Monte Carlo Uncertainty Quantification Using Quasi-1D SRM Ballistic Model

被引:5
|
作者
Vigano, Davide [1 ]
Annovazzi, Adriano [2 ]
Maggi, Filippo [1 ]
机构
[1] Politecn Milan, SPLab, Dept Aerosp Sci & Technol, I-20156 Milan, Italy
[2] AVIO SpA, Space Prop Design Dept, I-00034 Colleferro, Italy
关键词
D O I
10.1155/2016/3765796
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
Compactness, reliability, readiness, and construction simplicity of solid rocket motors make them very appealing for commercial launcher missions and embarked systems. Solid propulsion grants high thrust-to-weight ratio, high volumetric specific impulse, and a Technology Readiness Level of 9. However, solid rocket systems are missing any throttling capability at run-time, since pressure-time evolution is defined at the design phase. This lack of mission flexibility makes their missions sensitive to deviations of performance from nominal behavior. For this reason, the reliability of predictions and reproducibility of performances represent a primary goal in this field. This paper presents an analysis of SRM performance uncertainties throughout the implementation of a quasi-1D numerical model of motor internal ballistics based on Shapiro's equations. The code is coupled with a Monte Carlo algorithm to evaluate statistics and propagation of some peculiar uncertainties from design data to rocker performance parameters. The model has been set for the reproduction of a small-scale rocket motor, discussing a set of parametric investigations on uncertainty propagation across the ballistic model.
引用
收藏
页数:8
相关论文
共 50 条
  • [21] Uncertainty Quantification for the BGK Model of the Boltzmann Equation Using Multilevel Variance Reduced Monte Carlo Methods
    Hu, Jingwei
    Pareschi, Lorenzo
    Wang, Yubo
    SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION, 2021, 9 (02): : 650 - 680
  • [22] A QUASI-1D MODEL FOR TRANSONIC MULTISTAGE AXIAL COMPRESSORS
    Du, W.
    Leonard, O.
    9TH EUROPEAN CONFERENCE ON TURBOMACHINERY: FLUID DYNAMICS AND THERMODYNAMICS, VOLS I AND II, 2011, : 793 - 803
  • [23] Quasi-1D analysis model of rocket ejector mode
    College of Astronautics, Northwestern Polytechnical Univ., Xi'an 710072, China
    Tuijin Jishu, 2006, 6 (529-531+541):
  • [24] Multilevel Monte Carlo FDTD Method for Uncertainty Quantification
    Zhu, Xiaojie
    Di Rienzo, Luca
    Ma, Xikui
    Codecasa, Lorenzo
    IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, 2022, 21 (10): : 2030 - 2034
  • [25] Multicanonical sequential Monte Carlo sampler for uncertainty quantification
    Millar, Robert
    Li, Hui
    Li, Jinglai
    RELIABILITY ENGINEERING & SYSTEM SAFETY, 2023, 237
  • [26] An exact framework for uncertainty quantification in Monte Carlo simulation
    Saracco, P.
    Pia, M. G.
    20TH INTERNATIONAL CONFERENCE ON COMPUTING IN HIGH ENERGY AND NUCLEAR PHYSICS (CHEP2013), PARTS 1-6, 2014, 513
  • [27] Progress with Uncertainty Quantification in Generic Monte Carlo Simulations
    Saracco, P.
    Pia, M. G.
    2013 IEEE NUCLEAR SCIENCE SYMPOSIUM AND MEDICAL IMAGING CONFERENCE (NSS/MIC), 2013,
  • [28] Prediction and uncertainty quantification of shale well performance using multifidelity Monte Carlo
    Mehana, Mohamed
    Pachalieva, Aleksandra
    Kumar, Ashish
    Santos, Javier
    O'Malley, Daniel
    Carey, William
    Sharma, Mukul
    Viswanathan, Hari
    GAS SCIENCE AND ENGINEERING, 2023, 110
  • [29] Uncertainty quantification of offshore wind farms using Monte Carlo and sparse grid
    Richter, Pascal
    Wolters, Jannick
    Frank, Martin
    ENERGY SOURCES PART B-ECONOMICS PLANNING AND POLICY, 2022, 17 (01)
  • [30] Simplified models for uncertainty quantification of extreme events using Monte Carlo technique
    Hu, Xiaonong
    Fang, Genshen
    Yang, Jiayu
    Zhao, Lin
    Ge, Yaojun
    RELIABILITY ENGINEERING & SYSTEM SAFETY, 2023, 230