Flow simulation of the flight manoeuvres of a large transport aircraft with load alleviation

A new method for predicting manoeuvre loads on a large transport aircraft with a swept-back wing and a load alleviation system based on control surface deflections is developed. For this purpose, three-dimensional Reynolds-averaged Navier–Stokes (RANS) simulations of the rigid wing–fuselage configuration are performed while the aerodynamics of the tailplane are estimated by means of handbook methods. For a closer analysis, different quasi-steady pitching manoeuvres are chosen based on the CS-25 regulations. One of these manoeuvres is also simulated with active load alleviation, leading to a reduction in the wing-root bending moment by more than 40%. Besides demonstrating the potential of the considered load alleviation system, it is shown which manoeuvres are especially critical in this context and which secondary effects come along with load alleviation.

Estimation of Aircraft-Dependent Bumpiness Severity in Turbulent Flight

Atmospheric turbulence threatens flight safety of civil aviation aircraft by inducing aircraft bumpiness. A severity estimation method of aircraft bumpiness in turbulent flight is explored according to in-situ Eddy Dissipation Rate (EDR) indicator. With the turbulence intensity derived from EDR value, a time series of longitudinal and vertical turbulence was generated according to von Karman turbulence model. In order to obtain the vertical acceleration response of aircraft, the continuous change of aerodynamic force on the assembly of wing and horizontal tail was computed by Unsteady Vortex Lattice Method (UVLM). The computing accuracy was improved by using semi-circle division and assigning the vortex rings on the mean camber surface. Furthermore, the adverse effects of control surface deflections on bumpiness severity estimation can be effectively removed by separating turbulence-induced and aircraft maneuvers-induced aerodynamic force change. After that, the variance of vertical acceleration, as the severity indicator of aircraft bumpiness, was obtained by Welch spectrum estimation. With the refined grid level, the pitching moment change due to control surface deflections can be solved accurately by UVLM. The instantaneous acceleration change obtained by UVLM approximates recorded acceleration data with better accuracy than linear transfer function model. A further test with a set of flight data on the same airway shows that compared with in-situ EDR indicator, the proposed method gives an aircraft-dependent estimation of bumpiness severity, which can not only be used to estimate in-situ bumpiness but also be applied to forecast the bumpiness severity of other different aircrafts.

Estimation of Flexible Pavement Structural Capacity Using Machine Learning Techniques

The most common index for representing structural condition of the pavement is the structural number. The current procedure for determining structural numbers involves utilizing falling weight deflectometer and ground-penetrating radar tests, recording pavement surface deflections, and analyzing recorded deflections by back-calculation manners. This procedure has two drawbacks: 1. falling weight deflectometer and ground-penetrating radar are expensive tests, 2. back-calculation ways has some inherent shortcomings compared to exact methods as they adopt a trial and error approach. In this study, three machine learning methods entitled Gaussian process regression, m5p model tree, and random forest used for the prediction of structural numbers in flexible pavements. Dataset of this paper is related to 759 flexible pavement sections at Semnan and Khuzestan provinces in Iran and includes “structural number” as output and “surface deflections and surface temperature” as inputs. The accuracy of results was examined based on three criteria of R, MAE, and RMSE. Among the methods employed in this paper, random forest is the most accurate as it yields the best values for above criteria (R=0.841, MAE=0.592, and RMSE=0.760). The proposed method does not require to use ground penetrating radar test, which in turn reduce costs and work difficulty. Using machine learning methods instead of back-calculation improves the calculation process quality and accuracy.

Mechanistic-Based Approach to Utilize Traffic Speed Deflectometer Measurements in Backcalculation Analysis

Backcalculation analysis of pavement layer moduli is typically conducted based on falling weight deflectometer (FWD) deflection measurements; however, the stationary nature of the FWD requires lane closure and traffic control. In recent years, traffic speed deflection devices such as the traffic speed deflectometer (TSD), which can continuously measure pavement surface deflections at traffic speed, have been introduced. In this study, a mechanistic-based approach was developed to convert TSD deflection measurements into the equivalent FWD deflections. The proposed approach uses 3D-Move software to calculate the theoretical deflection bowls corresponding to FWD and TSD loading configurations. Since 3D-Move requires the definition of the constitutive behaviors of the pavement layers, cores were extracted from 13 sections in Louisiana and were tested in the laboratory to estimate the dynamic complex modulus of asphalt concrete. The 3D-Move generated deflection bowls were validated with field TSD and FWD data with acceptable accuracy. A parametric study was then conducted using the validated 3D-Move model; the parametric study consisted of simulating pavement designs with varying thicknesses and material properties and their corresponding FWD and TSD surface deflections were calculated. The results obtained from the parametric study were then incorporated into a Windows-based software application, which uses artificial neural network as the regression algorithm to convert TSD deflections to their corresponding FWD deflections. This conversion would allow backcalculation of layer moduli using TSD-measured deflections, as equivalent FWD deflections can be used with readily available tools to backcalculate the layer moduli.

Data correlation of non-destructive testing methods to assess asphalt pavement thickness

<p>Performing structural assessment at any time of asphalt pavements service life is an inherent process within pavement condition assessment. Layers thicknesses are among the major contributors to the overall pavement response and performance. Knowledge of layer thicknesses is imperative for both new and in-service pavements, because thickness data is usually combined with other response indicators (i.e. pavement deflections) in order to perform pavement evaluation during pavements service life. As such, inaccuracies in thickness assessment might result in erroneous response analysis and life expectancy estimation with a detrimental financial impact during maintenance planning.</p><p>Traditionally, layer thicknesses were retrieved through coring or digging test pits. Because of the limitations of these methods (including location-specific information, destructive nature, need for traffic disruptions), the pavement engineering community has consistently drawn its attention to a broadened utilization of advanced Non-Destructive Testing (NDT) systems in order to non-invasively determine the pavement cross-section. The most indicative NDT tool for that purpose is the Ground Penetrating Radar (GPR), which is systematically used for layers thickness evaluation. Within the framework of pavement evaluation processes, GPR is quite often combined with the Falling Weight Deflectometer (FWD), which provides with pavement response indications in terms of surface deflections.</p><p>It is worthwhile mentioning that GPR requires high expertise in order to reliably analyze the collected data and until now, there is none uniquely recognizable and universally accepted signal processing scheme. Supplementary to experienced users and analysts, investments in time and human resources are also needed to make reliable interpretations. Such reasons may potentially discourage related stakeholders from systematic GPR use, especially in cases where there are budgets constraints for the procurement and transportation logistics of multiple expensive equipment.</p><p>In light of the above, related research is pursed in respect to the investigation of the ability of FWD surface deflections indexes to provide with reliable information on the Asphalt Concrete (AC) layer thicknesses. For this purpose, Long-Term Pavement Performance (LTPP) data is analyzed including FWD and GPR data as well as sample coring. A nonlinear regression based relationship is under development that preliminarily exhibits a satisfactory performance both during model fit and model accuracy evaluation. Based on the above framework, it is suggested that the NDT analysis with deflection indexes seems promising in terms of roughly producing AC thickness, thereby balancing constraints at network level.</p>

Hiatus Mapping at a Continental Scale for Cretaceous and Cenozoic time

<p><span> Our results suggest that geologic maps yield geodynamically-relevant quantities, allowing one to constrain mantle-induced surface deflections of the lithosphere related to past dynamic topography.</span></p>