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The study of insect flight is important for conservation and sustainability efforts, as predicting insect dispersal can aid management programmes in tackling economic and ecological harm from, for example, invasive species. Flight mills are invaluable tools for measuring the factors of insect flight under laboratory conditions, as they lower several technical and financial barriers to conduct experiments. It is especially difficult, however, to make assumptions about the energetic cost of tethered flights conducted using different tethers, or even on different flight mills, due to the mechanical variability of the bearing friction and air resistance of the rotating assembly. This additional uncertainty necessitates a larger number of replicates for any given standard of statistical confidence. By characterising flight mill friction, this uncertainty can both be reduced in magnitude and assigned a specific, well‐defined numerical value. We present a simple methodology to characterise this friction through dynamic calibration of the flight mill, at a high statistical confidence. This study uses videography of a flight mill undergoing free velocity decay due to friction, using an in‐house developed software to extract angular velocity from video data. However, the technique is readily adaptable to other measurement techniques. Using the velocity, alongside the mass moment of inertia of the flight mill, allows us to determine the rotational friction coefficient. This friction coefficient provides precise measurements of thrust production, and therefore the energy expenditure of flight, by the tethered insect. [ABSTRACT FROM AUTHOR]

Six patents were secured by E. H. Lanier from 1930 to 1933 for aeroplane designs that were intended to be exceptionally stable. A feature of five of these was a flow-induced "vacuum chamber" which was thought to provide superior stability and increased lift compared to typical wing designs. Initially, this chamber was in the fuselage, but later designs placed it in the wing by replacing a section of the upper skin of the wing with a series of angled slats. We report upon an investigation of the Lanier wing design using inviscid aerodynamic theory and viscous numerical simulations. This took place at the 2005 Australia–New Zealand Mathematics-in-Industry Study Group. The evidence from this investigation does not support the claims but, rather, suggests that any improvement in lift and/or stability seen in the few prototypes that were built was, most probably, due to thicker airfoils than were typical at the time. [ABSTRACT FROM AUTHOR]

Direct numerical simulations of rolling wheels at Reynolds numbers, Re D , based on wheel diameter, D , in the range of Re D ∈ 3.0 × 10 3 − 3.0 × 10 5 corresponding with translation speed of U ∞ ∈ 0.1 − 10 m/s, and varying thickness-to-diameter ratios, R = W / D ∈ 0.040,0.127,0.400 , are conducted with the objective of characterizing the coherent flow structures and their effect on forces. Previous work by A. Javadi (2022)1 for Re D = 3.0 × 10 4 shows non-monotonic variation of drag coefficients with thickness change. Under the flow conditions essayed here, C D steadily decreases as Re D increases. If R = 0.04 the drag coefficient is C D ∈ 0.4 − 1.4 , while it decreases from unity to 0.5 if R = 0.400. The lift force is downward for the wheel with R = 0.040, while it changes its direction if the thickness and/or Re D number increases. The downward lift force for rolling wheel is associated with the Magnus effect. The positive lift force, provided on the rolling wheel with R = 0.127 and 0.400, is associated with strong (positive) peak of the static pressure in the upstream vicinity of the contact point with the ground. The positive lift force also significantly decreases from unity to 0.3 as Reynolds number increases. The effects of the thickness on the flow structures in the wake are revealed, especially in high Re D numbers. On the top of the wheel, there is hairpin vortex at Re D = 3.0 × 10 3 , while the structure becomes unsteady and loses its coherence in higher Reynolds numbers. There is no coherent structures on the top of the thickest wheel because the wheel rotation avoids forming any structure. The large dipole vortex pinch off from the sides of the wheel with R = 0.400 causes large force fluctuations. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to study on the power extraction of the hydrofoil by performance of the coupled oscillating heave and pitch motions. The numerical analysis is conducted by using the Reynolds Average Navier-Stokes (RANS) equations and the realizable k- ɛ turbulent model of the Star-CCM+ software. A 3D oscillating hydrofoil of NACA0015 section with aspect ratio of 7 is selected for the present analysis at two inflow velocities and three frequencies. The numerical results of lift and drag coefficients, horizontal and vertical forces coefficients, power efficiency in time domain and average value of those parameters are presented and discussed. According to the numerical results, the high efficiency of hydrofoil is found at the reduced frequency of 0.18 and the flow velocity of 1 m/s and the low efficiency is obtained at the reduction frequency of 0.06 and the flow velocity of 2 m/s. Moreover, the contour results of vorticity, streamline and pressure distribution are also presented and discussed. The computational model depicts clear vortex patterns surrounding the hydrofoil, which has a major impact on the power performance of oscillating hydrofoil.

A novel bluff-body control concept by using combined windward-and-leeward jets was introduced to modify the wake-vortex patterns and suppress the aerodynamic forces acting on a circular cylinder. Wind tunnel investigations are performed at a subcritical Reynolds number of Re = 3.33 × 104, which is a typical Re level that the wind-induced cable vibrations usually occur. The strength and effectiveness of active control in the present study are characterized with a dimensionless equivalent jet momentum coefficient Cµ. The particle image velocimetry (PIV) technique is employed to measure the wake flow patterns of the baseline and controlled cylinders to reveal the great modifications of active control to the cylinder wake. Except for PIV tests, surface pressure measurements are also conducted to obtain the pressure distribution around the cylinder surface so as to estimate the aerodynamic forces, i.e., drag and lift forces acting on the baseline and controlled cylinders. Pressure measurement results demonstrate clearly the control effectiveness and the wake flow topologies obtained by PIV system help to uncover the mechanism of windward-and-leeward jet control. [ABSTRACT FROM AUTHOR]

Leading edge surface erosion is an emerging issue in wind turbine blade reliability, causing a reduction in power performance, aerodynamic loads imbalance, increased noise emission, and, ultimately, additional maintenance costs, and, if left untreated, it leads to the compromise of the functionality of the blade. In this work, we first propose an empirical spatio-temporal stochastic model for simulating leading edge erosion, to be used in conjunction with aeroelastic simulations, and subsequently present a deep learning model to be trained on simulated data, which aims to monitor leading edge erosion by detecting and classifying the degradation severity. This could help wind farm operators to reduce maintenance costs by planning cleaning and repair activities more efficiently. The main ingredients of the model include a damage process that progresses at random times, across multiple discrete states characterized by a non-homogeneous compound Poisson process, which is used to describe the random and time-dependent degradation of the blade surface, thus implicitly affecting its aerodynamic properties. The model allows for one, or more, zones along the span of the blades to be independently affected by erosion. The proposed model accounts for uncertainties in the local airfoil aerodynamics via parameterization of the lift and drag coefficients’ curves. The proposed model was used to generate a stochastic ensemble of degrading airfoil aerodynamic polars, for use in forward aero-servo-elastic simulations, where we computed the effect of leading edge erosion degradation on the dynamic response of a wind turbine under varying turbulent input inflow conditions. The dynamic response was chosen as a defining output as this relates to the output variable that is most commonly monitored under a structural health monitoring (SHM) regime. In this context, we further proposed an approach for spatio-temporal dependent diagnostics of leading erosion, namely, a deep learning attention-based Transformer, which we modified for classification tasks on slow degradation processes with long sequence multivariate time-series as inputs. We performed multiple sets of numerical experiments, aiming to evaluate the Transformer for diagnostics and assess its limitations. The results revealed Transformers as a potent method for diagnosis of such degradation processes. The attention-based mechanism allows the network to focus on different features at different time intervals for better prediction accuracy, especially for long time-series sequences representing a slow degradation process.

Leading edge surface erosion is an emerging issue in wind turbine blade reliability, causing a reduction in power performance, aerodynamic loads imbalance, increased noise emission, and, ultimately, additional maintenance costs, and, if left untreated, it leads to the compromise of the functionality of the blade. In this work, we first propose an empirical spatio-temporal stochastic model for simulating leading edge erosion, to be used in conjunction with aeroelastic simulations, and subsequently present a deep learning model to be trained on simulated data, which aims to monitor leading edge erosion by detecting and classifying the degradation severity. This could help wind farm operators to reduce maintenance costs by planning cleaning and repair activities more efficiently. The main ingredients of the model include a damage process that progresses at random times, across multiple discrete states characterized by a non-homogeneous compound Poisson process, which is used to describe the random and time-dependent degradation of the blade surface, thus implicitly affecting its aerodynamic properties. The model allows for one, or more, zones along the span of the blades to be independently affected by erosion. The proposed model accounts for uncertainties in the local airfoil aerodynamics via parameterization of the lift and drag coefficients' curves. The proposed model was used to generate a stochastic ensemble of degrading airfoil aerodynamic polars, for use in forward aero-servo-elastic simulations, where we computed the effect of leading edge erosion degradation on the dynamic response of a wind turbine under varying turbulent input inflow conditions. The dynamic response was chosen as a defining output as this relates to the output variable that is most commonly monitored under a structural health monitoring (SHM) regime. In this context, we further proposed an approach for spatio-temporal dependent diagnostics of leading erosion, namely, a deep learning attention-based Transformer, which we modified for classification tasks on slow degradation processes with long sequence multivariate time-series as inputs. We performed multiple sets of numerical experiments, aiming to evaluate the Transformer for diagnostics and assess its limitations. The results revealed Transformers as a potent method for diagnosis of such degradation processes. The attention-based mechanism allows the network to focus on different features at different time intervals for better prediction accuracy, especially for long time-series sequences representing a slow degradation process. [ABSTRACT FROM AUTHOR]