![]() ![]() Kruschke J (2015) Doing Bayesian data analysis. Mach Learn 50:5–43Īn D, Choi JH (2013) Improved MCMC method for parameter estimation based on marginal probability density function. Reliab Eng Syst Saf 95:777–785Īndrieu C, Freitas ND, Doucet A, Jordan MI (2003) An introduction to MCMC for machine learning. Park I, Amarchinta HK, Grandhi RV (2010) A bayesian approach for quantification of model uncertainty. Kelly DL, Smith CL (2009) Bayesian inference in probabilistic risk assessment-the current state of the art. J Korean Soc Aeronaut Sp Sci 46(4):332–337 Jung KS, Yang JM, Lee SC, Yi YS, Lee JW (2018) Design optimization of fuel sensor location in aircraft conformal fuel tank. Jung JH, Lee SC, Park KK, Choi HJ, Bae KH, Kim YJ, Cho JW (2012) Development of aircraft attitude simulator for MUAV fuel system. Jung JH, Lee SC, Choi SG, Park KK, Choi HJ, Bae KH, Kim YJ, Cho JW (2011) Integrated fuel system test equipment for UAV. Pearson DB (1947) A capacitance-type fuel-measurement system for aircraft. Howe ME (2006) Supplementary fuel tank systems for aircraft and methods for their manufacture and use, U.S. Smith RK (1998) Seventy-five years of inflight refueling. Knight W, Bolkcom C (2008) Air force air refueling: the KC-X aircraft acquisition program, library of congress Washington DC congressional research service To validate the proposed fuel quantity estimation approach, a test with known fuel quantity is performed. The lower bound in the estimation result can be utilized as a conservative fuel quantity value for a reliable operation. ![]() As an estimation result, the probability density function of the fuel quantity is provided, which accounts for the uncertainties caused from the developed mathematical model and measured data. The parameter of the mathematical model is then estimated using the MCMC method. In the model, the fuel quantity is represented as a multivariate polynomial function of sensor output (i.e., frequency), aircraft roll and pitch angles. The first step of the estimation process is a mathematical modeling of the fuel quantity in a supplementary tank. Through reflecting uncertainties, the conservative bound of fuel quantity estimation results can be found, which is necessary for a reliable aircraft operation. Using the proposed method, fuel quantity uncertainty of an aircraft supplementary tank can be estimated when the roll and pitch attitudes of an aircraft change. This paper presents an aircraft fuel quantity estimation method using the Markov Chain Monte Carlo (MCMC) method.
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