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At least 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Validation of the SCALE/Polaris–PARCS Code Procedure With the ENDF/B-VII.1 AMPX 56-Group Library: Boiling Water Reactor

The SCALE/Polaris–PARCS code procedure has been used in the confirmatory analysis for boiling water reactors by the US Nuclear Regulatory Commission. In this study, the SCALE/Polaris v6.3.0–PARCS v3.4.2 code procedure with the Evaluated Nuclear Data File (ENDF)/B-VII.1 AMPX 56-group library was validated by comparing the simulated results with the measured data for operating boiling water reactors, including Peach Bottom Unit 2 cycles 1–3, Hatch Unit 1 cycles 1–3, and Quad Cities Unit 1 cycles 1–3. The uncertainties and biases of the SCALE/Polaris–PARCS code package for boiling water reactor physics analysis were evaluated in the validation for key nuclear parameters such as reactivity and traversing in-core probe data.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

A globally sampled high-resolution hand-labeled validation dataset for evaluating surface water extent maps

Effective monitoring of global water resources is increasingly critical due to climate change and population growth. Advancements in remote sensing technology, specifically in spatial, spectral, and temporal resolutions, are revolutionizing water resource monitoring, leading to more frequent and high-quality surface water extent maps using various techniques such as traditional image processing and machine learning algorithms. However, satellite imagery datasets contain trade-offs that result in inconsistencies in performance, such as disparities in measurement principles between optical (e.g., Sentinel-2) and radar (e.g., Sentinel-1) sensors and differences in spatial and spectral resolutions among optical sensors. Therefore, developing accurate and robust surface water mapping solutions requires independent validations from multiple datasets to identify potential biases within the imagery and algorithms. However, high-quality validation datasets are expensive to build, and few contain information on water resources. For this purpose, we introduce a globally sampled, high-spatial-resolution dataset labeled using 3 m PlanetScope imagery. Our surface water extent dataset comprises 100 images, each with a size of 1024×1024 pixels, which were sampled using a stratified random sampling strategy covering all 14 biomes. We highlighted urban and rural regions, lakes, and rivers, including braided rivers and coastal regions. We evaluated two surface water extent mapping methods using our dataset – Dynamic World, based on Sentinel-2, and the NASA IMPACT model, based on Sentinel-1. Dynamic World achieved a mean intersection over union (IoU) of 72.16 % and F1 score of 79.70 %, while the NASA IMPACT model had a mean IoU of 57.61 % and F1 score of 65.79 %. Performance varied substantially across biomes, highlighting the importance of evaluating models on diverse landscapes to assess their generalizability and robustness. Our dataset can be used to analyze satellite products and methods, providing insights into their advantages and drawbacks. Our dataset offers a unique tool for analyzing satellite products, aiding the development of more accurate and robust surface water monitoring solutions. The dataset can be accessed via https://doi.org/10.25739/03nt-4f29.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

FAST.Farm load validation for single wake situations at alpha ventus

The main objective of the presented work is the validation of the simulation tool FAST.Farm for the calculation of power and structural loads in single wake situations; the basis for the validation is the measurement database of the operating offshore wind farm alpha ventus. The approach is described in detail and covers the calibration of the aeroelastic turbine model, transfer of environmental conditions to simulations, and comparison between simulations and adequately filtered measurements. It is shown that FAST.Farm accurately predicts power and structural load distributions over wind direction with discrepancies of less than 10 % for most of the cases compared to the measurements. Additionally, the frequency response of the structure is investigated, and it is calculated by FAST.Farm in good agreement with the measurements. In general, the calculation of fatigue loads is improved with a wake-added turbulence model added to FAST.Farm in the course of this study.

17 WIND ENERGY↗
DOE OSTI2024

One-to-one aeroservoelastic validation of operational loads and performance of a 2.8 MW wind turbine model in OpenFAST

Abstract. This article presents a validation study of the popular aeroservoelastic code suite OpenFAST leveraging weeks of measurements obtained during normal operation of a 2.8 MW land-based wind turbine. Measured wind conditions were used to generate one-to-one turbulent flow fields (i.e., comparing simulation to measurement in 10 min increments, or bins) through unconstrained and constrained assimilation methods using the kinematic turbulence generators TurbSim and PyConTurb. A total of 253 bins of 10 min of normal turbine operation were selected for analysis, and a statistical comparison in terms of performance and loads is presented. We show that successful validation of the model was not strongly dependent on the type of inflow assimilation method used for mean quantities of interest, which had median modeling errors per wind-speed interval generally within 5 %–10 % of the measurement. The type of inflow assimilation method did have a larger effect on the fatigue predictions for blade-root flapwise and tower-base fore–aft quantities, which surprisingly saw larger errors from the assumed higher-fidelity assimilation methods. Avenues for further work are discussed and include possible improvements to the aerodynamic, structural, and controller modeling that may offer insight on the origin of the up to ∼ 40 % median overprediction of fatigue for these quantities.

17 WIND ENERGY↗
NASA NTRS2001

Noninvasive assessment of mitral inertness: clinical results with numerical model validation

Inertial forces (Mdv/dt) are a significant component of transmitral flow, but cannot be measured with Doppler echo. We validated a method of estimating Mdv/dt. Ten patients had a dual sensor transmitral (TM) catheter placed during cardiac surgery. Doppler and 2D echo was performed while acquiring LA and LV pressures. Mdv/dt was determined from the Bernoulli equation using Doppler velocities and TM gradients. Results were compared with numerical modeling. TM gradients (range: 1.04-14.24 mmHg) consisted of 74.0 +/- 11.0% inertial forcers (range: 0.6-12.9 mmHg). Multivariate analysis predicted Mdv/dt = -4.171(S/D (RATIO)) + 0.063(LAvolume-max) + 5. Using this equation, a strong relationship was obtained for the clinical dataset (y=0.98x - 0.045, r=0.90) and the results of numerical modeling (y=0.96x - 0.16, r=0.84). TM gradients are mainly inertial and, as validated by modeling, can be estimated with echocardiography.

NASA Discipline Cardiopulmonary↗
NASA NTRS2000

MISR - Science Data Validation Plan

This Science Data Validation Plan describes the plans for validating a subset of the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 algorithms and data products and supplying top-of-atmosphere (TOA) radiances to the In-flight Radiometric Calibration and Characterization (IFRCC) subsystem for vicarious calibration.

MISR science data validation Plan↗
NASA NTRS1996

NDSC Lidar Intercomparisons and Validation: OPAL and MLO3 Campaigns in 1995

The Network for the Detection of Stratospheric Change (NDSC) has developed and adopted a Validation Policy in order to ensure that the results submitted and stored in its archives are of a known, high quality. As a part of this validation policy, blind instrument intercomparisons are considered an essential element in the certification of NDSC instruments and a specific format for these campaigns has been recommended by the NDSC-Steering Committee.

Stratospheric Change The Network for the Detection↗
NASA NTRS1999

A Process for Verifying and Validating Requirements for Fault Tolerant Systems Using Model Checking

Model checking is shown to be an effective tool in validating the behavior of a fault tolerant embedded spacecraft controller. The case study presented here shows that by judiciously abstracting away extraneous complexity, the state space of the model could be exhaustively searched allowing critical functional requirement to be validated down to the design level.

model checking fault tolerant embedded spacecraft ↗
NASA NTRS2006

Early Validation Analyses of Atmospheric Profiles from EOS MLS on the Aura Satellite

We present results of early validation studies using retrieved atmospheric profiles from the Earth Observing System Microwave Limb Sounder (MLS) instrument on the Aura satellite. 'Global' results are presented for MLS measurements of atmospheric temperature, ozone, water vapor, hydrogen chloride, nitrous oxide, nitric acid, and carbon monoxide, with a focus on the January-March 2005 time period. These global comparisons are made using long-standing global satellites and meteorological datasets, as well as some measurements from more recently launched satellites. Comparisons of MLS data with measurements from the Ft. Sumner, NM, September 2004 balloon flights are also presented. Overall, good agreeement is obtained, often within 5% to 10%, but we point out certain issues to resolve and some larger systematic differences; some artifacts in the first publicly released MLS (version 1.5) dataset are noted.We comment briefly on future plans for validation and software improvements.

Atmospheric retrievals↗
NASA NTRS2013

Validating an Air Traffic Management Concept of Operation Using Statistical Modeling

Validating a concept of operation for a complex, safety-critical system (like the National Airspace System) is challenging because of the high dimensionality of the controllable parameters and the infinite number of states of the system. In this paper, we use statistical modeling techniques to explore the behavior of a conflict detection and resolution algorithm designed for the terminal airspace. These techniques predict the robustness of the system simulation to both nominal and off-nominal behaviors within the overall airspace. They also can be used to evaluate the output of the simulation against recorded airspace data. Additionally, the techniques carry with them a mathematical value of the worth of each prediction-a statistical uncertainty for any robustness estimate. Uncertainty Quantification (UQ) is the process of quantitative characterization and ultimately a reduction of uncertainties in complex systems. UQ is important for understanding the influence of uncertainties on the behavior of a system and therefore is valuable for design, analysis, and verification and validation. In this paper, we apply advanced statistical modeling methodologies and techniques on an advanced air traffic management system, namely the Terminal Tactical Separation Assured Flight Environment (T-TSAFE). We show initial results for a parameter analysis and safety boundary (envelope) detection in the high-dimensional parameter space. For our boundary analysis, we developed a new sequential approach based upon the design of computer experiments, allowing us to incorporate knowledge from domain experts into our modeling and to determine the most likely boundary shapes and its parameters. We carried out the analysis on system parameters and describe an initial approach that will allow us to include time-series inputs, such as the radar track data, into the analysis

Statistical emulation↗
NASA NTRS2012

SMAP Verification and Validation Project - Final Report

In 2007, the National Research Council (NRC) released the Decadal Survey of Earth science. In the future decade, the survey identified 15 new space missions of significant scientific and application value for the National Aeronautics and Space Administration (NASA) to undertake. One of these missions was the Soil Moisture Active Passive (SMAP) mission that NASA assigned to the Jet Propulsion Laboratory (JPL) in 2008. The goal of SMAP1 is to provide global, high resolution mapping of soil moisture and its freeze/thaw states. The SMAP project recently passed its Critical Design Review and is proceeding with its fabrication and testing phase.Verification and Validation (V&V) is widely recognized as a critical component in system engineering and is vital to the success of any space mission. V&V is a process that is used to check that a system meets its design requirements and specifications in order to fulfill its intended purpose. Verification often refers to the question "Have we built the system right?" whereas Validation asks "Have we built the right system?" Currently the SMAP V&V team is verifying design requirements through inspection, demonstration, analysis, or testing. An example of the SMAP V&V process is the verification of the antenna pointing accuracy with mathematical models since it is not possible to provide the appropriate micro-gravity environment for testing the antenna on Earth before launch.

Soil Moisture Active Passive (SMAP)↗
NASA NTRS2016

SOSS ICN Model Validation

Under the NASA-KAIA-KARI ATM research collaboration agreement, SOSS ICN Model has been developed for Incheon International Airport. This presentation describes the model validation work in the project. The presentation will show the results and analysis of the validation.

model validation↗
NASA NTRS2017

Strategies for Validating Satellite Soil Moisture Products Using in Situ Networks: Lessons from the USDA-ARS Watersheds

There are a variety of soil moisture station designs and networks deployed throughout the world, each with varying applications and uses. The purpose of satellite validation of soil moisture products, a dense network of soil moisture networks are required with soil moisture sensors at the near surface (approx. 5 cm or less) to correspond to the satellite footprints and signals. The USDA- Agricultural Research Service operates a collection of soil moisture networks as a part of the Long Term Agro-ecosystem Research (LTAR) network to this end. These networks have been used to validate products from AMSR-E, SMOS, Aquarius, and SMAP. A review of these results and a synopsis of successful scaling strategies are discussed.

soil moisture↗
NASA NTRS2010

Data Validation in the Kepler Science Operations Center Pipeline

We present an overview of the Data Validation (DV) software component and its context within the Kepler ScienceOperations Center (SOC) pipeline and overall Kepler Science mission. The SOC pipeline performs a transiting planetsearch on the corrected light curves for over 150,000 targets across the focal plane array. We discuss the DV strategy forautomated validation of Threshold Crossing Events (TCEs) generated in the transiting planet search. For each TCE, atransiting planet model is fitted to the target light curve. A multiple planet search is conducted by repeating the transitingplanet search on the residual light curve after the model flux has been removed; if an additional detection occurs, aplanet model is fitted to the new TCE. A suite of automated tests are performed after all planet candidates have beenidentified. We describe a centroid motion test to determine the significance of the motion of the target photocenterduring transit and to estimate the coordinates of the transit source within the photometric aperture; a series of eclipsingbinary discrimination tests on the parameters of the planet model fits to all transits and the sequences of odd and eventransits; and a statistical bootstrap to assess the likelihood that the TCE would have been generated purely by chancegiven the target light curve with all transits removed.

photometry↗
NASA NTRS2016

OVERFLOW Turbulence Modeling Resource Validation Results

Abstract:We exercise the computational fluid dynamics code OVERFLOW on sixteen turbulence model validation cases from the NASALangley Turbulence Model Resource web site. We give some information about the OVERFLOW options used to run these cases, and compare OVERFLOW results with results from other codes and with experiment. The goal is turbulence model validation for OVERFLOW.

computational fluid dynamics↗
NASA NTRS2018

Fuel Cell Model Validation with Power Module Test Data

The AES Modular Power Systems (AMPS) Fuel Cell team at the NASA Glenn Research Center (GRC) has created a Microsoft Excel model of a regenerative fuel cell (RFC) system for potential lunar and Martian applications. Due to some limitations of this model, including the ability to predict transient effects in the thermal and fluidic performance of the system, a system-level transient thermal/fluids model was desired. GT-SUITE is a transient multi-physics simulation tool that is popular in the automotive industry and has a built-in template for proton exchange membrane (PEM) fuel cells. However, terrestrial fuel cells used in the automotive industry vary significantly from aerospace fuel cells. A validation effort was undertaken to compare GT SUITE model results to actual data collected during the 2015 AMPS (Advanced Exploration Systems (AES) Modular Power Systems) Power Module Demonstration in which the AMPS team used a PEM fuel cell system to power a 478 kilogram rover assembly at the Dunes test site located at GRC. The purpose of the model was to evaluate the efficacy of using GT SUITE to model a non-flow through PEM fuel cell system. The validation effort examined electrochemical performance including fuel cell stack voltage and power supplied, the thermal performance of the stack namely stack temperature and heat rejection rates, and the fluidic performance of the system including pressures, reactant consumption, and water production rates.

Model Validation↗
NASA NTRS2019

Blind Validation Study of Parametric Cost Estimation Tool SEER-H for NASA Space Missions

One of the primary parametric cost modeling tools used by NASA and the aerospace industry to estimate the development and production cost of future spacecraft hardware is SEER-H by Galorath. To date, no independent validation of this tool for cost estimation of space missions has been reported in the literature. In the present validation study, cost estimators used SEER-H to estimate the cost of twelve different past NASA science missions. The estimators were prevented from knowing the actual cost of the missions in an effort to minimize cognitive biases. The point estimates of SEER-H had an average error of 23%, median error of -0.3%, and a standard deviation of 43%. Nine of the twelve mission's actual costs fell within the 80% confidence interval of SEER's probabilistic estimates. Several factors independent of SEER that may have affected the accuracy of the results have been identified and are discussed; these include: uncertainty in the technical data used for the estimates, the methods used to estimate uncertainty in spacecraft component mass and numbers of prototypes, and the experience of the estimators.

SEER-H↗