Pia N Sartor

Value of an Overload Indication System Assessed Through Analysis of Aviation Occurrences

This paper identifies the value of an aircraft landing gear overload indication system by comparing vertical descent velocity landing data extrapolated from a statistical analysis of the Federal Aviation Administrations Video Landing Parameter Survey data, with reported occurrence data. Data suggest that there should be between 455- 848 narrow-body aircraft and between 236―1279 heavy-wide-body aircraft per million departures, with a vertical descent velocity above the hard-landing threshold of 10 ft/s, and that flight crews declare hard landings at a frequency even higher than predicted by the Federal Aviation Administration data. Analysis of aviation authority and landing gear manufacturer data show that occurrences are reported at a much lower frequency than the 10 ft/s vertical descent velocity threshold or flight-crew declaration rates. This could be interpreted as suggesting that more hard landings occur than are reported. However, the discrepancy between the vertical descent velocity data and the reported occurrence data can be attributed to 1) Federal Aviation Administration data being based solely on vertical descent velocity without taking into account the other critical enveloping flight parameters required to calculate the loads in the landing gear structure and 2) reported occurrences being filtered in an authorized occurrence assessment process. Having reviewed the occurrence assessment process, it is argued that an overload indication system offers potential benefits through 1) improved aircraft operational availability; 2) reduced costs for the operator, aircraft manufacturer, and landing gear manufacturer; and 3) reduced risk to the aircraft and operator.

Robust and Reliability-Based Aeroelastic Design of Composite Plate Wings

Probabilistic design methods may be used to account for inherent variability in the manufacturing quality of aerospace structures. Two different probabilistic approaches are compared alongside a deterministic method for the aeroelastic design of composite plate wings with uncertain ply orientations. The instability speed is found to be a discontinuous function of the lamination parameters, which are themselves functions of the ply orientations. A surrogate modeling approach is presented in which Gaussian processes are combined with support vector machines to emulate the discontinuous instability speed to efficiently propagate uncertainty through the model. The surrogate model is used to calculate the objectives of two optimization strategies: a reliability-based design, in which the probability of aeroelastic instability occurring within the design envelope is minimized, and a robust design, in which the mean and standard deviation of the instability speed are traded off. A genetic algorithm is used to op...

Efficient prediction and uncertainty propagation of correlated loads

Aircraft structural design is influenced by the static and dynamic loads resulting from flight manoeuvres, gust/turbulence encounters and ground manoeuvres; thus the identification of such loads is crucial for the development and structural analysis of aircraft, requiring the solution of the aeroelastic dynamic responses. Numerical aeroelastic models are used to predict a large number (1000s) of “Interesting Quantities” (IQs), and for aircraft design the identification of the worst cases for each IQ is very important, but involves a significant computational effort. Of particular interest are the so-called correlated loads, where coincident values of pairs of IQs are plotted against each other. This paper demonstrates how to reduce the computational burden to determine the behaviour of the correlated loads envelopes with little reduction in the accuracy, and also to quantify the effects of uncertainty, for a range of different parameters. The methodology is demonstrated on a numerical aeroelastic wing model of a civil jet airliner.

Prediction of landing gear loads using machine learning techniques

This article investigates the feasibility of using machine learning algorithms to predict the loads experienced by a landing gear during landing. For this purpose, the results on drop test data and flight test data will be examined. This article will focus on the use of Gaussian process regression for the prediction of loads on the components of a landing gear. For the learning task, comprehensive measurement data from drop tests are available. These include measurements of strains at key locations, such as on the side-stay and torque link, as well as acceleration measurements of the drop carriage and the gear itself, measurements of shock absorber travel, tyre closure, shock absorber pressure and wheel speed. Ground-to-tyre loads are also available through measurements made with a drop test ground reaction platform. The aim is to train the Gaussian process to predict load at a particular location from other available measurements, such as accelerations, or measurements of the shock absorber. If models can be successfully trained, then future load patterns may be predicted using only these measurements. The ultimate aim is to produce an accurate model that can predict the load at a number of locations across the landing gear using measurements that are readily available or may be measured more easily than directly measuring strain on the gear itself (for example, these may be measurements already available on the aircraft, or from a small number of sensors attached to the gear). The drop test data models provide a positive feasibility test which is the basis for moving on to the critical task of prediction on flight test data. For this, a wide range of available flight test measurements are considered for potential model inputs (excluding strain measurements themselves), before attempting to refine the model or use a smaller number of measurements for the prediction.

Robust aeroelastic design of composite plate wings

An approach is presented for the robust stacking sequence design of composite plate wings with uncertain ply orientations. An aeroelastic model is constructed using the Rayleigh-Ritz technique coupled with modified strip theory aerodynamics. Gaussian processes are used as emulators for the aeroelastic instability speed in order to efficiently quantify the effects of uncertainty. The critical instability speed is discontinuous as a result of the different potential instability mechanisms, therefore multiple Gaussian processes are fitted to ensure computational efficiency. An order of two magnitude reduction in model runs is achieved for the majority of examples, and an order of magnitude reduction is achieved when a switch between flutter modes occurs. The emulators are used to estimate the probability that instability occurs at a given design speed, which is minimized using a genetic algorithm. Results are compared to deterministic optima for maximal instability speed. Two lay-up strategies are undertaken, a first in which ply orientations are limited to 0°, ±45° and 90°, and a second in which values of ±30° and ±60° may also be taken. Improvements in reliability of at least 85% are achieved. The inclusion of ±30° and ±60° plies enables a 1.7% increase in the nominal instability speed, and an increase in reliability of at least 59%.

Morphing Wing Design for Fixed Wing Aircraft

A design approach is described that considers the design of aircraft wing structures that incorporate morphing devices. The methodology is generic rather than device specific, and determines the best distribution of morphing concept that enables minimisation of weight for minimum morphing application across the design flight envelope. Examples of the approach are described using a regional jet aircraft configuration. As well as determining the morphing requirements, the effect of uncertainty in both the aircraft structure and aerodynamic on the design process is also considered.

Evaluation and Uncertainty Quantification of Bifurcation Diagram: Landing Gear, a Case Study

Bifurcations are features of dynamical systems and occur when a small change in a system parameter results in a sudden qualitative change in the system behaviour. Such events can occur in linear and non-linear systems. The analysis of this phenomenon is challenging in the presence of non-linearity even for simple systems since they lack the properties and principles common to linear systems, e.g. the superposition principle. In the last few decades, numerical methodologies have been developed in order to efficiently determine bifurcation diagrams. Such techniques include continuation, normal form analysis, harmonic balance, and more recently branch and bounds methods. However, very little work has been considered for quantification of uncertain non-linear systems. In this paper, a methodology is provided to propagate parametric uncertainties and define bounds for the bifurcation diagrams taking into account the factors that most influence the behaviour of the analysed dynamical system. To this end, the developed methodology includes sensitivity analysis and uncertainty quantification. The methodology exploits numerical continuation methods, (high order) singular value decompositions, interval analysis and Bayesian approach, which is adopted in the Kriging method to develop surrogate models . The proposed method is validated considering a complex non-linear system from the aeronautical field: a landing gear (LG) system. The LG system has been the subject of several deterministic studies predicting the occurrence of shimmy, which is a selfexcited oscillation dangerous for the integrity of the aircraft. This phenomenon arises from Hopf bifurcations. The case study explores the effects of uncertainty on this phenomenon.

Bayesian sensitivity analysis of flight parameters that affect main landing gear yield locations

An aircraft and landing gear loads model was developed to assess the Margin of Safety ( MS ) in main landing gear components such as the main fitting, sliding tube and shock absorber upper diaphragm tube. Using a technique of Bayesian sensitivity analysis, a number of flight parameters were varied in the aircraft and landing gear loads model to gain an understanding of the sensitivity of the MS of the main landing gear components to the individual flight parameters in symmetric two-point landings. The significant flight parameters to the main fitting MS , sliding tube bending moment MS and shock absorber upper diaphragm tube MS include: longitudinal tyre-runway friction coefficient, aircraft vertical descent velocity, aircraft Euler pitch angle and aircraft mass. It was also shown that shock absorber servicing state and tyre pressure do not contribute significantly to the MS .

Optimization of a Landing Gear System Including Uncertainties

The optimization of structural behaviour in the presence of uncertainty is very demanding, especially for complex structures. Robust design optimization (RDO) and reliability-based design optimization (RBDO) are two techniques commonly adopted to deal with the optimization of performance of systems under uncertainty. In the presence multi-objective problems for complex performance criteria, the traditional RDO and RBDO are not always suitable because of two main problems: the prohibitive computational cost and the neglect of higher-order moments. In this paper, a novel optimization strategy, based upon evolutionary algorithms and inverse reasoning, is presented. It was conceived in order to deal with problems that are difficult to solve when adopting RBDO or RDO. To this end, reduced order models are built up using surrogate models together with a singular value/high order singular value decomposition. The proposed algorithm is used to minimize the probability of failure assuring a reliable design, providing an understanding of the acceptable range of uncertainties and keeping robustness. A representative nonlinear landing gear design problem is used to demonstrate the approach, showing how an optimized structure can be found that avoids the “shimmy” phenomenon.

17th AIAA Non-Deterministic Approaches Conference

An approach is presented for the robust stacking sequence design of composite plate wings with uncertain ply orientations. An aeroelastic model is constructed using the Rayleigh-Ritz technique coupled with modified strip theory aerodynamics. Gaussian processes are used as emulators for the aeroelastic instability speed in order to efficiently quantify the effects of uncertainty. The critical instability speed is discontinuous as a result of the different potential instability mechanisms, therefore multiple Gaussian processes are fitted to ensure computational efficiency. An order of two magnitude reduction in model runs is achieved for the majority of examples, and an order of magnitude reduction is achieved when a switch between flutter modes occurs. The emulators are used to estimate the probability that instability occurs at a given design speed, which is minimized using a genetic algorithm. Results are compared to deterministic optima for maximal instability speed. Two lay-up strategies are undertaken, a first in which ply orientations are limited to 0°, ±45° and 90°, and a second in which values of ±30° and ±60° may also be taken. Improvements in reliability of at least 85% are achieved. The inclusion of ±30° and ±60° plies enables a 1.7% increase in the nominal instability speed, and an increase in reliability of at least 59%.