Search NASA⌕ Search

SEARCH · Search NASA

Results for “Validation”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Experimental validation of structural optimization methods

The topic of validating structural optimization methods by use of experimental results is addressed. The need for validating the methods as a way of effecting a greater and an accelerated acceptance of formal optimization methods by practicing engineering designers is described. The range of validation strategies is defined which includes comparison of optimization results with more traditional design approaches, establishing the accuracy of analyses used, and finally experimental validation of the optimization results. Examples of the use of experimental results to validate optimization techniques are described. The examples include experimental validation of the following: optimum design of a trussed beam; combined control-structure design of a cable-supported beam simulating an actively controlled space structure; minimum weight design of a beam with frequency constraints; minimization of the vibration response of helicopter rotor blade; minimum weight design of a turbine blade disk; aeroelastic optimization of an aircraft vertical fin; airfoil shape optimization for drag minimization; optimization of the shape of a hole in a plate for stress minimization; optimization to minimize beam dynamic response; and structural optimization of a low vibration helicopter rotor.

Adelman, Howard M.↗
NASA NTRS1992

Specification Reformulation During Specification Validation

The goal of the ARIES Simulation Component (ASC) is to uncover behavioral errors by 'running' a specification at the earliest possible points during the specification development process. The problems to be overcome are the obvious ones the specification may be large, incomplete, underconstrained, and/or uncompilable. This paper describes how specification reformulation is used to mitigate these problems. ASC begins by decomposing validation into specific validation questions. Next, the specification is reformulated to abstract out all those features unrelated to the identified validation question thus creating a new specialized specification. ASC relies on a precise statement of the validation question and a careful application of transformations so as to preserve the essential specification semantics in the resulting specialized specification. This technique is a win if the resulting specialized specification is small enough so the user my easily handle any remaining obstacles to execution. This paper will: (1) describe what a validation question is; (2) outline analysis techniques for identifying what concepts are and are not relevant to a validation question; and (3) identify and apply transformations which remove these less relevant concepts while preserving those which are relevant.

Benner, Kevin M.↗
NASA NTRS2003

EOS-Aura's Ozone Monitoring Instrument (OMI): Validation Requirements

OMI is an advanced hyperspectral instrument that measures backscattered radiation in the UV and visible. It will be flown as part of the EOS Aura mission and provide data on atmospheric chemistry that is highly synergistic with other Aura instruments HIRDLS, MLS, and TES. OMI is designed to measure total ozone, aerosols, cloud information, and UV irradiances, continuing the TOMS series of global mapped products but with higher spatial resolution. In addition its hyperspectral capability enables measurements of trace gases such as SO2, NO2, HCHO, BrO, and OClO. A plan for validation of the various OM1 products is now being formulated. Validation of the total column and UVB products will rely heavily on existing networks of instruments, like NDSC. NASA and its European partners are planning aircraft missions for the validation of Aura instruments. New instruments and techniques (DOAS systems for example) will need to be developed, both ground and aircraft based. Lidar systems are needed for validation of the vertical distributions of ozone, aerosols, NO2 and possibly SO2. The validation emphasis will be on the retrieval of these products under polluted conditions. This is challenging because they often depend on the tropospheric profiles of the product in question, and because of large spatial variations in the troposphere. Most existing ground stations are located in, and equipped for, pristine environments. This is also true for almost all NDSC stations. OMI validation will need ground based sites in polluted environments and specially developed instruments, complementing the existing instrumentation.

Brinksma, E. J.↗
NASA NTRS2003

Validation of ADAR System 5500 Digital Imagery: Delivery Task Order #1, Task Request #857 - Brookings, SD

This work was performed under NASA's Verification and Validation Program as an independent check of data supplied by Positive Systems, Inc. through the Earth Science Enterprise's Scientific Data Purchase (SDP) Program. This document serves as the basis for reporting results associated with validation of multispectral imagery according to the specifications of contract NAS 13-98049. The validation was performed under the Positive Systems Imaging System Validation Work Instruction CRSP-WI-28: Spectral registration, spatial resolution, endlaps, sidelaps, and image quality were evaluated. The validation was proceded by Shipment Verification, as described in the Work Instruction CRSP-WI-22: Every image was passed through an automatic ingest verification and thumbnail review process to identify omissions, problems with media integrity, and gross errors in data quality. Validation of metadata files is not within the scope of this report, but it was performed separately.

Blonski, Slawomir↗
NASA NTRS2004

Preparations for Global Precipitation Measurement(GPM)Ground Validation

The Global Precipitation Measurement (GPM) program is an international partnership led by the National Aeronautics and Space Administration (NASA) and the Japan Aerospace Exploration Agency (JAXA). GPM will improve climate, weather, and hydro-meterorological forecasts through more frequent and more accurate measurement of precipitation across the globe. This paper describes the concept and the preparations for Ground Validation within the GPM program. Ground Validation (GV) plays a critical role in the program by investigating and quantitatively assessing the errors within the satellite retrievals. These quantitative estimates of retrieval errors will assist the scientific community by bounding the errors within their research products. The two fundamental requirements of the GPM Ground Validation program are: (1) error characterization of the precipitation retrievals and (2) continual improvement of the satellite retrieval algorithms. These two driving requirements determine the measurements, instrumentation, and location for ground observations. This paper describes GV plans for estimating the systematic and random components of retrieval error and for characterizing the spatial and temporal structure of the error. This paper describes the GPM program for algorithm improvement in which error models are developed and experimentally explored to uncover the physical causes of errors within the retrievals. GPM will ensure that information gained through Ground Validation is applied to future improvements in the spaceborne retrieval algorithms. This paper discusses the potential locations for validation measurement and research, the anticipated contributions of GPM's international partners, and the interaction of Ground Validation with other GPM program elements.

Bidwell, S. W.↗
NASA NTRS2005

Validation Of The Airspace Concept Evaluation System Using Real World Data

This paper discusses the process of performing a validation of the Airspace Concept Evaluation System (ACES) using real world historical flight operational data. ACES inputs are generated from select real world data and processed to create a realistic reproduction of a single day of operations within the National Airspace System (NAS). ACES outputs are then compared to real world operational metrics and delay statistics for the reproduced day. Preliminary results indicate that ACES produces delays and airport operational metrics similar to the real world with minor variations of delay by phase of flight. ACES is a nation-wide fast-time simulation tool developed at NASA Ames Research Center. ACES models and simulates the NAS using interacting agents representing center control, terminal flow management, airports, individual flights, and other NAS elements. These agents pass messages between one another similar to real world communications. This distributed agent based system is designed to emulate the highly unpredictable nature of the NAS, making it a suitable tool to evaluate current and envisioned airspace concepts. To ensure that ACES produces the most realistic results, the system must be validated. There is no way to validate future concepts scenarios using real world historical data, but current day scenario validations increase confidence in the validity of future scenario results. Each operational day has unique weather and traffic demand schedules. The more a simulation utilizes the unique characteristic of a specific day, the more realistic the results should be. ACES is able to simulate the full scale demand traffic necessary to perform a validation using real world data. Through direct comparison with the real world, models may continuee to be improved and unusual trends and biases may be filtered out of the system or used to normalize the results of future concept simulations.

Zelinski, Shannon↗
NASA NTRS2005

Remote Sensing Product Verification and Validation at the NASA Stennis Space Center

Remote sensing data product verification and validation (V&V) is critical to successful science research and applications development. People who use remote sensing products to make policy, economic, or scientific decisions require confidence in and an understanding of the products' characteristics to make informed decisions about the products' use. NASA data products of coarse to moderate spatial resolution are validated by NASA science teams. NASA's Stennis Space Center (SSC) serves as the science validation team lead for validating commercial data products of moderate to high spatial resolution. At SSC, the Applications Research Toolbox simulates sensors and targets, and the Instrument Validation Laboratory validates critical sensors. The SSC V&V Site consists of radiometric tarps, a network of ground control points, a water surface temperature sensor, an atmospheric measurement system, painted concrete radial target and edge targets, and other instrumentation. NASA's Applied Sciences Directorate participates in the Joint Agency Commercial Imagery Evaluation (JACIE) team formed by NASA, the U.S. Geological Survey, and the National Geospatial-Intelligence Agency to characterize commercial systems and imagery.

Stanley, Thomas M.↗
NASA NTRS2005

Performance Evaluation of a Data Validation System

Online data validation is a performance-enhancing component of modern control and health management systems. It is essential that performance of the data validation system be verified prior to its use in a control and health management system. A new Data Qualification and Validation (DQV) Test-bed application was developed to provide a systematic test environment for this performance verification. The DQV Test-bed was used to evaluate a model-based data validation package known as the Data Quality Validation Studio (DQVS). DQVS was employed as the primary data validation component of a rocket engine health management (EHM) system developed under NASA's NGLT (Next Generation Launch Technology) program. In this paper, the DQVS and DQV Test-bed software applications are described, and the DQV Test-bed verification procedure for this EHM system application is presented. Test-bed results are summarized and implications for EHM system performance improvements are discussed.

Wong, Edmond↗
NASA NTRS2005

CFD Modeling Needs and What Makes a Good Supersonic Combustion Validation Experiment

If a CFD code/model developer is asked what experimental data he wants to validate his code or numerical model, his answer will be: "Everything, everywhere, at all times." Since this is not possible, practical, or even reasonable, the developer must understand what can be measured within the limits imposed by the test article, the test location, the test environment and the available diagnostic equipment. At the same time, it is important for the expermentalist/diagnostician to understand what the CFD developer needs (as opposed to wants) in order to conduct a useful CFD validation experiment. If these needs are not known, it is possible to neglect easily measured quantities at locations needed by the developer, rendering the data set useless for validation purposes. It is also important for the experimentalist/diagnostician to understand what the developer is trying to validate so that the experiment can be designed to isolate (as much as possible) the effects of a particular physical phenomena that is associated with the model to be validated. The probability of a successful validation experiment can be greatly increased if the two groups work together, each understanding the needs and limitations of the other.

Gaffney, Richard L., Jr.↗
NASA NTRS2005

Statistical Calibration and Validation of a Homogeneous Ventilated Wall-Interference Correction Method for the National Transonic Facility

Wind tunnel experiments will continue to be a primary source of validation data for many types of mathematical and computational models in the aerospace industry. The increased emphasis on accuracy of data acquired from these facilities requires understanding of the uncertainty of not only the measurement data but also any correction applied to the data. One of the largest and most critical corrections made to these data is due to wall interference. In an effort to understand the accuracy and suitability of these corrections, a statistical validation process for wall interference correction methods has been developed. This process is based on the use of independent cases which, after correction, are expected to produce the same result. Comparison of these independent cases with respect to the uncertainty in the correction process establishes a domain of applicability based on the capability of the method to provide reasonable corrections with respect to customer accuracy requirements. The statistical validation method was applied to the version of the Transonic Wall Interference Correction System (TWICS) recently implemented in the National Transonic Facility at NASA Langley Research Center. The TWICS code generates corrections for solid and slotted wall interference in the model pitch plane based on boundary pressure measurements. Before validation could be performed on this method, it was necessary to calibrate the ventilated wall boundary condition parameters. Discrimination comparisons are used to determine the most representative of three linear boundary condition models which have historically been used to represent longitudinally slotted test section walls. Of the three linear boundary condition models implemented for ventilated walls, the general slotted wall model was the most representative of the data. The TWICS code using the calibrated general slotted wall model was found to be valid to within the process uncertainty for test section Mach numbers less than or equal to 0.60. The scatter among the mean corrected results of the bodies of revolution validation cases was within one count of drag on a typical transport aircraft configuration for Mach numbers at or below 0.80 and two counts of drag for Mach numbers at or below 0.90.

Walker, Eric Lee↗
NASA NTRS2005

Statistical Calibration and Validation of a Homogeneous Ventilated Wall-Interference Correction Method for the National Transonic Facility

Wind tunnel experiments will continue to be a primary source of validation data for many types of mathematical and computational models in the aerospace industry. The increased emphasis on accuracy of data acquired from these facilities requires understanding of the uncertainty of not only the measurement data but also any correction applied to the data. One of the largest and most critical corrections made to these data is due to wall interference. In an effort to understand the accuracy and suitability of these corrections, a statistical validation process for wall interference correction methods has been developed. This process is based on the use of independent cases which, after correction, are expected to produce the same result. Comparison of these independent cases with respect to the uncertainty in the correction process establishes a domain of applicability based on the capability of the method to provide reasonable corrections with respect to customer accuracy requirements. The statistical validation method was applied to the version of the Transonic Wall Interference Correction System (TWICS) recently implemented in the National Transonic Facility at NASA Langley Research Center. The TWICS code generates corrections for solid and slotted wall interference in the model pitch plane based on boundary pressure measurements. Before validation could be performed on this method, it was necessary to calibrate the ventilated wall boundary condition parameters. Discrimination comparisons are used to determine the most representative of three linear boundary condition models which have historically been used to represent longitudinally slotted test section walls. Of the three linear boundary condition models implemented for ventilated walls, the general slotted wall model was the most representative of the data. The TWICS code using the calibrated general slotted wall model was found to be valid to within the process uncertainty for test section Mach numbers less than or equal to 0.60. The scatter among the mean corrected results of the bodies of revolution validation cases was within one count of drag on a typical transport aircraft configuration for Mach numbers at or below 0.80 and two counts of drag for Mach numbers at or below 0.90.

Walker, Eric L.↗
NASA NTRS2007

The Hyper-X Flight Systems Validation Program

For the Hyper-X/X-43A program, the development of a comprehensive validation test plan played an integral part in the success of the mission. The goal was to demonstrate hypersonic propulsion technologies by flight testing an airframe-integrated scramjet engine. Preparation for flight involved both verification and validation testing. By definition, verification is the process of assuring that the product meets design requirements; whereas validation is the process of assuring that the design meets mission requirements for the intended environment. This report presents an overview of the program with emphasis on the validation efforts. It includes topics such as hardware-in-the-loop, failure modes and effects, aircraft-in-the-loop, plugs-out, power characterization, antenna pattern, integration, combined systems, captive carry, and flight testing. Where applicable, test results are also discussed. The report provides a brief description of the flight systems onboard the X-43A research vehicle and an introduction to the ground support equipment required to execute the validation plan. The intent is to provide validation concepts that are applicable to current, follow-on, and next generation vehicles that share the hybrid spacecraft and aircraft characteristics of the Hyper-X vehicle.

Redifer, Matthew↗
NASA NTRS2006

Analysis of Raman Lidar and Radiosonde Measurements from the AWEX-G Field Campaign and Its Relation to Aqua Validation

Early work within the Aqua validation activity revealed there to be large differences in water vapor measurement accuracy among the various technologies in use for providing validation data. The validation measurements were made at globally distributed sites making it difficult to isolate the sources of the apparent measurement differences among the various sensors, which included both Raman lidar and radiosonde. Because of this, the AIRS Water Vapor Experiment-Ground (AWEX-G) was held in October-November 2003 with the goal of bringing validation technologies to a common site for intercomparison and resolving the measurement discrepancies. Using the University of Colorado Cryogenic Frostpoint Hygrometer (CFH) as the water vapor reference, the AWEX-G field campaign permitted correction techniques to be validated for Raman lidar, Vaisala RS80-H and RS90/92 that significantly improve the absolute accuracy of water vapor measurements from these systems particularly in the upper troposphere. Mean comparisons of radiosondes and lidar are performed demonstrating agreement between corrected sensors and the CFH to generally within 5% thereby providing data of sufficient accuracy for Aqua validation purposes. Examples of the use of the correction techniques in radiance and retrieval comparisons are provided and discussed.

Whiteman, D. N.↗
NASA NTRS2008

Improving Coastal Ocean Color Validation Capabilities through Application of Inherent Optical Properties (IOPs)

Understanding how the different components of seawater alter the path of incident sunlight through scattering and absorption is essential to using remotely sensed ocean color observations effectively. This is particularly apropos in coastal waters where the different optically significant components (phytoplankton, detrital material, inorganic minerals, etc.) vary widely in concentration, often independently from one another. Inherent Optical Properties (IOPs) form the link between these biogeochemical constituents and the Apparent Optical Properties (AOPs). understanding this interrelationship is at the heart of successfully carrying out inversions of satellite-measured radiance to biogeochemical properties. While sufficient covariation of seawater constituents in case I waters typically allows empirical algorithms connecting AOPs and biogeochemical parameters to behave well, these empirical algorithms normally do not hold for case I1 regimes (Carder et al. 2003). Validation in the context of ocean color remote sensing refers to in-situ measurements used to verify or characterize algorithm products or any assumption used as input to an algorithm. In this project, validation capabilities are considered those measurement capabilities, techniques, methods, models, etc. that allow effective validation. Enhancing current validation capabilities by incorporating state-of-the-art IOP measurements and optical models is the purpose of this work. Involved in this pursuit is improving core IOP measurement capabilities (spectral, angular, spatio-temporal resolutions), improving our understanding of the behavior of analytical AOP-IOP approximations in complex coastal waters, and improving the spatial and temporal resolution of biogeochemical data for validation by applying biogeochemical-IOP inversion models so that these parameters can be computed from real-time IOP sensors with high sampling rates. Research cruises supported by this project provides for collection and processing of seawater samples for biogeochemical (pigments, DOC and POC) and optical (CDOM and POM absorption coefficients) analyses to enhance our understanding of the linkages between in-water optical measurements (IOPs and AOPs) and biogeochemical constituents and to provide a more comprehensive suite of validation products.

Mannino, Antonio↗
NASA NTRS2008

Systematic Independent Validation of Inner Heliospheric Models

This presentation is the first in a series which will provide independent validation of community models of the outer corona and inner heliosphere. In this work we establish a set of measures to be used in validating this group of models. We use these procedures to generate a comprehensive set of results from the Wang- Sheeley-Arge (WSA) model which will be used as a baseline, or reference, against which to compare all other models. We also run a test of the validation procedures by applying them to a small set of results produced by the ENLIL Magnetohydrodynamic (MHD) model. In future presentations we will validate other models currently hosted by the Community Coordinated Modeling Center(CCMC), including a comprehensive validation of the ENLIL model. The Wang-Sheeley-Arge (WSA) model is widely used to model the Solar wind, and is used by a number of agencies to predict Solar wind conditions at Earth as much as four days into the future. Because it is so important to both the research and space weather forecasting communities, it is essential that its performance be measured systematically, and independently. In this paper we offer just such an independent and systematic validation. We report skill scores for the model's predictions of wind speed and IMF polarity for a large set of Carrington rotations. The model was run in all its routinely used configurations. It ingests line of sight magnetograms. For this study we generated model results for monthly magnetograms from the National Solar Observatory (SOLIS), Mount Wilson Observatory and the GONG network, spanning the Carrington rotation range from 1650 to 2068. We compare the influence of the different magnetogram sources, performance at quiet and active times, and estimate the effect of different empirical wind speed tunings. We also consider the ability of the WSA model to identify sharp transitions in wind speed from slow to fast wind. These results will serve as a baseline against which to compare future versions of the model

MacNeice, P. J.↗
NASA NTRS2010

Definition and Demonstration of a Methodology for Validating Aircraft Trajectory Predictors

This paper presents a new methodology for validating an aircraft trajectory predictor, inspired by the lessons learned from a number of field trials, flight tests and simulation experiments for the development of trajectory-predictor-based automation. The methodology introduces new techniques and a new multi-staged approach to reduce the effort in identifying and resolving validation failures, avoiding the potentially large costs associated with failures during a single-stage, pass/fail approach. As a case study, the validation effort performed by the Federal Aviation Administration for its En Route Automation Modernization (ERAM) system is analyzed to illustrate the real-world applicability of this methodology. During this validation effort, ERAM initially failed to achieve six of its eight requirements associated with trajectory prediction and conflict probe. The ERAM validation issues have since been addressed, but to illustrate how the methodology could have benefited the FAA effort, additional techniques are presented that could have been used to resolve some of these issues. Using data from the ERAM validation effort, it is demonstrated that these new techniques could have identified trajectory prediction error sources that contributed to several of the unmet ERAM requirements.

Vivona, Robert A.↗
NASA NTRS2014

Turbulence Modeling Verification and Validation

Computational fluid dynamics (CFD) software that solves the Reynolds-averaged Navier-Stokes (RANS) equations has been in routine use for more than a quarter of a century. It is currently employed not only for basic research in fluid dynamics, but also for the analysis and design processes in many industries worldwide, including aerospace, automotive, power generation, chemical manufacturing, polymer processing, and petroleum exploration. A key feature of RANS CFD is the turbulence model. Because the RANS equations are unclosed, a model is necessary to describe the effects of the turbulence on the mean flow, through the Reynolds stress terms. The turbulence model is one of the largest sources of uncertainty in RANS CFD, and most models are known to be flawed in one way or another. Alternative methods such as direct numerical simulations (DNS) and large eddy simulations (LES) rely less on modeling and hence include more physics than RANS. In DNS all turbulent scales are resolved, and in LES the large scales are resolved and the effects of the smallest turbulence scales are modeled. However, both DNS and LES are too expensive for most routine industrial usage on today's computers. Hybrid RANS-LES, which blends RANS near walls with LES away from walls, helps to moderate the cost while still retaining some of the scale-resolving capability of LES, but for some applications it can still be too expensive. Even considering its associated uncertainties, RANS turbulence modeling has proved to be very useful for a wide variety of applications. For example, in the aerospace field, many RANS models are considered to be reliable for computing attached flows. However, existing turbulence models are known to be inaccurate for many flows involving separation. Research has been ongoing for decades in an attempt to improve turbulence models for separated and other nonequilibrium flows. When developing or improving turbulence models, both verification and validation are important steps in the process. Verification insures that the CFD code is solving the equations as intended (no errors in the implementation). This is typically done either through the method of manufactured solutions (MMS) or through careful step-by-step comparisons with other verified codes. After the verification step is concluded, validation is performed to document the ability of the turbulence model to represent different types of flow physics. Validation can involve a large number of test case comparisons with experiments, theory, or DNS. Organized workshops have proved to be valuable resources for the turbulence modeling community in its pursuit of turbulence modeling verification and validation. Workshop contributors using different CFD codes run the same cases, often according to strict guidelines, and compare results. Through these comparisons, it is often possible to (1) identify codes that have likely implementation errors, and (2) gain insight into the capabilities and shortcomings of different turbulence models to predict the flow physics associated with particular types of flows. These are valuable lessons because they help bring consistency to CFD codes by encouraging the correction of faulty programming and facilitating the adoption of better models. They also sometimes point to specific areas needed for improvement in the models. In this paper, several recent workshops are summarized primarily from the point of view of turbulence modeling verification and validation. Furthermore, the NASA Langley Turbulence Modeling Resource website is described. The purpose of this site is to provide a central location where RANS turbulence models are documented, and test cases, grids, and data are provided. The goal of this paper is to provide an abbreviated survey of turbulence modeling verification and validation efforts, summarize some of the outcomes, and give some ideas for future endeavors in this area.

Rumsey, Christopher L.↗
NASA NTRS2015

System-Level Experimental Validations for Supersonic Commercial Transport Aircraft Entering Service in the 2018-2020 Time Period

This report describes the work conducted under NASA funding for the Boeing N+2 Supersonic Experimental Validation project to experimentally validate the conceptual design of a supersonic airliner feasible for entry into service in the 2018 -to 2020 timeframe (NASA N+2 generation). The primary goal of the project was to develop a low-boom configuration optimized for minimum sonic boom signature (65 to 70 PLdB). This was a very aggressive goal that could be achieved only through integrated multidisciplinary optimization tools validated in relevant ground and, later, flight environments. The project was split into two phases. Phase I of the project covered the detailed aerodynamic design of a low boom airliner as well as the wind tunnel tests to validate that design (ref. 1). This report covers Phase II of the project, which continued the design methodology development of Phase I with a focus on the propulsion integration aspects as well as the testing involved to validate those designs. One of the major airplane configuration features of the Boeing N+2 low boom design was the overwing nacelle. The location of the nacelle allowed for a minimal effect on the boom signature, however, it added a level of difficulty to designing an inlet with acceptable performance in the overwing flow field. Using the Phase I work as the starting point, the goals of the Phase 2 project were to design and verify inlet performance while maintaining a low-boom signature. The Phase II project was successful in meeting all contract objectives. New modular nacelles were built for the larger Performance Model along with a propulsion rig with an electrically-actuated mass flow plug. Two new mounting struts were built for the smaller Boom Model, along with new nacelles. Propulsion integration testing was performed using an instrumented fan face and a mass flow plug, while boom signatures were measured using a wall-mounted pressure rail. A side study of testing in different wind tunnels was completed as a precursor to the selection of the facilities used for validation testing. As facility schedules allowed, the propulsion testing was done at the NASA Glenn Research Center (GRC) 8 x 6-Foot wind tunnel, while boom and force testing was done at the NASA Ames Research Center (ARC) 9 x 7-Foot wind tunnel. During boom testing, a live balance was used for gathering force data. This report is broken down into nine sections. The first technical section (Section 2) covers the general scope of the Phase II activities, goals, a description of the design and testing efforts, and the project plan and schedule. Section 3 covers the details of the propulsion system concepts and design evolution. A series of short tests to evaluate the suitability of different wind tunnels for boom, propulsion, and force testing was also performed under the Phase 2 effort, with the results covered in Section 4. The propulsion integration testing is covered in Section 5 and the boom and force testing in Section 6. CFD comparisons and analyses are included in Section 7. Section 8 includes the conclusions and lessons learned.

Magee, Todd E.↗