Nonlinear programming extensions to rational function approximationsof unsteady aerodynamics

Tiffany, S. H.; Adams, W. M.

doi:10.2514/6.1987-854

Cited by 55 publications

(45 citation statements)

References 3 publications

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“…Although the choice of Roger's RFA and the independent fitting of the gust GAF leads to a state-space model whose size is greater compared to the Minimum-State method by Karpel [18], the model is afterwards reduced to a considerably smaller size through Model Order Reduction. Moreover, Roger's RFA is robust and offers less computational burden than the Minimum-State method [20], even though this cost, if a Model Order Reduction is not subsequently carried out, is ultimately overcome by the smaller resulting model employed in the simulations. The original formulation by Roger hereby used is extended considering the aerodynamic poles as free design variables of an optimization process, whose objective function is the minimization of the squared error between the approximated and tabulated GAF.…”

Section: Extended Rational Function Approximation Approachmentioning

confidence: 99%

“…It also allows adapting the RFA to each Mach number of interest, since commonly, in the standard approach, the poles are held arbitrarily constant over a range of Mach numbers, whereas the GAF can change significantly with Mach number. Several studies have been presented on nonlinear optimization of the aerodynamic poles [20], [21], [22], [23]. In this work, an optimization is performed to select the aerodynamic poles minimizing the functional ℱ such that…”

Section: Extended Rational Function Approximation Approachmentioning

confidence: 99%

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Parametric reduced order model approach for rapid dynamic loads prediction

Castellani¹,

Lemmens

Cooper³

2016

Aerospace Science and Technology

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A parametric reduced order methodology for loads estimation is described that produces a fast and accurate prediction of gust and manoeuvre loads for different flight conditions and structural parameter variations. The approach enables efficient prediction of the peak loads whilst maintaining the correlated time histories for different loads. It is then possible to determine correlated loads plots with reduced computation without losing accuracy. The effectiveness of the methodology is demonstrated by considering loads arising from families of gusts and pitching manoeuvres acting upon a numerical transport aircraft aeroservoelastic model with varying flight conditions and structural properties.

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Section: Extended Rational Function Approximation Approachmentioning

confidence: 99%

Section: Extended Rational Function Approximation Approachmentioning

confidence: 99%

Parametric reduced order model approach for rapid dynamic loads prediction

Castellani¹,

Lemmens

Cooper³

2016

Aerospace Science and Technology

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“…In this work, Roger's formulation is extended considering the aerodynamic poles as free design variables of an optimization process whose objective function is the minimization of the squared error between the approximated and tabulated GAF. Several studies have been published on the nonlinear optimization of the aerodynamic poles [10,11,12]. In the present work, nonlinear non-gradient constrained optimizations are performed to select the aerodynamic poles minimizing the following objective function…”

Section: State-space Modelmentioning

confidence: 99%

Reduced Order Model Approach for Efficient Aircraft Loads Prediction

Castellani¹,

Lemmens²,

Cooper³

2015

SAE Int. J. Aerosp.

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General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/pure/about/ebr-terms Page 1 of 9 2015-01-0066 Reduced Order Model Approach for Efficient Aircraft Loads PredictionAuthor, co-author (Do NOT enter this information. It will be pulled from participant tab in MyTechZone)Affiliation (Do NOT enter this information. It will be pulled from participant tab in MyTechZone) AbstractFlight loads calculations play a fundamental role in the development and certification of an aircraft and have an impact on the structural sizing and weight. The number of load cases required by the airworthiness regulations is in the order of tens of thousands and the analysis must be repeated for each design iteration. On large aircraft, CS-25 explicitly requires taking into account for loads prediction, airframe flexibility, unsteady aerodynamics and interaction of systems and structure, leading to computationally expensive numerical models. Thus there is a clear benefit in speeding-up this calculation process. This paper presents a methodology aiming to significantly reduce the computational time to predict loads due to gust and maneuvers. The procedure is based on Model Order Reduction, whose goal is the generation of a Reduced Order Model (ROM) able to limit the computational cost compared to a full analysis whilst retaining accuracy. The method is applied to a commercial transport aircraft modeled with beam elements, unsteady aerodynamics based on Doublet Lattice Method and servo-hydraulic actuators for the control surfaces. The aeroelastic equations of motion are formulated in the time-domain, through the Rational Function Approximation and application of the Balanced Truncation method. The results obtained with the reduced model shows a very good accuracy with respect to the full model and a significant saving in computational time. The impact of flexibility on the gust load factor is also highlighted, comparing it with the quasi-static analysis by Pratt's formula, current standard for Part 23 aircraft.

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“…where the n coefficients j A and j B are still derived from constrained nonlinear curve-fitting [69]. Alternatively to employing a further exponential term [103], the proposed approximation has inherently been tuned to satisfy Kutta-Joukowsky condition at the impulsive start of the wake, since the latter initially moves as an "extension" of the wing and the resulting apparent flow inertia is obtained by considering the pressure difference , x p due to the kinetic flow potential , x over each two-dimensional wing section in normal motion, namely [20]:…”

Section: Appendix A: Generalised Structural Matricesmentioning

confidence: 99%