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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Acoustic Liner Drag: Measurement Uncertainty Reduction and Application to Novel Perforate Geometries

In addition to developing acoustic liner concepts and characterizing their performance, the NASA Langley Liner Physics Team has investigated issues related to liner drag. A method to quantify relative drag of liner configurations was developed and employed to understand the effects of various liner features. It was observed that perforate shape could have a marked effect on the measured liner drag and led to the discovery of a low-drag geometry that cut the drag penalty between a perforate and a smooth wall by approximately 50%. Investigations of novel perforates to further reduce drag were stymied by measurement uncertainty that prevented further resolution of drag differences between configurations. The current study details efforts to understand the causes of this uncertainty and describe changes made to testing methods to reduce it. Previously evaluated perforates were retested with these improvements in the NASA Langley Grazing Flow Impedance Tube to determine their relative drag at a flow speed of Mach 0.5 without acoustic excitation. Their performance was compared to the previously identified low-drag geometry to see if further drag reductions could be realized.

drag↗
NASA NTRS2014

Estimating Sampling Biases and Measurement Uncertainties of AIRS-AMSU-A Temperature and Water Vapor Observations Using MERRA Reanalysis

We use MERRA (Modern Era Retrospective-Analysis for Research Applications) temperature and water vapor data to estimate the sampling biases of climatologies derived from the AIRS/AMSU-A (Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit-A) suite of instruments. We separate the total sampling bias into temporal and instrumental components. The temporal component is caused by the AIRS/AMSU-A orbit and swath that are not able to sample all of time and space. The instrumental component is caused by scenes that prevent successful retrievals. The temporal sampling biases are generally smaller than the instrumental sampling biases except in regions with large diurnal variations, such as the boundary layer, where the temporal sampling biases of temperature can be +/- 2 K and water vapor can be 10% wet. The instrumental sampling biases are the main contributor to the total sampling biases and are mainly caused by clouds. They are up to 2 K cold and greater than 30% dry over mid-latitude storm tracks and tropical deep convective cloudy regions and up to 20% wet over stratus regions. However, other factors such as surface emissivity and temperature can also influence the instrumental sampling bias over deserts where the biases can be up to 1 K cold and 10% wet. Some instrumental sampling biases can vary seasonally and/or diurnally. We also estimate the combined measurement uncertainties of temperature and water vapor from AIRS/AMSU-A and MERRA by comparing similarly sampled climatologies from both data sets. The measurement differences are often larger than the sampling biases and have longitudinal variations.

MERRA↗
NASA NTRSDate not supplied

Atmospheric Variability and Measurement Uncertainty: Pitfalls in Averaging in situ Data

Satellite measurements and atmospheric models, two essential components of the integrated global observing system, provide crucial tools for monitoring and predicting regional and global foci, spanning numerous Earth Science fields. Unfortunately, models can lack the spatial and temporal resolution needed to resolve finer scale structure. Satellite measurements also have similar tempo-spatial restriction issues, but additionally can only measure certain species and can have biases that must be evaluated. Ground measurement networks are critical components of this system, by providing both independent inputs of species needed by models (including those that satellites do not provide) and assisting with investigation of biases in satellite products. Similarly, aircraft measurements play a vital role in closing the gaps between satellite measurements, model products, and ground monitoring networks by providing high accuracy, high-resolution data on local to regional spatial scales. Therefore, both ground-based and airborne observations are widely used to assess model predictions and satellite observations. One of the great challenges in using both ground and aircraft data in this fashion is matching the data temporally and spatially to the model/satellite data. A meaningful comparison with model or satellite requires a solid assessment of the variability of the in-situ measurements, which include both the instrument uncertainty and the statistical uncertainty due to atmospheric variability. While instrument uncertainty is generally more straightforwardly characterized, it can be challenging to accurately capture this variability uncertainty as it often presents in a non-Gaussian manner (e.g. emission plumes, frontal passages). We will present results examining spatial and temporal variability over a selection of scales relevant to satellite measurements and models of several in situ measurement species spanning both airborne and ground measurements. The extent of non-Gaussian variability will be quantified, and we will discuss additional statistical parameters that help assess the fitness of gaussian variability assumption when temporally or spatially averaging.

satellite validation↗
NASA NTRS1994

Measurement uncertainty and feasibility study of a flush airdata system for a hypersonic flight experiment

Presented is a feasibility and error analysis for a hypersonic flush airdata system on a hypersonic flight experiment (HYFLITE). HYFLITE heating loads make intrusive airdata measurement impractical. Although this analysis is specifically for the HYFLITE vehicle and trajectory, the problems analyzed are generally applicable to hypersonic vehicles. A layout of the flush-port matrix is shown. Surface pressures are related airdata parameters using a simple aerodynamic model. The model is linearized using small perturbations and inverted using nonlinear least-squares. Effects of various error sources on the overall uncertainty are evaluated using an error simulation. Error sources modeled include boundarylayer/viscous interactions, pneumatic lag, thermal transpiration in the sensor pressure tubing, misalignment in the matrix layout, thermal warping of the vehicle nose, sampling resolution, and transducer error. Using simulated pressure data for input to the estimation algorithm, effects caused by various error sources are analyzed by comparing estimator outputs with the original trajectory. To obtain ensemble averages the simulation is run repeatedly and output statistics are compiled. Output errors resulting from the various error sources are presented as a function of Mach number. Final uncertainties with all modeled error sources included are presented as a function of Mach number.

Whitmore, Stephen A.↗
NASA NTRS2017

Interval Predictor Models for Data with Measurement Uncertainty

An interval predictor model (IPM) is a computational model that predicts the range of an output variable given input-output data. This paper proposes strategies for constructing IPMs based on semidefinite programming and sum of squares (SOS). The models are optimal in the sense that they yield an interval valued function of minimal spread containing all the observations. Two different scenarios are considered. The first one is applicable to situations where the data is measured precisely whereas the second one is applicable to data subject to known biases and measurement error. In the latter case, the IPMs are designed to fully contain regions in the input-output space where the data is expected to fall. Moreover, we propose a strategy for reducing the computational cost associated with generating IPMs as well as means to simulate them. Numerical examples illustrate the usage and performance of the proposed formulations.

Lacerda, Marcio J.↗
NASA NTRS1983

Reduction of flow-measurement uncertainties in laser velocimeters with nonorthogonal channels

An analysis of certain geometrical limitations inherent in the application of laser velocimeters with nonorthogonal channels has led to the development of advanced-LDA-calibration and data-acquisition techniques that minimize systematic and statistical errors, respectively. The data-acquisition technique optimizes the number of velocity samples collected from three velocimeter channels as a function of local turbulence intensity, vector direction, and prescribed confidence interval. Linear velocity surveys and streamline traces measured in a turbulent flow field with a three-dimensional laser velocimeter are presented and the validity and accuracy of the theoretical analysis are discussed.

Snyder, P. K.↗
NASA NTRS1991

Measuring uncertainty by extracting fuzzy rules using rough sets

Despite the advancements in the computer industry in the past 30 years, there is still one major deficiency. Computers are not designed to handle terms where uncertainty is present. To deal with uncertainty, techniques other than classical logic must be developed. The methods are examined of statistical analysis, the Dempster-Shafer theory, rough set theory, and fuzzy set theory to solve this problem. The fundamentals of these theories are combined to possibly provide the optimal solution. By incorporating principles from these theories, a decision making process may be simulated by extracting two sets of fuzzy rules: certain rules and possible rules. From these rules a corresponding measure of how much these rules is believed is constructed. From this, the idea of how much a fuzzy diagnosis is definable in terms of a set of fuzzy attributes is studied.

Worm, Jeffrey A.↗
NASA NTRS2016

Influence of Measurement Uncertainties on Fractional Solubility of Iron in Mineral Aerosols over the Oceans

The atmospheric supply of mineral dust iron (Fe) plays a crucial role in the Earths biogeochemical cycle and is of specific importance as a micronutrient in the marine environment. Observations show several orders of magnitude variability in the fractional solubility of Fe in mineral dust aerosols, making it hard to assess the role of mineral dust for the global ocean biogeochemical Fe cycle. In this study we compare the operational solubility of mineral dust aerosol Fe associated with the flow-through leaching protocol to the results of the global 3-D chemical transport model GEOS-Chem. According to the protocol, aerosol Fe is defined as soluble by first deionized water leaching of mineral dust through a 0.45 m pore size membrane followed by acidification and storage of the leachate over a long period of time prior to analysis. To estimate the uncertainty in soluble Fe results introduced by the flow-through leaching protocol, we prescribe an average 50 (range of 30 to 70) fractional solubility to sub-0.45 m sized mineral dust particles that may inadvertently pass the filter and end up in the acidified (at pH1.7) leachate for a couple of month period. In the model, the fractional solubility of Fe is either explicitly calculated using a complex mineral aerosol Fe dissolution module, or prescribed to be 1 and 4. Calculations show that the fractional solubility of Fe derived through the flow-through leaching is higher compared to the model results. The largest differences (40) are predicted to occur farther away from the dust source regions, over the areas where sub-0.45 m sized mineral dust particles contribute a larger fraction of the total mineral dust mass. This study suggests that different methods used in soluble Fe measurements and inconsistences in the operational definition of filterable Fe in marine environment and soluble Fe in atmospheric aerosols are likely to contribute to the wide range of fractional solubility of aerosol Fe reported in the literature.

Soluble Iron↗