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All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Development and Use of Engineering Standards for Computational Fluid Dynamics for Complex Aerospace Systems

Computational fluid dynamics (CFD) and other advanced modeling and simulation (M&S) methods are increasingly relied on for predictive performance, reliability and safety of engineering systems. Analysts, designers, decision makers, and project managers, who must depend on simulation, need practical techniques and methods for assessing simulation credibility. The AIAA Guide for Verification and Validation of Computational Fluid Dynamics Simulations (AIAA G-077-1998 (2002)), originally published in 1998, was the first engineering standards document available to the engineering community for verification and validation (V&V) of simulations. Much progress has been made in these areas since 1998. The AIAA Committee on Standards for CFD is currently updating this Guide to incorporate in it the important developments that have taken place in V&V concepts, methods, and practices, particularly with regard to the broader context of predictive capability and uncertainty quantification (UQ) methods and approaches. This paper will provide an overview of the changes and extensions currently underway to update the AIAA Guide. Specifically, a framework for predictive capability will be described for incorporating a wide range of error and uncertainty sources identified during the modeling, verification, and validation processes, with the goal of estimating the total prediction uncertainty of the simulation. The Guide's goal is to provide a foundation for understanding and addressing major issues and concepts in predictive CFD. However, this Guide will not recommend specific approaches in these areas as the field is rapidly evolving. It is hoped that the guidelines provided in this paper, and explained in more detail in the Guide, will aid in the research, development, and use of CFD in engineering decision-making.

Lee, Hyung B.↗
NASA NTRS2024

Development of a Data Fusion Methodology for Lineload Aerodynamic Databases for a Launch Vehicle during Liftoff and Transition

The need for databases for the distributed loading on launch vehicles during the early portion of flight necessitates the use of expensive computational flows in regimes where wake effects dominate. While also being expensive, this is a regime that computational tools tend to historically have problems simulating accurately. To help tackle this problem, a method of data fusion to combine computational results to wind tunnel derived force and moment data is developed. Using this method, significant reduction in computational costs and increases in confidence of the final product is possible and has been used to generate several databases for the Space Launch System (SLS) at NASA. While the full details of database generation are not part of this work, the crucial method at its core is developed here. Two SLS geometries are used throughout the work to demonstrate the techniques. These are two of the larger geometries and represent both planned crewed missions to the Moon as well as potential cargo missions to deep space. The method uses principal component analysis (PCA) to generate a reduced ordered model (ROM) to help fill in the full parameter space. Other similar techniques are explored, but were not found to have a significant result on the predictions of the ROM. Because the full number of components are kept to generate the model, this lack of difference is expected. This method is then extended to ensure that predicted surfaces match trusted force and moment data derived from wind tunnel testing. This extension is done by setting up a constrained optimization problem in order to minimize the deviation from the surface resolved computational data while still integrating to the desired values. When generating the constrained optimization problem, a weighting factor to balance these competing needs is introduced. The work compares previously introduced weighting terms from similar work to the proposed terms and shows that the previously used terms do not have as desirable behavior in this flow regime. This method is then expanded by developing a technique to incorporate uncertainty quantification into the developed data fusion methodology. This expansion takes a two pronged approach. One examines transferring the uncertainties in the force and moment database and characterizes how those adjustments change the predicted lineloads. The second looks at model form error and looks how rebuilding the model using slightly different data changes the predictions. These two terms are then combined in order to create an uncertainty model that takes both effects into account. The limitations of the proposed methods is then discussed as well as possible techniques to address these shortcomings.

Launch Vehicles↗
NASA NTRS1989

Model-based quantification of image quality

In 1982, Park and Schowengerdt published an end-to-end analysis of a digital imaging system quantifying three principal degradation components: (1) image blur - blurring caused by the acquisition system, (2) aliasing - caused by insufficient sampling, and (3) reconstruction blur - blurring caused by the imperfect interpolative reconstruction. This analysis, which measures degradation as the square of the radiometric error, includes the sample-scene phase as an explicit random parameter and characterizes the image degradation caused by imperfect acquisition and reconstruction together with the effects of undersampling and random sample-scene phases. In a recent paper Mitchell and Netravelli displayed the visual effects of the above mentioned degradations and presented subjective analysis about their relative importance in determining image quality. The primary aim of the research is to use the analysis of Park and Schowengerdt to correlate their mathematical criteria for measuring image degradations with subjective visual criteria. Insight gained from this research can be exploited in the end-to-end design of optical systems, so that system parameters (transfer functions of the acquisition and display systems) can be designed relative to each other, to obtain the best possible results using quantitative measurements.

Hazra, Rajeeb↗
NASA NTRS2014

Assessment of Satellite Ocean Colour Radiometry and Derived Geophysical Products

Standardization of methods to assess and assign quality metrics to satellite ocean color radiometry and derived geophysical products has become paramount with the inclusion of the marine reflectance and chlorophyll-a concentration (Chla) as essential climate variables (ECV; [1]) and the recognition that optical remote sensing of the oceans can only contribute to climate research if and when a continuous succession of satellite missions can be shown to collectively provide a consistent, long-term record with known uncertainties. In 20 years, the community has made significant advancements toward that objective, but providing a complete uncertainty budget for all products and for all conditions remains a daunting task. In the retrieval of marine water-leaving radiance from observed top-of-atmosphere radiance, the sources of uncertainties include those associated with propagation of sensor noise and radiometric calibration and characterization errors, as well as a multitude of uncertainties associated with the modeling and removal of effects from the atmosphere and sea surface. This chapter describes some common approaches used to assess quality and consistency of ocean color satellite products and reviews the current status of uncertainty quantification in the field. Its focus is on the primary ocean color product, the spectrum of marine reflectance Rrs, but uncertainties in some derived products such as the Chla or inherent optical properties (IOPs) will also be considered.

Ocean↗
NASA NTRS1977

Fundamental Analysis of the Linear Multiple Regression Technique for Quantification of Water Quality Parameters from Remote Sensing Data

Constituents with linear radiance gradients with concentration may be quantified from signals which contain nonlinear atmospheric and surface reflection effects for both homogeneous and non-homogeneous water bodies provided accurate data can be obtained and nonlinearities are constant with wavelength. Statistical parameters must be used which give an indication of bias as well as total squared error to insure that an equation with an optimum combination of bands is selected. It is concluded that the effect of error in upwelled radiance measurements is to reduce the accuracy of the least square fitting process and to increase the number of points required to obtain a satisfactory fit. The problem of obtaining a multiple regression equation that is extremely sensitive to error is discussed.

Whitlock, C. H., III↗
NASA NTRS2025

Uncertainty Quantification for Empirical X-59 Sonic Boom Loudness Levels

Estimates of the total uncertainty for empirically determined loudness levels are documented when GRS (Ground Recording System) noise monitors are used to record X 59 sonic boom waveforms. The total uncertainty is characterized by combining nine different sources of uncertainty that may affect the apparent gain of the measurement chain. These uncertainty estimates are presented as expected measurement error relative to the true loudness level, and separate error estimates are provided for eight different noise metrics in which NASA has interest. The behavior of the Perceived Level (PL) metric is studied within the report body, while the total uncertainties for the seven other noise metrics are summarized in appendices for brevity. The effects of four sources of uncertainty are estimated simply from information found on hardware specification sheets provided by the manufacturer. However, mock acoustic recordings are created to estimate the effects of other sources because those effects are expected to induce spectral coloration, so they may vary with noise metric type and sound level. These sources are not well modeled by simple gain adjustments. Importantly, measurement error is computable when processing mock recordings since the true levels are knowable, which is not the case when processing data recorded in the field. Specifically, the true levels are knowable because the components of the mock recordings are separable – e.g., loudness levels of booms can be computed with or without superimposed background noise. Mock acoustic recordings also have the benefit of allowing analysis of sonic booms from vehicles that are not yet flying, like the X-59, since the mock recordings are created by combining vehicle-specific predicted waveforms with other audio sources. The estimates of total measurement error are documented as a function of the signal-to-noise ratio (SNR) of the loudness level, where the corrected SNR is computed while accounting for the effects of the method that is used to correct for background noise contamination when computing the noise metric values. The corrected SNR calculations used here can be applied to both mock recordings and in-field measurements, so the uncertainty of in-field recordings can be found using pre-computed lookup tables that identify the relationship between metric type, corrected SNR, and the expected measurement error.

Sonic Boom↗
NASA NTRS2001

Updated Electronic Testbed System

As we continue to advance in exploring space frontiers, technology must also advance. The need for faster data recovery and data processing is crucial. In this, the less equipment used, and lighter that equipment is, the better. Because integrated circuits become more sensitive in high altitude, experimental verification and quantification is required. The Center for Applied Radiation Research (CARR) at Prairie View A&M University was awarded a grant by NASA to participate in the NASA ER-2 Flight Program, the APEX balloon flight program, and the Student Launch Program. These programs are to test anomalous errors in integrated circuits due to single event effects (SEE). CARR had already begun experiments characterizing the SEE behavior of high speed and high density SRAM's. The research center built a error testing system using a PC-104 computer unit, an Iomega Zip drive for storage, a test board with the components under test, and a latchup detection and reset unit. A test program was written to continuously monitor a stored data pattern in the SRAM chip and record errors. The devices under test were eight 4Mbit memory chips totaling 4Mbytes of memory. CARR was successful at obtaining data using the Electronic TestBed System (EBS) in various NASA ER-2 test flights. These series of high altitude flights of up to 70,000 feet, were effective at yielding the conditions which single event effects usually occur. However, the data received from the series of flights indicated one error per twenty-four hours. Because flight test time is very expensive, the initial design proved not to be cost effective. The need for orders of magnitude with more memory became essential. Therefore, a project which could test more memory within a given time was created. The goal of this project was not only to test more memory within a given time, but also to have a system with a faster processing speed, and which used less peripherals. This paper will describe procedures used to build an updated Electronic Testbed System.

Brewer, Kevin L.↗
NASA NTRS2012

Use of In Situ and Airborne Multiangle Data to Assess MODIS- and Landsat-based Estimates of Surface Albedo

The quantification of uncertainty of global surface albedo data and products is a critical part of producing complete, physically consistent, and decadal land property data records for studying ecosystem change. A current challenge in validating satellite retrievals of surface albedo is the ability to overcome the spatial scaling errors that can contribute on the order of 20% disagreement between satellite and field-measured values. Here, we present the results from an uncertain ty analysis of MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat albedo retrievals, based on collocated comparisons with tower and airborne multi-angular measurements collected at the Atmospheric Radiation Measurement Program s (ARM) Cloud and Radiation Testbed (CART) site during the 2007 Cloud and Land Surface Interaction Campaign (CLAS33 IC 07). Using standard error propagation techniques, airborne measurements obtained by NASA s Cloud Absorption Radiometer (CAR) were used to quantify the uncertainties associated with MODIS and Landsat albedos across a broad range of mixed vegetation and structural types. Initial focus was on evaluating inter-sensor consistency through assessments of temporal stability, as well as examining the overall performance of satellite-derived albedos obtained at all diurnal solar zenith angles. In general, the accuracy of the MODIS and Landsat albedos remained under a 10% margin of error in the SW(0.3 - 5.0 m) domain. However, results reveal a high degree of variability in the RMSE (root mean square error) and bias of albedos in both the visible (0.3 - 0.7 m) and near-infrared (0.3 - 5.0 m) broadband channels; where, in some cases, retrieval uncertainties were found to be in excess of 20%. For the period of CLASIC 07, the primary factors that contributed to uncertainties in the satellite-derived albedo values include: (1) the assumption of temporal stability in the retrieval of 500 m MODIS BRDF values over extended periods of cloud-contaminated observations; and (2) the assumption of spatial 45 and structural uniformity at the Landsat (30 m) pixel scale.

Roman, Miguel O.↗
NASA NTRS2013

Use of In Situ and Airborne Multiangle Data to Assess MODIS and Landsat-Based Estimates of Directional Reflectance and Albedo

The quantification of uncertainty in satellite-derived global surface albedo products is a critical aspect in producing complete, physically consistent, and decadal land property data records for studying ecosystem change. A challenge in validating albedo measurements acquired from space is the ability to overcome the spatial scaling errors that can produce disagreements between satellite and field-measured values. Here, we present the results from an accuracy assessment of MODIS and Landsat-TM albedo retrievals, based on collocated comparisons with tower and airborne Cloud Absorption Radiometer (CAR) measurements collected during the 2007 Cloud and Land Surface Interaction Campaign (CLASIC). The initial focus was on evaluating intersensor consistency through comparisons of intrinsic bidirectional reflectance estimates. Local and regional assessments were then performed to obtain estimates of the resulting scaling uncertainties, and to establish the accuracy of albedo reconstructions during extended periods of precipitation. In general, the satellite derived estimates met the accuracy requirements established for the high-quality MODIS operational albedos at 500 m (the greater of 0.02 units or +/-10% of surface measured values). However, results reveal a high degree of variability in the root-mean-square error (RMSE) and bias of MODIS visible (0.3-0.7 μm) and Landsat-TM shortwave (0.3-5.0 μm) albedos; where, in some cases, retrieval uncertainties were found to be in excess of 15%. Results suggest that an overall improvement in MODIS shortwave albedo retrieval accuracy of 7.8%, based on comparisons between MODIS and CAR albedos, resulted from the removal of sub-grid scale mismatch errors when directly scaling-up the tower measurements to the MODIS satellite footprint.

biosphere↗
NASA NTRSDate not supplied

Uncertainty Quantification of Expected Time-of-Arrival in UAV Flight Trajectory

One of the foremost requirements for accurate in-flight safety monitoring of autonomous unmanned aerial vehicles (UAVs) is tracking of their flight trajectory. Existing UAVs leverage autonomous flight functionalities based on trajectory generation algorithms developed in robotic applications such as polynomial or spline curves in order to facilitate kinematic smoothness, minimum vibrations and fuel efficiency. However in practice, the actual path may be subjected to unexpected local weather conditions, unexpected obstacles along the path or abrupt traffic changes in the low-altitude airspace resulting in large errors of the predicted time-of-arrival at way-points. In this study, an approach to quantify and propagate uncertainty in 4D trajectories is proposed. The paper presents a simple error interval propagation method based on the expected cruise speed of the UAV and its associated uncertainty. The uncertainty is then propagated in time to estimate reasonable confidence intervals on the times-of-arrival of the vehicle at each way-point as well as along the entire flight-path. The uncertainty propagation is demonstrated on a state-of-the-art trajectory generation algorithm based on non-uniform rational B-spline (NURBS) curves. Further, the effect of a stationary wind field is incorporated in the uncertainty propagation approach. The proposed method is implemented on synthetic and real data obtained from flight experiments with a small UAV.

Uncertainty Quantification↗
NASA NTRSDate not supplied

HIFiRE Flight 2 Unstart Reliability Analysis

The objective of this work was to assess the unstart reliability of the HIFiRE Flight 2 system. To do this, a quantification of margins and uncertainties framework was used for comparing model predictions of the predicted combustion induced shock location to the predicted last stable shock location within the isolator. Uncertainty sources included parametric uncertainty in the flight conditions, the heat release model, and turbulence modeling, as well as model verification errors. Additionally, an estimate of model-form uncertainty was established by comparing the model to measured ground test data. A computationally efficient non-intrusive polynomial chaos approach was used to propagate parametric uncertainty through the computational flu-ids dynamics models of both the ground test configuration and the flight vehicle. Compared to direct-connect ground test data, computational fluid dynamics predictions yielded about two duct heights of model-form uncertainty. This was applied to a prediction of flight vehicle unstart margin at the Mach 6.5 flight condition. Building up all of the computational model uncertainty, including parametric uncertainty, verification errors, and the determined model-form uncertainty, a 95% probability level based confidence ratio, or a ratio of total uncertainty to a statistical margin measure, was found to be 0.31 for the flight system.

Thomas K West↗
NASA NTRS2011

Application of Commercial Non-Dispersive Infrared Spectroscopy Sensors for Sub-Ambient Carbon Dioxide Detection

Monitoring carbon dioxide (CO2) concentration within a spacecraft or spacesuit is critically important to ensuring the safety of the crew. Carbon dioxide uniquely absorbs light at wavelengths of 3.95 micrometers and 4.26 micrometers. As a result, non-dispersive infrared (NDIR) spectroscopy can be employed as a reliable and inexpensive method for the quantification of CO2 within the atmosphere. A multitude of commercial-off-the-shelf (COTS) NDIR sensors exist for CO2 quantification. The COTS sensors provide reasonable accuracy so long as the measurements are attained under conditions close to the calibration conditions of the sensor (typically 21.1 C and 1 atm). However, as pressure deviates from atmospheric to the pressures associated with a spacecraft (8.0-10.2 PSIA) or spacesuit (4.1-8.0 PSIA), the error in the measurement grows increasingly large. In addition to pressure and temperature dependencies, the infrared transmissivity through a volume of gas also depends on the composition of the gas. As the composition is not known a priori, accurate sub-ambient detection must rely on iterative sensor compensation techniques. This manuscript describes the development of recursive compensation algorithms for sub-ambient detection of CO2 with COTS NDIR sensors. In addition, the basis of the exponential loss in accuracy is developed theoretically considering thermal, Doppler, and Lorentz broadening effects which arise as a result of the temperature, pressure, and composition of the gas mixture under analysis. As a result, this manuscript provides an approach to employing COTS sensors at sub-ambient conditions and may also lend insight into designing future NDIR sensors for aerospace application.

Swickrath, Michael J.↗
NASA NTRS2015

Sonic Boom Pressure Signature Uncertainty Calculation and Propagation to Ground Noise

The objective of this study was to outline an approach for the quantification of uncertainty in sonic boom measurements and to investigate the effect of various near-field uncertainty representation approaches on ground noise predictions. These approaches included a symmetric versus asymmetric uncertainty band representation and a dispersion technique based on a partial sum Fourier series that allows for the inclusion of random error sources in the uncertainty. The near-field uncertainty was propagated to the ground level, along with additional uncertainty in the propagation modeling. Estimates of perceived loudness were obtained for the various types of uncertainty representation in the near-field. Analyses were performed on three configurations of interest to the sonic boom community: the SEEB-ALR, the 69o DeltaWing, and the LM 1021-01. Results showed that representation of the near-field uncertainty plays a key role in ground noise predictions. Using a Fourier series based dispersion approach can double the amount of uncertainty in the ground noise compared to a pure bias representation. Compared to previous computational fluid dynamics results, uncertainty in ground noise predictions were greater when considering the near-field experimental uncertainty.

West, Thomas K., IV↗
NASA NTRS1992

Full field flow visualization and computer-aided velocity measurements in a bank of cylinders in a wind tunnel

The full field flow tracking (FFFT) method that is presented in this paper uses a laser-generated, mechanically strobed planar sheet of light, a low luminosity TV camera coupled with a long distance microscope, and a computer-controlled video recorder to study nonintrusively and qualitatively the flow structures in a bank of cylinders that are placed in a wind tunnel. This setup simulates an upscale version of the geometry of internal cooling passageways characteristic of small air-cooled radial turbines. The qualitative images supplied by the FFFT system are processed by means of a computer-integrated image quantification (CIIQ) method into quantitative information, trajectories and velocities, that describe the flow upstream of and within the bank of cylinders. The tracking method is Lagrangian in concept, and permits identification and tracking of the same particle, thus facilitating construction of time dependent trajectories and the calculation of true velocities and accelerations. The error analysis evaluates the accuracy with which the seed particles follow the flow and the errors incurred during the quantitative processing of the raw data derived from the FFFT/CIIQ method.

Braun, M. J.↗
NASA NTRS2016

Snow Process Estimation Over the Extratropical Andes Using a Data Assimilation Framework Integrating MERRA Data and Landsat Imagery

A data assimilation framework was implemented with the objective of obtaining high resolution retrospective snow water equivalent (SWE) estimates over several Andean study basins. The framework integrates Landsat fractional snow covered area (fSCA) images, a land surface and snow depletion model, and the Modern Era Retrospective Analysis for Research and Applications (MERRA) reanalysis as a forcing data set. The outputs are SWE and fSCA fields (1985-2015) at a resolution of 90 m that are consistent with the observed depletion record. Verification using in-situ snow surveys showed significant improvements in the accuracy of the SWE estimates relative to forward model estimates, with increases in correlation (0.49-0.87) and reductions in root mean square error (0.316 m to 0.129 m) and mean error (-0.221 m to 0.009 m). A sensitivity analysis showed that the framework is robust to variations in physiography, fSCA data availability and a priori precipitation biases. Results from the application to the headwater basin of the Aconcagua River showed how the forward model versus the fSCA-conditioned estimate resulted in different quantifications of the relationship between runoff and SWE, and different correlation patterns between pixel-wise SWE and ENSO. The illustrative results confirm the influence that ENSO has on snow accumulation for Andean basins draining into the Pacific, with ENSO explaining approximately 25% of the variability in near-peak (1 September) SWE values. Our results show how the assimilation of fSCA data results in a significant improvement upon MERRA-forced modeled SWE estimates, further increasing the utility of the MERRA data for high-resolution snow modeling applications.

SWE↗
NASA NTRS2019

Uncertainty Quantification for JPL Retrievals

We define the notions of verification, validation, and uncertainty quantification (VVUQ), and place them in the context of retrievals of Earth science data. Uncertainty quantification (UQ) is the process of characterizing the uncertainty of a measurement system relative to the true, physical quantity it measures. UQ is needed when measurements are used for scientific inference, assimilated into models with other data, and used for decision-making and risk analysis. Many measurements come with measures of standard error and bias, but these uncertainty measures are often inaccurate, due to the operational need to assume crisp values for parameters which are in fact uncertain, as well as the failure of technical assumptions needed by operational error-estimation approaches. UQ provides a way to study which assumptions fail to hold, and a tool to improve the measurement. We describe a particular, broadly-applicable UQ setup that has been successful for JPL retrievals including OCO-2, and provide recommendations for furthering the UQ state of the practice at JPL.

Turmon, Michael↗
NASA NTRS2014

Detailed Uncertainty Analysis of the ZEM-3 Measurement System

The measurement of Seebeck coefficient and electrical resistivity are critical to the investigation of all thermoelectric systems. Therefore, it stands that the measurement uncertainty must be well understood to report ZT values which are accurate and trustworthy. A detailed uncertainty analysis of the ZEM-3 measurement system has been performed. The uncertainty analysis calculates error in the electrical resistivity measurement as a result of sample geometry tolerance, probe geometry tolerance, statistical error, and multi-meter uncertainty. The uncertainty on Seebeck coefficient includes probe wire correction factors, statistical error, multi-meter uncertainty, and most importantly the cold-finger effect. The cold-finger effect plagues all potentiometric (four-probe) Seebeck measurement systems, as heat parasitically transfers through thermocouple probes. The effect leads to an asymmetric over-estimation of the Seebeck coefficient. A thermal finite element analysis allows for quantification of the phenomenon, and provides an estimate on the uncertainty of the Seebeck coefficient. The thermoelectric power factor has been found to have an uncertainty of +9-14 at high temperature and 9 near room temperature.

Seebeck effect↗
NASA NTRS2015

Sensor Selection for Aircraft Engine Performance Estimation and Gas Path Fault Diagnostics

This paper presents analytical techniques for aiding system designers in making aircraft engine health management sensor selection decisions. The presented techniques, which are based on linear estimation and probability theory, are tailored for gas turbine engine performance estimation and gas path fault diagnostics applications. They enable quantification of the performance estimation and diagnostic accuracy offered by different candidate sensor suites. For performance estimation, sensor selection metrics are presented for two types of estimators including a Kalman filter and a maximum a posteriori estimator. For each type of performance estimator, sensor selection is based on minimizing the theoretical sum of squared estimation errors in health parameters representing performance deterioration in the major rotating modules of the engine. For gas path fault diagnostics, the sensor selection metric is set up to maximize correct classification rate for a diagnostic strategy that performs fault classification by identifying the fault type that most closely matches the observed measurement signature in a weighted least squares sense. Results from the application of the sensor selection metrics to a linear engine model are presented and discussed. Given a baseline sensor suite and a candidate list of optional sensors, an exhaustive search is performed to determine the optimal sensor suites for performance estimation and fault diagnostics. For any given sensor suite, Monte Carlo simulation results are found to exhibit good agreement with theoretical predictions of estimation and diagnostic accuracies.

Gas Turbine Engines↗