Risk Assessment of Fuel Property Variability Using Quasi-Random Sampling/Design of Experiments Methodologies

Increases in on-board heat generation in modern military aircraft have led to a reliance on thermal management techniques using fuel as a primary heat sink. However, recent studies have found that fuel properties, such as specific heat, can vary greatly between batches, affecting the amount of heat delivered to the fuel. With modern aircraft systems utilizing the majority of available heat sink capacity, an improved understanding of the effects of fuel property variability on overall system response is important. One way to determine whether property variability inside a thermal system causes failure is to perform uncertainty analyses on fuel thermophysical properties and compare results to a risk assessment metric. A sensitivity analysis can be performed on any properties that cause inherent system variability to determine which properties contribute the most significant impact. For the current study, a quasi-random sampling based uncertainty analysis was combined with a surrogate model based sensitivity analysis. Combining sample based and surrogate-based methodologies provided statistical information from the sampling based approach and sensitivity information from the design of experiments from one test series. Using the two methods simultaneously combined the advantages of both methods, while reducing the number of trials required for a statistically significant sample. The methodologies were applied to fuel property variability by sampling deviating thermophysical properties and analyzing the system impact for a sample mission profile. The fuel property variabilities examined were fuel density, specific heat, viscosity, and thermal conductivity. The analysis utilized the architecture’s feed tank temperature as the failure metric and the probability of system failure was determined using a confidence interval. The determined point of failure was analyzed using a sensitivity analysis to determine dominant fuel properties. The sample sizes were compared using computation time and sample size effect on statistical variation to determine the optimal setting for risk analysis.