A Multi-Objective Design Optimization Approach for the Preliminary Design of High Speed Low Pressure Turbine Disks for Green Engine Architectures

摘要

The so-called Green Engine architectures are deeply investigated by the scientific community with the aim of reducing fuel consumption and noise emissions by 50%, and pollutants by 80%, environmental targets established by the Advisory Council for Aeronautical Research in Europe to be achieved by 2020. Low pressure turbine improvements will be important to increase the efficiency of the two most innovative propulsive architectures, the Geared Turbofan and the Geared Open Rotor. The low pressure turbine is released from the fan and can rotate at higher speed values, implying a reduction in fuel consumption. Due to a higher rotational speed, low pressure turbine disks design needs careful considerations due to their higher stress level and reduced burst limit. The presented study falls within the preliminary design phase of an engine, thus a code based on finite differences was used to perform disks analyses and optimization studies were set up. This paper presents two multi-objective hybrid optimization methodologies designed to study high speed low pressure turbine disks. The objective functions of both the constrained multi-objective optimizations were the minimization of disks weight and the maximization of their burst speed, and a hybrid approach was pursued to better investigate the design space and find the optimum. The code-based methodology provides an innovative approach to design such critical components by reducing disks weight by 15% and computational time by 30%, if compared to reference corporate design methodologies. The second study was conducted by considering a surrogate-model-based optimization to further reduce computational time while ensuring the analysis accuracy. A trade-off of most common approximation strategies was conducted and main results are shown in the paper. Distributed computing was used in both the considered optimization strategies, by involving three national research centers, and parallel computing was adopted to spread the calculation tasks on local workstations. Numeric and Finite-Element-Methodology-based validations followed to ensure the goodness of the achievements. Finally a good match between the predicted optimal solutions was found, thus justifying a surrogate-model-based-approach that led to further gains in computational time.