Wind tunnel facility calibrations and experimental uncertainty

Kammeyer, Mark

doi:10.2514/6.1998-2715

Cited by 10 publications

(2 citation statements)

References 1 publication

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“…One contributing source not often appreciated and often overlooked is the variance in the flow conditions. As shown by Kammeyer,7 the uncertainty in a calculated pressure or drag coefficient can be dominated by the uncertainty in the Mach number, with much less contribution coming from the force balance, pressure transducer, or other sources. The uncertainty in the flow conditions stems from two sources: the calibration of the facility itself and spatial variations in the flow.…”

Section: Aiaa-2005-4274mentioning

confidence: 99%

Hypersonic Wind Tunnel Calibration Using the Modern Design of Experiments

Rhode

DeLoach²

2005

41st AIAA/ASME/SAE/ASEE Joint Propulsion Conference &Amp;amp; Exhibit

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A calibration of a hypersonic wind tunnel has been conducted using formal experiment design techniques and response surface modeling. Data from a compact, highly efficient experiment was used to create a regression model of the pitot pressure as a function of the facility operating conditions as well as the longitudinal location within the test section. The new calibration utilized far fewer design points than prior experiments, but covered a wider range of the facility's operating envelope while revealing interactions between factors not captured in previous calibrations. A series of points chosen randomly within the design space was used to verify the accuracy of the response model. The development of the experiment design is discussed along with tactics used in the execution of the experiment to defend against systematic variation in the results. Trends in the data are illustrated, and comparisons are made to earlier findings. = stagnation pressure, measured in settling chamber of the wind tunnel P T,2 = post-normal-shock stagnation pressure, or pitot pressure Re 2 = Reynolds number based on static conditions behind a normal shock t = time from start of data acquisition for a given run t α = t-statistic associated with the Type I inference error probability t β = t-statistic associated with the Type II inference error probability T T,1 = stagnation temperature, measured in the settling chamber of the wind tunnel X = axial location in the test section, referenced from upstream edge of injection opening y' = spanwise distance along pitot rake, referenced from centerline γ = ratio of specific heats λ = precision requirement for response prediction ρ 2 = static density behind normal shock ρ ∞ = freestream density σ = standard deviation of measurement

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Section: Aiaa-2005-4274mentioning

confidence: 99%

Hypersonic Wind Tunnel Calibration Using the Modern Design of Experiments

Rhode

DeLoach²

2005

41st AIAA/ASME/SAE/ASEE Joint Propulsion Conference &Amp;amp; Exhibit

View full text Add to dashboard Cite

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“…It is important to exercise the relevant variables present in an experiment, where possible, and obtain uncertainty information other than just replication of a set of conditions. For wind tunnel experiments, uncertainty and spatial non-uniformity in the flow conditions can be a significant, if not the dominant, contributor to the overall uncertainty of a quantity of interest [9]. Additionally, deviations from the ideal surface geometry can alter a measurement, as can instrumentation installation effects, such as a leaking pressure port.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Uncertainties for a Supersonic Retro-Propulsion Model Validation Experiment in a Wind Tunnel

Rhode

Oberkampf²

2012

42nd AIAA Fluid Dynamics Conference and Exhibit

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A high-quality model validation experiment was performed in the NASA Langley Research Center Unitary Plan Wind Tunnel to assess the predictive accuracy of computational fluid dynamics (CFD) models for a blunt-body supersonic retro-propulsion configuration at Mach numbers from 2.4 to 4.6. Static and fluctuating surface pressure data were acquired on a 5-inch-diameter test article with a forebody composed of a sphericallyblunted, 70-degree half-angle cone and a cylindrical aft body.One non-powered configuration with a smooth outer mold line was tested as well as three different powered, forward-firing nozzle configurations: a centerline nozzle, three nozzles equally spaced around the forebody, and a combination with all four nozzles. A key objective of the experiment was the determination of experimental uncertainties from a range of sources such as random measurement error, flowfield non-uniformity, and model/instrumentation asymmetries. This paper discusses the design of the experiment towards capturing these uncertainties for the baseline non-powered configuration, the methodology utilized in quantifying the various sources of uncertainty, and examples of the uncertainties applied to non-powered and powered experimental results. The analysis showed that flowfield nonuniformity was the dominant contributor to the overall uncertainty -a finding in agreement with other experiments that have quantified various sources of uncertainty.

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Verification and validation in computational fluid dynamics

Oberkampf

Trucano

2002

Progress in Aerospace Sciences

836

360

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Verification and validation (V&V) are the primary means to assess accuracy and reliability in computational simulations. This paper presents an extensive review of the literature in V&V in computational fluid dynamics (CFD), discusses methods and procedures for assessing V&V, and develops a number of extensions to existing ideas. The review of the development of V&V terminology and methodology points out the contributions from members of the operations research, statistics, and CFD communities. Fundamental issues in V&V are addressed, such as code verification versus solution verification, model validation versus solution validation, the distinction between error and uncertainty, conceptual sources of error and uncertainty, and the relationship between validation and prediction. The fundamental strategy of verification is the identification and quantification of errors in the computational model and its solution. In verification activities, the accuracy of a computational solution is primarily measured relative to two types of highly accurate solutions: analytical solutions and highly accurate numerical solutions. Methods for determining the accuracy of numerical solutions are presented and the importance of software testing during verification activities is emphasized. The fundamental strategy of 1 Accepted for publication in the review journal Progress in Aerospace Sciences.3 validation is to assess how accurately the computational results compare with the experimental data, with quantified error and uncertainty estimates for both. This strategy employs a hierarchical methodology that segregates and simplifies the physical and coupling phenomena involved in the complex engineering system of interest. A hypersonic cruise missile is used as an example of how this hierarchical structure is formulated. The discussion of validation assessment also encompasses a number of other important topics. A set of guidelines is proposed for designing and conducting validation experiments, supported by an explanation of how validation experiments are different from traditional experiments and testing. A description is given of a relatively new procedure for estimating experimental uncertainty that has proven more effective at estimating random and correlated bias errors in wind-tunnel experiments than traditional methods. Consistent with the authors' contention that nondeterministic simulations are needed in many validation comparisons, a three-step statistical approach is offered for incorporating experimental uncertainties into the computational analysis. The discussion of validation assessment ends with the topic of validation metrics, where two sample problems are used to demonstrate how such metrics should be constructed. In the spirit of advancing the state of the art in V&V, the paper concludes with recommendations of topics for future research and with suggestions for needed changes in the implementation of V&V in production and commercial software.4 Acknowledgements

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Wind tunnel facility calibrations and experimental uncertainty

Cited by 10 publications

References 1 publication

Hypersonic Wind Tunnel Calibration Using the Modern Design of Experiments

Hypersonic Wind Tunnel Calibration Using the Modern Design of Experiments

Estimation of Uncertainties for a Supersonic Retro-Propulsion Model Validation Experiment in a Wind Tunnel

Verification and validation in computational fluid dynamics

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