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NASA NTRS1975

The accuracy of far-field noise obtained by the mathematical extrapolation of near-field noise data

Results are described of an analytical study of the accuracy and limitations of a technique that permits the mathematical extrapolation of near-field noise data to far-field conditions. The effects of the following variables on predictive accuracy of the far-field pressure were examined: (1) number of near-field microphones; (2) length of source distribution; (3) complexity of near-field and far-field distributions; (4) source-to-microphone distance; and (5) uncertainties in microphone data and imprecision in the location of the near-field microphones. It is shown that the most important parameters describing predictive accuracy are the number of microphones, the ratio of source length to acoustic wavelength, (L/wavelength), and the error in location of near-field microphones. If microphone measurement and location errors are not included, then far-field pressures can be accurately predicted up to L/wavelength values of 15 using approximately 50 microphones. For maximum microphone location errors of + or - 1 cm, only an accuracy of + or - 2-1/2 db can be attained with approximately 40 microphones for the highest L/wavelength of 10.

Ahtye, W. F.↗

NASA NTRSDate not supplied

Flow Boiling & Condensation Experiment (FBCE): Flow Boiling in Earth Gravity and Onboard the International Space Station

Two phase thermal management systems that capitalize on both latent and sensible heats of the working fluid can yield orders of magnitude enhancements in flow boiling and condensation heat transfer coefficients and reduce size and weight of future space systems. Because the understanding of microgravity influences on two-phase flow and heat transfer is quite limited, there is presently an urgent need for a new experimental microgravity facility to enable investigators to perform long-duration flow boiling and condensation experiments in pursuit of reliable databases. This presentation will discuss results from the Flow Boiling and Condensation Experiment (FBCE), a collaborative effort between Purdue University and NASA Glenn Research Center. Experiments have been performed using the final system with the Flow Boiling Module (FBM) in vertical orientation in Earth gravity (Mission Sequence Tests, MST) and in microgravity onboard the International Space Station (ISS). High-speed-video flow visualization, heat transfer, and critical heat flux (CHF) results from the MST are presented for both subcooled liquid and saturated liquid-vapor inlet conditions. CHF predictions made using the Interfacial Lift-off Model are compared with a consolidated database made by compiling FBM datasets obtained in prior years for different orientations in Earth gravity and on parabolic flights. New explicit correlations for CHF and subcooled flow boiling heat transfer coefficient are developed and shown to be excellent in their predictive accuracies against consolidated experimental databases. Computations are performed for flows in microgravity and horizontal flows in Earth gravity, the results of which show a good predictive accuracy for both void fraction and wall temperature. Finally, similar preliminary results from the recent ISS experiments are presented.

Issam Mudawar↗

NASA NTRS1975

The accuracy of far-field noise obtained by the mathematical extrapolation of near-field noise data

Results are presented for an analytical study of the accuracy and limitations of a technique that permits the mathematical extrapolation of near-field noise data to far-field conditions. The effects of the following variables on predictive accuracy of the far-field pressure were examined: (1) number of near-field microphones; (2) length of source distribution; (3) complexity of near-field and far-field distributions; (4) source-to-microphone distance; and (5) uncertainties in microphone data and imprecision in the location of the near-field microphones. It is shown that the most important parameters describing predictive accuracy are the number of microphones, the ratio of source length to acoustic wavelength (L/lambda), and the error in location of near-field microphones. For maximum microphone location errors of plus or minus 1 cm, only an accuracy of plus or minus 2.5 dB can be attained with approximately 40 microphones for the highest L/lambda of 10.

Ahtye, W. F.↗

NASA NTRS1992

Interval prediction in structural dynamic analysis

Methods for assessing the predictive accuracy of structural dynamic models are examined with attention given to the effects of modal mass, stiffness, and damping uncertainties. The methods are based on a nondeterministic analysis called 'interval prediction' in which interval variables are used to describe parameters and responses that are unknown. Statistical databases for generic modeling uncertainties are derived from experimental data and incorporated analytically to evaluate responses. Covariance matrices of modal mass, stiffness, and damping parameters are propagated numerically in models of large space structures by means of three methods. The test data tend to fall within the predicted intervals of uncertainty determined by the statistical databases. The present findings demonstrate the suitability of using data from previously analyzed and tested space structures for assessing the predictive accuracy of an analytical model.

Hasselman, Timothy K.↗

NASA NTRS2016

Dynamic Filtering Improves Attentional State Prediction with fNIRS

Brain activity can predict a person's level of engagement in an attentional task. However, estimates of brain activity are often confounded by measurement artifacts and systemic physiological noise. The optimal method for filtering this noise - thereby increasing such state prediction accuracy - remains unclear. To investigate this, we asked study participants to perform an attentional task while we monitored their brain activity with functional near infrared spectroscopy (fNIRS). We observed higher state prediction accuracy when noise in the fNIRS hemoglobin [Hb] signals was filtered with a non-stationary (adaptive) model as compared to static regression (84% +/- 6% versus 72% +/- 15%).

Harrivel, Angela R.↗

NASA NTRS1987

NORAD/SPACECMD comments concerning the atmospheric density models

Recent models do not produce more accurate neutral densities (although they require more computer time), regardless of the level of solar activity. This implies that there has been no measurable improvement of the calculation of neutral density since the early 60s. With known solar flux and geomagnetic activity (Ap) inputs, the density evaluation needs improvement. For highly eccentric orbits which span low to high altitude, the accuracy generated which has an altitude limitation of 1000 km remains comparable with those obtained by more recent models. This implies that either the density at 1000 km and above is insignificant or that the values provided by the recent models at high altitudes may not be reliable or both. Prediction accuracies obtained through the use of precision data from the Defense Mapping Agency are generally comparable to those obtained by using operational sensor data. This implies that the prediction accuracy problem is not necessarily caused by less accurate observations. The definition of the mean solar flux F10.7 is not universal.

Liu, Joseph J. F.↗

NASA NTRS2019

Isotopologue Consistency of Semi-Empirically Computed InfraRed Line Lists and Further Improvement for Rare Isotopologues: CO2 and SO2 Case Studies.

The semi-empirical molecular rovibrational IR line lists, such as ExoMol, TheoReTs, and Ames, combine the experimental accuracy and theoretical power to reach better than 0.1 cm-1 accuracy for line positions and better than 80-90% agreement for line intensities. The quality of these existing semi-empirical IR lists allows further improvements of intensity and line positions for those unobserved minor isotopologues. This paper presents our new BTRHE (Best Theory + Reliable High-resolution Experiment) strategy implementation. For line intensity, the isotopologue consistency and the patterns of mass dependence in the Ames-296K SO2 and CO2 IR lists are quantitatively presented along the mass-inverse coordinates. The consistency and patterns are better than those in existing experimental data. The methodology proposed here can be used to identify inconsistencies, outliers, and mistakes in intensities, and help improve Effective Dipole Model (EDM) and molecular IR databases. We call for an experimental study on the 50006 and 60007 bands of CO2 628. For line position predictions, a simple approach combining the variational IR line lists with Effective Hamiltonian (EH) model may refine the effective rotational constants A0/B0/C0 and quartic centrifugal distortion constants of minor isotopologues. The prediction accuracy may be improved by two orders of magnitude, i.e. reaching 0-5 MHz prediction accuracy in the range of J<20-30, Ka<10-20, and 0.01-0.02 MHz accuracy for A0/B0/C0. Several important factors have been systematically investigated and discussed, e.g. convergence, uncertainties, higher order terms, fixing EH parameters, mass coordinates, etc. Amicrowave (MW) line set consisting of 644,636 strong transitions for all 30 isotopologues and corresponding refined EH(Ames) parameters are reported in the supplementary material. This approach may be easily extended to rovibrational bands, hot bands, and other molecular systems.

Xinchuan Huang↗

NASA NTRS2017

Filter Tuning Using the Chi-Squared Statistic

This paper examines the use of the Chi-square statistic as a means of evaluating filter performance. The goal of the process is to characterize the filter performance in the metric of covariance realism. The Chi-squared statistic is the value calculated to determine the realism of a covariance based on the prediction accuracy and the covariance values at a given point in time. Once calculated, it is the distribution of this statistic that provides insight on the accuracy of the covariance. The process of tuning an Extended Kalman Filter (EKF) for Aqua and Aura support is described, including examination of the measurement errors of available observation types, and methods of dealing with potentially volatile atmospheric drag modeling. Predictive accuracy and the distribution of the Chi-squared statistic, calculated from EKF solutions, are assessed.

Filter Tuning↗

NASA NTRS1991

Assessing damping uncertainty in space structures with fuzzy sets

NASA has been interested in the development of methods for evaluating the predictive accuracy of structural dynamic models. This interest stems from the use of mathematical models in evaluating the structural integrity of all spacecraft prior to flight. Space structures are often too large and too weak to be tested fully assembled in a ground test lab. The predictive accuracy of a model depends on the nature and extent of its experimental verification. The further the test conditions depart from in-service conditions, the less accurate the model will be. Structural damping is known to be one source of uncertainty in models. The uncertainty in damping is explored in order to evaluate the accuracy of dynamic models. A simple mass-spring-dashpot system is used to illustrate a comparison among three methods for propagating uncertainty in structural dynamics models: the First Order Method, the Numerical Simulation Method, and the Fuzzy Set Method. The Fuzzy Set Method is shown to bound the range of possible responses and thus to provide a valuable limiting check on the First Order Method near resonant conditions. Fuzzy Methods are a relative inexpensive alternative to numerical simulation.

Ross, Timothy J.↗

NASA NTRS2017

Filter Tuning Using the Chi-Squared Statistic

The Goddard Space Flight Center (GSFC) Flight Dynamics Facility (FDF) performs orbit determination (OD) for the Aqua and Aura satellites. Both satellites are located in low Earth orbit (LEO), and are part of what is considered the A-Train satellite constellation. Both spacecraft are currently in the science phase of their respective missions. The FDF has recently been tasked with delivering definitive covariance for each satellite.The main source of orbit determination used for these missions is the Orbit Determination Toolkit developed by Analytical Graphics Inc. (AGI). This software uses an Extended Kalman Filter (EKF) to estimate the states of both spacecraft. The filter incorporates force modelling, ground station and space network measurements to determine spacecraft states. It also generates a covariance at each measurement. This covariance can be useful for evaluating the overall performance of the tracking data measurements and the filter itself. An accurate covariance is also useful for covariance propagation which is utilized in collision avoidance operations. It is also valuable when attempting to determine if the current orbital solution will meet mission requirements in the future.This paper examines the use of the Chi-square statistic as a means of evaluating filter performance. The Chi-square statistic is calculated to determine the realism of a covariance based on the prediction accuracy and the covariance values at a given point in time. Once calculated, it is the distribution of this statistic that provides insight on the accuracy of the covariance.For the EKF to correctly calculate the covariance, error models associated with tracking data measurements must be accurately tuned. Over estimating or under estimating these error values can have detrimental effects on the overall filter performance. The filter incorporates ground station measurements, which can be tuned based on the accuracy of the individual ground stations. It also includes measurements from the NASA space network (SN), which can be affected by the assumed accuracy of the TDRS satellite state at the time of the measurement.The force modelling in the EKF is also an important factor that affects the propagation accuracy and covariance sizing. The dominant force in the LEO orbit regime is the drag force caused by atmospheric drag. Accurate accounting of the drag force is especially important for the accuracy of the propagated state. The implementation of a box and wing model to improve drag estimation accuracy, and its overall effect on the covariance state is explored.The process of tuning the EKF for Aqua and Aura support is described, including examination of the measurement errors of available observation types (Doppler and range), and methods of dealing with potentially volatile atmospheric drag modeling. Predictive accuracy and the distribution of the Chi-square statistic, calculated based of the ODTK EKF solutions, are assessed versus accepted norms for the orbit regime.

Covariance Analysis↗

NASA NTRSDate not supplied

Comparison of Two Load Prediction Methods for Strain-Gage Balances

Data from a five-component semi-span balance is used to perform a systematic comparison of the load prediction accuracy of two load prediction methods. Both methods independently obtain the load prediction equations from multivariate least squares fits of balance calibration data. The first method is called the Non-Iterative Method. This approach directly uses regression models of the individual load components of a balance for the load prediction. The second method is called the Iterative Method. This alternate approach uses a load iteration equation for the load prediction that is constructed from the regression coefficients of the gage outputs of the balance. Basic characteristics of the two methods are reviewed. Afterwards, both methods are applied to calibration, check load, and wind tunnel test data of a five-component semi-span balance. Selected analysis results are compared. These comparisons confirm that the accuracy of the two methods is the same for all practical purposes.

strain-gage balance↗

NASA NTRS1989

More accurate predictions with transonic Navier-Stokes methods through improved turbulence modeling

Significant improvements in predictive accuracies for off-design conditions are achievable through better turbulence modeling; and, without necessarily adding any significant complication to the numerics. One well established fact about turbulence is it is slow to respond to changes in the mean strain field. With the 'equilibrium' algebraic turbulence models no attempt is made to model this characteristic and as a consequence these turbulence models exaggerate the turbulent boundary layer's ability to produce turbulent Reynolds shear stresses in regions of adverse pressure gradient. As a consequence, too little momentum loss within the boundary layer is predicted in the region of the shock wave and along the aft part of the airfoil where the surface pressure undergoes further increases. Recently, a 'nonequilibrium' algebraic turbulence model was formulated which attempts to capture this important characteristic of turbulence. This 'nonequilibrium' algebraic model employs an ordinary differential equation to model the slow response of the turbulence to changes in local flow conditions. In its original form, there was some question as to whether this 'nonequilibrium' model performed as well as the 'equilibrium' models for weak interaction cases. However, this turbulence model has since been further improved wherein it now appears that this turbulence model performs at least as well as the 'equilibrium' models for weak interaction cases and for strong interaction cases represents a very significant improvement. The performance of this turbulence model relative to popular 'equilibrium' models is illustrated for three airfoil test cases of the 1987 AIAA Viscous Transonic Airfoil Workshop, Reno, Nevada. A form of this 'nonequilibrium' turbulence model is currently being applied to wing flows for which similar improvements in predictive accuracy are being realized.

Johnson, Dennis A.↗

NASA NTRSDate not supplied

Modeling Shock-Separated Flow with the Active Model Split

The predictive accuracy of typical hybrid RANS/LES models is limited by several shortcomings, such as modeled-stress depletion and a dependence on scalar grid measures. The "active model-split" (AMS) hybrid RANS/LES model was developed by Haering, Oliver, and Moser to address these shortcomings. In this model, the mean and fluctuating portions of the stress are modeled separately; this allows for a consistent treatment of the mean flow even in the presence of resolved fluctuations. Part of this hybrid model is an active forcing, which generates turbulent fluctuations in regions that support LES-type fluctuations. In this presentation, some recent results are shown using the active model-split for shock-separated flows. The effects of the active forcing are examined, including the effects on anisotropy and acoustics. Superior predictive accuracy over RANS is shown for some quantities of interest, such as the shock location.

RANS↗

NASA NTRS1975

The Annular Suspension and Pointing (ASP) system for space experiments and predicted pointing accuracies

An annular suspension and pointing system consisting of pointing assemblies for coarse and vernier pointing is described. The first assembly is attached to a carrier spacecraft (e.g., the space shuttle) and consists of an azimuth gimbal and an elevation gimbal which provide 'coarse' pointing. The second or vernier pointing assembly is made up of magnetic actuators of suspension and fine pointing, roll motor segments, and an instrument or experiment mounting plate around which is attached a continuous annular rim similar to that used in the annular momentum control device. The rim provides appropriate magnetic circuits for the actuators and the roll motor segments for any instrument roll position. The results of a study to determine the pointing accuracy of the system in the presence of crew motion disturbances are presented. Typical 3 sigma worst-case errors are found to be of the order of 0.001 arc-second.

Anderson, W. W.↗

NASA NTRS2016

Improving the Accuracy of Predicting Maximal Oxygen Consumption (VO2pk)

Maximal oxygen (VO2pk) is the maximum amount of oxygen that the body can use during intense exercise and is used for benchmarking endurance exercise capacity. The most accurate method to determineVO2pk requires continuous measurements of ventilation and gas exchange during an exercise test to maximal effort, which necessitates expensive equipment, a trained staff, and time to set-up the equipment. For astronauts, accurate VO2pk measures are important to assess mission critical task performance capabilities and to prescribe exercise intensities to optimize performance. Currently, astronauts perform submaximal exercise tests during flight to predict VO2pk; however, while submaximal VO2pk prediction equations provide reliable estimates of mean VO2pk for populations, they can be unacceptably inaccurate for a given individual. The error in current predictions and logistical limitations of measuring VO2pk, particularly during spaceflight, highlights the need for improved estimation methods.

Downs, Meghan E.↗

NASA NTRSDate not supplied

Exploring the Accuracy of RANS Simulations for Mars Entry Vehicles

Accurate yet inexpensive predictions of aerodynamic coefficients for Mars entry vehicles have remained a consistent challenge over the past five decades. Below Mach 6, drag on the backshell becomes significant and must be accurately predicted. Steady Reynolds-averaged Navier-Stokes (RANS) models are commonly used, despite their poor predictions of backshell pressure. While scale-resolving simulations have shown promise in the past decade, there is still a need for cheap, accurate RANS predictions for large aerodynamic databases. The Mars Science Laboratory (MSL) is used as a case study to examine predictive accuracy and known shortcomings for RANS predictions of Mars entry vehicles. Several different grid generation techniques are compared, including a comparison between prismatic boundary layer grids and fully unstructured, tetrahedral grids. Comparisons are made to experimental data for Mach 2.5, 3.5, and 4.5. The accuracy of predicted aerodynamic coefficients is examined. Overpredictions in axial force and drag are explained by a closer examination of the surface pressure. These findings document sensitivities and best practices for future RANS database development of Mars entry vehicles.

RANS↗

NASA NTRSDate not supplied

Exploring the Accuracy of RANS Simulations for Mars Entry Vehicles

CFD↗

NASA NTRS2019

Solar Activity Modeling: From Subgranular Dynamical Scales to the Solar Cycles

Dynamical effects of solar magnetoconvection span a wide range spatial and temporal scales that extends from the interior to the corona and from fast turbulent motions to the global-Sun magnetic activity. To study the solar activity on short temporal scales (from minutes to hours), we use 3D radiative MHD simulations that allow us to investigate complex turbulent interactions that drive various phenomena, such as plasma eruptions, spontaneous formation of magnetic structures, funnel-like structures and magnetic loops in the corona, and others. In particular, we focus on multi-scale processes of energy exchange across the different layers, which contribute to the corona heating and eruptive dynamics, as well as interlinks between different layers of the solar interior and atmosphere. For modeling the global-scale activity we use the data assimilation approach that has demonstrated great potential for building reliable long-term forecasts of solar activity. In particular, it has been shown that the Ensemble Kalman Filter (EnKF) method applied to the Parker-Kleeorin-Ruzmakin dynamo model is capable of predicting solar activity up to one sunspot cycle ahead in time, as well as estimating the properties of the next cycle a few years before it begins. In this presentation, using the available magnetogram data, we discuss development of the methodology and forecast quality criteria (including forecast uncertainties and sources of errors). We demonstrate the influence of observational limitation on the prediction accuracy. We present the EnKF predictions of the upcoming Solar Cycle 25 based on both the sunspot number series and observed magnetic fields, and discuss the uncertainties and potential of the data assimilation approach for modeling and forecasting the solar activity.

Kitiashvili, I. N.↗