Casings of machinery and support structures have an influence on the rotordynamic behaviour which is commonly considered by simplified models (e.g. one degree of freedom models). These are in many cases insufficient. Hence, more accurate modelling approaches are required which can be used in the design process or the rotordynamic calculation to achieve a better representation of the overall vibrational behaviour. To study the effects of casing and supporting structures on rotordynamics, the casing modal parameters of an axial compressor are determined by an experimental modal analysis. In parallel, a numerical model is established. As experimental data are rarely found in the literature, this work focuses on the parameter identification of the casing structure. The results are subsequently incorporated into a model updating strategy, in order to tune and improve the numerical model. Experimental and numerical data are compared to assess the quality of the data and the results gained. The ultimate objective is a reduced order model, which can be integrated in existing rotordynamic tools via an interface while keeping the calculation time low.
Recent developments in turbomachinery design require an improved prediction accuracy of blade vibrations in order to maintain safe operations. This paper aims to investigate the accuracy of numerical aeroelastic approaches for the calculation of blade vibrations. For validation, extensive aerodynamic and forced response measurements in an 1.5-stage axial compressor with a blade integrated disk (Blisk) are presented. The excitation intensity of the vibration is controlled by varying the stagger angle of the Inlet Guide Vane (IGV). Additionally, a second engine order is imposed by a nonsymmetric circumferential vane angle distribution to simulate a multistage behavior. Experimental validated Reynoldsaveraged Navier-Stokes (RANS) simulations in both, the frequency and the time domain, are compared to assess the prediction accuracy of the numerical approaches. The numerical results agree with the experiments for low and intermediate vane angles. However, at high IGV stagger angles and when exciting multiple engine orders, the inaccuracy in the prediction of flow separation by the RANS simulations leads to an over prediction of vibration amplitudes. This exaggeration becomes even more pronounced in the frequency domain simulations. Time domain methods with a time lag formulation tend to be efficient and more accurate approaches for large separated flow regimes.
Modal parameter identification can be a valuable tool in mechanical engineering to predict vibrational behaviour and avoid machine damage during operation. Operational modal analysis is an output-only identification tool motivated by the structural identification of civil engineering structures, which are excited by ambient conditions. This technique is increasingly applied in mechanical engineering in order to characterise the system behaviour during operation as modal parameters can vary under operating conditions. The following study investigates the application of operational modal analysis on an axial compressor under operating conditions. Since the modal parameters of the system change depending on the life history and during the operation of the system, a corresponding data analysis might allow us to identify the present status of the system. Eigenfrequencies and eigenvectors are studied for the use of structural health monitoring approaches. According to the analysis, eigenfrequencies represent robust parameters for the studied purpose. Eigenvectors are sensitive to damages but need further investigation, especially for rotating machinery. This study will help the user to set up a virtual model, which describes the system behaviour for different boundary conditions. This in turn, will provide an accurate prediction of the vibrational behaviour in order to assure a safe operation.
In reasons of the new working conditions and globalization, manufacturing organizations seek to reduce their production costs. One of the factors that affect production costs is the supply chain and inventorying costs. This research presents a model of multi-stage supply chain system that operates under just-in-time (JIT) policy and supplies a fixed quantity of finished products to single customer, at a fixed time interval. Raw materials enter into the manufacturing system from two different channels. The first is brought to a consolidation centre where several items from several long-distance suppliers are collected according to single ordering policy, divided into small shipments, and redirected according to multi-ordering policy to the manufacturing system. The second type includes raw materials that are brought from JIT delivery suppliers according to the multi-ordering policy. Inbound logistics of raw materials are managed by third-party logistics (3PL) firms to coordinate and consolidate the transportation flow. The deliveries of raw material from suppliers, the work-in-process in the production stages, and the finished goods are all controlled by kanbans. For this supply chain system, the batch size and the number of batches in each stage that is to be shipped by kanbans, and the total quantity over one period are to be determined optimally. The supply chain system is modelled as a nonlinear integer programming (NLIP) problem.
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