# A Study on Aeroengine Conceptual Design Considering Multi-Mission Performance Reliability
## 2 Literature Review  
The investigation of uncertainty is the basis of uncertainty-based analysis and design problem research. Currently, uncertainty is mainly classified into two types, including epistemic uncertainty and stochastic uncertainty. More specifically, epistemic uncertainty is a potential inaccuracy due to the incompleteness in knowledge (either in historical/statistical data or theories) [10; 11]. Thus, a progress of knowledge or more collected data are beneficial for eliminating epistemic uncertainty. Stochastic uncertainty describes the inherent variation of the physical system or the environment under consideration, and it appears more frequently in the actual situation of aviation and aerospace engineering [7]. Emerged as the effective tools to solve stochastic uncertainty problem, probabilistic methods have attracted extensive interest. So far, probabilistic-based analysis and design methods have been successfully conducted in the fields of aerospace engineering and civil engineering that mainly focused on reliability and robustness of aerodynamic design, control system, space vehicle structure, and aeroengine alloys. For instance, PW Corp developed the Probabilistic Design System for gas turbine rotors, which integrates existing deterministic and probabilistic design techniques to assess mechanical failure modes for developing lighter weight engine components [12]. Nowadays, probabilistic analysis methods effectively promote the research advances in superalloys, which are extensively used in gas turbine engines owing to the excellent corrosion resistance and mechanical properties. The assessment of the global stability and reliability of GH4133B superalloy is implemented by using three-parameter Weibull distribution model [13]. Based on finite element simulation, the probabilistic analysis methods precisely measured the surface properties and fatigue life of Incoloy A286 alloy [14]. Lately, probabilistic methodology is successfully applied in the emerging field of aviation additive manufacturing. The newly proposed inspection scheduling approach of gas turbine welded structures considers the influences of uncertainty in material properties, weld geometry, and loads, etc. [15]. Using the first-order reliability method, the proposed routine accessed the failure rate and inspection intervals of the welded components to reduce the computational cost of probabilistic defect-propagation analysis [16]. In recent years, tremendous research has been donated to the cumulative effect of uncertainty on aeroengine overall performance. Based on Monte-Carlo probabilistic analysis method, [PERSON] et al. proposed a feasible methodology to quantify the impacts of uncertainty in component performance on the overall performance of conventional gas turbine engine [17]. Their later research has studied the impact of component performance deviation (CPD) on adaptive cycle engine in multiple operating conditions. An interval analysis method was presented to set the standard of CPD based on the first order Taylor series expansion, which only require less computation [18]. Furthermore, this research team proposed a linear substitute model for the rapid quantification of uncertainty, which greatly simplifies the computation process and can be easily applied to other complex non-linear energy systems [19]. Above studies prove that the probabilistic method is an effective method to considerably solve the uncertainty-based problem of aeronautical science field. On this basis, the probabilistic method is introduced to solve the design problems related to the uncertainty overall performance of aeroengine. In order to assist the decision maker during the early stages of ambiguity engine design process, [PERSON] to utilize the probabilistic methods for analysis of the effects of component performance uncertainty on the sizing of an unmanned combat aerial vehicle engine, including payload, range, maneuver requirements [20].