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One of the most widely applied identification methods for stall modeling using flight test data is based on Kirchhoff’s method of flow separation. However, this approach has not lead to a satisfactory aerodynamic pitching moment model. The introduction of the so-called X-variable, representing the point of flow separation on the wing, interferes with identification of a pitch damping term, that is required for dynamic stability. In general, Kirchhoff methods lead to models that are incompatible with nominal flight envelope models. This paper presents a nonlinear unsteady model of the pitching moment using lag states of the angle of attack measurements, identified from flight test data collected with a Cessna Citation II laboratory aircraft. The model is formulated in terms of well-known stability derivatives and is a one-on-one extension of the nominal envelope model. Model regressors are selected from a large pool of candidates using Multivariate Orthogonal Function Modeling. The candidate pool is based on a newly formulated mathematical model, such that each model contribution has a clear physical interpretation. The model has good predictive abilities and results in a reduction of 55.9% in validation MSE compared to Kirchhoff based pitching moment models.
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One of the most widely applied identification methods for stall modeling using flight test data is based on Kirchhoff’s method of flow separation. However, this approach has not lead to a satisfactory aerodynamic pitching moment model. The introduction of the so-called X-variable, representing the point of flow separation on the wing, interferes with identification of a pitch damping term, that is required for dynamic stability. In general, Kirchhoff methods lead to models that are incompatible with nominal flight envelope models. This paper presents a nonlinear unsteady model of the pitching moment using lag states of the angle of attack measurements, identified from flight test data collected with a Cessna Citation II laboratory aircraft. The model is formulated in terms of well-known stability derivatives and is a one-on-one extension of the nominal envelope model. Model regressors are selected from a large pool of candidates using Multivariate Orthogonal Function Modeling. The candidate pool is based on a newly formulated mathematical model, such that each model contribution has a clear physical interpretation. The model has good predictive abilities and results in a reduction of 55.9% in validation MSE compared to Kirchhoff based pitching moment models.
This research investigated how the variation of temperature and shear rate affects the viscosity of ethanol gel propellants that use methyl cellulose as gellant and, in parts, use boron as energetic additive. Using a rotational viscometer in a cone-and-plate configuration, propellant viscosity data was recorded across a range of temperatures and applied shear rates. The temperaturedependence of the viscosity was modelled using an Arrhenius-type equation. For the high shear rates, the data was modelled using the Power Law, Herrschel–Bulkley model, Carreau model, and Cross model. For low shear rates the used model was the rearranged Herrschel–Bulkley model. The temperature investigation suggested that the trend of decreasing viscosity with increasing temperature, predicted by the Arrhenius-type equation, is only applicable until approximately 320 K, after which the gel viscosity increased strongly. At high shear rates, the gel behaved in a shear thinning manner and was modelled most accurately by the Cross model. At low shear rates, the gel was shear thickening up to its elastic limit, which was found to lie at 0.41 s–1.
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This research investigated how the variation of temperature and shear rate affects the viscosity of ethanol gel propellants that use methyl cellulose as gellant and, in parts, use boron as energetic additive. Using a rotational viscometer in a cone-and-plate configuration, propellant viscosity data was recorded across a range of temperatures and applied shear rates. The temperaturedependence of the viscosity was modelled using an Arrhenius-type equation. For the high shear rates, the data was modelled using the Power Law, Herrschel–Bulkley model, Carreau model, and Cross model. For low shear rates the used model was the rearranged Herrschel–Bulkley model. The temperature investigation suggested that the trend of decreasing viscosity with increasing temperature, predicted by the Arrhenius-type equation, is only applicable until approximately 320 K, after which the gel viscosity increased strongly. At high shear rates, the gel behaved in a shear thinning manner and was modelled most accurately by the Cross model. At low shear rates, the gel was shear thickening up to its elastic limit, which was found to lie at 0.41 s–1.
