conditions for optimal performance.

The rotor–stator interaction noise is a major source of fan noise. Especially for low-speed fan stages, the tonal component is typically a dominant noise source. A challenge is to reduce this tonal noise, as it is typically perceived as unpleasant. Therefore, in this paper, we analytically, numerically and experimentally investigate an acoustic effect to lower the tonal noise excitation. Our study on an existing low-speed fan indicates a reduction in tonal interaction noise of more than 9 dB at the source if the excited acoustic modes propagate parallel to the stator leading edge angle. Moreover, a design-to-low-noise approach is demonstrated in order to apply this effect to two new fan stages with fewer stator than rotor blades. The acoustic design of both fans is determined by an appropriate choice of the rotor and stator blade numbers in order to align the modal propagation angle with the stator stagger angle. The blade geometries are obtained from aerodynamic optimization. Both fans provide similar aerodynamic but opposing acoustic radiation characteristics compared to the baseline fan and a significant tonal noise reduction resulting from the impact of the modal propagation angle on noise excitation. To ensure that this effect can also be applied to other low-speed fans, a design rule is derived and validated.

In order to improve the design level of common aero vehicle (CAV), three parametric methods are used to carry out parametric modeling studies on two different types of CAV configurations. Combining engineering estimation methods and optimization algorithms, the aircraft configuration optimization with the goal of maximum lift-to-drag ratio and multi-objective design optimization with the goals of maximum lift-to-drag ratio, maximum volumetric efficiency and minimum heat flow are carried out respectively. The results show that the configuration with large maximum lift-to-drag ratio is often relatively slender, and the lift-to-drag ratio, volumetric efficiency and heat flow cannot be optimized at the same time. The configuration only taking the lift-to-drag as the optimization goal is difficult to meet the actual needs. The multi-objective optimization process obtains the non-inferior solution set under the three optimization objectives, which can provide a reference for the selection of CAV configuration in engineering.

The traditional aerodynamic optimization design requires computational fluid dynamics(CFD)analysis,and the surrogate-based optimization(SBO)method can effectively reduce the number of CFD analyse,but it cannot speed up a single CFD analysis time.A hot-start strategy using the proper orthogonal decomposition-back propagation based neural network(POD-BPNN)model is proposed,and applied in surrogate-based aerodynamic optimization.The prediction model from geometric design variables to flowfield data through the initial samples is built with SBO POD-BPNN model.During iteration of the SBO,the flowfield of a new sample is predicted by the built model,and the flowfield is used as the initial flowfield of the hot-start CFD analysis for the new sample.The new sample is used to update the POD-BPNN model until the end of the optimization.The comparison verification of proposed strategy is performed with instance.The results show that,in the aerodynamic optimization design of the transonic airfoil,the hot-start strategy based on POD-BPNN model can reduce the time of a single CFD analysis by 68%,and improve the efficiency of the SBO by 37%.

In this study, the results of genetic algorithm and sequential quadratic programming are discussed for NACA four-digit airfoil series optimization. The lift-to-drag ratio is defined as the objective function. The aerodynamic constraints related to the lift, drag, moment values of the airfoil at high and low angle of attack, and the geometric constraints related to the wetted length and internal volume are set. Results show that the sequential use of the former and latter methods and the stand-alone use of the latter method yield an increment of approximately 20 % in the objective function. The change in the geometric design variables reveals that the optimum airfoils are obtained by the variation in the maximum camber and thickness. Sequential quadratic programming is proven to converge to the global optimum in a significantly shorter solution time than genetic algorithm when the practical constraints are defined into the optimization problems. [ABSTRACT FROM AUTHOR]

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To explore the internal flow mechanism and improve the performance of a supersonic compressor, an efficient global optimization design method was developed for an axial flow compressor and applied in the optimization design of a prototype supersonic compressor. Based on the multiple circular arc (MCA) blade parameters, the method can be used to parameterize the elementary stage of the blade. The optimized solution is obtained by changing the elementary stage and stacking lines of the blade during the optimization process. It has the advantages of fewer optimization variables, strong physical intuition, and a smooth surface. The optimization results show that a change in the rotor blade shape parameters has a significant effect on the compressor efficiency under design conditions, while a change in the skewed-swept parameters of the stator is the main factor that improves the compressor's performance under near-stall conditions. Further numerical results show that the optimized rotor changes the form of the shock, weakens the degree of shock boundary layer interference, inhibits the radial migration flow of the supersonic rotor, reduces the loss of the rotor blade top, and improves the performance of the compressor under design conditions. The stator's optimization restrains the generation of a concentrated shedding vortex at the root of the blades and greatly improves the stall margin of the compressor. Finally, the total pressure ratio and flow rate are less than 1% of the values based on the prototype operating conditions, the design mass flow of the optimized high-load supersonic compressor is increased by 0.25%, the isentropic efficiency is increased by 1.05%, and the stall margin is enhanced by 3.5%, thus verifying the effectiveness of the optimization method. [ABSTRACT FROM AUTHOR]

Since the taper ratio of most wings is not equal to 1, the beam-disk trailing edge deflection mechanism originally designed for the rectangular wing is not fully applicable to the non-equal chord wing. Moreover, it is not only expected that the wing shape can achieve excellent aerodynamic performance under different flight conditions, but one also needs to consider whether the flexible skin can achieve this deformation. This paper used the honeycomb composite structure with zero Poisson's ratio as the flexible skin of the trailing edge for the variable camber wing, and designed the beam-disk trailing edge deflection mechanism for the non-equal chord wing. The aerodynamic configuration was optimized considering the deformation capability of the skin, and the multidisciplinary design and optimization method of the variable camber wing with non-equal chord was studied. The results show that the aerodynamic performances of the optimized non-equal chord wings were better than before under all given flight conditions. The flexible skin could withstand the strain caused by the maximum deflection of the trailing edge of the wing, and the weight of the wing structure was reduced by 47.1% compared with the initial design when the structural stiffness and strength were satisfied. [ABSTRACT FROM AUTHOR]

A slot formed between the slat and the main wing of the 2D high-lift device is used to accelerate the convergence of the flow to the upper surface of the main wing to improve the flow field quality. In order to further enhance flow characteristics, this paper proposes a design method for multi-channel leading-edge slats based on grid flow channels. On the one hand, a specific shape of a shrinking expansion tube is formed to improve the lift characteristics of the leading slat. On the other hand, the newly formed slot plays a similar role to that of the jet stream, delaying the separation on the upper surface of the main wing, making the separation point move back and helping to improve the lift characteristics of the main wing. The optimization of coupled slat-grid channel configuration is developed by using the DOE algorithm. The geometric parameters, such as coordinates and curve slope, are considered as design variables, and the maximum lift–drag ratio is taken as the optimization objective required to obtain the optimal configuration. The simulation and optimization results show that the lift coefficient increases by 3.3%, the drag coefficient decreases by 12.7%, and the lift–drag ratio increases by 18.4% of the optimal configuration compared with the original airfoil at an angle of attack of 16.3°. [ABSTRACT FROM AUTHOR]

To evaluate novel turbine designs, the wind energy sector extensively depends on computational fluid dynamics (CFD). To use CFD in the design optimization process, where lower-fidelity approaches such as blade element momentum (BEM) are more popular, new tools to increase the accuracy must be developed as the latest wind turbines are larger and the aerodynamics and structural dynamics become more complex. In the present study, a new concurrent aerodynamic shape optimization approach towards multidisciplinary design optimization (MDO) that uses a Reynolds-averaged Navier–Stokes solver in conjunction with a numerical optimization methodology is introduced. A multidisciplinary design optimization tool called DAFoam is used for the NREL phase VI turbine as a baseline geometry. Aerodynamic design optimizations in terms of five different schemes, namely, cross-sectional shape, pitch angle, twist, chord length, and dihedral optimization are conducted. Pointwise, a commercial mesh generator is used to create the numerical meshes. As the adjoint approach is strongly reliant on the mesh quality, up to 17.8 million mesh cells were employed during the mesh convergence and result validation processes, whereas 2.65 million mesh cells were used throughout the design optimization due to the computational cost. The Sparse Nonlinear OPTimizer (SNOPT) is used for the optimization process in the adjoint solver. The torque in the tangential direction is the optimization's merit function and excellent results are achieved, which shows the promising prospect of applying this approach for transient MDO. This work represents the first attempt to implement DAFoam for wind turbine aerodynamic design optimization. [ABSTRACT FROM AUTHOR]