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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.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Tiltrotor Test Rig Aerodynamic Tares

The Tiltrotor Test Rig (TTR) is a NASA facility designed to test full-scale proprotors in the National Full-Scale Aerodynamics Facility (NFAC). The first wind-tunnel entry included measurements of aerodynamic loads (tares) on the spinner without a rotor. The TTR has an onboard balance for direct measurement of rotor loads, so these tares comprise only spinner loads. Spinner tares also affect shaft loads measured independently of the balance. The results show that in axial flow, spinner drag is a linear function of dynamic pressure as expected, but at other flow angles, spinner loads include substantial lift and drag. Furthermore, there is a pronounced Magnus effect when the spinner is sideways to the flow. There is a sharp change in aerodynamic load at 90-deg yaw angle, indicating separation. Equations were developed to characterize the spinner tares at any arbitrary combination of yaw angle and airspeed.

TTR↗
NASA NTRS1956

Lift hysteresis at stall as an unsteady boundary-layer phenomenon

Analysis of rotating stall of compressor blade rows requires specification of a dynamic lift curve for the airfoil section at or near stall, presumably including the effect of lift hysteresis. Consideration of the magnus lift of a rotating cylinder suggests performing an unsteady boundary-layer calculation to find the movement of the separation points of an airfoil fixed in a stream of variable incidence. The consideration of the shedding of vorticity into the wake should yield an estimate of lift increment proportional to time rate of change of angle of attack. This increment is the amplitude of the hysteresis loop. An approximate analysis is carried out according to the foregoing ideas for a 6:1 elliptic airfoil at the angle of attack for maximum lift. The assumptions of small perturbations from maximum lift are made, permitting neglect of distributed vorticity in the wake. The calculated hysteresis loop is counterclockwise. Finally, a discussion of the forms of hysteresis loops is presented; and, for small reduced frequency of oscillation, it is concluded that the concept of a viscous "time lag" is appropriate only for harmonic variations of angle of attack with time at mean conditions other than maximum lift.

Moore, Franklin K↗
NASA NTRSDate not supplied

ArcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession, sting arm motion, and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials.

machine learning↗
NASA NTRSDate not supplied

ArcjetCV: a new machine learning application for extracting time-resolved recession measurements from arc jet test videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession, sting arm motion, and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials.

machine learning↗
NASA NTRSDate not supplied

ArcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

machine learning↗
NASA NTRSDate not supplied

arcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

machine learning↗
NASA NTRSDate not supplied

arcjetCV: automating recession extraction from video

Arc jet Computer Vision (arcjetCV)[1][2] is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗
NASA NTRSDate not supplied

ArcjetCV: Automating Recession Tracking

Arc jet Computer Vision (arcjetCV) is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗
NASA NTRS1973

Viscous flow over spinning cones at angle of attack.

A numerical finite-difference method is developed for evaluating the Magnus coefficients on spinning cones in laminar flow. The merged layer, the strong interaction region, and the downstream boundary layer are all considered. The numerical method is a predictor-corrector scheme developed for three-dimensional flows with or without crossflow diffusion. This method is particularly useful in problems in which a symmetry plane does not exist. Several contributions to the Magnus force and moments are considered. These include asymmetries in displacement thickness, centrifugal force and crossflow shear, and the effects of crossflow separation and vortex formation. Comparisons are made with experimental data and other analyses.

Lin, T. C.↗
NASA NTRSDate not supplied

ArcjetCV: Automating Arc Jet Analysis

Arc jet Computer Vision (arcjetCV) is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗
NASA NTRS2009

Right Ventricular Tissue Doppler Assessment in Space During Circulating Volume Modification using the Braslet-M Device

This joint U.S. - Russian work aims to establish a methodology for assessing cardiac function in microgravity in association with manipulation of central circulating volume. Russian Braslet-M occlusion cuffs were used to temporarily increase the volume of blood in the lower extremities, which effectively reduces the volume returning to the heart in the central circulation. A novel methodology was tested on the International Space Station (ISS) to assess the volume status of crewmembers by evaluating the responses to application and release of the Braslet-on-occlusion cuffs, as well as to modified Valsalva and Mueller maneuvers. Baseline echocardiographic tissue Doppler imaging (TDI) of the right ventricular free wall with no Braslet applied shows early diastolic E' (16 cm/sec), late diastolic A' (14 cm/sec), and systolic (12 cm/sec) velocities compatible with normal subjects on Earth. TDI of the RV free wall with Braslet applied shows that early diastolic E' decreased by 50% (8 cm/sec), late diastolic A' increased by 45%, and systolic S' remains unchanged. TDI of the RV free wall approximately 8 beats after the Braslet was released shows early diastolic E' (8 cm/sec), late diastolic A' (12 cm/sec), and systolic S' (13 cm/sec) velocities. During this portion of the release, early diastolic E' did not recover to baseline values but late diastolic A' and systolic S' recovered to pre-Braslet values. The pre-systolic cross-sectional area of the internal jugular vein with Braslet off was 1.07 cm(sup 2) and 1.13 cm(sup 2) 10 min after the Braslet was applied. The presystolic cross-sectional area of the common femoral vein with Braslet off was 0.50 cm(sup 2), and was 0.54 cm(sup 2) 10 min after the Braslet was applied. The right ventricular myocardial performance Tei index also was calculated for comparison with typical values found in healthy subjects on Earth. Baseline and Braslet-on values for Tei index were 0.25 and 0.22 respectively. Braslet Tei indices are within normal ranges found in healthy subjects and temporarily become greater than 0.4 during the dynamic Braslet release portion of this study. Tissue Doppler imaging of the right ventricle revealed that the Braslet influenced cardiac preload and that fluid was sequestered in the lower-extremity interstitial and vascular space after only 10 minutes of application. This report demonstrates that Braslet application affects right ventricular physiology in long-duration space flight based on TDI and that this effect is in part due to venous hemodynamics.

Hamilton, D. R.↗
NASA NTRS2008

A Modification and Analysis of Lagrangian Trajectory Modeling and Granular Dynamics of Lunar Dust Particles

A previously developed mathematical model is amended to more accurately incorporate the effects of lift and drag on single dust particles in order to predict their behavior in the wake of high velocity gas flow. The model utilizes output from a CFD or DSMC simulation of exhaust from a rocket nozzle hot gas jet. An extension of the Saffman equation for lift based on the research of McLaughlin (1991) and Mei (1992) is used, while an equation for the Magnus force modeled after the work of Oesterle (1994) and Tsuji et al (1985) is applied. A relationship for drag utilizing a particle shape factor (phi = 0.8) is taken from the work of Haider and Levenspiel (1989) for application to non-spherical particle dynamics. The drag equation is further adjusted to account for rarefaction and compressibility effects in rarefied and high Mach number flows according to the work of Davies (1945) and Loth (2007) respectively. Simulations using a more accurate model with the correction factor (Epsilon = 0.8 in a 20% particle concentration gas flow) given by Richardson and Zaki (1954) and Rowe (1961) show that particles have lower ejection angles than those that were previously calculated. This is more prevalent in smaller particles, which are shown through velocity and trajectory comparison to be more influenced by the flow of the surrounding gas. It is shown that particles are more affected by minor changes to drag forces than larger adjustments to lift forces, demanding a closer analysis of the shape and behavior of lunar dust particles and the composition of the surrounding gas flow.

Long, Jason M.↗
NASA NTRSDate not supplied

Assessment of ProgPy - An Open-Source Condition Monitoring and Diagnostics Tool

Traditional maintenance programs, such as corrective and preventive strategies, may lead to high costs and operational inefficiencies. Condition Monitoring and Diagnostics (CM&D) aims to improve these maintenance strategies by enabling timely insights into equipment health and performance. However, implementation of CM&D can be challenging without a robust framework that manages data efficiently, supports interoperability and simplifies integration. To address these challenges ProgPy, an open-source Python-based prognostics tool developed by NASA Ames Research Center, offers a structured solution for broader Prognostics and Health Management (PHM) applications. Ongoing research is assessing the feasibility of implementing ProgPy as a Condition Monitoring and Diagnostics (CM&D) solution by comparing its framework to the guidelines for open CM&D systems recommended in the ISO 13374-2 standard. This evaluation aims to highlight ProgPy’s strengths and identify opportunities for improvement, thereby, contributing to its advancement as an effective tool for Prognostics and Health Management (PHM). This paper presents the results of an initial assessment of the ProgPy toolbox through a gearbox case study using open-source datasets.

Condition-Monitoring, Diagnostics, Failure, Gearbo↗
NASA NTRS2002

STS-112 Flight Day 7 Highlights

On this seventh day of STS-112 mission members of the crew (Commander Jeff Ashby; Pilot Pam Melroy; Mission Specialist Sandy Magnus, Piers Sellers, Dave Wolf, and Fyodor Yurchikhin) along with the Expedition Five crew (Commander Valery Korzun; Flight Engineer Peggy Whitson, and Sergei Treschev) are seen answering questions during the mission's press interview and photo opportunity. They answered various questions regarding the mission's objectives, the onboard science experiments, the extravehicular activities (EVAs) and the effects of living in space. Shots of the test deployment of the S1 truss radiator and Canadarm rotor joint are also shown.

Source record↗
NASA NTRS1970

Calculation of the planar supercritical flow over a NASA supercritical profile

An unsteady finite difference procedure was used to calculate the steady inviscid flow over an 11% thickness ratio NASA supercritical profile of LWP 505 at M sub infinity = 0.80 and alpha = 0. An attempt is made to include the viscous effects using a modified form of Head's entrainment method to calculate the turbulent boundary layer. The attempt to predict the viscous effects using the compressible form of Head's integral method with a modified auxiliary equation for the form factors was unsatisfactory. Though a reasonably separation bubble was established on the lower surface, a grossly exaggerated displacement effect resulted downstream of the shock on the upper surface. There clearly is substantial further effort required to evolve a satisfactory boundary layer procedure, which must then be coupled in a still unproven manner with the inviscid procedure.

Yoshihara, H.↗
NASA NTRSDate not supplied

Preliminary 3D Simulations of Anomalous Thermocouple Coupling to Surface Electric Fields

Anomalous measurements from subsurface thermocouples have been observed during re-entry of the Orion capsule (EFT-1 plugs). These measurements are considered anomalous because the amplitude and frequency of the inferred temperature modulations are too large to be due to plausible heating processes. Consequently, it is believed the anomalies are caused by electrical interference generated by the re-entry plasma. Quantifying these effects requires several steps: modeling the external plasma flows, modeling the plasma-surface interaction, and finally modeling the surface-thermocouple coupling. This paper addresses this last step using 3D finite element simulations of a wire embedded in a weakly conductive medium.

thermocouple anomaly↗
NASA NTRSDate not supplied

Electromagnetic Thermocouple (TC) Anomaly Mechanisms During Atmospheric Entry

Thermocouples (TCs) are a critical diagnostic sensor for high enthalpy environments including Thermal Protection Systems (TPS) for atmospheric entry, hypersonic wind tunnels, and arc jet test facilities. They are used for both evaluating facility environmental conditions (enthalpy, and heat flux) as well as for measuring in-situ material thermal response under test conditions (thermal conductivity, heat capacity, etc.). Consequently, accurate TC measurements are fundamental for both material and facility Verification and Validation (V&V). However, a number of electromagnetic TC anomalies have been observed in both ground tests and flight instrumentations. These anomalies are caused via various coupling mechanisms: surface voltage coupling, Hall effect currents, and RF rectification. All of these mechanisms can, if unaddressed, result in obviously compromised results or worse, non-physical measurements which appear reasonable. This talk will provide an overview of each of these anomaly mechanisms and discuss ongoing efforts to quantify and mitigate them.

Magnus Haw↗