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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

The influence of intake flow and coolant temperature on gasoline spray morphology during early-injection DISI engine operation

Multi-hole gasoline injectors operating at conditions spanning throttled early-intake stroke operation produce spray plumes that either remained separated or merge and collapse due to flash boiling. Flash boiling occurs due to the sudden expansion of gas bubbles in the liquid fuel at high fuel temperature and low ambient pressure. This study records high-speed images of spray-morphology changes due to in-cylinder flow, thereby revealing operating conditions that do and do not affect the self-induced morphology observed in quiescent vessels. Specifically, in a central-injection, four-valve, high-tumble engine, where the thermodynamic state and in-cylinder cross flow are dynamic. Motivated by cold start and hot restart operation, the fuel pressure, coolant temperature, in-cylinder air pressure, and engine rpm were systematically varied over relevant operating conditions, which bracketed the range from non- to flash-boiling sprays. The results reveal the operating conditions at which the in-cylinder cross flow disrupts the spray morphology as well as the extent of the disruption. At 650 rpm, the spray morphology was similar to that observed in quiescent vessels at nominally equivalent fuel temperature and in-cylinder pressure, indicating that the spray’s self-induced entrainment flow dominated the in-cylinder flow. However, for fuel temperature and ambient pressure near the transition between non- and flash-boiling, the intake cross flow at higher engine speed (1950 rpm) significantly disrupted the spray morphology. The high cross-flow velocity appears to induce plume merging and collapse, whereas none was evident at low rpm (650 rpm). This study led to the postulate that the spray merging and collapse are governed by the rate of atomization near the nozzle exit, presumed to be controlled by either or both aerodynamic atomization and flash-boiling intensity. It would then follow that spray modeling in CFD requires atomization models that blend the effects of both physical processes.

42 ENGINEERING↗
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↗
DOE OSTI2020

Dynamics of Magnus-dominated particle clusters, collisions, pinning, and ratchets

Motivated by the recent work in skyrmions and active chiral matter systems, we examine pairs and small clusters of repulsively interacting point particles in the limit where the dynamics is dominated by the Magnus force. We find that particles with the same Magnus force can form stable pairs, triples, and higher ordered clusters or exhibit chaotic motion. For mixtures of particles with opposite Magnus force, particle pairs can combine to form translating dipoles. Under an applied drive, particles with the same Magnus force translate; however, particles with different or opposite Magnus force exhibit a drive-dependent decoupling transition. When the particles interact with a repulsive obstacle, they can form localized orbits with depinning or unwinding transitions under an applied drive. We examine the interaction of these particles with clusters or lines of obstacles and find that the particles can become trapped in orbits that encircle multiple obstacles. Under an ac drive, we observe a series of ratchet effects, including ratchet reversals, for particles interacting with a line of obstacles due to the formation of commensurate orbits. Lastly, in assemblies of particles with mixed Magnus forces of the same sign, we find that the particles with the largest Magnus force become localized in the center of the cluster, while for mixtures with opposite Magnus forces, the motion is dominated by transient local pairs or clusters, where the translating pairs can be regarded as a form of active matter.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
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.↗
DOE OSTI2020

Determination of a macro- to micro-scale progression leading to a magnetized plasma disruption

We report here the observations of a plasma jet evolving through a macro- to micro-scale progression sequence. This leads to a fast magnetic reconnection that results in the jet breaking off from its originating electrode and forming a force-free state. A sausage-like pinching occurs first and squeezes an initially fat, short magnetized jet so that it becomes thin. The thin jet then becomes kink unstable. The lengthening of the jet by the kinking thins the jet even more since the kink is an incompressible instability. When the jet radius becomes comparable to the ion-skin depth, Hall and electron inertial physics become important and establish the environment for a fast magnetic reconnection. This fast reconnection occurs, disrupting the jet and establishing a force-free state. X-ray bursts and whistler waves, evidence of a magnetic reconnection, are observed when the plasma jet breaks off from the electrode. This experimentally confirmed sequence of successive thinning from pinching followed by kinking is reproduced in a three-dimensional ideal Magnetohydrodynamic (MHD) numerical simulation. The results of the experiment and the numerical simulation, together demonstrate a viable path from macro-scale MHD physics to micro-scale non-MHD physics where fast reconnection occurs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Diode effect for skyrmions interacting with linear protrusion defects

We simulate collectively interacting skyrmions in a channel with periodic asymmetry, and find a strong diode effect for the skyrmion flow. There is also an asymmetry in the skyrmion annihilation rate for currents applied along the hard or easy substrate asymmetry direction, with a higher annihilation rate for hard direction currents. We map out the diode efficiency as a function of magnetic field and substrate asymmetry angle. We also show that the Magnus force impacts the diode motion and annihilation rate asymmetry by forcing skyrmions into corners of the protrusion geometry.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Numerical Investigation of Fuel Property Effects on Mixed-Mode Combustion in a Spark-Ignition Engine

In this research, lean mixed-mode combustion is numerically investigated using computational fluid dynamics (CFD) in a spark-ignition engine. A new E30 fuel surrogate is developed using a neural network model with matched octane numbers. A skeletal mechanism is also developed by automated mechanism reduction and by incorporating a NO x submechanism. A hybrid approach that couples the G-equation model and the well-stirred reactor model is employed for turbulent combustion modeling. The developed CFD model is shown to well predict pressure and apparent heat release rate (AHRR) traces compared with experiment. Two types of combustion cycles (deflagration-only and mixed-mode cycles) are observed. The mixed-mode cycles feature early flame propagation and subsequent end-gas auto-ignition, leading to two distinctive AHRR peaks. The validated CFD model is then employed to investigate the effects of NO x chemistry. The NO x chemistry is found to promote auto-ignition through the residual gas, while the deflagration phase remains largely unaffected. Sensitivity analysis is finally performed to understand effects of fuel properties, including heat of vaporization (HoV) and laminar flame speed (S L ). An increased HoV tends to suppress auto-ignition through charge cooling, while the impact of HoV on flame propagation is insignificant. In contrast, an increased S L is found to significantly promote both flame propagation and end-gas auto-ignition. The promoting effect of S L on auto-ignition is not a direct chemical effect; it is rather caused by an advancement of the combustion phasing, which increases compression heating of the end-gas.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗