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This paper examines the performance of a 3D printed acoustic metamaterial as an acoustic treatment for aircraft engine nacelles in the Advanced Noise Control Fan. As the level of air travel continues to increase, so too does the demand for better noise-reduction technologies for aircraft. Engines are one of the two main sources of noise generated by aircraft, with fan noise, in particular, being of concern due to its broadband and tonal contributions. Small and lightweight methods of addressing both broadband and tonal noise are necessary due to the limitations presented by the current engine design. Presented in this paper is a novel acoustic metamaterial that has undergone design optimization for broadband noise reduction. The final design was produced using 3D printing and tested using the Advanced Noise Control Fan at the University of Notre Dame. It was found that the material is capable of reducing the first harmonic of the blade passing frequency by up to 18.5 dB, with an overall noise reduction of 3.7 dB. [ABSTRACT FROM AUTHOR]

The structural safety factor is an essential parameter in aircraft design, representing the ratio of the design load to the operating load. Traditional design methods rely on subjective determination of safety factor values based on experience, lacking objectivity in quantifying uncertainty. However, with advancements in aircraft design technology and increasing competition in the commercial space market, new-generation hypersonic aircraft with complex load environments require a more optimal approach. Applying a uniform safety factor to each component within subregions of the aircraft leads to overly conservative results and impacts flight performance. To address this limitation, a design scheme that incorporates subregional, differentiated safety factors is necessary. This approach allows for better material utilization and ensures compliance with safety requirements. This paper utilizes reliability-based design optimization theory to consider uncertainty in structural systems. It establishes a mapping relationship between structural reliability and differentiated safety factors, providing safety under uncertainty while guaranteeing weight reduction. Additionally, this paper develops a subregional, differentiated safety factors distribution program to determine the safety factors of different subregions of the structure. Consequently, a refined subregional differentiated safety factors scheme that balances safety and economy is derived. [ABSTRACT FROM AUTHOR]

This paper focuses on the development of a deep-learning framework for predicting distributed quantities around aircraft flying in the transonic regime. These quantities play a crucial role in determining aerodynamic loads and conducting aeroelastic analysis. Angle of attack and Mach number are chosen as the two independent parameters for the reduced-order models. A comparative assessment is conducted between the proposed deep-learning framework and the proper orthogonal decomposition approach to identify the strengths and weaknesses of each method. The accuracy of the data-driven machine-learning method in modeling steady-state transonic aerodynamics is assessed against three benchmark cases of three-dimensional test cases: Benchmark Super Critical Wing and ONERA M6 wings, and the wing-body Common Research Model configuration. Despite the challenges of the analyzed scenarios, promising results are obtained for each test case, showing the effectiveness of the model implemented. Furthermore, the paper demonstrates the application of the method for aeroelastic analysis and uncertainty quantification. This quantifies the robustness and versatility of the implemented model. [ABSTRACT FROM AUTHOR]

Deployable mechanisms have the characteristics of complex loops and strong coupling. As a result, the solution to the degrees of freedom (DOFs) and kinematic characteristics of the basic mechanism unit are often complicated, and the solution for combined mechanisms is rarely reported in the relevant literature. In this paper, the kinematic characteristics of the rectangular pyramid deployable mechanism (RPDM) are analyzed, and the DOFs of the RPDM are calculated based on the screw theory. In addition, the DOF number and properties of its moving components are analyzed. Based on the condition that the nonhomogeneous linear equations have a unique solution, the singularity analysis of the mechanism is complete, which indicates that the mechanism can be continuously deployed and folded. Based on the geometric characteristics of the mechanism, its folded ratio is calculated. Finally, a ring, a planar, and a parabolic truss deployable antenna mechanism are assembled based on the RPDM, and the DOF variation for the different assembly modes is obtained via the screw constraint matrix. This study provides a good reference for the design and analysis of such multi-closed-loop deployable mechanisms, and the large-scale space-deployable mechanisms constructed in this paper have good application prospects in the aerospace field. [ABSTRACT FROM AUTHOR]

Stowage schemes based on origami wrapping patterns are useful for the design of various deployable spacecraft structures. These origami patterns allow for the compact stowage of flat sheets of small thickness. Typically, some other structure is needed to stiffen this thin sheet when it is fully deployed and flat. This paper describes a class of thin-shell structures that can be compactly stowed using origami wrapping methods but have a corrugated form when deployed. These structures cannot reach a flat state by design. The persistent corrugations provide stiffness without the need for an external structure, thus leading to overall savings in mass and complexity. The geometrical design of the corrugations is highly tunable. This paper describes a method for generating the form of these folding structures; the method guarantees that the structure can be unstrained both when compactly wrapped and when fully deployed. Test articles are fabricated to demonstrate the concept, and stowage experiments and stiffness experiments are conducted. A structural finite element modeling procedure is described to predict the deployed stiffness of these structures, and the stiffness predictions match the experimental results. [ABSTRACT FROM AUTHOR]

This paper considers the lift coefficient of an airfoil with nonzero prescribed thickness and camber, along with a prescribed porosity distribution in a steady incompressible flow. In similar prior work, considering a Darcy-type porosity condition on the airfoil surface provides a Fredholm integral equation that can be solved exactly for Hölder-continuous porosity distributions. However, the generated lift predicted by the model diverges from the experimental data for porous airfoils with large values of the porosity parameter. This indicates a fundamental characteristic is missing in the mathematical model. Consequently, in this paper, the (linear) Darcy porosity condition is replaced by the (nonlinear) Forchheimer porosity condition. The Forchheimer porosity condition is decomposed into linear sections and furnishes a modified system of Fredholm integral equations, enabling an approximate solution by superposition for Hölder-continuous porosity distributions. The solution is then verified against the SD7003 airfoil results, and the comparison shows better agreement than considering the Darcy porosity condition. The methodology and results presented here may be combined with previous work on the aeroacoustics of porous airfoils with a Forchheimer boundary condition to address the conflicting aims of improving aerodynamic performance while reducing unwanted aeroacoustic emissions. [ABSTRACT FROM AUTHOR]

Accurate prediction of nonlinear aerodynamics is essential for the transonic aeroelastic analysis of flight vehicles. Though reduced-order aerodynamic models are cheap and reasonable tools, it is still a tough problem to accurately evaluate the unsteady pressure distributions on the surface of an elastic structure. This paper presents a nonlinear data-driven modeling approach based on the high-fidelity simulations in the following three steps. The first step is to compute the dominant modes of unsteady pressure distributions through the proper orthogonal decomposition. The pressure snapshots used for the feature extraction are sampled under a multilevel sine-sweep excitation. The second step is to obtain the low-dimensional temporal dynamics of the coefficients of these modes via polynomial nonlinear state-space identification. The linear estimation implemented by employing the dynamic mode decomposition with control algorithm serves as the initialization of the nonlinear optimization. The third step is to reconstruct the unsteady pressure distributions under arbitrary structural excitation from the temporal coefficients. The paper validates the approach via two numerical examples of the transonic aerodynamic-structural coupling problem. One is an NACA0012 airfoil, and the other is an AGARD 445.6 wing. The examples show that the proposed approach exhibits both accurate and robust performance in the prediction of unsteady pressure distributions, aerodynamic forces, and aeroelastic responses. In particular, the approach well predicts the physical features at the fluid-structure coupling interface, previously neglected in the system identification of aerodynamic systems. Therefore, the approach serves as a promising tool for data-driven aeroelastic analysis. [ABSTRACT FROM AUTHOR]

The Koiter-Newton method improves the computational efficiency of nonlinear buckling analysis; however, the construction of reduced-order models using fully nonlinear kinematics is still a tedious and time-consuming work. In this paper, the Koiter-Newton reduced-order method using mixed nonlinear kinematics is presented for the geometrically nonlinear buckling analysis of thin-walled structures. Strain energy variations up to the fourth order were achieved using mixed kinematics for the improved Koiter theory. Corotational kinematics, which is inconvenient for high-order variations, was applied to calculate the first- and second-order variations for the internal force and tangent stiffness, respectively, whereas the third- and fourth-order strain energy variations were facilitated by explicit algebraic formulations using updated von Kármán kinematics. A reduced-order model with 1+m degrees of freedom was established, of which m perturbation loads were considered to make the method applicable for buckling problems. The geometrically nonlinear response was traced using a predictor-corrector strategy by combining the nonlinear prediction solved by the reduced-order model and the correction using Newton iterations. Numerical examples of structures with various buckling behaviors demonstrate that the performance of the proposed method is not obviously affected by using simplified kinematics, and sometimes it even exhibits a superior capability for path-following analysis. [ABSTRACT FROM AUTHOR]

This paper presents a novel gradient-based optimization algorithm for improving the accuracy of experimentally estimated modal parameters with the assistance of finite element models. Initially, we recast the discrete vibration response equation into a matrix form and formulate the parameter estimation problem in modal analysis as an optimization problem. Then the problem is solved with a gradient-based iterative algorithm, which explicitly exhibits the closed form of gradients used in optimization. Initial values for this iteration are parameters derived from finite element models, since every important engineering structure should be analyzed with a finite element model before it is constructed. Subsequently, the performance of this algorithm is validated by both pure numerical experiments, which simulate the physical world, and experiments using real measurement data gathered by sensors in the real physical world. The algorithm's performance is further enhanced by incorporating gradient clipping and an adaptive iteration threshold. As a comparison, a discussion on classical least-squares time-domain method for the problem is provided. For practical applications, the Shi-Tomasi corner detection and Lucas-Kanade optical flow methods are deployed to detect corner points from videos taken during the vibration of a structure and track the motion of these points in the videos. [ABSTRACT FROM AUTHOR]

This paper describes the implementation and linearization of a coupled panel and vortex particle method in a state-variable form. More specifically, the coupled panel and vortex particle dynamics are formulated as a nonlinear system of ordinary differential equations in first-order form to be self-contained and inherently linearizable. Linearization of this coupled panel and vortex particle method is demonstrated via finite differencing and a novel analytical linearization technique to yield a linear time-invariant representation. The code is implemented in MATLAB® and validated against an open-source aerodynamic solver for a fixed wing in both stationary and unsteady conditions. The linearized models are verified against the nonlinear dynamics both in the time and frequency domains. Linearized models accurately represent the wake dynamics about the equilibrium condition for moderate amplitude inputs and for input frequencies covering the typical frequency range of flight dynamics and flight controls, i.e., 0.3-30 rad/s. Analytical linearization is shown to abate the cost of linearization over perturbation methods by O(n2), where n is the total number of states of the system. Linearized models of the coupled panel and vortex particle dynamics have applications in the flight dynamics of both fixed- and rotary-wing vehicles, where these dynamics can be used to augment the rigid-body dynamics. The coupled rigid-body and wake dynamics can be leveraged to assess the stability, response characteristics, and handling qualities of vehicles experiencing aerodynamic interactions between rotors, wings, and/or obstacles. [ABSTRACT FROM AUTHOR]

A hypersonic airborne radome must possess multiband electromagnetic transparency, high specific strength, and thermomechanical resistance to ensure proper antenna operation. However, achieving these multifunctional properties in radome wall design is challenging due to conflicting material requirements. Moreover, existing design methods for arbitrary multiband and broadband hypersonic radomes are insufficient. This paper presents an efficient method that integrates artificial intelligence and an evolutionary algorithm to enable the multidisciplinary synchronous design of the radome wall. The designed radome walls using multilayered porous silicon-nitride ceramics achieve power transmission efficiency exceeding 90% in a broadband of 30 GHz or arbitrary multiple bands with bandwidths of 5 GHz, covering the centimeter-to-millimeter wave range. Moreover, the designed laminate also meets requirements of flexural strength surpassing 60 MPa and a maximum thermal stress-strength ratio below 1. Notably, the utilization of artificial intelligence accelerates the design process by at least 1093 times compared to conventional methods. [ABSTRACT FROM AUTHOR]

The ability to distribute entanglement between quantum nodes may unlock new capabilities in the future that include teleporting information across multinode networks, higher resolution detection via entangled sensor arrays, and measurements beyond the quantum limit enabled by networked atomic clocks. These new quantum networks also hold promise for the Aerospace community in areas such as deep space exploration, improved satellite communication, and synchronizing drone swarms. Although exciting, these applications are a long way off from providing a "real-world" benefit, as they have only been theoretically explored or demonstrated in small-scale experiments. An outstanding challenge is to identify near-term use cases for quantum networks; this may be an intriguing new area of interest for the aerospace community, as the quantum networking field would benefit from more multidisciplinary collaborations. This paper introduces quantum networking, discusses the difficulties in distributing entanglement within these networks, highlights recent progress toward this endeavor, and features two current case studies on mobile quantum nodes and an entangled clock network, both of which are relevant to the aerospace community. [ABSTRACT FROM AUTHOR]

Multirotor vehicles typically use either complex mechanisms to vary blade pitch angle or speed control to vary thrust and torque to attain stability and maneuverability. Alternatively, thrust and torque can be varied by morphing the blades without the use of complex mechanisms and without varying the rotor speed. This method could be faster to change thrust, more efficient, and it may require fewer mechanical components. This paper presents an experimentally validated aerodynamic model with an empirical static-aeroelastic correction to predict the thrust and torque coefficient of a mechanism-free rotor with Macro-Fiber Composite actuators. A variable-camber piezocomposite rotor prototype is experimentally tested on a static-thrust setup. The experiment is used to identify the so-called unknown coefficients of the model (e.g., related to the shape of the blade induced by the piezoelectric device) and to identify a correction to account for flow-induced deformations. Using the identified model, several parametric analyses are conducted to predict thrust and torque coefficients as a function of rotor speed and the electrical excitation of the piezocomposite actuators. In addition, the model is used for design optimization, leading to several optimal designs based on a limited parameter space. Three different objective functions are used: maximizing thrust, minimizing torque, and maximizing thrust-to-torque ratio. The operational aerodynamic characteristics of these designs are also presented. [ABSTRACT FROM AUTHOR]

This paper proposes a mathematics-informed neural network (MINN) approach for resolving the long-term challenge in wave scattering modeling. The central innovation lies in integrating Cauchy-Riemann equations into machine learning architectures. By incorporating Cauchy integrals and boundary conditions, the neural network successfully learns to numerically produce matrix kernel factorization for Wiener-Hopf analytical models. To validate and demonstrate the approach, a benchmark case of wave scattering from parallel hard-soft plates is studied by comparing the machine learning results with the available analytical solutions. The proposed MINN approach could provide a new route to extensively enhance the theoretical modeling capability for several wave scattering and fluid mechanics problems. The code can be found at https://github.com/lscapku/MINN. [ABSTRACT FROM AUTHOR]

Trailing edge serration is an effective method to reduce broadband noise generation by an airfoil. However, the noise reduction performance can be significantly reduced when there is flow misalignment at the serration. This experimental study investigates the impact of a shift in the serration position downstream of the airfoil trailing edge on the noise reduction performance, referred to in this paper as serration extension. Experiments were performed on a 100-mm-chord NACA 0012 wing model with sawtooth trailing edge serrations. The serration performance was studied at 0° and 7° flap-down configurations at various angles of attack. The serrations with three different extension lengths of 5, 10, and 15 mm were tested and compared with the baseline case without extension. The emitted noise was measured with a phased microphone array. The results show a significant reduction in broadband high-frequency noise by extension under loading conditions. Particle image velocimetry measurements of root flow along the wall-normal plane reveal diminished crossflow across the serration after extension. Furthermore, the extensions cause an up-to-25% increase in the maximum lift coefficient and improved or unaltered lift-to-drag ratios. [ABSTRACT FROM AUTHOR]

This paper presents the assessment of several recent enhancements for a high-order wall-modeled large-eddy simulation (WMLES) approach and demonstrates order independence with a fixed data exchange location in the wall model. The two enhancements include the use of isotropic tetrahedral elements to improve accuracy and an explicit subgrid-scale model, the Vreman model, to improve accuracy and robustness. The P-refinement study focused on the high-lift Common Research Model (HL-CRM) at the angle of attack of 19.57 deg, a benchmark problem from the 4th AIAA High-Lift Prediction Workshop. Solution polynomial orders of P=2,3,4, and 5 were used in the study. The study demonstrated P independence in integrated forces, pitch moment, velocity profile in the wall-normal direction, and surface flow topology. It also showed that a P order of at least 3 (P3) was needed to correctly predict the external inviscid flow and the surface flow topology. Thereafter, P3 simulations over several other angles of attack demonstrated that the high-order WMLES approach can correctly predict the maximum lift and flow separation regions for HL-CRM with about 40 million degrees of freedom (DOF) compared to at least 250 million DOF required by second-order methods. [ABSTRACT FROM AUTHOR]

Various types of sensors are needed to monitor the health state of smart deep-space habitats. However, measured data can be affected by sensor faults, which influence the health management system and consequently the decision-making. In this paper, an unsupervised learning approach based on convolutional autoencoders (CAEs) is developed to detect anomalies in temperature and pressure sensors. The proposed method is systematically investigated using a habitat simulator (HabSim). Several illustrative examples are demonstrated in the nominal and hazardous states of the habitat, including micrometeorite impact and fire scenarios. The performance of the proposed method using CAEs is compared with that of existing methods using auto-associative neural networks (AANNs) and variational autoencoders. This comparison is based on typical evaluation metrics, including precision, recall, F1 score, training time, and testing time. The effect of temperature-pressure coupling on the detection performance of CAEs and AANNs is explored by training different data-driven models, including one with temperature sensors, one with pressure sensors, and one with both temperature and pressure sensors. The effect of the number of faulty sensors on the performance of CAEs is studied, as with an increase in the number of faulty sensors, redundant information among the sensors is reduced. The capability of CAEs to change the number of sensors without redesigning the network architecture and retraining the neural network is investigated and demonstrated. The capabilities and limitations of the proposed solution are discussed. [ABSTRACT FROM AUTHOR]

The momentum coefficient is one of the most commonly used parameters to quantify the aerodynamic performance and system output of active flow control (AFC). Estimating the momentum coefficient accurately through experiments is challenging. Several approaches exist to estimate the momentum coefficient in laboratory conditions. However, the question arises as to whether these models hold as ambient and supply conditions change during operation in flight. Therefore, computational fluidic dynamic (CFD) simulations were carried out on two exemplary AFC actuators (i.e., a fluidic oscillator and a steady jet nozzle) in a quiescent environment with supply pressure ratios ranging from 1.5 to 4; supply temperatures at 293, 550, and 800 K; and ambient altitude conditions at sea level and 10 km. When scaled with √Tsup/p∞, the mass flow for different conditions collapses as a function of the pressure ratio. Furthermore, the momentum coefficient collapses when scaled with 1/p∞. The existing models to calculate the momentum coefficient hold for changing ambient and supply conditions. Therefore, these models, in conjunction with scaling parameters, can be employed during the operation of an AFC system in flight with changing conditions in order to provide the desired momentum output. The paper discusses the details of the CFD results and the derivation of the scaling parameters. [ABSTRACT FROM AUTHOR]

There has been a growing interest in utilizing flared folding wingtips as an in-flight load alleviation device to enable increased wing spans that meet airport gate limits but with little increase in wing weight. The semi-aeroelastic hinge (SAH) concept is implemented in high-aspect-ratio wings to enable wingtips to be released during severe load cases such as maneuvers and gusts to alleviate the bending moments while maintaining optimum aerodynamic shape for the rest of the flight. In this paper, scaling methods for wings incorporating the SAH are explored, allowing for the development of equivalent scaled unmanned aerial vehicles or wind tunnel models with similar aeroelastic behavior as full-size aircraft. Three scaling approaches are considered in this study, namely, Iso-Froude, Iso-Frequency, and Iso-Strain, where a set of governing nondimensional quantities and scaling factors are determined. Despite the significant nonlinearities resulting from large wingtip fold angles, it is shown that a linear scaling approach can be appropriate for such a wing configuration. Furthermore, the aeroelastic properties of each scaled model are compared to those of the full-scale model, where the best match was obtained from the Iso-Strain model, although it is challenging to meet the required operational conditions. [ABSTRACT FROM AUTHOR]

Aluminum and boron nanoparticles are added to kerosene in this paper to improve the combustion properties of kerosene. When the particle concentration is 30 g/L, the volumetric heat value of fuel increases by 3 and 5%, respectively, with the addition of aluminum and boron nanoparticles. Meanwhile, combustion experiments in a supersonic combustor are conducted to study the combustion characteristics of Al-kerosene nanofuels and B-kerosene nanofuels. The air flow rates of all experiments are about 1.77 kg/s, the total temperature is 1500 K, and the total pressure is 1.2 MPa. The combustion flow and flame structures of kerosene with different particles and different concentrations are studied, and the results indicate that the addition of nanoparticles to the fuel enhances combustion and heat release enhanced, and the flame stabilization mode is changed from the cavity stabilization to the jet-wake stabilization mode with a higher particle concentration. Meanwhile, the unsteady characteristics of flame are studied. The flame oscillation is intensified, and the fundamental frequency of the flame increases with the increase in particle concentration. Besides, the addition of nanoparticles significantly improves the combustion efficiency of kerosene. When the particle concentration is 30 g/L, the combustion efficiencies of nanofuels are increased by 15% and 17.5%, respectively, with the addition of aluminum and boron nanoparticles. [ABSTRACT FROM AUTHOR]

Uncrewed air vehicles in urban environments will have flights during terminal operations that are dominated by strong transient aerodynamics. These vehicles are not only lighter and smaller than traditional rotorcraft and helicopters, but in many instances they may be hybrid configurations with lifting surfaces similar to those of fixed-wing aircraft. These differences require further understanding of the physics of these transient aerodynamics, specifically large-amplitude transverse gusts and the resulting vehicle response, where classic indicial theory is no longer valid. This is crucial to the safety and certification of these air vehicles near buildings and populations. Prior efforts identified that gust responses depart from traditional linear theory when the leading-edge vortex (LEV) forms as a distinct feature and departs from the lifting surface, resulting in flow nonlinearities. This paper expands our understanding of the interactional physics of these nonlinear transverse gusts with flow separation. LEV and trailing-edge vortex (TEV) behavior are correlated, and these vortex interactions are studied to understand the impact on flow separation and subsequent aerodynamic behavior. Larger gust ratios are observed to increase the LEV normal translation, while flow separation is driven by the location and magnitude of the TEV. [ABSTRACT FROM AUTHOR]

In this paper, we present a wall model based on a modified version of the Spalart-Allmaras turbulence model. Unlike typical wall models, this method avoids the need to query the interior solution by utilizing solution information solely at the boundary, making it well-suited for unstructured grids and mesh adaptation. Furthermore, this wall-modeling method is formulated to lessen the near-wall grid resolution requirements by, below the log layer, making the eddy viscosity approach a constant, nonzero value and the velocity varying approximately linearly with distance from the wall. Using output-based mesh adaptation, this new wall-modeling method is compared to standard Reynolds-Averaged Navier-Stokes (RANS-SA). It is shown that this wall model results in accurate predictions of quantities of interest such as aerodynamic coefficients, surface pressure and temperature, skin friction, and heat transfer compared with RANS-SA, while requiring substantially less near-wall grid. [ABSTRACT FROM AUTHOR]

State feedback is mostly used in the active vibration control of structural systems. However, the most challenging problem is to achieve the whole state of the structure. Another issue in active vibration control is that, in many cases, only some particular modes need to be controlled, while the other modes can remain unchanged in order to save on the costs of the control. To solve the above issues, this paper proposes a state-observing method and conducts a non-spillover partial pole placement control using a multistep method. Using the finite element method, the electromechanical coupling equations of motion of the beam are formulated. The reduced finite element model is then obtained by using the system equivalent reduction expansion process method. Introducing the real measurements by sensors and applying the local corresponding principle, the modes of the structural system are corrected, which are then utilized to predict all the degrees of freedom without measuring by sensors. Then, the non-spillover partial pole placement based on the state feedback is conducted using the multistep method. The optimal locations of actuators are derived by the genetic algorithm to control the specific structural modes effectively. The numerical simulations and experimental studies are also carried out. [ABSTRACT FROM AUTHOR]

Aerodynamic simulations were carried out in the study presented in this paper focusing on the stall performance of the High-Lift Common Research Model obtained from the fourth AIAA High-Lift Prediction Workshop. Various turbulence models of Reynolds-averaged Navier-Stokes simulations are analyzed. A modified version of the transitional 𝑘-̅ν²-𝜔 model was developed to enhance stall prediction accuracy for high-lift configurations with a nacelle chine. The vortex generator, three-element airfoil, and high-lift model are numerically simulated. The results reveal that implementing a 𝑘-̅ν² -𝜔 model with the separation shear layer fixed notably enhances the stall prediction behavior for both the three-element airfoil and high-lift configuration without affecting the prediction of the vortex strength of a vortex generator. Moreover, incorporating rotation correction into the SPF 𝑘-̅ν² -𝜔 model improves the prediction of vortex strength and further enhances stall prediction for the high-lift configuration. The relative error in predicting the maximum lift coefficient is less than 5% of the experimental data. The study also investigated the impact of the nacelle chine on the stall behavior of the high-lift configuration. The results demonstrate that the chine vortex can mitigate the adverse effects of the nacelle/pylon vortex system and increase the maximum lift coefficient. [ABSTRACT FROM AUTHOR]

This paper investigates the combination of a slit at the blunt trailing edge of the flat plate and dielectric barrier discharge plasma to control the vortex shedding of the plate and its associated tonal noise. The noise and flow characteristics of the plate were measured using a far-field microphone array and the particle image velocimetry technique, respectively. The results show that the vortex shedding and the tonal noise can be significantly suppressed by the slit alone, with an average noise reduction of approximately 10 dB in the test Reynolds number. In addition, installing a plasma actuator inside the slit further suppresses the vortex shedding and reduces tonal noise. However, the additional control efficiency of the plasma decreases with increasing wind speeds, with a further 8 dB reduction at a wind speed of U∞=10  m/s (corresponding to an inducing blowing rate BR of 4.5%). However, only an additional 1.5 dB noise reduction is achieved at U∞=20  m/s (BR=2.25%). The particle image velocimetry snapshots were analyzed by proper orthogonal decomposition. The measurements clearly show the variation in vortex shedding at the trailing edge of the plate, revealing the underlying flow mechanisms that lead to the observed noise variations and frequency changes. [ABSTRACT FROM AUTHOR]

This paper presents an improved mathematical expression for semi-empirical wall pressure spectra modeling based on gene expression programming (GEP). The main focus of this work is to obtain a model that applies to a wide range of cases in terms of parameters and the source of data. The dataset comprises flat plate and airfoil cases with adverse and favorable pressure gradients at various Reynolds numbers. First, a characterization of the dataset is performed to understand the low-dimensional distribution of parameters. Then, a feature importance study is conducted to choose the most suitable model input variables from the exhaustive list of nondimensional parameters. The GEP algorithm is modified to ensure that trained models adhere to the basic structure of previously published semi-empirical models. Following training on the diverse database, the new model is compared against existing, best-performing empirical models to quantify the performance improvements. The models are tested on cases with completely different configurations and parameter ranges, unseen during training, and maintain their superior performance. Finally, a comparison is made between models developed with GEP and neural networks in terms of their efficacy, complexity, and interpretability. [ABSTRACT FROM AUTHOR]

Fast and accurate evaluation of aerodynamic characteristics is essential for aerodynamic design optimization because aircraft programs require many years of design and optimization. Therefore, it is imperative to develop sufficiently fast, robust, and accurate computational tools for industry routine analysis. This paper presents a nonintrusive machine-learning method for building reduced-order models (ROMs) using an autoencoder neural network architecture. An optimization framework was developed to identify the optimal solution by exploring the low-dimensional subspace generated by the trained autoencoder. To demonstrate the convergence, stability, and reliability of the ROM, a subsonic inverse design problem and a transonic drag minimization problem of the airfoil were studied and validated using two different parameterization strategies. The robustness and accuracy demonstrated by the method suggest that it is valuable in parametric studies, such as aerodynamic design and optimization, and requires only a small fraction of the cost of full-order modeling. [ABSTRACT FROM AUTHOR]

Accurate real-time prediction of aerodynamic forces is crucial for the navigation of unmanned aerial vehicles (UAVs). This paper presents a data-driven aerodynamic force prediction model based on a small number of pressure sensors located on the surface of a UAV. The model is built on a linear term that can make a reasonably accurate prediction and a nonlinear correction for accuracy improvement. The linear term is based on a reduced basis reconstruction of surface pressure, with the basis extracted from simulation data and the basis coefficients determined by solving linear pressure reconstruction equations at a set of optimal sensor locations, which are obtained by using the discrete empirical interpolation method (DEIM). The nonlinear term is an artificial neural network that is trained to bridge the gap between the DEIM prediction and the ground truth, especially when only low-fidelity simulation data are available. The model is tested on numerical and experimental dynamic stall data of a two-dimensional NACA0015 airfoil and numerical simulation data of the dynamic stall of a three-dimensional drone. Numerical results demonstrate that the machine-learning-enhanced model is accurate, efficient, and robust, even for the NACA0015 case, in which the simulations do not agree well with the wind tunnel experiments. [ABSTRACT FROM AUTHOR]

This paper analyzes ionic wind production and propulsive force in various electrode configurations under atmospheric conditions. By considering the aerodynamic forces in addition to previously considered electric ones, new predictions for steady-state forces and ionic wind flow velocity are successfully compared with experimental measurements, providing convincing quantitative evidence of the predictive capabilities of drift-diffusion modeling associated with one-way Coulomb forcing of Navier-Stokes equations for ionic wind generation. Furthermore, various electrode configurations are analyzed, some of them streamlined, reducing wakes downstream collectors on the one hand and providing additional thrust on the other. The quantification of these additional thrusts is analyzed, physically discussed, and explored in various configurations. [ABSTRACT FROM AUTHOR]

This paper concerns the interaction of an impinging shock wave with a supersonic turbulent boundary layer over several distinct and permanently deformed surfaces, resulting in differences in the shock-boundary-layer interaction and the surface acoustic loading. High-order numerical simulations featuring two-dimensional surface deformations typically encountered in experiments are performed. The deformation amplitudes are up to half the incoming turbulent boundary-layer thickness. The results show that the high-pressure region about the shock impingement is significantly altered and can become narrower or wider depending on the local surface inclination of the deformed panel mode. The surface curvature is found to not significantly affect the separation and reattachment locations of the recirculation bubble. The power spectrum analysis of the pressure fluctuations along the panel's midspan, where the surface attains the largest deformation amplitude, exhibits a rich and varied response. The pressure power spectrum is amplified in all of the surface deformation modes examined, with the magnitude of the amplification varying in the frequency domain, depending on the location and mode. [ABSTRACT FROM AUTHOR]

Under random-interval mixed uncertainties of structures, failure-probability upper-bound function (FPUBF), which varies with the distribution parameters of random inputs, can not only provide the influence of distribution parameters on the failure-probability upper bound (FPUB), but also contribute to decoupling a reliability-based design optimization model. Although FPUBF can be estimated by repeatedly evaluating FPUBs at different distribution parameter realizations, it suffers from unaffordable computational cost resulting from this double-loop framework. To address this issue, this paper proposes a single-loop sampling strategy (SL) to estimate FPUBF at arbitrary realizations in the interested distribution parameter region. Instead of the huge computational cost of a double-loop framework, the SL estimates the entire FPUBF only by one simulation analysis. Moreover, importance sampling (IS) variance reduction technique is introduced, and a single-loop IS probability density function (PDF), or SL-IS-PDF, is constructed to more efficiently estimate FPUBF by reducing the required size of the candidate sample pool. For approximating the optimal SL-IS-PDF and identifying the states of candidate samples efficiently, the double-loop adaptive Kriging model of performance function is introduced to further reduce the number of performance function evaluations. A numerical example and two composite structure examples are employed to verify the accuracy, efficiency, and feasibility of the proposed methods. [ABSTRACT FROM AUTHOR]

The geometrical arrangement of metamaterials controls their mechanical properties, such as Young's modulus and the shear modulus. However, optimizing the geometrical arrangement for user-defined performance criteria leads to an inverse problem that is intractable when considering numerous combinations of properties and underlying geometries. Machine-learning techniques have been proven to be effective and practical to accomplish such nonintuitive design tasks. This paper proposes an inverse design framework using conditional generative adversarial networks (CGANs) to explore and optimize two-dimensional metamaterial designs consisting of spinodal topologies, called spinodoids. CGANs are capable of solving the many-to-many inverse problem, which requires generating a group of geometric patterns of representative volume elements with target combinations of mechanical properties. The performance of the networks was validated by numerical simulations with the finite element method. The proposed inverse design framework vastly improves the efficiency of design exploration and optimization of spinodoid metamaterials. [ABSTRACT FROM AUTHOR]

The accurate prediction of transient thermal fields of space structures is of paramount significance for the analysis of geometrical stability and thermal management systems. This paper proposes a novel method to solve the transient thermal field of the thin-walled cross section of beams in outer space. High-order beam elements are derived by means of the Carrera unified formulation (CUF), where the longitudinal direction of the beam is discretized by one-dimensional two-node and four-node elements, whereas arbitrary high-order expansion functions are used for the description of the temperature expansion within the cross section. The governing differential equations of the transient temperature field are derived according to the transient thermal conduction theory and the weighted residual method. The solution of the initial value problem for ordinary differential equations is obtained through the application of the Adams-Bashforth method, leading to the determination of the transient thermal field. In order to demonstrate the accuracy of the temperature results, a convergence analysis is conducted. Finally, temperature distributions within the cross section and along the longitudinal direction of the model are discussed to show the effects of several factors, including material properties, angle of solar radiation, and shadows induced by the shelter of other components. [ABSTRACT FROM AUTHOR]

Understanding the acoustic source characteristics of supersonic jets is vital to accurate noise field modeling and jet noise reduction strategies. This paper uses advanced, coherence-based partial field decomposition methods to characterize the acoustic sources in an installed, supersonic GE F404 engine. Partial field decomposition is accomplished using an equivalent source reconstruction via acoustical holography. Bandwidth is extended through the application of an array phase-unwrapping and interpolation scheme. The optimized-location virtual reference method is used. Apparent source distributions and source-related partial fields are shown as a function of frequency. Local maxima are observed in holography reconstructions at the nozzle lipline, distinct in frequency and space. The lowest-frequency local maximum may relate to noise generated by large-scale turbulence structures in the convectively subsonic region of the flow. Other local maxima are correlated primarily with Mach wave radiation originating from throughout the shear layer and into the fully mixed region downstream of the potential core tip. Source-elucidating decompositions show that the order and behavior of the decomposition lend to the local maxima being related to distinct subsources. Between the local maxima, however, there may be a combination of sources active, which is likely the cause of the spatiospectral lobes observed in other full-scale, supersonic jets. [ABSTRACT FROM AUTHOR]

A fast method for predicting turbofan fan-stage broadband interaction noise is being developed. The downstream propagating acoustic power in the bypass duct due to the response of the fan exit guide vane (FEGV) to fan wake turbulence is computed based on two-dimensional flat-plate cascade analysis and Green's method. This study focused on using machine learning to define the fan wake parameters used as inputs to the FEGV response and noise calculation. Machine-learning algorithms are being trained using computational fluid dynamics results. This paper describes the accuracy of machine learning given the available rotor wake data. Further, the effect of errors in the learned input data on the acoustic prediction was studied. Based on this study, the method shows great promise. [ABSTRACT FROM AUTHOR]

Mesh-agnostic models have advantages in terms of processing unstructured spatial data and incorporating partial differential equations. Recently, they have been widely studied for constructing physics-informed neural networks, but they need to be trained on a case-by-case basis and require long training times. On the other hand, fast prediction and design tools are desired for aerodynamic shape designs, and data-driven mesh-based models have achieved great performance. Therefore, this paper proposes a data-driven mesh-agnostic decoder that combines the fast prediction ability of data-driven models and the flexibility of mesh-agnostic models. The model is denoted by an implicit decoder, which consists of two subnetworks, i.e., ShapeNet and HyperNet. ShapeNet is based on implicit neural representation, and HyperNet is a simple neural network. The implicit decoder is trained for the fast prediction of supercritical airfoils. Different activation functions are compared, and a spatial constraint is proposed to improve the interpretability and generalization ability of the model. Then, the implicit decoder is used together with a mesh-based encoder to build a generative model, which is used for the inverse design of supercritical airfoils with specified physical features. [ABSTRACT FROM AUTHOR]

Attempts to extend a Reynolds-stress model (RSM) for transition flow simulations have begun to cause concern in recent years based on the fact that the RSM has more potential than an eddy viscosity model when applied to complex three-dimensional flowfields. In terms of the RSM, however, coupling more transport equations will further increase its complexity (may worsen robustness and convergence rate). In this paper, a 𝛾 -based algebraic transition model originating from the SA-BC model is coupled with an 𝜈𝑡 -scale RSM (EV-RSM) to form a new transition/turbulence model, called tEV-RSM. Some numerical tests are conducted with a fifth-order scheme, WCNS, including the well-known T3 flat plate series, a two-dimensional airfoil (S809), a two-dimensional three-element airfoil (30P-30N), a three-dimensional prolate-spheroid, and a three-dimensional circular cylinder. The proposed model is compared with the SA-BC and 𝛾-𝑅⁢𝑒𝜃⁢𝑡 SST models, which indicates that it can predict the flows primarily governed by Tollmien-Schlichting transition or bypass transition well and has an advantage in predicting the flows with detached separation. Meanwhile, the iteration convergence rate of the tEV-RSM is fast enough considering it as a seven-equation model. [ABSTRACT FROM AUTHOR]

Identification of flutter margins in modern aeroelastic systems is a challenging task due to increased nonlinearities in novel designs, which can result in instabilities occurring below the linear flutter speed. These instabilities pose a significant risk as they may involve multiple stable solutions, such as large-amplitude self-sustained oscillations. The lack of efficient nonlinear bifurcation analysis methods for experimental systems exacerbates the challenges associated with postflutter analysis. This paper presents a data-driven method for predicting flutter instabilities and bifurcation diagrams of an experimental nonlinear 2-degree-of-freedom (2-DOF) airfoil. The approach uses measurement data from the preflutter regime to forecast the postflutter dynamics, eliminating the need for computationally expensive models. This study is the first application of the recently introduced data-driven bifurcation forecasting method to experimental aeroelastic systems. The results show that the proposed method is accurate, with predictions matching the measured behavior of the system. The presented study provides valuable insights into the nonlinear stability and dynamics of experimental airfoils and demonstrates the potential for applicability of this approach in the analysis of experimental systems. The findings have significant implications for online monitoring and evaluation of the nonlinear dynamics of aeroelastic systems in the aerospace industry, where safety is of crucial importance. [ABSTRACT FROM AUTHOR]

The prediction and control of thermoacoustic instability play a crucial role in combustion systems. This paper presents a novel methodology that employs attention transfer learning for the prediction of multivariate thermoacoustic signals, including acoustic pressure and heat release rate. By leveraging attention transfer learning, the proposed model can be generalized to thermoacoustic signal prediction under a variety of conditions with little additional training resources. Experimental data from various conditions in annular combustors are employed to evaluate the predictive performance of the model. The results demonstrate its ability to accurately predict future pressure signals over a wide-ranging time horizon while maintaining consistent dynamics, thus providing valuable information for active control. The proposed model is expected to be an effective step toward a data-driven solution for the early detection of thermoacoustic instability. [ABSTRACT FROM AUTHOR]

A single dielectric-barrier discharge plasma actuator is an active flow control device that imparts momentum to the fluid through ion acceleration using electromagnetic forces and has been used to suppress flow separation. This paper studies flow over an airfoil and how adding a single dielectric-barrier discharge actuator influences flow characteristics through numerical modeling. Using the spectral proper orthogonal decomposition and large-eddy simulation, flow instabilities are analyzed at their different temporal and spatial scales in the wake region. This study aims to explore the viability of spectral proper orthogonal decomposition for separation control and correlate the decomposed flow modes to the different actuation modes. [ABSTRACT FROM AUTHOR]

Because of the requirements of payload capability, flight range, and maneuverability, hypersonic aircraft demand high aerodynamic performance. Waveriders have attracted significant attention because of their high lift-to-drag ratios in hypersonic states. Many methods to design waveriders have been proposed for more than 60 years, among which the osculating method features unique advantages in terms of design space, efficiency, and flexibility. Moreover, nearly all waverider design methods can be realized using this approach. This paper presents an overview of research on waverider design methodologies using osculating theory developed until 2023. The design theory and detailed implementation of the osculating cone method are first described, followed by the progress and improvement of the method and the derived osculating flowfield method. The planform-customized waverider from the osculating cone method, which has developed rapidly in recent years, is discussed emphatically. In addition, the connection between the osculating cone and other waverider design methods is plotted to show its powerful applicability. The application of the osculating waverider in practical aircraft design is also summarized. Finally, some perspectives and suggestions for future development are concluded. [ABSTRACT FROM AUTHOR]

The paper delves into the detailed sound generation and propagation mechanisms associated with tripping-induced flow perturbations and trailing-edge scattering using wall-resolved large-eddy simulations. Two distinct boundary-layer tripping techniques, namely a geometrically resolved stair strip and an artificially modeled trip using suction and blowing, are investigated. To facilitate comparison, the natural boundary-layer transition is also simulated as a baseline scenario. The analysis takes into account a Reynolds number of 4×105, a Mach number of 0.058, and a nonzero angle of attack of 6.25° over a NACA 0012 airfoil configuration. The mechanisms for sound generation and propagation related to trailing-edge noise remain consistent across the three transition scenarios. However, boundary-layer tripping notably leads to intricate, scenario-specific noise generation: there is an interaction between the laminar separation bubble and tripping for the stair strip case, whereas laminar boundary-layer instability is evident for the suction and blowing scenario. Aerodynamic flowfields involving acoustic noise sources, their propagating natures near the wall, and far-field acoustics are cross-examined in detail. The comprehensive analysis of observed phenomena provides valuable insights for understanding nonlinear flow and acoustic interactions relevant to airfoil noise and designing new types of trips under adverse pressure gradient flows. [ABSTRACT FROM AUTHOR]

Composition noise has recently received increasing attention for its potential to contribute significantly to the indirect noise mechanism. In this study, the importance and definition of composition noise are revisited by proposing a new proper decomposition between entropy and mixture compositional fluctuations. When assuming quasi-one-dimensional, multispecies, isentropic, and nonreactive flow in nozzles, the resulting system of equations shows a new and remarkable one-way coupling between composition waves and both acoustic and entropy waves. Relying on the Magnus-expansion methodology, an exact solution of that system is investigated. The proposed theory is validated by comparing the model predictions with direct numerical simulations of nozzle flows in which compositional fluctuations are pulsed. It is shown that composition transfer functions from the unsteady simulations are in agreement with the analytical model of this paper. Finally, a hybrid approach is investigated consisting of extracting waves from a large-eddy simulation of a real helicopter engine and propagating those through different nozzle geometries. Composition noise is found to be negligible compared to direct or indirect entropy noise since it is at least 20 dB lower than other noise mechanisms for all tested cases. [ABSTRACT FROM AUTHOR]

Many modern structural health monitoring (SHM) systems use piezoelectric transducers to induce and measure guided waves propagating in structures for structural damage detection. To increase the detection capabilities of SHM systems, gradient-based optimization of sensor placement is frequently necessary. However, available numerical differentiation methods for mechanical wave propagation problems suffer from truncation and subtraction errors and are difficult to extend to high-order sensitivities. This paper addresses these issues by introducing an approach to obtain highly accurate numerical sensitivities of arbitrary order in mechanical wave propagation problems. The hypercomplex time-domain spectral finite element method (ZSFEM) couples the hypercomplex Taylor series expansion method with the time-domain spectral finite element method. We show how ZSFEM can be implemented within the commercial finite element package ABAQUS/Explicit. For verification, we compared the numerical and analytical results of the displacement and its sensitivities with respect to mechanical parameters, geometry, and boundary conditions for a rod subjected to a sudden, distributed axial load. First- and second-order sensitivities were obtained with normalized root mean square deviations below 4×10-3. Mesh convergence analyses revealed that p -refinement offered better convergence rates than h -refinement for the outputs and their sensitivities. Also, the sensitivities obtained with ZSFEM were compared with finite differences showing higher accuracy and step-size independence (e.g., no iteration is needed to determine the step size that minimizes the error). For simplicity, ZSFEM was presented only for one-dimensional truss elements, but the method is general and can be applied to other elements. [ABSTRACT FROM AUTHOR]

Rigid-flex printed circuit board (PCB) robotics have been investigated for exploring planetary terrain currently inaccessible to traditional aluminum body rovers. To push the technology to flight readiness, there must be rigorous methodologies for modeling these structures. This paper presents a modeling methodology for rigid-flex PCB robots subjected to large deformations, failure criteria for each component of the robot (PCB, Nomex hinges, flex cable), validation with drop testing of the rigid-flex PCB robot Pop-Up Flat Folding Explorer Robot (PUFFER), and an application of the method to assess survivability under lunar impact scenarios. A numerically stable and computationally efficient model of rigid-flex PCB robotics was permitted by using a combination of pre-existing element types and a hyperelastic material model for the flexible hinges. [ABSTRACT FROM AUTHOR]

The constants and functions in Reynolds-averaged Navier-Stokes (RANS) turbulence models are coupled. Consequently, modifications of a RANS model often negatively impact its basic calibrations, which is why machine-learned augmentations are often detrimental outside the training dataset. A solution to this is to identify the degrees of freedom that do not affect the basic calibrations and only modify these identified degrees of freedom when recalibrating the baseline model to accommodate a specific application. This approach is colloquially known as the "rubber-band" approach, which we formally call "constrained model recalibration" in this paper. To illustrate the efficacy of the approach, we identify the degrees of freedom in the Spalart-Allmaras model that do not affect the log law calibration. By subsequently interfacing data-based methods with these degrees of freedom, we train models to solve historically challenging flow scenarios, including the round-jet/plane-jet anomaly, airfoil stall, secondary flow separation, and recovery after separation. In addition to good performance inside the training dataset, the trained models yield similar performance as the baseline model outside the training dataset. [ABSTRACT FROM AUTHOR]

The column bending test (CBT) is an experimental method to measure the moment-curvature behavior and failure curvature of high-strain composites (HSCs), designed to apply a relatively constant bending moment through a sample up to very high curvatures. In this paper, we first calculate the errors involved in the simplified geometric analysis used to model the CBT by considering two different effects: variations in the bending moment through the sample and nonlinearities in the material response of the fibers. The first effect, modeled using Euler's elastica, results in a set of design guidelines for the test geometry (i.e., length of the grips and the specimens) in order to achieve moderate errors in moment and curvature predictions. The nonlinearity in the fiber behavior (tension stiffening and compression softening) does not have a strong effect on the curvature but produces significant variations in the bending stiffness of HSCs as well as the maximum strain observed in the fibers due to the shift in the neutral axis. This indicates that accounting for the nonlinear behavior of fibers is necessary in order to accurately predict the failure properties of HSCs under bending. [ABSTRACT FROM AUTHOR]

Skimming the sea at ultralow altitudes can be achieved by repeatedly touching and bouncing off water surface (commonly referred to as skipping), thus giving birth to skippable anti-ship missiles. However, the high impact load during skipping poses a safety hazard to the structure. Whether a missile can rebound primarily depends on the initial kinematic parameters, which also influence the impact load along with the configuration parameters. Therefore, it is important to optimize both of them. Due to the differences in types and complicated coupling of these parameters, the optimization problem becomes highly complex. In this paper, the grouped sequential quadratic programming (GSQP) algorithm is proposed to reduce the nonlinearity and nonconvexity of the problem. By bringing together the global search capability of cooperative co-evolutionary algorithm and the local search strength of GSQP, a combined optimization framework using decomposition strategies is constructed. With the support of fluid-structure interaction models, the optimization is performed to reduce impact load for the missile. The results demonstrate that simultaneously optimizing the configuration and initial kinematic parameters yields a reduction of 12.2% in the impact load on the premise of successful skipping. This research provides a valuable reference for the design of future skippable anti-ship missiles. [ABSTRACT FROM AUTHOR]

Flexible structures show low natural frequencies and light damping and often suffer from various dynamic loads, leading to low-frequency vibrations. Active vibration control is necessary for precision operation and long-term service of such structures, and a flexible actuator with a large actuation force and stroke is a key limitation. In this paper, super-coiled (SC) actuators, fabricated from fibers by twisting and inserting, were introduced to actively suppress the low-frequency vibration of flexible structures. Two SC actuators were symmetrically mounted on both surfaces of a cantilever beam to generate actuation forces. The force model of thermally driven SC actuators was developed, and a dynamic model of a cantilever beam with SC actuators was then proposed and validated via experimental actuation. Three types of control strategies, open loop, velocity negative feedback, and linear quadratic regulator (LQR), were adopted for vibration suppression. The results show that the proposed model fits quite well with the experimental data, and the maximum error between the experiment and the prediction is 3.90% among various driven currents. In velocity negative feedback control and LQR control, a 91.83% reduction of dynamic response is achieved, which validates the feasibility of using the actuator for low-frequency vibration control. [ABSTRACT FROM AUTHOR]

In recent years, fiber-optic shape sensing, which means reconstructing the deformation state of structures from strain information measured by fiber-optic sensors, has attracted interest from many fields. Most of the existing shape-sensing research for flexible three-dimensional (3D) slender structures is based on simple strain-curvature transformation, relying on standardized substrates with a circular cross section and particular forms of fiber layouts. To develop a more general shape-sensing method that can be applied to a wider range of practical engineering conditions, in this paper, the strain-displacement relationship of 3D slender structures under the effect of multiple deformation modes coupling is described by introducing absolute nodal coordinate formulation element, and the deformation reconstruction problem is reorganized into a nonlinear optimization problem that can be applied to large deformation and accommodates different cross-sectional shapes and optical fiber layouts. Due to the rapid increase in the calculation difficulties of nonlinear optimization problems with the number of variables, an element-by-element solving strategy is adopted, and nodal degrees of freedom that have less influence on the overall shape of the structure are merged. In addition, in order to address the issue of local strain anomalies caused by unmodeled factors such as section warping, the pointwise matching between the theoretical strains and measured strains is relaxed to an average matching in subregions to capture the overall deformation, improving the robustness and computational efficiency of the solution process. The accuracy and computational performance of the proposed method are verified through numerical simulation and experiment. [ABSTRACT FROM AUTHOR]