Search Results (104)

This study investigates the degradation of aircraft paint and its impact on aerodynamic performance, using the PZL M-20 “Mewa” aircraft as a case study. Paint samples were collected from both damaged and intact areas of the airframe and analyzed using electron paramagnetic resonance (EPR) spectroscopy, scanning electron microscopy (SEM), and aerodynamic testing. One of the major challenges addressed in this work was the non-destructive identification of chemical aging effects in operational paint coatings and their correlation with aerodynamic behavior. The application of EPR spectroscopy in conjunction with real-world aerodynamic testing on naturally degraded surfaces represents an innovative approach that offers both scientific insight and practical guidance for maintenance practices. The results indicate significant deterioration in aerodynamic characteristics—such as increased drag and reduced lift—due to coating damage, particularly around riveted and bolted joints. EPR spectra revealed a notable increase in the density of unpaired electron spins in aged coatings, confirming ongoing oxidative degradation processes. While this study was limited to a single aircraft, the findings highlight the critical importance of regular inspection and maintenance of paint coatings to ensure flight safety and operational efficiency. Full article

This study investigates the connection between coherent structures in the flow around a vehicle and the aerodynamic forces acting on its body. Dynamic Mode Decomposition (DMD) was applied to analyze the flow field of a squareback Ahmed body at $R{e}_H=7.7\times {10}^5$. DMD enabled the identification of coherent structures in the near and far wake by isolating their individual oscillation frequencies and spatial energy distributions. These structures were classified into three regimes based on their underlying mechanisms: symmetry breaking, bubble pumping, and large-scale vortex shedding in range of $St\le 0.2$. The energy contributions of these flow regimes were quantified across different regions of the flow field and compared to the aerodynamic forces on the body. Additionally, the linear correlation between pressure and velocity components was examined using Pearson correlation coefficients of DMD spectral amplitudes. The locations of maximum and minimum correlation values, as well as their relationship to energy contributions, were identified and analyzed in detail. Full article

To study the interference effects of the fuselage/rear-mounted propeller on the aerodynamic and aeroacoustic characteristics at a forward speed of Ma = 0.323, a multi-component flowfield simulation and an aeroacoustic prediction method were employed. Firstly, hybrid grids were adopted in the embedded grid system, and a new boundary identification method was developed to address the overlap problem by adjusting the grid boundary based on entities. The simulations were based on the URANS and FW-H equations. The employed numerical analysis methods were validated through comparisons with experimental data. Then, the aerodynamic and aeroacoustic characteristics of the propeller were analyzed, and the interference of the fuselage with the propeller was discussed in detail. Key findings included the following. Under fuselage interference, the sound pressure level (SPL) of the propeller at those observers near the forward flight direction increased dramatically, by more than 10 dB, especially in the range of two to six times the fundamental frequency. A downward vertical velocity reduced the SPLs beneath the fuselage, while an upward one had the opposite effect. The flat/vertical tails’ deceleration effect caused a thrust surge in the propeller, with most magnitudes around 20%. At different forward speeds, the thrust surge and SPL changes were similar. Full article

The dynamic mode decomposition serves as a useful tool for the coherent structure extraction of the complex flow fields with characteristic frequency identification, but the phase information of the flow modes is paid less attention to. In this study, phase information around the locally flexible membrane airfoil is quantitatively studied using dynamic mode decomposition (DMD) to unveil the physical mechanism of the lift improvement of the membrane airfoil. The flow over the airfoil at a low Reynolds number (Re = 5500) is computed parametrically across a range of angles of attack (AOA = 4°–14°) and membrane lengths (LM = 0.55c–0.70c) using a verified fluid–structure coupling framework. The lift enhancement is analyzed by the dynamic coherent patterns of the membrane airfoil flow fields, which are quantified by the DMD modal phase propagation. A downstream propagation pressure speed (DPP) on the upper surface is defined to quantify the propagation speed of the lagged maximal pressure in the flow separation zone. It is found that a faster DPP speed can induce more vortices. The correlation coefficient between the DPP speed and lift enhancement is above 0.85 at most cases, indicating the significant contribution of vortex evolution to aerodynamic performance. The DPP speed greatly impacts the retention time of dominant vortices on the upper surface, resulting in the lift enhancement. Full article

A new method to discover open-loop, unstable, longitudinal aerodynamic parameters, using a ‘two-stage optimization approach’ for designing optimal inputs, and with an application on the fighter aircraft platform, has been presented. System identification of supersonic aircraft requires formulating optimal inputs due to the extremely limited maneuver time, high angles of attack, restricted flight conditions, and the demand for an enhanced computational effect. A pre-requisite of the parametric model identification is to have a priori aerodynamic parameter estimates, which were acquired using linear regression and Least Squares (LS) estimation, based upon simulated time histories of outputs from heuristic inputs, using an F-16 Flight Dynamic Model (FDM). In the ‘first stage’, discrete-time pseudo-random binary signal (PRBS) inputs were optimized using a minimization algorithm, in accordance with aircraft spectral features and aerodynamic constraints. In the ‘second stage’, an innovative concept of integrating the Fisher Informative Matrix with cost function based upon D-optimality criteria and Crest Factor has been utilized to further optimize the PRBS parameters, such as its frequency, amplitude, order, and periodicity. This unique optimum design also solves the problem of non-convexity, model over-parameterization, and misspecification; these are usually caused by the use of traditional heuristic (doublets and multistep) optimal inputs. After completing the optimal input framework, parameter estimation was performed using Maximum Likelihood Estimation. A performance comparison of four different PRBS inputs was made as part of our investigations. The model performance was validated by using statistical metrics, namely the following: residual analysis, standard errors, t statistics, fit error, and coefficient of determination $({\mathrm{R}}^2)$. Results have shown promising model predictions, with an accuracy of more than 95%, by using a Single Sequence Band-limited PRBS optimum input. This research concludes that, for the identification of the decoupled longitudinal Linear Time Invariant (LTI) aerodynamic model of supersonic aircraft, optimum PRBS shows better results than the traditional frequency sweeps, such as multi-sine, doublets, square waves, and impulse inputs. This work also provides the ability to corroborate control and stability derivatives obtained from Computational Fluid Dynamics (CFD) and wind tunnel testing. This further refines control law design, dynamic analysis, flying qualities assessments, accident investigations, and the subsequent design of an effective ground-based training simulator. Full article

The aim of the present work is the improvement of a free-wake model for the analysis of a small-scale two-bladed propeller in hover. The simulations are carried out using a BEM approach implemented in the medium-fidelity solver RAMSYS. An acoustic validation is also performed using the developed tool ACO-FWH. The work proves that even mild discrepancies in the propeller geometry must be accounted for as their influence is not negligible, especially on the aeroacoustics of the propeller. In particular, the proper modeling of the blades enables the correct identification of the sub-harmonics of the SPL spectra. An optimization procedure based on the application of the evolutionary Genetic Algorithm is followed to identify the values of the parameters describing the dissipative and diffusive properties in the Bhagwat–Leishman vortex core model, an upgraded version of the classical Lamb–Oseen one. On average, this approach enabled the further improvement of the accuracy of the numerical model in terms of acoustic signature evaluation with respect to the one obtained by only modeling blade dissimilarities. The results obtained demonstrate the promising capabilities of a fine-tuned free-wake medium-fidelity approach to simulate the aerodynamic and acoustic details of a small-scale propeller in hover, provided the accurate geometrical modeling of the propeller and the selection of suitable parameters to be used in the wake modeling. Full article

Aerodynamics is a key factor in time-trial cycling. Over the years, various aspects have been investigated, including positioning, clothing, bicycle design, and helmet shape. The present study focuses on the development of a methodology for the aerodynamic optimization of a time-trial helmet through the implementation of a reduced-order model, alongside advanced simulation techniques, such as computational fluid dynamics, radial basis functions, mesh morphing, and response surface methodology. The implementation of a reduced-order model enhances the understanding of aerodynamic interactions compared to traditional optimization workflows reported in sports-related research, facilitating the identification of an optimal helmet shape during the design phase. The study offers practical insights for refining helmet design. Starting with a baseline teardrop profile, several morphing configurations are systematically tested, resulting in a 10% reduction in the drag force acting on the helmet. The reduced-order model also facilitates the analysis of turbulent flow patterns on the cyclist’s body, providing a detailed understanding of aerodynamic interactions. By leveraging reduced-order models and advanced simulation techniques, this study contributes to ongoing efforts to reduce the aerodynamic resistance of time-trial helmets, ultimately supporting the goal of improved athlete performance. Full article

An enhanced understanding of the mechanisms responsible for wind turbine blade leading-edge erosion (LEE) and advancing technology readiness level (TRL) solutions for monitoring its environmental drivers, reducing LEE, detecting LEE evolution, and mitigating its impact on power production are a high priority for all wind farm owners/operators and wind turbine manufacturers. Identifying and implementing solutions has the potential to continue historical trends toward lower Levelized Cost of Energy (LCoE) from wind turbines by reducing both energy yield losses and operations and maintenance costs associated with LEE. Here, we present results from the first Phenomena Identification and Ranking Tables (PIRT) assessment for wind turbine blade LEE. We document the LEE-relevant phenomena/processes that are deemed by this expert judgment assessment tool to be the highest priorities for research investment within four themes: atmospheric drivers, damage detection and quantification, material response, and aerodynamic implications. The highest priority issues, in terms of importance to LEE but where expert judgment indicates that there is a lack of fundamental knowledge, and/or implementation in measurement, and modeling is incomplete include the accurate quantification of hydrometeor size distribution (HSD) and phase, the translation of water impingement to material loss/stress, the representation of operating conditions within rain erosion testers, the quantification of damage and surface roughness progression through time, and the aerodynamic losses as a function of damage morphology. We discuss and summarize examples of research endeavors that are currently being undertaken and/or could be initiated to reduce uncertainty in the identified high-priority research areas and thus enhance the TRLs of solutions to mitigate/reduce LEE. Full article

It is meaningful to develop an accurate model to predict the dynamical motion of an unmanned sailboat. Considering cost and convenience, this work proposes a parameter identification method based on the combination of experiments and numerical analysis. Firstly, a free-running trial is carried out by utilizing the propellers on the studied sailboat to acquire real navigation information. Secondly, particle swarm optimization (PSO), which is highly efficient and easily implemented, is designed to acquire the hydrodynamic parameters of the sailboat’s hull. At the same time, the aerodynamic parameters of the sail are acquired by computational fluid dynamics (CFD) simulation. Finally, a three degree-of-freedom (DOF) model is established, the effectiveness of which is verified through comparisons between sea trials and simulation. The results prove that this parameter identification method has the desired accuracy and reliability. Full article

In this paper, nonlinear system identification using Bayesian network has been implemented to discover open-loop lateral-directional aerodynamic model parameters of an agile aircraft using a grey box modelling structure. Our novel technique has been demonstrated on simulated flight data from an F-16 nonlinear simulation of its Flight Dynamic Model (FDM). A mathematical model has been obtained using time series analysis of a Box–Jenkins (BJ) model structure, and parameter refinement has been performed using Bayesian mechanics. The aircraft nonlinear Flight Dynamic Model is adequately excited with doublet inputs, as per the dictates of its natural frequency, in accordance with non-parametric modelling (Finite Impulse Response) estimates. Time histories of optimized doublet inputs in the form of aileron and rudder deflections, and outputs in the form of roll and yaw rates are recorded. Dataset is pre-processed by implementing de-trending, smoothing, and filtering techniques. Blend of System Identification time-domain grey box modelling structures to include Output Error (OE) and Box–Jenkins (BJ) Models are stage-wise implemented in multiple flight conditions under varied stochastic models. Furthermore, a reduced order parsimonious model is obtained using Akaike information Criteria (AIC). Parameter error minimization activity is conducted using the Levenberg–Marquardt (L-M) Algorithm, and parameter refinement is performed using the Bayesian Algorithm due to its natural connection with grey box modelling. Comparative analysis of different nonlinear estimators is performed to obtain best estimates for the lateral–directional aerodynamic model of supersonic aircraft. Model Quality Assessment is conducted through statistical techniques namely: Residual Analysis, Best Fit Percentage, Fit Percentage Error, Mean Squared Error, and Model order. Results have shown promising one-step model predictions with an accuracy of 96.25%. Being a sequel to our previous research work for postulating longitudinal aerodynamic model of supersonic aircraft, this work completes the overall aerodynamic model, further leading towards insight to its flight control laws and subsequent simulator design. Full article

Acquiring knowledge of aircraft flight dynamics is crucial for simulation, control, mission performance and safety assurance analysis. In the fast-paced UAV market, long flight testing campaigns are hard to achieve, leaving limited controlled flight data and a significant amount of unplanned flight data. This work delves into the application of system identification techniques on unplanned flight data when faced with a shortage of dedicated flight test data. Based on a medium-sized, fixed-wing UAV, it focuses on the system identification of longitudinal dynamics using structural routine flight test data of pitch down and pitch up manoeuvres with no specific guidelines for the control inputs given. The proposed solution uses first- and second-order parameter-based models to build a non-linear dynamic model which, using a least square error optimisation algorithm in a time domain formulation, has its parameters tuned to converge the model behaviour with the real aircraft dynamics. The optimisation uses a combination of pitch, altitude, airspeed and pitch rate responses as a measure of model accuracy. Very significant improvements regarding the UAV model response are found when trimmed flight manoeuvres are used, resulting in proper estimation of important aerodynamic and control derivatives. Pitching moment and control derivatives are shown to be the crucial parameters. However, difficulties in estimation are shown for untrimmed flight manoeuvres. Better results were obtained when using multiple manoeuvres simultaneously in the optimisation error metric, as opposed to single manoeuvres that led to system bias. The proposed system identification procedure can be applied to any fixed-wing UAV without the need for specific flight testing campaigns. Full article

Traditional tower galloping theory is founded on the quasi-steady assumption, which has inherent limitations. By treating tower galloping as a single-degree-of-freedom crosswind bending flutter problem and introducing flutter derivatives into the expression of the crosswind aerodynamic force acting on the tower, the unsteady effects induced by motion can be incorporated into the analysis of tower galloping. An actual chamfered square cross-section tower was used as the research subject, and static tests and flutter derivative identification tests were performed on tower segment models without any modifications and with two types of aerodynamic measures: added arc-shaped fairings and vertical fin plates. Predictions of the aerodynamic damping of the tower structure were made and compared based on two different galloping theories: one under the quasi-steady assumption and the other considering unsteady effects. Experimental results indicate that both theories lead to the same conclusion about the galloping stability of the chamfered square tower. The original cross-section tower exhibited significant galloping instability problems, but the addition of arc-shaped fairings or vertical fin plates effectively improved its galloping stability performance. The predicted results of the tower’s aerodynamic damping based on the two different galloping theories differed by at most 34% at dimensionless wind speeds below 25. However, some differences were observed, and these differences between the two theories were noticeably affected by the magnitude of the dimensionless wind speed. Full article

This article investigates the design of time-accurate input signals in the angle-of-attack and pitch rate space to identify the aerodynamic characteristics of a generic triple-delta wing configuration at subsonic speeds. Regression models were created from the time history of signal simulations in DoD HPCMP CREATE^TM-AV/Kestrel software. The input signals included chirp, Schroeder, pseudorandom binary sequence (PRBS), random, and sinusoidal signals. Although similar in structure, the coefficients of these regression models were estimated based on the specific input signals. The signals covered a wide range of angle-of-attack and pitch rate space, resulting in varying regression coefficients for each signal. After creating and validating the models, they were used to predict static aerodynamic data at a wide range of angles of attack but with zero pitch rate. Next, slope coefficients and dynamic derivatives in the pitch direction were estimated from each signal. These predictions were compared with each other as well as with the ONERA wind tunnel data and some CFD calculations from the DLR TAU code provided by the NATO Science and Technology Organization research task group AVT-351. Subsequently, the models were used to predict different pitch oscillations at various mean angles of attack with given amplitudes and frequencies. Again, the model predictions were compared with wind tunnel data. Final predictions involved responses to new signals from different models. A feed-forward neural network was then used to model pressure coefficients on the upper surface of the vehicle at different spanwise sections for each signal and the validated models were used to predict pressure data at different angles of attack. Overall, the models predict similar integrated forces and moments, with the main discrepancies appearing at higher angles of attack. All models failed to predict the stall behavior observed in the measurements and CFD data. Regarding the pressure data, the PRBS signal provided the best accuracy among all the models. Full article

Air pollution is recognized by the World Health Organization as the major environmental threat; therefore, air quality is constantly being monitored by monitoring stations. However, the most common atmospheric pollutants being monitored do not include pollen. Among the reasons for the lack of pollen control is that there are different types and sizes of pollen. The largest particles commonly being monitored by air monitoring stations have a maximum aerodynamic diameter of 10 microns, while the aerodynamic diameter of most pollen grains is known to range from 10 to 100 microns. For this reason, most pollen is not being detected by air monitoring stations. For the patents found in a literature review, monitoring pollen concentration in the air requires the discrimination of pollen grains from particulate matter of a similar size, as well as the identification of the type of pollen grains detected, since different pollen types may produce different effects, such as allergic reactions, asthma, and lung cancer, in exposed people. In this work, 15 patent documents regarding pollen monitoring were identified and reviewed using three search engines: Google Patents, WIPO’s PatentScope, and the United States Patent and Trademark Office (USPTO) database. The extracted data from the patents included whether they differentiate pollen type, pollen size, and sensor type and whether they provide real-time data. The results show that 93.33% of the patents identify pollen type, while 80% of the patents identify pollen size. Most of the patents use light-scattering and image sensors and use image processing techniques to analyze particles. Furthermore, 40% of the patents were found to implement artificial intelligence. Further, it was found that only nine patents provide real-time data, which is an important feature of an air monitoring system. Full article

This study explores the design and development of a human-powered aircraft (HPA), leveraging modern engineering techniques, materials science, and advanced CAD/CAM tools. The project addresses key aspects of aircraft design, including the geometry of wings and tail, control and power transmission mechanisms, propeller selection, and material identification to achieve ultra-lightweight construction. The 3DExperience platform facilitated comprehensive model creation, simulation, and production process development, while XFLR5 was employed for aerodynamic profile analysis using the vortex lattice and panel methods. JavaProp aided in evaluating propeller thrust and power requirements. Computational fluid dynamics (CFD) simulations using the SST k-ω turbulence model provided critical insights into flow behavior. The design was found to be theoretically capable of flight, although challenges arose in selecting appropriate software for aerodynamic analysis, leading to the use of XFLR5 for early-stage design and the more advanced 3DExperience platform for final evaluations. Although structural strength analyses were not performed due to the complexity of composite materials, future work in this area could enhance the precision of component selection and aircraft mass estimation. Full article