An Improved Methodology for Gas Turbine Technology Portfolio Planning, Including Technology Synergy Matrices and Real Options Analysis

摘要

The management of technologies remains a challenging and risky constituent of gas turbine component design. The performance impact of a single new technology on a gas turbine component is often uncertain, requiring statistical methods to fully quantify. The inclusion of multiple new technologies further complicates gas turbine component performance prediction because there may be significant non-linear technology interactions. When designing gas turbine components to operate within the context of a fully specified gas turbine engine, each component introduces its own mix of new technologies. Because gas turbine components are linked thermodynamically, a new technology in one component may induce secondary impacts in other engine components. Thus, technology portfolio analysis for gas turbines is further complicated by the dynamics of component interaction. In this paper, a new technology portfolio planning methodology is presented that directly addresses these three inherent complexities of gas turbine component design. This methodology includes Technology Impact Matrices to quantify component level technology impacts as well as Technology Compatibility Matrices and Technology Synergy Matrices to account for non-linear component level technology interactions. Through surrogate modeling, a physics-based dynamic engine model is formed that quickly translates component technology impacts into total-system thermodynamic performance. Real Options analysis is used to translate engine performance metrics to net engine economic performance. Finally, a probabilistic multi-objective genetic algorithm is used to discover a robust Pareto front of optimal technology combinations with respect to system level thermodynamic and economic performance metrics. This paper first describes the proposed methodology in detail. Next, a benchmark gas turbine technology portfolio problem is outlined. Finally, a baseline technology portfolio planning methodology and the newly proposed methodology are both executed upon the benchmark problem, allowing for the benefits of the new methodology to be quantified.