The neural architecture search technique is used to automate the engineering of neural network models. Several studies have applied this approach, mainly in the fields of image processing and natural language processing. Its application generally requires very long computing times before converging on the optimal architecture. This study proposes a hybrid approach that combines transfer learning and dynamic search space adaptation (TL-DSS) to reduce the architecture search time. To validate this approach, Long Short-Term Memory (LSTM) models were designed using different evolutionary algorithms, including artificial bee colony (ABC), genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO), which were developed to predict trends in global horizontal irradiation data. The performance measures of this approach include the performance of the proposed models, as evaluated via RMSE over a 24-h prediction window of the solar irradiance data trend on one hand, and CPU search time on the other. The results show that, in addition to reducing the search time by up to 89.09% depending on the search algorithm, the proposed approach enables the creation of models that are up to 99% more accurate than the non-enhanced approach. This study demonstrates that it is possible to reduce the search time of a neural architecture while ensuring that models achieve good performance.

The adjoint method is efficient for computing derivatives, enabling gradient-based optimization to manage systems with many design variables. Therefore, this paper aims to investigate the aerodynamic optimization design of morphing wings with different leading-edge shapes. The wing shapes include a clean UAS-S45 wing and different tubercle-based wing shapes. This study emphasizes optimizing the various shapes in the leading edge to delay stall and increase the aerodynamic performance of the wing. Firstly, baseline wing design studies were carried out, followed by experimental wind tunnel validation to ensure accuracy and methodology validation. After establishing the baseline, optimization was conducted using a multidisciplinary design optimization (MDO) approach with DAFoam, a Reynolds-averaged Navier-Stokes solver. The optimization process employs a Free Form Deformation approach to produce different variants of the leading-edge shapes. Numerical investigations of flow characteristics, performed using computational fluid dynamics (CFD) computations, validate the numerical scheme against experimental data. The optimization strategy combines the Interior Point OPTimizer (IPOPT) within the adjoint solver framework with ICEM to generate high-quality numerical meshes. The experimental and numerical results showed the advantages of tubercles in maintaining aerodynamic effectiveness, particularly at high angles of attack. The study confirms that tubercle-profiled leading edges improve post-stall aerodynamic behavior by mitigating abrupt flow separation and facilitating smoother transitions during stall. For optimized tubercle-based leading edges, the peaks and valleys generate alternating regions of high and low vorticity, forming counter-rotating vortices that enhance mixing, improving aerodynamic performance. The peak configuration, in particular, exhibits smooth airflow acceleration over the crest of the tubercle, creating higher velocity regions along the leading edge and downstream, demonstrating enhanced flow attachment. While conventional bio-inspired designs already offer significant performance benefits at high angles of attack, further optimization of tubercle shapes for morphing leading edges has demonstrated additional improvements in aerodynamic efficiency at low angles of attack. However, achieving convergence during optimization remains challenging due to mesh deformation and movement complexities in these geometries. Future work will aim to expand the dataset, refine the optimization process, and enable more detailed analyses to unlock the full potential of tubercle-based designs.

Monte Carlo localization methods deploy a particle filter to resolve a hidden Markov process based on recursive Bayesian estimation, which approximates the internal states of a dynamic system given observation data. When the observed data are corrupted by outliers, the particle filter’s performance may deteriorate, preventing the algorithm from accurately computing dynamic system states such as a robot’s position, which in turn reduces the accuracy of the localization and navigation. In this paper, the notion of information entropy is used to identify outliers. Then, a probability-based approach is used to remove the discovered outliers. In addition, a new mutation process is added to the localization algorithm to exploit the posterior probability density function in order to actively detect the high-likelihood region. The goal of incorporating the mutation operator into this method is to solve the problem of algorithm impoverishment which is due to insufficient representation of the complete probability density function. Simulation experiments are used to confirm the effectiveness of the proposed techniques. They also are employed to predict the remaining viability of a lithium-ion battery. Furthermore, in an experimental study, the modified Monte Carlo localization algorithm was applied to a mobile robot to demonstrate the local planner’s improved accuracy. The test results indicate that developed techniques are capable of effectively capturing the dynamic behavior of a system and accurately tracking its characteristics.

An integrated approach to active flow control is proposed by finding both the drooping leading edge and the morphing trailing edge for flow management. This strategy aims to manage flow separation control by utilizing the synergistic effects of both control mechanisms, which we call the combined morphing leading edge and trailing edge (CoMpLETE) technique. This design is inspired by a bionic porpoise nose and the flap movements of the cetacean species. The motion of this mechanism achieves a continuous, wave-like, variable airfoil camber. The dynamic motion of the airfoil’s upper and lower surface coordinates in response to unsteady conditions is achieved by combining the thickness-to-chord (t/c) distribution with the time-dependent camber line equation. A parameterization model was constructed to mimic the motion around the morphing airfoil at various deflection amplitudes at the stall angle of attack and morphing actuation start times. The mean properties and qualitative trends of the flow phenomena are captured by the transition SST (shear stress transport) model. The effectiveness of the dynamically morphing airfoil as a flow control approach is evaluated by obtaining flow field data, such as velocity streamlines, vorticity contours, and aerodynamic forces. Different cases are investigated for the CoMpLETE morphing airfoil, which evaluates the airfoil’s parameters, such as its morphing location, deflection amplitude, and morphing starting time. The morphing airfoil’s performance is analyzed to provide further insights into the dynamic lift and drag force variations at pre-defined deflection frequencies of 0.5 Hz, 1 Hz, and 2 Hz. The findings demonstrate that adjusting the airfoil camber reduces streamwise adverse pressure gradients, thus preventing significant flow separation. Although the trailing-edge deflection and its location along the chord influence the generation and separation of the leading-edge vortex (LEV), these results show that the combined effect of the morphing leading edge and trailing edge has the potential to mitigate flow separation. The morphing airfoil successfully contributes to the flow reattachment and significantly increases the maximum lift coefficient (cl,max)). This work also broadens its focus to investigate the aerodynamic effects of a dynamically morphing leading and trailing edge, which seamlessly transitions along the side edges. The aerodynamic performance analysis is investigated across varying morphing frequencies, amplitudes, and actuation times.

This paper aims to present a new methodology to model the aerodynamic coefficients and predict the flow structure and the behavior of dynamic stall vortices surrounding a pitching CRJ-700 airfoil. This new methodology, called the Combined Morphing Leading Edge and Trailing Edge (CoMpLETE), aimed to manage dynamic stall control using the effects of leading and trailing edge morphing mechanisms. A framework for unsteady parametrization was created to simulate the transient leading edge and trailing edge motions. The parabolic airfoil parametrization approach was used to obtain its morphing motion, and it was coupled with Laplace Diffusion dynamic mesh techniques. Precise and reliable simulations validated the geometry deflection and mesh deformation schemes because the mesh quality criteria were respected throughout the deformation process. The gamma - Re-theta turbulence model adequately captured the flow structures of dynamic airfoils associated with leading-edge vortex formations for a wide range of Reynolds numbers. The numerical results have shown that the new radius of curvature of the CRJ-700 morphing airfoil can minimize the streamwise adverse pressure gradient and further prevent significant flow separation by delaying the occurrence of Dynamic Stall Vortex (DSV). Results with the pitching-oscillation motion of the CRJ-700 airfoil and its parameters, such as the droop nose amplitude and the time at which the leading-edge morphing starts, revealed better aerodynamic performance. The CoMpLETE airfoil successfully contributes to the flow reattachment and significantly increases the maximum lift coefficient (c(l,max)).

This study investigates the design and optimization of a flexible Droop Nose Leading Edge (DNLE) based on the composite laminate skin for the UAS-S45. The concept of a morphing DNLE airfoil has excellent potential for drag and airframe noise reduction. The essential part of this DNLE airfoil type is the easiness of its mechanism deformation while maintaining the wing's structural integrity. The morphing DNLE must change the baseline shape under the influence of the aerodynamic loads to obtain its desired optimized target shape. This study proposes an optimization method to evaluate the skin design's feasibility and check the composite's failure index when the morphing DNLE changes shape. The approach yielded the desired aerodynamics shape of flexible droop nose leading edge. Wing leading edge topology depends on composite wing properties, such as ply-orientation, ply-thickness, and other composite wing parameters. This paper presents a design methodology of stiffness coefficients and lamination parameters for the stacking sequence optimization during the DNLE morphing deformation. In addition to increasing the deformation accuracy of the final airfoil shape made of composite skin, the use of stiffness coefficients and lamination parameters also effectively and efficiently defines the sequence of the composite lay-up. The deformed airfoil shapes between the optimized lay-ups and their modified shapes are compared to confirm their abilities to morph and aerodynamically optimize their shapes. The numerical results of the droop nose morphing with composite materials proved structure morphing capacity and showed feasibility of wing leading edge design mechanism and show the ability and accuracy of the methodology to obtain their morphing wing shapes.

This paper investigates the effect of the optimised morphing leading edge (MLE) and the morphing trailing edge (MTE) on dynamic stall vortices (DSV) for a pitching aerofoil through numerical simulations. In the first stage of the methodology, the optimisation of the UAS-S45 aerofoil was performed using a morphing optimisation framework. The mathematical model used Bezier-Parsec parametrisation, and the particle swarm optimisation algorithm was coupled with a pattern search with the aim of designing an aerodynamically efficient UAS-45 aerofoil. The $\gamma - R{e_\theta }$ transition turbulence model was firstly applied to predict the laminar to turbulent flow transition. The morphing aerofoil increased the overall aerodynamic performances while delaying boundary layer separation. Secondly, the unsteady analysis of the UAS-S45 aerofoil and its morphing configurations was carried out and the unsteady flow field and aerodynamic forces were analysed at the Reynolds number of 2.4 × 106 and five different reduced frequencies of k = 0.05, 0.08, 1.2, 1.6 and 2.0. The lift ( ${C_L})$ , drag ( ${C_D})$ and moment ( ${C_M})\;$ coefficients variations with the angle-of-attack of the reference and morphing aerofoils were compared. It was found that a higher reduced frequencies of 1.2 to 2 stabilised the leading-edge vortex that provided its lift variation in the dynamic stall phase. The maximum lift $\left( {{C_{L,max}}} \right)$ and drag $\left( {{C_{D,max}}} \right)\;$ coefficients and the stall angles of attack are evaluated for all studied reduced frequencies. The numerical results have shown that the new radius of curvature of the MLE aerofoil can minimise the streamwise adverse pressure gradient and prevent significant flow separation and suppress the formation of the DSV. Furthermore, it was shown that the morphing aerofoil delayed the stall angle-of-attack by 14.26% with respect to the reference aerofoil, and that the ${C_{L,max}}\;$ of the aerofoil increased from 2.49 to 3.04. However, while the MTE aerofoil was found to increase the overall lift coefficient and the ${C_{L,max}}$ , it did not control the dynamic stall. Vorticity behaviour during DSV generation and detachment has shown that the MTE can change the vortices’ evolution and increase vorticity flux from the leading-edge shear layer, thus increasing DSV circulation. The conclusion that can be drawn from this study is that the fixed drooped morphing leading edge aerofoils have the potential to control the dynamic stall. These findings contribute to a better understanding of the flow analysis of morphing aerofoils in an unsteady flow.

The large-scale optimization problem requires some optimization techniques, and the Metaheuristics approach is highly useful for solving difficult optimization problems in practice.The purpose of the research is to optimize the transportation system with the help of this approach.We selected forest vehicle routing data as the case study to minimize the total cost and the distance of the forest transportation system.Matlab software helps us find the best solution for this case by applying three algorithms of Metaheuristics: Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Extended Great Deluge (EGD).The results show that GA, compared to ACO and EGD, provides the best solution for the cost and the length of our case study.EGD is the second preferred approach, and ACO offers the last solution.

A pharmaceutical supply chain (PSC) is a system of processes, operations, and organisations for drug delivery. This paper provides a new PSC mathematical cost model, which includes Blockchain technology (BT), that can improve the safety, performance, and transparency of medical information sharing in a healthcare system. We aim to estimate the costs of the BT-based PSC model, select algorithms with minimum prediction errors, and determine the cost components of the model. After the data generation, we applied four Supervised Learning algorithms (k-nearest neighbour, decision tree, support vector machine, and naive Bayes) combined with two Evolutionary Computation algorithms (ant colony optimization and the firefly algorithm). We also used the Feature Weighting approach to assign appropriate weights to all cost model components, revealing their importance. Four performance metrics were used to evaluate the cost model, and the total ranking score (TRS) was used to determine the most reliable predictive algorithms. Our findings show that the ACO-NB and FA-NB algorithms perform better than the other six algorithms in estimating the costs of the model with lower errors, whereas ACO-DT and FA-DT show the worst performance. The findings also indicate that the shortage cost, holding cost, and expired medication cost more strongly influence the cost model than other cost components.

Deduplication has become a widely used technique to reduce space requirements for storage systems by replacing redundant chunks of data with references. While storage systems continue to grow in size, there remain practical limits to the size of any deduplication node, and enterprise businesses may have dozens to hundreds of nodes. It is important to place datasets on nodes in a multi-node environment to take advantage of deduplication savings globally. For customers of the DD File System (DDFS) 1 , we provide the Global Deduplication Service that advises customers on data placement to maximize deduplication-related space savings. This paper describes our currently shipping approach that uses a Fingerprint Dictionary to intelligently cluster customer data and generate a plan to relocate datasets to improve global deduplication. We report results from thousands of deployed systems at customer sites. We have also developed a further improvement using MinHashes that lowers resource requirements, and we provide proofs of the similarity estimates. Our results on a real-world dataset show that MinHashes improve the clustering speed up to 400X relative to our previous method and reduce memory consumption up to 260X.

Cost prediction can provide a pharma supply chain industry with completing their projects on schedule and within budget. This paper provides a new multi-function Blockchain Technology-enabled Pharmaceutical Supply Chain (BT-enabled PSC) mathematical cost model, including PSC costs, BT costs, and uncertain demand. The purpose of this study is to find the most appropriate algorithm(s) with minimum prediction errors to predict the costs of the BT-enabled PSC model. This paper also aims to determine the importance and cost of each component of the multi-function model. To reach these goals, we combined four Supervised Learning algorithms (KNN, DT, SVM, and NB) with two Evolutionary Computation algorithms (HS and PSO) after data generation. Each component of the multi-function model has its importance, and we applied the Feature Weighting approach to analyze their importance. Next, four performance metrics evaluated the multi-function model, and the Total Ranking Score determined predictive algorithms with high reliability. The results indicate the HS-NB and PSO-NB algorithms perform better than the other six algorithms in predicting the costs of the multi-function model with small errors. The findings also show that the Raw Materials cost has a more substantial influence on the model than the other components. This study also introduces the components of the multi-function BT-enabled PSC model.

Increasing fuel costs have necessitated the need for highly fuel-efficient aircraft. Industry and academic researchers are continually looking for ways to increase aircraft performance. One way is to reduce total aircraft drag, thereby improving aerodynamic efficiency without compromising structural integrity. The increase in efficiency directly benefits airlines by allowing for more frequent flights with less fuel consumption, resulting in economic benefits. Wingtip devices are already available in various shapes and sizes, and they all serve to minimize drag by recovering tip vortex energy, thereby improving fuel efficiency. Several methods have been proposed in this study for achieving the required morphing wing adaptability, resulting in considerable performance improvements over conventional wing design, such as a camber morphing wing flap.

The purpose of this study is to design a variable camber morphing winglet with a composite laminate structure for the UAS-S45. The camber morphing winglet will be designed using a honeybee-inspired abdomen structure for the deformation mechanism. It was found that the morphing winglet could improve wing aerodynamic efficiency compared to its reference geometry. The leading edge and trailing edge deflections of the proposed morphing winglet design were generated by the bending and flexing motion mechanism represented by a honeybee abdomen and a flexible wing skin. The controlled winglet deformation was achieved by linear servos that stretched and retracted the flexible mechanism. The morphing winglet was controlled by three servos. The kinematic working mechanism will be presented in the study. The stress analysis is also performed on composite laminates winglet under various loads, used as pre-analysis data. A laminate with a stacking sequence of [0/90/±45/90/0] s was calculated in detail using the Classical Lamination Theory (CLT). The analysis consisted in the comparison of two materials, carbon epoxy and Glass Fibre Reinforced Plastics (GFRP). Ansys ACP was used to model the geometry, while Ansys Mechanical was utilized to model the loading cases and to perform the stress analysis to confirm the results. A demonstrative mechanism for morphing winglet was manufactured of thermoplastic polylactic acid (PLA), and its deformations were measured. The structural optimization analysis results will be presented, and investigated, therefore the optimization will result in a better orientation of composite lay-up and will minimize the morphing winglet weight.

This study uses a multi-objective Non-Dominated Sorting Genetic Algorithm to optimise the aerodynamics of a well-known UAV, the UAS-S45. The optimization algorithm is combined with updated Class Shape Transformation (CST) parameterization to improve aerodynamic performance by increasing lift-to-drag ratio and aerodynamic endurance at various angles of attack. The reference airfoil is parameterized using the CST to give local shape changes and skin flexibility for optimum morphing airfoil combinations. The optimization scheme was carried out with an in-house MATLAB code and this procedure is based on a multi-objective Non-Dominated Sorting Genetic Algorithm coupled to XFoil solver, and validation is done using the SST-K Omega model. The results of the optimizations carried out using different operating conditions are presented; starting from the optimal Pareto fronts, several solutions are selected and compared in terms of airfoil shapes and performance. The results show that the morphing improves the UAS-S45 airfoil's aerodynamic efficiency. The improved airfoils have shown a high improvement in overall aerodynamic performance by up to 65.3% in lift to drag ratio at 8 degrees angle of attack compared to the reference airfoil, and an increase in C_L^(3/2)/C_D of up to 98.8% at 8 degrees for the UAS-S45 optimized airfoil configurations. The optimization increases the overall aerodynamic performance of the configuration and the stall angle from 12º to at least 16º. The method used in this work can be employed as a valuable tool for replacing the traditional slats and flaps at the leading edge and the trailing edge of the airfoil. The pareto optimization analysis will be presented with the optimization results and numerical analysis using high fidelity CFD.

This paper investigates the effect of the Dynamically Morphing Leading Edge (DMLE) on the flow structure and the behavior of dynamic stall vortices around a pitching UAS-S45 airfoil with the objective of controlling the dynamic stall. An unsteady parametrization framework was developed to model the time-varying motion of the leading edge. This scheme was then integrated within the Ansys-Fluent numerical solver by developing a User-Defined-Function (UDF), with the aim to dynamically deflect the airfoil boundaries, and to control the dynamic mesh used to morph and to further adapt it. The dynamic and sliding mesh techniques were used to simulate the unsteady flow around the sinusoidally pitching UAS-S45 airfoil. While the γ-Reθ turbulence model adequately captured the flow structures of dynamic airfoils associated with leading-edge vortex formations for a wide range of Reynolds numbers, two broader studies are here considered. Firstly, (i) an oscillating airfoil with the DMLE is investigated; the pitching-oscillation motion of an airfoil and its parameters are defined, such as the droop nose amplitude (AD) and the pitch angle at which the leading-edge morphing starts (MST). The effects of the AD and the MST on the aerodynamic performance was studied, and three different amplitude cases are considered. Secondly, (ii) the DMLE of an airfoil motion at stall angles of attack was investigated. In this case, the airfoil was set at stall angles of attack rather than oscillating it. This study will provide the transient lift and drag at different deflection frequencies of 0.5 Hz, 1 Hz, 2 Hz, 5 Hz, and 10 Hz. The results showed that the lift coefficient for the airfoil increased by 20.15%, while a 16.58% delay in the dynamic stall angle was obtained for an oscillating airfoil with DMLE with AD = 0.01 and MST = 14.75°, as compared to the reference airfoil. Similarly, the lift coefficients for two other cases, where AD = 0.05 and AD = 0.0075, increased by 10.67% and 11.46%, respectively, compared to the reference airfoil. Furthermore, it was shown that the downward deflection of the leading edge increased the stall angle of attack and the nose-down pitching moment. Finally, it was concluded that the new radius of curvature of the DMLE airfoil minimized the streamwise adverse pressure gradient and prevented significant flow separation by delaying the Dynamic Stall Vortex (DSV) occurrence.

This paper provides a new multi-function Blockchain Technology-enabled Pharmaceutical Supply Chain (BT-enabled PSC) mathematical cost model, including PSC costs, BT costs, and uncertain demand fluctuations. The purpose of this study is to find the most appropriate algorithm(s) with minimum prediction errors to predict the costs of the BT-enabled PSC model. This paper also aims to determine the importance and cost of each component of the multi-function model. To reach these goals, we combined four Supervised Learning algorithms (KNN, DT, SVM, and NB) with two Evolutionary Computation algorithms (HS and PSO) after data generation. Each component of the multi-function model has its own importance, and we applied the Feature Weighting approach to analyse their importance. Next, four performance metrics evaluated the multi-function model, and the Total Ranking Score determined predictive algorithms with high reliability. The results indicate the HS-NB and PSO-NB algorithms perform better than the other six algorithms in predicting the costs of the multi-function model with small errors. The findings also show that the Raw Materials cost has a stronger influence on the model than the other components. This study also introduces the components of the multi-function BT-enabled PSC model.

This chapter presents an aerodynamic optimization for a Morphing Leading Edge (MLE) winglet of a well-known UAV, the UAS-S45. The optimization algorithm is integrated with the modified Class Shape Transformation (CST) parameterization method and had the aim to enhance aerodynamic performance by minimizing drag and maximizing aerodynamic endurance at the cruise flight condition. The optimization scheme was carried out with in-house MATLAB code and by employing the Vortex Lattice Method (VLM) to calculate the aerodynamic properties of the morphing leading-edge winglet. This study presents the optimization technique and compares winglet geometries results by demonstrating that changing the winglet geometry in flight can enhance aircraft performance while lowering drag, therefore the fuel consumption. The optimized airfoils have shown a significant improvement in the overall aerodynamic performance by up to 8.55

The unsteady flow characteristics and responses of the UAS-S45 airfoil with a morphing trailing edge shape at high angles of attack undergoing deflections are investigated at a Reynolds number of 2.4 × 106. The flexible trailing edge was simulated using a computational fluid dynamics approach using a dynamic mesh and user-defined functions. The goal was to achieve a dynamically deflected trailing edge in an unsymmetrical airfoil and assess the influence of unsteady morphing trailing edge deflection on transient forces and flow field unsteadiness. The steady aerodynamic characteristics of the morphing deflection and the conventional deflection was initially studied. Then, the unsteady aerodynamic characteristics of the morphing wing was investigated as the trailing edge deflects at different rates. The dynamic flow responses to downward deflections are studied using the turbulence model. The time histories of the lift and drag coefficient responses exhibit a proportional relationship between the morphing frequency and the slope of response at which these parameters evolve. Coefficients of lift, drag, and moment of the deflected trailing edge airfoils were compared to those of the reference airfoils for various angles of attack. The numerical results show that the transient lift coefficient in the deflection process was higher than that of the static case at different angles of attack. The transient lift coefficient were higher as the deflection frequency increased. It was also revealed that the trailing edge deflection did not favor the flow reattachment. In addition, the dynamic mesh strategy, cell quality, and the proposed method of deforming the morphing trailing-edge was presented. increased. It was also revealed that the trailing edge deflection did not favor the flow reattachment. In addition, the dynamic mesh strategy, cell quality, and the proposed method of deforming the morphing trailing-edge was presented.

This work presents an aerodynamic and structural optimization for a Droop Nose Leading Edge Morphing airfoil as a high lift device for the UAS-S45. The results were obtained using three optimization algorithms: coupled Particle Swarm Optimization-Pattern Search, Genetic Algorithm, and Black Widow Optimization algorithm. The lift-to-drag ratio was used as the fitness function, and the impact of the choice of optimization algorithm selection on the fitness function was evaluated. The optimization was carried out at various Mach numbers of 0.08, 0.1, and 0.15, respectively, and at the cruise and take-off flight conditions. All these optimization algorithms obtained effectively comparable lift-to-drag ratio results with differences of less than 0.03% and similar airfoil geometries and pressure distributions. In addition, an unsteady analysis of a Variable Morphing Leading Edge airfoil with a dynamic meshing scheme was carried out to study its flow behaviour at different angles of attack and the feasibility of leading-edge downward deflection as a stall control mechanism. The numerical results showed that the variable morphing leading edge reduces the flow separation areas over an airfoil and increases the stall angle of attack. Furthermore, a preliminary investigation was conducted into the design and sensitivity analysis of a morphing leading-edge structure of the UAS-S45 wing integrated with an internal actuation mechanism. The correlation and determination matrices were computed for the composite wing geometry for sensitivity analysis to obtain the parameters with the highest correlation coefficients. The parameters include the composite material qualities, thickness, ply angles, and the ply stacking sequence. These findings can be utilized to design the flexible skin optimization framework, obtain the target droop nose deflections for the morphing leading edge, and design an improved model.