High-Efficient parametric aeroelastic modeling and experimental validation for a morphing aircraft testbed

The folding wing, as one of possible concept for designing morphing aircraft, has been gained special attention. During the in-flight morphing operation (that is, variation in folding angle), the natural frequencies and mode shapes could be dramatically changed, which may induce aeroelastic and aeroservoelastic instabilities. The paper presents a novel parameterized aeroservoelastic modeling approach for a folding wing with a variation in the folding angle. The most attractive feature of the methodology is that the mathematical aeroservoelastic model of the folding wing with different aerodynamic configurations can be efficiently obtained via the proposed parameterized modeling method. Based on the present parameterized aeroservoelastic model, the control law design and closed-loop aeroservoelastic analysis can be quickly implemented without the need of repeated finite element modeling for each specific folding angle. To demonstrate the accuracy of the present parameterized modeling method in predicting the aeroservoelastic behaviors, the frequency responses at different folding angles are compared with the direct results computed via the commercial software. As the second task of this study, the closed-loop stability analysis of active flutter suppression for the morphing wing is also investigated via the present parameterized aeroservoelastic model. The numerical simulations show that the aeroservoelastic behaviors of the morphing wing can be investigated efficiently via the present parameterized modeling method.

The purpose of this paper is to present linear parameter varying modeling and simulation of a folding-wing morphing aircraft in the wing-folding process. Morphing aircraft can alter the shape to adapt to mission environment changing. When the wing shape is varying, the dynamic responses of the morphing aircraft will be dependent on the time-varying aerodynamic forces and moments, which will be a function of the wing shape change by the morphing command. The folding-wing morphing aircraft is regarded as a variable geometry rigid body, and the six-degree-of-freedom nonlinear dynamic model in the wing-folding process is founded. The decoupled longitudinal dynamic equation of the morphing process is deduced by simplification, and the longitudinal dynamic responses of the wing morphing process are numerically simulated by the quasi-steady aerodynamic assumption. Furthermore, using the Jacobian linearization approach, the longitudinal nonlinear dynamics equations of morphing aircraft are linearized, and the longitudinal linear parameter varying model of the morphing aircraft in the wing-folding process is obtained. Finally, the linear parameter varying model is validated by the open-loop time responses compared to the nonlinear aircraft responses. The research results can be as great reference for the linear parameter varying controller design for the morphing aircraft in the wing shape varying.

The physical model of a variable-span variable-sweep morphing aircraft was simplified. Based on Kane's method,the morphing movements were assumed to be controllable known input,and the wings' movements were expressed by constraint equations. The six components of the translational velocity and the angular velocity of the morphing aircraft's body in the body coordinate frame were selected as the generalized speeds. Six degree-of-freedom dynamic model was built. Additional forces and additional moments were defined to describe the dynamic influence imposed on the morphing aircraft by the morphing movement. The simulation results show that,in the condition of steady-state flight,the additional forces and moments cased by the wings' morphing are small compared to the variations of aerodynamic forces. The aircraft's longitudinal dynamic responses during the morphing process are simulated and analyzed at different morphing speeds. Results show that the aircraft 's height,velocity and pitch angle will change considerably during morphing process.

Morphing aircraft can change their shapes in order to acquire enhanced flight performance. In the transition process of shape change, the aerodynamics and dynamics of aircraft would change dramatically, and the aircraft is a time-varying and highly nonlinear system. This article presents a tensor product (TP) modeling method and a TP model-based control design method for a kind of morphing aircraft in transition process. First, a new linear parameter varying (LPV) modeling method is proposed to model the longitudinal dynamics of a kind of morphing aircraft undergoing large shape change. This LPV modeling method does not need trim map and numerical calculation. Then, the LPV model is transformed into a TP type polytopic model. Furthermore, the TP model is augmented by a slow reference dynamics of flight speed. Based on the augmented TP model, a TP model-based LPV controller is designed by using parallel-distributed-compensation (PDC) control techniques and regional pole placement techniques. Finally, simulations are performed on a variable-sweep morphing aircraft undergoing large-scale changes in both shape and flight speed, and the results demonstrate the effectiveness of the proposed methodology.

he JWC is represented by a highly detailed, stress-level finite element model, as shown in Figure 1. The JWC structural model considered here is for the 100% (full) fuel configuration. There are three models used for the aeroelastic analyses. The Doublet Lattice Aeroelastic (DLA) model (Figure 2) couples with the finite element model directly for trim analysis, and uses its modal basis representation for gust analysis. The Low Definition Geometry Aeroelastic (LDG) model (Figure 3) performs both trim and gust analyses using the modal basis, as does the High Definition Geometry Aeroelastic (HDG) model (Figures 4 and 5). The HDG model is an outer moldline of the structural model, and uses triangular panels to account for control surfaces not aligned with the streamwise axis (Figure 5). The input required to generate the HDG model (95%) can also be coupled with automatic grid generation (5%) input to generate a skewed-Cartesian grid for trimmed, unsteady Euler aeroelastic analysis. All contours for Pressure Coefficient, Cp and deflection, dZ are to the same scale for comparison purposes (Figures 6 to 23). Similarly, all gust load monitor plots on the same page are to the same scale (Figures 24-38). All steady state symmetric trim and discrete gust results presented (Figures 6-35) are for critical gust flight condition (M=0.255, altitude=0 feet, gust velocity=62.4 fps). This critical flight condition for the 100% fuel configuration was identified by all 3 aeroelastic models (e.g. Figures 36-38), but the HDG aeroelastic model produced distinctly different force magnitudes and distributions. Figures 6-23 present trim results for 1g level flight (6-11), a 2g pullup (12-17) and a 1g pushover (18-23). The top figure on each page is a trim result using the HDG model by solving a 2-dof determined trim system, while the middle figure presents results for the LDG model. The bottom figure on each page is a trim result using the HDG model and solving an indeterminate 2-dof trim system by optimizing independent control surface psotitions to minimize wing root bending. All pages of Figures show nearly identical results for the 2-dof determined trim system (top vs. middle), while pressure distribution and wing deflection which are less severe and more evenly dispersed (bottom) when all the forward and aft wing control surfaces are utilized independently. This results in a much less stressed configuration, especially for the 1g level flight (Figures 6-11), which is the initial stress state prior to gust analysis.

Chiral mechanical metamaterial has become topical in energy absorption for the unusual compression-to-twist deformation determined by the asymmetric chirality geometry of their microstructures. However, the insufficient research on the dynamic crash behavior with high nonlinearity and the lack of efficient design and modeling method have blocked the development of chiral mechanical metamaterials considering crashworthiness. In this work, the structure-property relationship of chiral mechanical metamaterials under medium-strain-rate impact was systematically investigated. We proposed an aperiodic framework considering additive manufacturing which enabled both the ordered and disordered chiral mechanical metamaterials utilizing a screw-theory-based assembly rule. The strut components had variable cross-sections and the joints were fillet-enhanced in order to improve the compressive performance. Meanwhile, the corresponding efficient modeling method for finite element analysis (FEA) was introduced. The constitutive relation, damage criteria, and damage revolution of the 316L stainless steel were established considering strain rate effect. Finally, the deformation mechanisms and energy absorbing capabilities of the proposed chiral mechanical metamaterials were demonstrated and discussed based on the mechanical behaviors and the proposed chirality indexes. The numerical results indicated that the proposed design and modeling method provided an effective paradigm for the crashworthiness design of light-weight materials and structures, facilitating the further applications in fields of automobile, civil engineering, aerospace, etc.

Standard, nonparametric statistical methods estimate only the impact of therapy on survival rate up to a selected follow-up interval. In contrast, parametric methods can estimate the impact of treatment on the two cardinal parameters of malignancy: likelihood of cure and recurrence free survival time among uncured patients. The authors screened a total of six parametric survival models. Three of these, including the log normal model, were applied to survival data from five clinical trials of adjuvant therapy for Stage II breast cancer. For comparison, the log rank test, a standard nonparametric method, was also applied to the same data. Both parametric and nonparametric methods identified a significant therapeutic in three of the five trials. In only one of these three trials, however, did parametric analysis identify a significant difference in the likelihood of cure between treatment groups. In the remaining two trials, a significant difference was found in recurrence free survival time among uncured patients. The three parametric survival models gave similar results. These findings suggest that parametric analysis may warrant further study as a method for measuring the long term clinical impact of adjuvant therapy on Stage II breast cancer.

The morphing aircraft can obtain the best aerodynamic characteristics by changing its shape in order to adapt to the current mission and environment. In this paper, a non-affine parameter-dependent linear variable parameter (LPV) system model of a morphing aircraft whose wingspan and sweep angle can be changed is used as the object. First, the linear fractional form (LFR) is used to design an integral sliding mode that converges in a finite time. Then, the fuzzy switching gain adjustment method is introduced to eliminate the interference items and uncertain items in the nonlinear system. The simulation results show that the method can ensure the stability of the variant process in the presence of large disturbances and uncertainties. This controller design method has universal adaptability to LPV system.

Modeling and switching adaptive control for nonlinear morphing aircraft considering actuator dynamics

In order to study the galloping mechanism of ice-covered four-split transmission lines in oblique flows, the aerodynamic forces and aero-elastic response characteristics of the crescent-shaped four-split ice-covered transmission lines are investigated through wind tunnel tests on rigid and aero-elastic models. According to Den Hartog and Nigel’s galloping theories, the damping coefficients are calculated based on the experimental data. The results show that the crescent-shaped ice-covered four-split transmission lines usually suffer from torsional galloping. Furthermore, based on the aero-elastic wind tunnel data, the galloping is characterized by an elliptical trajectory, negative damping ratio, and a negative strain at hanging position. In addition, the galloping appears to be more prone to occur under oblique flows, with a larger galloping amplitude and a lower critical wind speed. This might be because an out-of-plane vibration of the third-order mode is excited at a lower wind speed, leading to a coupled resonance between in-plane and out-of-plane vibrations at the third-order mode with a frequency ratio of 1:1. The experimental results in this paper can also be used to verify the fluid-structure interaction simulation method of ice-covered transmission lines.

Sleeve is one of important components of garment. This paper presents a 3D sleeve modelling method. It is a parametric design based on hybrid dimension. There are two ways to provide parameters for sleeve parametric modelling. One is starting from 2D by recognizing sizes of sleeve pattern. The other is from 3D by recognizing sizes of a human body. The sleeve model is made up of parametric curve network with skeleton curves. Skeleton curves include mesh curve, center line, feature curve and contour curve. With the given parameters, all curves are generated using parametric method. Sleeve modification is done by editing skeleton curves. When editing skeleton curves in hybrid dimension, maintaining constraints among curves is a difficult task. Constraints are extracted and maintained using the technology of constraint solution and encoding/decoding. A sleeve surface and a sleeve mesh are created by interpolating the skeleton curves, which split a sleeve surface into three-side and four-side patches. After merging sleeve mesh patches, a genetic algorithm is utilized to improve surface developability of the sleeve surface. Examples show that this method of sleeve modelling is robust and powerful.

ملخص:- تحتوي نماذج الانحدار على مکونين أحدهما مکون عشوائي يمثل حد الخطأ (Error Term) والأخر مکون غير عشوائي يمثل متغير الاستجابة(Response Variable) وهذا المکون هو محل الاهتمام فيتم تقديره بإحدى الطريقتين هما: الطريقة المعلمية أو الطريقة اللامعلمية أو کلاهما. نموذج الانحدار المعلمي (Parametric Regression Model) هو النموذج الأکثر شيوعا حيث أنه يفترض أن متغير الاستجابة له صيغه دالية محدده مسبقا والتي تم الحصول عليها من معلومات سابقه حول هيکل دالة الانحدار من خلال ذلک يتم تقدير فئة من المعالم. ورغم أن النماذج المعلمية تعد أحد أهم أدوات تحليل البيانات؛ لأنها فعاله ويمکن تفسيرها بسهوله، إلا انها لم تستطع ان تکون کافية في بعض الحالات. أما نموذج الانحدار اللامعلمي (Nonparametric Regression Model) فانه يعتمد على تقدير دالة الانحدار مباشرة من البيانات کما أنه لا يفترض فروضا معينه لتقدير معالم النموذج مثل التي يفترضها النموذج المعلمي وبالتالي فهو يتمتع بمرونة أکثر لاکتشاف البيانات التي قد تکون مفقودة او في الحالات التي لا تتوافر فيها معلومات سابقة. يتم انشاء نموذج الانحدار شبه المعلمي (Semi parametric Regression Model) من خلال مزج الطريقتين السابقتين معا. فغالبا ما يتم استخدام هذا النموذج في المواقف التي تکون فيها الافتراضات المعلمية غير محددة وغير متسقة، أو أن النموذج اللامعلمي لا يعمل بشکل کامل. يشتمل هذا البحث على تطبيق نماذج الانحدار المعلمية وشبه المعلمية بالإضافة إلى النموذج اللامعلمى کحالة خاصة من نموذج الانحدار شبه المعلمى لتقدير الدالة الافتراضية وتحديد شکل العلاقة بين المتغير التابع والمتغيرات المستقلة بالاضافة الى شکل انتشار البيانات مع خط (أو منحني) التقدير وذلک على بيانات واقعية تم الحصول عليها من الإتحاد المصري لألعاب القوى والمعروفة بـبيانات لعبة العشاري . Regression models contain two components, one of which is a random component that represents the Error Term and the other is a non-random component that represents the response variable and this component is of interest and is estimated in one of two methods: the parametric method or the nonparametric method or both. Parametric regression model is the most common model since it assumes that the response variable has a predetermined functional formula which was obtained from previous information about the structure of the regression function by which a class of parameters is estimated. Although parameter models are one of the most important tools for analyzing data; Because it is effective and can be explained easily, but it could not be sufficient in some cases. As for the nonparametric regression model, it relies on estimating the regression function directly from the data, as it does not assume specific assumptions for estimating the model parameters, such as those assumed by the parametric model, and thus it has more flexibility to discover data that may be missing or in cases where previous information is not available. The semi-parametric regression model is constructed by blending the two previous methods together. This model is often used in situations where the parametric assumptions are indeterminate and inconsistent, or the nonparametric model is not fully functional. This research includes the application of parametric and semi-parametric regression models in addition to the nonparametric model as a special case of the semi-parametric regression model for estimating the hypothetical function and determining the form of the relationship between the dependent variable and the independent variables in addition to the data spread form with a line (or curve) estimation on realistic data On it from the Egyptian Athletics Federation, known as Decathlon data.

This article studies the application of nonparametric modeling approach to model magnetorheological (MR) dampers. For comparison purposes, another typical parametric modeling method for electrorheological (ER) and MR dampers is reviewed. The existing parametric MR damper model includes a stiff Bouc-Wen model that is not friendly for simulation study and real time implementation of model-based advanced control algorithms. In order to avoid the difficulties by using the existing parametric model, the test data from a commercialized MR damper is employed to develop nonparametric models, which can consist of a series of numerically efficient mathematic functions. In addition, the selected functions are required to be continuous and differentiable for potential model-based control algorithms. The results of the nonparametric models show that such different models are comparable. Furthermore, one nonparametric model is selected to be compared with a parametric model and the test data to illustrate the accuracy of the model. The comparison shows that the proposed nonparametric models are able to accurately predict the damper force characteristics, damper bilinear behavior, hysteresis, and electromagnetic saturation. It is further shown that the nonparametric models can be numerically solved with an integration step size of the order of 10 2 s, much faster than the parametric models of the order of 10 5 s, which clearly shows that the proposed nonparametric models are feasible even for real time model-based control algorithms.

Forecasting of sea-state characteristics has a great importance in coastal and ocean engineering studies. Therefore, the purpose of this study was to investigate performances of Adaptive-Network-Based Fuzzy Inference System (ANFIS) and several parametric methods in the Black Sea. For this purpose, different fuzzy models with different input combinations were developed for two different wind data sources (TSMS and ECMWF) at two offshore buoy stations. It also aimed to apply several approaches to event-based data sets for wave predictions. Generally, in literature the tendency is to use time series data for wave predictions. In this kind of prediction approach, lagged time series data are taken as inputs and current or future variables are taken as output. In this study, event-based data for each independent storm were extracted from time series data. Simultaneous or concurrent data of wind speed, blowing duration, fetch length and wave characteristics were detected for each single storm. These event data were then used to set up models. The hindcast results were validated with significant wave height and mean wave period data recorded in Hopa and Sinop buoy stations. The performance of developed fuzzy models were also compared with that of four different parametric methods (Wilson, SPM, Jonswap, and CEM methods) applied for two wind data sources at both buoy stations. Finally, it was determined that in the prediction of both wave parameters (Hs and Tz) the ANFIS models (R = 0.66, squared correlation coefficient, and MAE = 0.37 m, mean absolute error, for the best model in prediction of Hs) were more accurate than the parametric methods (R = 0.63 and MAE = 0.75 m for the best model in prediction of Hs).

A parametric 3D geological modeling method considering stratigraphic interface topology optimization and coding expert knowledge