Ruihua ZHANG, Weihua LIU, Wenyi LIU. Calculation method of fuel temperature conforming to requirements of airworthiness certification[J]. Journal of Aerospace Power, 2022, 37(2): 443-448.
DOI:
Ruihua ZHANG, Weihua LIU, Wenyi LIU. Calculation method of fuel temperature conforming to requirements of airworthiness certification[J]. Journal of Aerospace Power, 2022, 37(2): 443-448.DOI: 10.13224/j.cnki.jasp.20210008.
Calculation method of fuel temperature conforming to requirements of airworthiness certification
Based on the requirements of airworthiness certification,the measured fuel temperature change curve of the central wing fuel tank compartment of a certain aircraft under specified working conditions was taken as the input,and the high⁃order functional expression of the fuel temperature change rule in each stage was obtained by means of MATLAB polynomial piecewise fitting function.The exponential attenuation equation stipulated in the Monte Carlo evaluation model was used to characterize the change law of fuel temperature.The total ambient temperature (TAT) was obtained by the method specified in the evaluation model,and the equilibrium temperature difference and time constant were obtained by the improved genetic algorithm.The equilibrium temperature difference and time constant obtained by the inversion were then input into Monte Carlo evaluation model for calculation,the calculated temperature data and the measured polynomial data were compared.The results showed that both trends were exactly the same,the temperature difference between each moment did not exceed 1.67 K,the mean difference between the calculated value and the measured value of fuel temperature during the entire flight segment was about 0.050 1 K; in the stage with large error value,the value calculated by program was higher than the value measured by flight,which met the relevant requirements of airworthiness standards.The inversion method of time constant and equilibrium temperature difference is scientific and reliable,which can solve the bottleneck problem in airworthiness certification process and can effectively support the smooth development of airworthiness certification of large aircraft.
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EVOSEVICH B J , JOJIC I . Aircraft fuel tank flammability reduction method and system : CA2873226C [P]. 2017⁃12⁃19 .
MICHAEL B , WILLIAM M C . Inerting of a vented aircraft fuel tank test article with nitrogen⁃enriched air [R]. DOT/FAA/AR⁃01/6 , 2001 .
LANGTON R , CLARK C , HEWITT M , et al . Aircraft fuel system [M]. New York : John Wiley & Sons , 2010 : 225 ⁃ 237 .
MARTINOV N P . Aircraft fuel tank explosion reduction safety method : US6343465 [P]. 2002⁃02⁃05 .
EVOSEVICH B J , JOJIC I . Aircraft fuel tank flammability reduction method and system : US10000294 [P]. 2018⁃06⁃19 .
Federal Aviation Administration . Fuel tank flammability reduction means [R]. FAA 25 . 981 ⁃ 2 A, 2008 .
PAUL M D , SURAWSKI E . Temperature control system for fuel tank inerting system : US15208139 [P]. 2018⁃01⁃18 .
KANG Zhenye , LIU Zhenxia , REN Guozhe , et al . Simulation and calculation of fuel temperature in aircraft fuel tank based on MATLAB/simulink [J]. Journal of Propulsion Technology , 2014 , 35 ( 1 ): 62 ⁃ 69 . (in Chinese)
ZHANG Xingjuan , ZHANG Zuoqi , GAO Feng . Fuel temperature analysis of advanced fighter aircraft during supersonic cruise [J]. Journal of Aerospace Power , 2010 , 25 ( 2 ): 258 ‑ 263 . (in Chinese)
WANG Ri , DONG Sujun , MENG Fanchao , et al . A shortcut method for solving long⁃term thermal dynamic analysis of aircraft fuel tank by thermal node network analysis and OpenFOAM software [J]. Advances in Mechanical Engineering , 2018 , 10 ( 11 ): 16 ⁃ 19 .
SIVARAM M , BATRI K , MOHAMMED A S , et al . Exploiting the local optima in genetic algorithm using tabu search [J]. Indian Journal of Science and Technology , 2019 , 12 ( 1 ): 9 ⁃ 13 .
ARMAGHANI D J , HASANIPANAH M , MAHDIYAR A , et al . Airblast prediction through a hybrid genetic algorithm⁃ANN model [J]. Neural Computing and Applications , 2018 , 29 ( 9 ): 619 ⁃ 629 .
CHATTERJEE S , SARKAR S , DEY N , et al . Hybrid non⁃dominated sorting genetic algorithm:Ⅱ neural network approach [M] Hershey,US : IGI Global , 2018 : 264 ⁃ 286 .