Results for “test vector” — Page 13

NASA NTRS1993

Actuator and aerodynamic modeling for high-angle-of-attack aeroservoelasticity

Accurate prediction of airframe/actuation coupling is required by the imposing demands of modern flight control systems. In particular, for agility enhancement at high angle of attack and low dynamic pressure, structural integration characteristics such as hinge moments, effective actuator stiffness, and airframe/control surface damping can have a significant effect on stability predictions. Actuator responses are customarily represented with low-order transfer functions matched to actuator test data, and control surface stiffness is often modeled as a linear spring. The inclusion of the physical properties of actuation and its installation on the airframe is therefore addressed using detailed actuator models which consider the physical, electrical, and mechanical elements of actuation. The aeroservoelastic analysis procedure is described in which the actuators are modeled as detailed high-order transfer functions and as approximate low-order transfer functions. The impacts of unsteady aerodynamic modeling on aeroservoelastic stability are also investigated by varying the order of approximation, or number of aerodynamic lag states, in the analysis. Test data from a thrust-vectoring configuration of an F/A-l8 aircraft are compared to predictions to determine the effects on accuracy as a function of modeling complexity.

Brenner, Martin J.↗

NASA NTRS2001

Static and Wind Tunnel Aero-Performance Tests of NASA AST Separate Flow Nozzle Noise Reduction Configurations

This report presents the results of cold flow model tests to determine the static and wind tunnel performance of several NASA AST separate flow nozzle noise reduction configurations. The tests were conducted by Aero Systems Engineering, Inc., for NASA Glenn Research Center. The tests were performed in the Channels 14 and 6 static thrust stands and the Channel 10 transonic wind tunnel at the FluiDyne Aerodynamics Laboratory in Plymouth, Minnesota. Facility checkout tests were made using standard ASME long-radius metering nozzles. These tests demonstrated facility data accuracy at flow conditions similar to the model tests. Channel 14 static tests reported here consisted of 21 ASME nozzle facility checkout tests and 57 static model performance tests (including 22 at no charge). Fan nozzle pressure ratio varied from 1.4 to 2.0, and fan to core total pressure ratio varied from 1.0 to 1.19. Core to fan total temperature ratio was 1.0. Channel 10 wind tunnel tests consisted of 15 tests at Mach number 0.28 and 31 tests at Mach 0.8. The sting was checked out statically in Channel 6 before the wind tunnel tests. In the Channel 6 facility, 12 ASME nozzle data points were taken and 7 model data points were taken. In the wind tunnel, fan nozzle pressure ratio varied from 1.73 to 2.8, and fan to core total pressure ratio varied from 1.0 to 1.19. Core to fan total temperature ratio was 1.0. Test results include thrust coefficients, thrust vector angle, core and fan nozzle discharge coefficients, total pressure and temperature charging station profiles, and boat-tail static pressure distributions in the wind tunnel.

Mikkelsen, Kevin L.↗

NASA NTRS1998

PAB3D Simulations of a Nozzle with Fluidic Injection for Yaw Thrust-Vector Control

An experimental and computational study was conducted on an exhaust nozzle with fluidic injection for yaw thrust-vector control. The nozzle concept was tested experimentally in the NASA Langley Jet Exit Test Facility (JETF) at nozzle pressure ratios up to 4 and secondary fluidic injection flow rates up to 15 percent of the primary flow rate. Although many injection-port geometries and two nozzle planforms (symmetric and asymmetric) were tested experimentally, this paper focuses on the computational results of the more successful asymmetric planform with a slot injection port. This nozzle concept was simulated with the Navier-Stokes flow solver, PAB3D, invoking the Shih, Zhu, and Lumley algebraic Reynolds stress turbulence model (ASM) at nozzle pressure ratios (NPRs) of 2,3, and 4 with secondary to primary injection flow rates (w(sub s)/w(sub p)) of 0, 2, 7 and 10 percent.

Deere, Karen A.↗

NASA NTRS2004

Solar Sail GN and C Model Comparisons

The Solar Sail Propulsion project is engaged in an ambitious program to raise the Technology Readiness Level of solar sails and prepare for a validation flight via a series of hardware ground demonstrations and development of a number of high fidelity simulations and models. Guidance, navigation, and control of solar sails is a key part of this effort. The large flexible structure and optical nature of solar sails create a considerable challenge for attitude control, thrust modeling, and navigation. In this paper, we present an overview and comparison of two recently delivered prototype solar sail guidance, navigation, and control software tools currently funded by the Solar Sail Propulsion project. The results of some key test cases are presented. Where possible, we also make comparisons to other software tools. We discuss the implications of the results of these comparative studies to the future direction and scope of development efforts for guidance, navigation and control software for solar sails, including the relationship to hardware test efforts such as the Thrust Vector Control Authority Demonstration.

Heaton, Andrew F.↗

NASA NTRS2020

Cold-Flow Model Tests to Determine Static Performance of a NASA One-Sided Ejector Nozzle System

This report presents the results of cold-flow model tests to determine the static performance of multiple configurations of a NASA one-sided ejector nozzle system. The existing ejector nozzle system hardware was provided by NASA and was previously used for acoustic tests. A new facility adapter duct and ejector box support brackets were designed and fabricated by the FluiDyne Aerotest Laboratory of Aero Systems Engineering, Inc. The tests were performed in the Channel 8 static thrust stand at ASE’s FluiDyne Aerotest Laboratory in Plymouth, Minnesota. Facility checkout tests were made using a standard American Society of Mechanical Engineers (ASME) long-radius metering nozzle. These tests demonstrated facility data accuracy at flow conditions similar to the model tests. Channel 8 static tests included 40 ASME nozzle facility checkout tests and 24 model tests (plus an additional 1 at no charge). The model nozzle pressure ratio varied from 1.4 to 3.0. Test results include: thrust coefficients, thrust vector angles and location, nozzle discharge coefficients, charging station total and static pressures, and model static pressure distributions (in the Data Appendix).

McDonald, Timothy J.↗

NASA NTRS2018

Modelling and Laboratory Testing of Particle Resuspension and Transport for the Assessment of Terrestrial-Borne Biological Contamination of the Samples on the Mars 2020 Mission

The Mars 2020 mission will land a rover on the surface of Mars that will acquire, encapsulate, and cache scientifically selected samples of martian material for possible return to Earth by a future mission. The samples will be individually encapsulated and sealed in sample tubes. Each sample, and therefore each sample tube, must be kept clean of viable organisms with a terrestrial origin, which may adhere to the rover on their own and/or on other non-biological particles. Therefore, contrary to previous missions to the Red Planet, Mars 2020 is subject to new and more stringent biological, organic and inorganic contamination requirements. This paper reports on the analyses and testing performed to assess the various vectors that can lead to the terrestrial-borne contamination of the samples, focusing on those that are predicted to be the larger contributors. Specifically, the contamination of the sample tubes is expected to be very small prior to the commencement of the mission’s science phase since these tubes are protected by so-called Fluid Mechanical Particle Barriers. Once on the surface of Mars however the sample tubes will be removed from their FMPBs and be subject to contamination from the rover. Of specific interest is the vector by which winds dislodge some particles from the surface of the rover and transport them to the surrounding soil. Naturally, such assessments require multi-disciplinary analyses involving at minimum the physics of particle adhesion and resuspension from surfaces, fluid mechanics and aerosols. Here we provide an overview of these models. We also report on particle resuspension experiments we have performed at the Jet Propulsion Laboratory to both guide and validate the aforementioned physics models.

Steltzner, Adam↗

NASA NTRS1975

Effects of upper-surface blowing and thrust vectoring on low-speed aerodynamic characteristics of a large-scale supersonic transport model

Tests were conducted in the Langley full-scale tunnel to determine the low-speed aerodynamic characteristics of a large-scale arrow-wing supersonic transport configured with engines mounted above the wing for upper surface blowing, and conventional lower surface engines with provisions for thrust vectoring. A limited number of tests were conducted for the upper surface engine configuration in the high lift condition for beta = 10 in order to evaluate lateral directional characteristics, and with the right engine inoperative to evaluate the engine out condition.

Coe, P. L., Jr.↗

NASA NTRS1994

Static investigation of two fluidic thrust-vectoring concepts on a two-dimensional convergent-divergent nozzle

A static investigation was conducted in the static test facility of the Langley 16-Foot Transonic Tunnel of two thrust-vectoring concepts which utilize fluidic mechanisms for deflecting the jet of a two-dimensional convergent-divergent nozzle. One concept involved using the Coanda effect to turn a sheet of injected secondary air along a curved sidewall flap and, through entrainment, draw the primary jet in the same direction to produce yaw thrust vectoring. The other concept involved deflecting the primary jet to produce pitch thrust vectoring by injecting secondary air through a transverse slot in the divergent flap, creating an oblique shock in the divergent channel. Utilizing the Coanda effect to produce yaw thrust vectoring was largely unsuccessful. Small vector angles were produced at low primary nozzle pressure ratios, probably because the momentum of the primary jet was low. Significant pitch thrust vector angles were produced by injecting secondary flow through a slot in the divergent flap. Thrust vector angle decreased with increasing nozzle pressure ratio but moderate levels were maintained at the highest nozzle pressure ratio tested. Thrust performance generally increased at low nozzle pressure ratios and decreased near the design pressure ratio with the addition of secondary flow.

Wing, David J.↗

NASA NTRSDate not supplied

Space Launch System Core Stage Green Run Base Heating: Anomaly, Mitigation and Flight Redesign

The NASA Space Launch System (SLS) vehicle is composed of four RS-25 liquid oxygen and hydrogen rocket engines in the Core Stage (CS). The SLS Core Stage went through Green Run hot-fire testing at NASA Stennis Space Center’s B-2 test facility in 2021. The main goal of this testing was to confirm Core Stage tanking, propulsion and thrust vector control systems operations and performance to verify with predicted models. Two hot-fire (HF) test sequences were performed with the first one (HF1) in January for a test duration of 70 seconds and the second (HF2) testing completed in March for a test duration of 500 seconds. This paper focuses on the base heating anomalies observed during HF1 and HF2 where an extensive fire was observed along the Core Stage base heat shield during test operations. This environment was not anticipated and led to extensive unplanned damage to the thermal protection system which was augmented for flight. Green Run observations also led to a reassessment of flight environments for Artemis I. This paper discusses the potential cause of the anomalies, the flow physics, the reconstructed base environments, and mitigation plans for HF2 and flight.

aerothermodynamics↗

NASA NTRSDate not supplied

Space Launch System Core Stage Green Run Base Heating: Anomaly, Mitigation and Flight Redesign

The NASA Space Launch System (SLS) vehicle is composed of four RS-25 liquid oxygen and hydrogen rocket engines in the Core Stage (CS). The SLS Core Stage went through Green Run hotfire testing at NASA Stennis Space Center’s B-2 test facility in 2021. The main goal of this testing was to confirm Core Stage tanking, propulsion and thrust vector control systems operations and performance to verify with predicted models. Two hot-fire (HF) test sequences were performed with the first one (HF1) in January for a test duration of 70 seconds and the second (HF2) testing completed in March for a test duration of 500 seconds. This paper focuses on the base heating anomalies observed during HF1 and HF2 where an extensive fire was observed along the Core Stage base heat shield during test operations. This environment was not anticipated and led to extensive unplanned damage to the thermal protection system which was augmented for flight. Green Run observations also led to a reassessment of flight environments for Artemis I. This paper discusses the potential cause of the anomalies, the flow physics, the reconstructed base environments, and mitigation plans for HF2 and flight.

aerothermodynamics↗

NASA NTRS1988

Parafrase restructuring of FORTRAN code for parallel processing

Parafrase transforms a FORTRAN code, subroutine by subroutine, into a parallel code for a vector and/or shared-memory multiprocessor system. Parafrase is not a compiler; it transforms a code and provides information for a vector or concurrent process. Parafrase uses a data dependency to reveal parallelism among instructions. The data dependency test distinguishes between recurrences and statements that can be directly vectorized or parallelized. A number of transformations are required to build a data dependency graph.

Wadhwa, Atul↗

NASA NTRS1972

Performance testing of a solid propellant pulsed plasma microthruster.

Extensive performance testing has been carried out on a solid propellant, pulsed plasma microthruster essentially identical to the four units aboard the DOD, Lincoln Experimental Satellite (LES)-6. Tests include measurements of thrust, specific impulse, thrust vector, impulse bit repeatability, intermittency and endurance. The results show good impulse bit repeatability and a well defined thrust vector. The impulse bit, with a 1.85-joule input, was found to be approximately 26 micronewton-seconds at 190 seconds specific impulse. Based upon the results of the intermittency tests, a better understanding of the intermittency mechanism has been achieved.

Williams, T. E.↗

NASA NTRS1993

Development of iterative techniques for the solution of unsteady compressible viscous flows

During the past two decades, there has been significant progress in the field of numerical simulation of unsteady compressible viscous flows. At present, a variety of solution techniques exist such as the transonic small disturbance analyses (TSD), transonic full potential equation-based methods, unsteady Euler solvers, and unsteady Navier-Stokes solvers. These advances have been made possible by developments in three areas: (1) improved numerical algorithms; (2) automation of body-fitted grid generation schemes; and (3) advanced computer architectures with vector processing and massively parallel processing features. In this work, the GMRES scheme has been considered as a candidate for acceleration of a Newton iteration time marching scheme for unsteady 2-D and 3-D compressible viscous flow calculation; from preliminary calculations, this will provide up to a 65 percent reduction in the computer time requirements over the existing class of explicit and implicit time marching schemes. The proposed method has ben tested on structured grids, but is flexible enough for extension to unstructured grids. The described scheme has been tested only on the current generation of vector processor architecture of the Cray Y/MP class, but should be suitable for adaptation to massively parallel machines.

Hixon, Duane↗

NASA NTRS2017

Performance Verification and Testing of the COWVR Instrument Antenna

The Compact Ocean Wind Vector Radiometer (COWVR) is a technology demonstration mission, developed at the Jet Propulsion Laboratory (JPL), and scheduled for launch in 2016. The goal of COWVR is to provide the same windvector retrieval accuracy of other instruments, like WindSat, while reducing the total mass and using less power. In this paper, we present an overview of the COWVR instrument, and a detailed description of the EM (Electromagnetic) modeling of the antenna system and the test campaign carried out at JPL to assess its performance. Special emphasis has been placed on assessing the accuracy of the predictions made with the RF (Radio Frequency) model. We will show that the predicted radiation patterns are accurate enough so one can use them for orbit radiometer calibration.

Radiation Patterns↗

NASA NTRS1982

Tests and analysis of a vented D thrust deflecting nozzle on a turbofan engine

The objectives were to: obtain nozzle performance characteristics in and out of ground effects; demonstrate the compatibility of the nozzle with a turbofan engine; obtain pressure and temperature distributions on the surface of the D vented nozzle; and establish a correlation of the nozzle performance between small scale and large scale models. The test nozzle was a boilerplate model of the MCAIR D vented nozzle configured for operation with a General Electric YTF-34-F5 turbofan engine. The nozzle was configured to provide: a thrust vectoring range of 0 to 115 deg; a yaw vectoring range of 0 to 10 deg; variable nozzle area control; and variable spacing between the core exit and nozzle entrance station. Compatibility between the YTF-34-T5 turbofan engine and the D vented nozzle was demonstrated. Velocity coefficients of 0.96 and greater were obtained for 90 deg of thrust vectoring. The nozzle walls remained cool during all test conditions.

Roseberg, E. W.↗

NASA NTRS1993

Direct correlation of test-analysis cross-orthogonality

This paper presents an alternative to the correlation of individual components of a mode shape vectors by directly examining the sensitivity of the cross-orthogonality between test and analytical mode shapes. If the test and analysis mode shapes are identical, the diagonal elements of the cross-orthogonality will be identical to the test orthogonality matrix, so the cross-orthogonality matrix provides a concise measure of the 'closeness' between test and analysis mode shapes. There are two major advantages to the cross-orthogonality correlation approach. The first is that a direct correlation of this matrix will more directly meet the goal of the correlation effort (measured by cross-orthogonality). Secondly, and more importantly, the correlation of cross-orthogonality greatly reduces the amount of data that needs to be handled when compared to the correlation of mode shapes.

Blelloch, P. A.↗

NASA NTRSDate not supplied

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

J Lemery↗

NASA NTRS2024

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

Medical Operations↗