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

Abstract

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 Open Rotor Engine. 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. This paper presents a multi-objective hybrid optimization methodology designed to study high speed low pressure turbine disks. The presented study falls within the preliminary design phase, thus a code based on finite differences was used to perform an optimization study of high speed low pressure turbine disks. The objective functions of the constrained multi-objective optimization were the minimization of the disks weight and the maximization of the burst speed, and a hybrid approach was pursued to better investigate the design space and find the optimum. Then a surrogate-model-based hybrid optimization was conducted to reduce computational time while ensuring the analysis accuracy. A trade-off of most common approximation strategies was conducted. Distributed computing was used 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. This study provides an innovative approach to design such critical components by reducing disks weight by 15% and computational time by 30% if compared to traditional design methodologies. Furthermore, a good match between optimal solutions was found, thus justifying a surrogate-model-based-approach that led to further gains in computational time.