Development of the In Situ Load System for Internal Wind-Tunnel Balances

Commo, Sean A.; Lynn, Keith C.; Toro, Kenneth G.; Landman, Drew

doi:10.2514/1.c032691

Cited by 2 publications

(3 citation statements)

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“…It is worth noting that applying the exact combination as stated in Table 2 is not important because an AMS measures the orientation of the balance, and the applied forces and moments are easily calculated from the physics-based equations given by Eqs. (5) and (6). In this example, the actual load for five of the six components falls within the prediction intervals on the estimated load.…”

Section: Prediction Interval Capture Probability For the Ils Case supporting

confidence: 51%

“…The in-situ load system (ILS) is a new system designed at NASA LaRC to help address the issues surrounding a system-level validation or calibration of a wind-tunnel model system (WTMS) [6]. Together, the aircraft model, model sting, balance, angle measurement system (AMS), and other instrumentation make up the WTMS.…”

Section: A In-situ Load System Conceptmentioning

confidence: 99%

“…1. The details of the ILS geometry and measured mass properties may be found in [6]. It is known that the force due to the lower bearing mount acts through the load point, and therefore the location of the center of gravity is not required.…”

Section: A Uncertainty In the Ntf-113c Balance Responsesmentioning

confidence: 99%

See 2 more Smart Citations

Prediction Interval Development for Wind-Tunnel Balance Check-Loading

Landman¹,

Toro²,

Commo³

et al. 2015

Journal of Aircraft

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The current approach used to apply uncertainty intervals to balance estimated loads is based on the root mean square error from calibration. Using the root mean square error, a constant interval is applied around the estimated load and it is expected that a predetermined percentage of the check-loads applied fall within this constant uncertainty interval. However, this approach ignores additional sources of uncertainty and assumes constant uncertainty regardless of the load combination and magnitude applied to the balance. Rigorous prediction interval theory permits varying interval widths but fails to account for the additional error sources that are unrelated to the mathematical modeling. An engineered solution is proposed that combines prediction interval theory and the need to account for the additional sources of uncertainty from calibration and check loading. Results from a case study using the in-situ load system show improved probabilistic behavior in terms of uncertainty interval capture percentage when compared with the current root mean square error method.Nomenclature CG x = location of center of gravity along x bal axis from balance moment center CG y = location of center of gravity along y bal axis from balance moment center CG z = location of center of gravity along z bal axis from balance moment center d BMC = distance vector from balance moment center to load point x BMC y BMC z BMC 0 F = expanded load calibration matrix F app = applied force, lbf. F bal = applied force vector F x F y F x 0 F x = force along x bal axis, lbf. F y = force along y bal axis, lbf. F z = force along z bal axis, lbf. F 0 = expanded load vector g = gravity vector g x g y g z 0 g x = x component of the gravity vector g y = y component of the gravity vector g z = z component of the gravity vector M bal = applied aerodynamic moment vector M x M y M z 0 M x = moment about x bal axis, in-lbf. M y = moment about y bal axis, in-lbf. M z = moment about z bal axis, in-lbf. n = number of calibration points p = number of terms in calibration model rF = strain-gage bridge response, mV∕V t= value from Student's t distribution x BMC = distance from balance moment center to load point along x bal axis, in. y BMC = distance from balance moment center to load point along y bal axis, in. z BMC = distance from balance moment center to load point along z bal axis, in. α = level of significance β = regression coefficient in balance calibration model σ 2 = error variance from calibration, also known as the mean squared error

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Section: Prediction Interval Capture Probability For the Ils Case supporting

confidence: 51%

Section: A In-situ Load System Conceptmentioning

confidence: 99%