Results for “data base for model validation” — Page 54

NASA NTRS2021

SNOOPI: Demonstrating P-Band Reflectometry from Orbit

SigNals Of Opportunity: P-band Investigation (SNOOPI)will be the first on-orbit demonstration of remote sensing using Signals of Opportunity (SoOp) in P-band (240-380 MHz). P-band is needed to penetrate through dense vegetation and into the root zone. The longer wavelength of P-band also increases the unwrapping interval for phase observations. These observations hold the potential for spaceborne remote sensing of root-zone soil moisture (RZSM) and snow water equivalent(SWE), two variables identified as priorities in the 2017-2027 Decadal Survey for Earth Science and Applications from Space. SNOOPI will provide in-space validation of both the P-band SoOp technique and a science instrument prototype. SNOOPI technology validation goals will be met by targeting observations within 9 km of the SMAP calibration/validation sites in the continental United States. A secondary priority is collection of continuous phase data over snow-covered regions. These goals are evaluated under constraints of a limited data budget and mission lifetime, with a launch readiness in early2022. Updates on the development of measurement models and mission planning to support SNOOPI are provided. Aground-based station will be deployed to monitor the non-cooperative sources, in order to reduce risk due to uncertainty in knowledge of the broadcast power, spectrum shape, and orbital position.

J L Garrison↗

NASA NTRS2018

Performance Metrics for the Assessment of Satellite Data Products: An Ocean Color Case Study

Performance assessment of ocean color satellite data has generally relied on statistical metrics chosen for their common usage and the rationale for selecting certain metrics is infrequently explained. Commonly reported statistics based on mean squared errors, such as the coefficient of determination (r2), root mean square error, and regression slopes, are most appropriate for Gaussian distributions without outliers and, therefore, are often not ideal for ocean color algorithm performance assessment, which is often limited by sample availability. In contrast, metrics based on simple deviations, such as bias and mean absolute error, as well as pair-wise comparisons, often provide more robust and straightforward quantities for evaluating ocean color algorithms with non-Gaussian distributions and outliers. This study uses a SeaWiFS chlorophyll-a validation data set to demonstrate a framework for satellite data product assessment and recommends a multimetric and user-dependent approach that can be applied within science, modeling, and resource management communities.

remote sensing↗

NASA NTRS1998

Prediction and Validation of Mars Pathfinder Hypersonic Aerodynamic Data Base

Postflight analysis of the Mars Pathfinder hypersonic, continuum aerodynamic data base is presented. Measured data include accelerations along the body axis and axis normal directions. Comparisons of preflight simulation and measurements show good agreement. The prediction of two static instabilities associated with movement of the sonic line from the shoulder to the nose and back was confirmed by measured normal accelerations. Reconstruction of atmospheric density during entry has an uncertainty directly proportional to the uncertainty in the predicted axial coefficient. The sensitivity of the moment coefficient to freestream density, kinetic models and center-of-gravity location are examined to provide additional consistency checks of the simulation with flight data. The atmospheric density as derived from axial coefficient and measured axial accelerations falls within the range required for sonic line shift and static stability transition as independently determined from normal accelerations.

Gnoffo, Peter A.↗

NASA NTRSDate not supplied

Variable Lidar Ratios for Marine and Dusty Marine Using MODIS AOD Constrained Retrievals and GOCART Model Simulations and their Impact on Level 2 CALIPSO Data Products in Version 5

The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard CALIPSO provided global measurements of attenuated backscatter profiles of various tropospheric aerosol species from June 2006 through June 2023. Extinction profiles are retrieved from these backscatter measurements by assuming a value for the extinction-to-backscatter or lidar ratio (LR). Up until version V4.51 of the data products, only a constant global value for each of the species has been used, spatially and temporally. However, it has been well known from various ground-based measurements that LRs of each aerosol species can potentially vary spatially as well seasonally. For the forthcoming Version 5 of the CALIPSO products, the CALIPSO team will implement variable lidar ratios at 532 nm for the species classified as “marine” and “dusty marine”. Here we present the essential elements of this scheme, which involves estimating lidar ratios by constraining Fernald solutions for the CALIOP particulate backscatter profiles using aerosol optical depths (AOD) from collocated MODIS retrievals. To account for the lack of measurements in various regions and seasons (e.g., Arctic winters), we leverage collocated model sea-salt volume fractions (SSVF) simulated by the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model and use an empirical relationship between the SSVF and LR to build hybrid climatological maps for the entire globe, i.e., using the modelled values when reliable values of the constrained LRs are not available. Initial evaluation of the resulting extinction profiles and corresponding AODs, both globally and in specific regions are presented using a limited amount of data. The primary improvement in AOD occurs in the coastal areas, like the Arabian Sea and Bay of Bengal. The estimated marine LR values in these regions are significantly higher than the constant value of 23 sr used in previous data releases, likely due to mixing with offshore pollution which results in higher AODs. Initial validation results using the CALIPSO Ocean Derived Column Optical Depth (ODCOD) retrievals and ground-based AERONET AOD measurements are presented.

CALIPSO↗

NASA NTRS1977

An in-flight simulation of VTOL hover control concepts

Results of several parametric ground-based simulations covering a variety of VTOL in-hover control concepts are reviewed. The systems considered are angular acceleration, rate, and attitude control, as well as translational rate control. Since many cues are severely restricted by ground-based simulation (e.g., motion, peripheral vision, and environment), some form of in-flight validation of these results is desired. Such a study has been undertaken utilizing the NASA Ames X-14B VTOL aircraft. This in-flight simulator has been configured with a fly-by-wire capability in the hover mode through an analog-/digital variable stability system. This system permits the implementation of either response-feedback or model-following type of control. A comparison of flight- and ground-based data is shown for the attitude control system with the X-14B being flown in both a tethered hover and a free-flight hover.

Corliss, L. D.↗

NASA NTRS1991

Software reliability report

There are many software reliability models which try to predict future performance of software based on data generated by the debugging process. Unfortunately, the models appear to be unable to account for the random nature of the data. If the same code is debugged multiple times and one of the models is used to make predictions, intolerable variance is observed in the resulting reliability predictions. It is believed that data replication can remove this variance in lab type situations and that it is less than scientific to talk about validating a software reliability model without considering replication. It is also believed that data replication may prove to be cost effective in the real world, thus the research centered on verification of the need for replication and on methodologies for generating replicated data in a cost effective manner. The context of the debugging graph was pursued by simulation and experimentation. Simulation was done for the Basic model and the Log-Poisson model. Reasonable values of the parameters were assigned and used to generate simulated data which is then processed by the models in order to determine limitations on their accuracy. These experiments exploit the existing software and program specimens which are in AIR-LAB to measure the performance of reliability models.

Wilson, Larry↗

NASA NTRS1993

Transmission over EHF mobile satellite channels

Land mobile satellite communications at Ka-band (30/20 GHz) are attracting an increasing interest among researchers because of the frequency band availability and the possibility of small earth station designs. However, communications at the Ka-band pose significant challenges in the system designs due to severe channel impairments. Because only very limited experimental data for mobile applications at Ka-band is available, this paper studies the channel characteristics based on experimental data at L-band (1.6/1.5 GHz) and the use of frequency scaling. The land mobile satellite communication channel at Ka-band is modelled as log-normal Rayleigh fading channel. The first and second-order statistics of the fading channel are studied. The performance of a coherent BPSK system over the fading channel at L-band and K-band is evaluated theoretically and validated by computer simulations. Conclusions on the communication channel characteristics and system performance at L-band and Ka-band are presented.

Zhuang, W.↗

NASA NTRS2001

The Use of CASES-97 Observations to Assess and Parameterize the Impact of Land-Surface Heterogeneity on Area-Average Surface Heat Fluxes for Large-Scale Coupled Atmosphere-Hydrology Models

To understand the effects of land-surface heterogeneity and the interactions between the land-surface and the planetary boundary layer at different scales, we develop a multiscale data set. This data set, based on the Cooperative Atmosphere-Surface Exchange Study (CASES97) observations, includes atmospheric, surface, and sub-surface observations obtained from a dense observation network covering a large region on the order of 100 km. We use this data set to drive three land-surface models (LSMs) to generate multi-scale (with three resolutions of 1, 5, and 10 kilometers) gridded surface heat flux maps for the CASES area. Upon validating these flux maps with measurements from surface station and aircraft, we utilize them to investigate several approaches for estimating the area-integrated surface heat flux for the CASES97 domain of 71x74 square kilometers, which is crucial for land surface model development/validation and area water and energy budget studies. This research is aimed at understanding the relative contribution of random turbulence versus organized mesoscale circulations to the area-integrated surface flux at the scale of 100 kilometers, and identifying the most important effective parameters for characterizing the subgrid-scale variability for large-scale atmosphere-hydrology models.

Chen, Fei↗

NASA NTRS2005

F119 Nozzle Flaps Tested at Lewis' CE-22 Facility

The next generation of aircraft fighters requires higher engine performance and enhanced stealth characteristics for air superiority. A Lockheed-Martin/Boeing aircraft with a Pratt & Whitney F119 engine was selected by the Air Force for the next advanced tactical fighter (ATF). As part of this program, the NASA Lewis Research Center entered into a cooperative test program with Pratt & Whitney along with the Air Force to study the performance for various advanced nozzle concepts for the F119 engine. The area of interest was to measure the internal performance (both thrust and flow coefficients) of nozzle flaps redesigned for low observability with minimal performance loss. The experimental program was successfully completed May 1995 in Lewis' CE-22 facility. The models were tested over a wide range of geometric variations and nozzle pressure ratios. Results confirmed that the redesigned nozzle flaps had an insignificant effect on the thrust performance and that the resulting flow patterns should not be a problem in the cooling flow design. The results also agreed well with Pratt & Whitney's computational fluid dynamics analysis. The data obtained from this test were added to the current data base to help validate other performance prediction methodology.

Source record↗

NASA NTRS2005

Empirical Prediction of Aircraft Landing Gear Noise

This report documents a semi-empirical/semi-analytical method for landing gear noise prediction. The method is based on scaling laws of the theory of aerodynamic noise generation and correlation of these scaling laws with current available test data. The former gives the method a sound theoretical foundation and the latter quantitatively determines the relations between the parameters of the landing gear assembly and the far field noise, enabling practical predictions of aircraft landing gear noise, both for parametric trends and for absolute noise levels. The prediction model is validated by wind tunnel test data for an isolated Boeing 737 landing gear and by flight data for the Boeing 777 airplane. In both cases, the predictions agree well with data, both in parametric trends and in absolute noise levels.

Golub, Robert A.↗

NASA NTRS2014

A Population Pharmacokinetic Model for Disposition in Plasma, Saliva and Urine of Scopolamine after Intranasal Administration to Healthy Human Subjects

An intranasal gel formulation of scopolamine (INSCOP) was developed for the treatment of Space Motion Sickness. The bioavailability and pharmacokinetics (PK) were evaluated under the Food and Drug Administration guidelines for clinical trials with an Investigative New Drug (IND) protocol. The aim of this project was to develop a PK model that can predict the relationship between plasma, saliva and urinary scopolamine concentrations using data collected from the IND clinical trials with INSCOP. Methods: Twelve healthy human subjects were administered three dose levels (0.1, 0.2 and 0.4 mg) of INSCOP. Serial blood, saliva and urine samples were collected between 5 min and 24 h after dosing and scopolamine concentrations were measured by using a validated LC‐MS‐MS assay. Pharmacokinetic Compartmental models, using actual dosing and sampling times, were built using Phoenix (version 1.2). Model selection was based on the likelihood ratio test on the difference of criteria (‐2LL) and comparison of the quality of fit plots. Results: The best structural model for INSCOP (minimal ‐2LL= 502.8) was established. It consisted of one compartment each for plasma, saliva and urine, respectively, which were connected with linear transport processes except the nonlinear PK process from plasma to saliva compartment. The best‐fit estimates of PK parameters from individual PK compartmental analysis and Population PK model analysis were shown in Tables 1 and 2, respectively. Conclusion: A population PK model that could predict population and individual PK of scopolamine in plasma, saliva and urine after dosing was developed and validated. Incorporating a non‐linear transfer from plasma to saliva compartments resulted in a significantly improved model fitting. The model could be used to predict scopolamine plasma concentrations from salivary and urinary drug levels, allowing non‐invasive therapeutic monitoring of scopolamine in space and other remote environments.

Wu, L.↗

NASA NTRSDate not supplied

Airport Runway Configuration Management with Offline Model-free Reinforcement Learning

Runway configuration management (RCM) deals with the optimal selection of runways to operate on (for arrivals and departures) based on traffic, surface wind speed, wind direction and other environmental variables. RCM is one of the most challenging tasks in air traffic management, as it relies on operational and environmental variables (e.g., weather forecast) that are highly uncertain and complex to model. In this paper, an innovative and automated approach is deployed using offline model-free reinforcement learning to provide decision-support for RCM. The proposed technology processes historical data about variables of interest, decisions made regarding RCM, and their subsequent outcome, to identify a policy that would encourage good decisions and avoid the poor ones. The policy search is guided by an appropriately chosen weighted utility function (e.g., based on minimizing delays and go-arounds). Finally, the performance of the proposed tool is validated using Charlotte Douglas International Airport as the case study, which shows that the proposed method is superior to other conventional rule-based approaches.

Milad Memarzadeh↗

NASA NTRSDate not supplied

Airport Runway Configuration Management with Offline Model-free Reinforcement Learning

Runway configuration management (RCM) deals with the optimal selection of runways to operate on (for arrivals and departures) based on traffic, surface wind speed, wind direction and other environmental variables. RCM is one of the most challenging tasks in air traffic management, as it relies on operational and environmental variables (e.g., weather forecast) that are highly uncertain and complex to model. In this paper, an innovative and automated approach is deployed using offline model-free reinforcement learning to provide decision-support for RCM. The proposed technology processes historical data about variables of interest, decisions made regarding RCM, and their subsequent outcome, to identify a policy that would encourage good decisions and avoid the poor ones. The policy search is guided by an appropriately chosen weighted utility function (e.g., based on minimizing delays and go-arounds). Finally, the performance of the proposed tool is validated using Charlotte Douglas International Airport as the case study, which shows that the proposed method is superior to other conventional rule-based approaches.

Milad Memarzadeh↗

NASA NTRSDate not supplied

Impact of Canadian Wildfires on Mid Atlantic’s Region Air Quality: An Analysis Using ASDC Data

Wildfires pose a growing concern in North America due to their harmful impacts on air quality and public health, with increased wildfire activity in recent years leading to widespread smoke plumes that can transcend borders. The exposure of New York City (NYC), the most populous city in North America, to Canadian wildfire smoke highlights the substantial implications for public health and urban environments. To better understand the impact of Canadian wildfires on air quality in NYC, satellite data from the NASA Atmospheric Science Data Center (ASDC) at Langley Research Center, along with ground-based measurements and atmospheric modeling results, are analyzed. We examine concentrations of atmospheric aerosols—particularly PM2.5 particulate matter originating from Canadian wildfires—their dispersion patterns, and the duration and intensity of smoke events impacting NYC. Data from multiple satellites, such as those from the Earth Polychromatic Imaging Camera (EPIC), are synergistically used to identify regions affected by wildfires and estimate aerosol loading. Ground-based measurements, including data from air quality monitoring stations, provide localized information for validation and calibration purposes. The findings of this study contribute to our understanding of the impact of Canadian wildfires on NYC's air quality and emphasize the importance of monitoring and prediction of transboundary smoke events using data synthesized from multiple sources, such as those provided by the ASDC. This information is crucial for policymakers, public health officials, and residents in affected areas to develop effective strategies for mitigating the health risks associated with wildfire smoke and improving air quality during wildfire seasons. The utilization of ASDC data in this research highlights the critical role of atmospheric remote sensing in addressing the challenges posed by wildfires and their consequences on regional scales.

Ingrid Garcia-Solera↗

NASA NTRSDate not supplied

Analyzing the Impact of Canadian Wildfires on Air Quality in the U.S. Mid-Atlantic: with Data and Tools from NASA’s Atmospheric Sciences Data Center

Wildfires pose a growing concern in North America due to their harmful impacts on air quality and public health, with increased wildfire activity in recent years leading to widespread smoke plumes that can transcend borders. The exposure of New York City (NYC), the most populous city in North America, to Canadian wildfire smoke highlights the substantial implications for public health and urban environments. To better understand the impact of Canadian wildfires on air quality in NYC, satellite data from the NASA Atmospheric Science Data Center (ASDC) at Langley Research Center, along with ground-based measurements and atmospheric modeling results, are analyzed. We examine concentrations of atmospheric aerosols—particularly PM2.5 particulate matter originating from Canadian wildfires—their dispersion patterns, and the duration and intensity of smoke events impacting NYC. Data from multiple satellites, such as those from the Earth Polychromatic Imaging Camera (EPIC), are synergistically used to identify regions affected by wildfires and estimate aerosol loading. Ground-based measurements, including data from air quality monitoring stations, provide localized information for validation and calibration purposes. The findings of this study contribute to our understanding of the impact of Canadian wildfires on NYC's air quality and emphasize the importance of monitoring and prediction of transboundary smoke events using data synthesized from multiple sources, such as those provided by the ASDC. This information is crucial for policymakers, public health officials, and residents in affected areas to develop effective strategies for mitigating the health risks associated with wildfire smoke and improving air quality during wildfire seasons. The utilization of ASDC data in this research highlights the critical role of atmospheric remote sensing in addressing the challenges posed by wildfires and their consequences on regional scales.

Ingrid Garcia-Solera↗

NASA NTRSDate not supplied

Bond Line Thickness Estimation in Composite Structures Using Multiple Inspection Techniques

Imaging and other nondestructive evaluation techniques are commonly used for material characterization and defect recognition in safety critical aerospace applications, with data fusion providing the framework for uncertainty quantification in these contexts. Most commonly, forward physics-based modeling predicts the response conditioned on material properties and defect assumptions, and probabilistic methods are used to infer the hidden state of subject of the inspection from a combination of prior information, likelihoods, and inspection data. In this paper Bayesian methods are used to estimate bond thickness in lap joints comprised of aluminum adherends using a combination of infrared thermography, digital radiography, and ultrasound. The accuracy of the fused estimates are validated against data generated from synthetic specimens, and by comparison against high resolution X-ray computed tomography inspections of built specimens.

thermal nondestructive evaluation↗

NASA NTRSDate not supplied

Gateway bi-propellant refueling development testing and model validation activities

The Gateway is an upcoming long term lunar exploration program to be completed by NASA in partnership with ESA and other US and international partners. The system design of the Gateway contains both a high performance Xenon based Solar Electric Propulsion system, as well as a bi-propellant attitude control system. Both propulsion systems are designed for on-orbit refueling to enable long life performance of the Gateway. The ESPRIT-RM is a module which will expand the pressurized volume of Gateway, while also providing refueling capability for both the Xenon and Bipropellant propulsion systems, therefore extending the Gateway life on orbit. As part of the Gateway bi-propellant refueling system development, a simplified fluidic breadboard system was created to evaluate system performance and response using simulant fluids. The test plan includes verification activities with simulant (water, HFE-7100) to verify joined subsystem behavior in the critical operations, including propellant transfer demonstration between modules, transient tests and venting tests. Integrated testing occurred at TASUK in collaboration with NASA to support joint verification activities to de-risk the major functions of the ESPRIT BTS and the overall CONOPS of the refueling of the Gateway chemical propulsion system. Initially collected test data from the system testing is presented, as well as initial model validation, and correlated to collected high frequency test data and system response is shown. Initially collected data has shown the architected system performance is closing initial design assumptions, but much forward work remains to characterize and develop the system.

Christopher Daniel Radke↗

NASA NTRS2016

Unsteady Aerodynamic Force Sensing from Measured Strain

A simple approach for computing unsteady aerodynamic forces from simulated measured strain data is proposed in this study. First, the deflection and slope of the structure are computed from the unsteady strain using the two-step approach. Velocities and accelerations of the structure are computed using the autoregressive moving average model, on-line parameter estimator, low-pass filter, and a least-squares curve fitting method together with analytical derivatives with respect to time. Finally, aerodynamic forces over the wing are computed using modal aerodynamic influence coefficient matrices, a rational function approximation, and a time-marching algorithm. A cantilevered rectangular wing built and tested at the NASA Langley Research Center (Hampton, Virginia, USA) in 1959 is used to validate the simple approach. Unsteady aerodynamic forces as well as wing deflections, velocities, accelerations, and strains are computed using the CFL3D computational fluid dynamics (CFD) code and an MSC/NASTRAN code (MSC Software Corporation, Newport Beach, California, USA), and these CFL3D-based results are assumed as measured quantities. Based on the measured strains, wing deflections, velocities, accelerations, and aerodynamic forces are computed using the proposed approach. These computed deflections, velocities, accelerations, and unsteady aerodynamic forces are compared with the CFL3D/NASTRAN-based results. In general, computed aerodynamic forces based on the lifting surface theory in subsonic speeds are in good agreement with the target aerodynamic forces generated using CFL3D code with the Euler equation. Excellent aeroelastic responses are obtained even with unsteady strain data under the signal to noise ratio of -9.8dB. The deflections, velocities, and accelerations at each sensor location are independent of structural and aerodynamic models. Therefore, the distributed strain data together with the current proposed approaches can be used as distributed deflection, velocity, and acceleration sensors. This research demonstrates the feasibility of obtaining induced drag and lift forces through the use of distributed sensor technology with measured strain data. An active induced drag control system thus can be designed using the two computed aerodynamic forces, induced drag and lift, to improve the fuel efficiency of an aircraft. Interpolation elements between structural finite element grids and the CFD grids and centroids are successfully incorporated with the unsteady aeroelastic computation scheme. The most critical technology for the success of the proposed approach is the robust on-line parameter estimator, since the least-squares curve fitting method depends heavily on aeroelastic system frequencies and damping factors.

shape sensing↗