This paper investigates the feasibility of using machine learning algorithms to predict the loads experienced by a landing gear during landing. For this purpose, results on drop test data and flight test data will be examined. This paper will focus on the use of Gaussian Process regression for the prediction of loads on 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 the 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 GP 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 by 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 is 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.

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Abstract:The identification of correlated quantities is of particular interest in several fields of engineering and physics, for example in the development of reliable structural designs. When 'time-varying' quantities are analyzed, pairs of correlated Interesting Quantities (IQs) e.g. bending moments, torques etc., can be displayed by plotting them against each other, and the critical conditions determined by the extreme values of the envelope (convex hull). In this paper, a reduced order singular value based modelling technique is developed that enables a fast computation of the correlated loads envelopes for systems where the effect of variation of design parameters needs to be considered. The approach is extended to efficiently quantify the effects of uncertainty in the system parameters. The effectiveness of the method is demonstrated by consideration of the gust loads occurring from the aeroelastic numerical model of a civil jet airliner.

Abstract. This paper presents a review of the conservatism approaches applied by different industrial sectors to the stress-life (S-N) analysis of 'life-limited' or 'safe-life' components. A comparison of the fatigue design standards for 6 industrial sectors identified that the conservatism approaches are highly inconsistent when comparing the areas of variability and uncertainty accounted for along with the conservatism magnitude and method of application. Through the use of a case-study based on the SAE keyhole benchmark and 4340 steel S-N data, the industrial sector which introduces the greatest reduction of a component life-limit was identified as the nuclear sector. The results of the case-study also highlighted that conservatism applied to account for scatter in S-N data currently provides the greatest contribution to the reduction of component life-limits.

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A methodology is developed to eciently, yet accurately, determine the uncertainty bounds of the bifurcation loci of nonlinear dynamic systems subjected to parametric variations. The chosen approach make use of numerical continuation, the higher order singular value decomposition and surrogate modelling. The technique is demonstrated on a representative model of an aircraft undercarriage where the eect of uncertainty in the structural parameters is propagated onto the prediction of the conditions for the onset of shimmy. Comparison with numerical integration results demonstrates the accuracy of the methodology.

The variability present in S-N datasets is typically characterised using probability distributions to enable the construction of Probability-S-N curves for design. 3-Parameter Log-Normal and Weibull distributions have been proposed as alternative distributions to the commonly assumed 2-Parameter Log-Normal distribution. This paper performs statistical characterisation of a 4340 steel S-N dataset from the Engineering Sciences Data Unit using a systematic methodology. The 3-Parameter Weibull distribution provided improved characterisation of the S-N dataset. Using a case study, it was also demonstrated that use of a 3-Parameter Weibull distribution can increase component safe-life values by 20% when compared to the 2-Parameter Log-Normal distribution.