Abstract
In order to obtain better integrated performance of aero-engine during the conceptual design stage, multiple disciplines such as aerodynamics, structure, weight, and aircraft mission are required. Unfortunately, the couplings between these disciplines make it difficult to model or solve by conventional method. MDO (Multidisciplinary Design Optimization) methodology which can well deal with couplings of disciplines is considered to solve this coupled problem. Approximation method, optimization method, coordination method, and modeling method for MDO framework are deeply analyzed. For obtaining the more efficient MDO framework, an improved CSSO (Concurrent Subspace Optimization) strategy which is based on DOE (Design Of Experiment) and RSM (Response Surface Model) methods is proposed in this paper; and an improved DE (Differential Evolution) algorithm is recommended to solve the system-level and discipline-level optimization problems in MDO framework. The improved CSSO strategy and DE algorithm are evaluated by utilizing the numerical test problem. The result shows that the efficiency of improved methods proposed by this paper is significantly increased. The coupled problem of VCE (Variable Cycle Engine) conceptual design is solved by utilizing improved CSSO strategy, and the design parameter given by improved CSSO strategy is better than the original one. The integrated performance of VCE is significantly improved.
Nomenclature
- Am1
forward VABI sub inlet area
- Am2
rear VABI sub inlet area
- B
bypass ratio
- Fn
thrust
- Fs
specific thrust
- Ma
Mach number
- n
rotating speed
- sfc
specific fuel consumption
- SM
surge margin
- T
temperature
pressure ratio
inlet guide vane angle
- Subscripts
- b
burner
- c
compressor
- d
CDFS
- des
engine design point
- eng
engine
- f
fan
- Abbreviations
- CSSO
concurrent subspace optimization
- CDFS
core drive fan stage
- DE
differential evolution
- DOE
design of experiment
- HPC
high pressure compressor
- HPT
high pressure turbine
- LPT
low pressure turbine
- MDO
multidisciplinary design optimization
- RSM
response surface model
- SQP
sequential quadratic programming
- VCE
variable cycle engine
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