Low pressure turbine (LPT) rotor discs undergo high thermal and mechanical loads during normal aircraft missions. Therefore, to meet the minimum requirement for life, temperatures and stresses in the disk need to be maintained within certain limits. This is achieved by carefully designing the disk shape and the cooling system. The complexity of this multi-physics problem together with a large number of design parameters require the use of numerical optimization methods for the Secondary Air System (SAS) design. Moreover, possible variations in the boundary conditions due to ambient parameters (e.g. temperatures, pressures) and manufacturing tolerances of the SAS components should be taken into account within the system design and optimization phase. In this paper an application of robust optimization methods for the design of a LPT secondary air system is proposed. The objective is to increase the engine efficiency by minimizing the amount of cooling flow, which is needed to guarantee a minimum required number of life cycles and to keep maximal temperatures within the limits. In order to predict the disks life accurately, transient thermal-structural analysis is used, which is computationally demanding. For this reason, optimization should be performed with a very limited amount of system evaluations. The dimension of the parameter space is reduced through the application of global sensitivity analysis methods by selecting the parameters that most affect the results. Optimization methods are sped up by the use of surrogate models, created over the reduced parameter space, which approximate the objective function and the constraints.

In this paper several stochastic methods are evaluated with respect to their applicability for the analysis of fluid networks. The methods are applied for the analysis of a 1D flow model of the Secondary Air System (SAS) of a three stages low pressure turbine (LPT) of a jet engine. The stochastic analysis is comprised of a sensitivity analysis followed by an uncertainty analysis. The sensitivity analysis is performed to gain a better understanding of the SAS physics and robustness, to identify the important variables and to reduce the number of parameters involved in the simulations for the uncertainty analysis. The uncertainty analysis, using probability distributions derived from the manufacturing process, allows to determine the effect of the input uncertainties on responses such as pressures, fluid temperatures and mass flow rates. A review of the most common and relevant sampling methods is performed. A comparison of the respective computational cost and of the sample points distribution is proposed with the aim of finding the most suited method. The study shows that some of the sampling methods can not be recommended since they produce spurious correlations between independent input variables. With regards to the sensitivity analysis, many literature sources state that the Pearson correlation method is only valid for linear models when assessing the importance of input variables. As the SAS is highly non-linear, non-parametric variance based methods are introduced here to make up for the limitations of the correlation method. Following the results of the study, it is recommended to combine the sampling method with a non-parametric variance based method. Thus, the main effects as well as all the interactions among variables are captured.

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This paper aims at investigating, both through a theoretical and an experimental analysis, the discharging phase of a blowing unit of compressed air, used for the industrial production of plastic made bottles. The proposed mathematical model leads to a system of differential equations describing the flow through an open system. The solution was found by numerical simulations using the software Matlab, determining the gas density, pressure, temperature and mass flow rate, as functions of time. The pressure loss across the down flow has been tackled with a theoretical investigation, determining the mechanical loss coefficient and evaluating the effect of these losses on the emptying time of the blowing unit. The numerical results agree with the real discharging times obtained by experimental tests, and the proposed improvements allow to reduce loss of pressure and the emptying time of 35% and 20% respectively.

The development of jet engine components requires a detailed quantification of different uncertainty sources to improve the quality and robustness of the design. In order to get a better understanding of the entire interdisciplinary jet engine design process, the detailed uncertainty quantification of the performance parameters is of vital importance. This paper demonstrates a new approach how to represent the uncertainties in a jet engine performance model caused by the manufacturing and assembly process. In earlier research studies, the manufacturing process was modeled with a probabilistic approach, i.e. by assuming a multivariate normal distribution for the corresponding parameters. Within the scope of this paper, the uncertainty of the components’ flow capacity and efficiency is quantified based on a limited set of data. Due to the extreme scarcity of the data set, it is proposed to use methods from the field of non-probabilistic uncertainty quantification. In this paper, three different approaches to derive the variation of the components’ flow capacity and efficiency are compared with each other. In contrast to probabilistic methodologies, all approaches are able to represent the lack of data without making any additional assumptions regarding the underlying type of distribution. As a result, each of the methodologies describes the uncertain parameters by probability-boxes. After clarifying the theoretical background, the results obtained from the different approaches are discussed in detail. It figured out that the propagation method for probability-boxes plays a crucial role for the uncertainty quantification.