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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

Object Based Numerical Zooming Between the NPSS Version 1 and a 1-Dimensional Meanline High Pressure Compressor Design Analysis Code

Within NASA's High Performance Computing and Communication (HPCC) program, NASA Glenn Research Center is developing an environment for the analysis/design of propulsion systems for aircraft and space vehicles called the Numerical Propulsion System Simulation (NPSS). The NPSS focuses on the integration of multiple disciplines such as aerodynamics, structures, and heat transfer, along with the concept of numerical zooming between 0- Dimensional to 1-, 2-, and 3-dimensional component engine codes. The vision for NPSS is to create a "numerical test cell" enabling full engine simulations overnight on cost-effective computing platforms. Current "state-of-the-art" engine simulations are 0-dimensional in that there is there is no axial, radial or circumferential resolution within a given component (e.g. a compressor or turbine has no internal station designations). In these 0-dimensional cycle simulations the individual component performance characteristics typically come from a table look-up (map) with adjustments for off-design effects such as variable geometry, Reynolds effects, and clearances. Zooming one or more of the engine components to a higher order, physics-based analysis means a higher order code is executed and the results from this analysis are used to adjust the 0-dimensional component performance characteristics within the system simulation. By drawing on the results from more predictive, physics based higher order analysis codes, "cycle" simulations are refined to closely model and predict the complex physical processes inherent to engines. As part of the overall development of the NPSS, NASA and industry began the process of defining and implementing an object class structure that enables Numerical Zooming between the NPSS Version I (0-dimension) and higher order 1-, 2- and 3-dimensional analysis codes. The NPSS Version I preserves the historical cycle engineering practices but also extends these classical practices into the area of numerical zooming for use within a companies' design system. What follows here is a description of successfully zooming I-dimensional (row-by-row) high pressure compressor results back to a NPSS engine 0-dimension simulation and a discussion of the results illustrated using an advanced data visualization tool. This type of high fidelity system-level analysis, made possible by the zooming capability of the NPSS, will greatly improve the fidelity of the engine system simulation and enable the engine system to be "pre-validated" prior to commitment to engine hardware.

Follen, G.↗
DOE OSTI2022

Toward fully automated UED operation using two-stage machine learning model

To demonstrate the feasibility of automating UED operation and diagnosing the machine performance in real time, a two-stage machine learning (ML) model based on self-consistent start-to-end simulations has been implemented. This model will not only provide the machine parameters with adequate precision, toward the full automation of the UED instrument, but also make real-time electron beam information available as single-shot nondestructive diagnostics. Furthermore, based on a deep understanding of the root connection between the electron beam properties and the features of Bragg-diffraction patterns, we have applied the hidden symmetry as model constraints, successfully improving the accuracy of energy spread prediction by a factor of five and making the beam divergence prediction two times faster. The capability enabled by the global optimization via ML provides us with better opportunities for discoveries using near-parallel, bright, and ultrafast electron beams for single-shot imaging. It also enables directly visualizing the dynamics of defects and nanostructured materials, which is impossible using present electron-beam technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Imaging in Diagnostic Nuclear Medicine

Single-photon emission computed tomography (SPECT) and positron emission tomography (PET) are modern methods for visualization in diagnostic nuclear medicine. SPECT is known as a workhorse in cardiology, and PET is the gold standard in oncology. The development of nuclear medicine is provided by cooperation of physicists, mathematicians, biologists, medical doctors, and radio-chemists. In spite of extensive clinical applications, several problems may lead to false diagnoses. In particular, the correction of attenuation of gamma radiation in human organs must be taken into account. For interpretation and evaluation of such an effect on the clinical results, we perform physico-mathematical simulation of the SPECT diagnostics in cardiology in the absence and presence of the correction of attenuation. A brief review of the state-of-the art is presented. The simulation employs the first Russian anthropomorphic mathematical phantom that describes the distribution of radio-pharmacological agent ({sup 99m }Tc-methoxyisobutylisonitrile) in chest organs of a typical male patient. A model for calculation of raw images is developed with allowance for attenuation of radiation in biological tissues and the effect of collimator and detector. The results of the proposed models and calculated images are compared with clinical images obtained at the Meshalkin Institute of Circulation Pathology (Novosibirsk) and Myasnikov Institute of Clinical Cardiology (Moscow). Statistical algorithms are developed for the solution of the inverse problem of image reconstruction based on the entropy principle. The clinical and physico-mathematical approaches are compared in the evaluation of the effect of correction on the quality of reconstructed images of the left ventricle of myocardium.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS2004

Examples of Current and Future Uses of Neural-Net Image Processing for Aerospace Applications

Feed forward artificial neural networks are very convenient for performing correlated interpolation of pairs of complex noisy data sets as well as detecting small changes in image data. Image-to-image, image-to-variable and image-to-index applications have been tested at Glenn. Early demonstration applications are summarized including image-directed alignment of optics, tomography, flow-visualization control of wind-tunnel operations and structural-model-trained neural networks. A practical application is reviewed that employs neural-net detection of structural damage from interference fringe patterns. Both sensor-based and optics-only calibration procedures are available for this technique. These accomplishments have generated the knowledge necessary to suggest some other applications for NASA and Government programs. A tomography application is discussed to support Glenn's Icing Research tomography effort. The self-regularizing capability of a neural net is shown to predict the expected performance of the tomography geometry and to augment fast data processing. Other potential applications involve the quantum technologies. It may be possible to use a neural net as an image-to-image controller of an optical tweezers being used for diagnostics of isolated nano structures. The image-to-image transformation properties also offer the potential for simulating quantum computing. Computer resources are detailed for implementing the black box calibration features of the neural nets.

Decker, Arthur J.↗
DOE OSTI2015

The effect of non-uniform temperature and velocity fields on long range ultrasonic measurement systems in MYRRHA

SCK.CEN, the Belgian Nuclear Research Center, is developing MYRRHA, a generation IV liquid metal cooled nuclear research reactor. As the liquid metal coolant is opaque to light, normal visual feedback during fuel manipulations is not available and must therefore be replaced by a system that is not hindered by the opacity of the coolant. In this respect ultrasonic based instrumentation is under development at SCK.CEN to provide feedback during operations under liquid metal. One of the tasks that will be tackled using ultrasound is the detection and localization of a potentially lost fuel assembly. In this application, the distance between ultrasonic sensor and target may be as large as 2.5 m. At these distances, non uniform velocity and temperature fields in the liquid metal potentially influence the propagation of the ultrasonic signals, affecting the performance of the ultrasonic systems. In this paper, we investigate how relevant temperature and velocity gradients inside the liquid metal influence the propagation of ultrasonic waves. The effect of temperature and velocity gradients are simulated by means of a newly developed numerical ray-tracing model. The performance of the model is validated by dedicated water experiments. The setup is capable of creating velocity and temperature gradients representative for MYRRHA conditions. Once validated in water, the same model is used to make predictions for the effect of gradients in the MYRRHA liquid metal environment. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Impurity gas detection for SNF canisters using probabilistic deep learning and acoustic sensing *

Abstract Monitoring impurity gases in spent nuclear fuel (SNF) canisters is a novel structural health monitoring approach for SNF in dry storage. The SNF canisters are sealed containers that do not facilitate visual access to the inside. Acoustic sensing can be deployed by taking advantage of the pathways unobstructed by internal hardware. Although the ultrasonic time-of-flight measurement can provide valuable information, it is limited in its ability to discern the concentration of only one impurity gas. As such, deep learning algorithms, particularly convolutional neural networks (CNNs), offer a promising solution. In this study, CNN-based probabilistic deep learning models were implemented to detect and quantify multiple impurity gases in helium. An experimental platform was established to simulate canister conditions, and ultrasonic test data were collected. The presence of argon and air in helium at concentrations ranging from 0% to 1.2% at increments of 0.05% was considered. The multi-layer perceptron, decision tree, and logistic regression classifiers achieved high accuracies when distinguishing pure helium from helium with impurities. CNN with dropout layers and CNN using maximum likelihood estimation showed a similar performance, indicating their ability to capture uncertainties. The ensemble CNN model exhibited improved predictions and the ability to balance individual gas concentration by integrating 1D- and 2D-CNN models. These findings contribute probabilistic deep learning solutions for impurity gas detection and analysis within SNF canisters, thus ensuring safe storage and management of SNFs.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Numerical Analysis of Detonability Assessment in a Natural Gas-Air Fueled Rotating Detonation Engine

Rotating Detonation Engines (RDE) offer an alternative combustion strategy to replace conventional constant pressure combustion with a process that could produce a pressure gain without the use of a mechanical compressor. Recent numerical and experimental publications that consider air as the oxidizer have primarily focused on the ability of these annular combustors to sustain a stable continuous detonation wave when fueled by hydrogen. However, for this to be a viable consideration for the land-based power generation it is necessary to explore the ability to detonate natural gas and air within the confines of the annular geometry of an RDE. Previous studies on confined detonations have expressed the importance of permitting detonation cells to fully form within the combustor in order to achieve stability. This poses a challenge for natural gas–air fueled processes as their detonation cell size can be quite large even at moderate pressures. Despite the practical importance, only a few studies are available on natural gas detonations for air-breathing RDE applications. Moreover, the extreme thermodynamic condition (high temperature inside the combustor) allows limited accessibility inside the combustor for detailed experimental instrumentations, providing mostly single-point data. Recent experimental studies at the National Energy Technology Laboratory (NETL) have reported detonation failure at higher methane concentration in an air-breathing RDE fueled by natural gas-hydrogen fuel blends. This encourages to perform a detailed numerical investigation on the wave characteristics of detonation in a natural gas-air fueled RDE to understand the various aspects of instability associated with the natural gas-air detonation.This study is a numerical consideration of a methane-air fueled RDE with varying operating conditions to ascertain the ability to achieve a stable, continuous detonation wave. The simulations have been performed in a 2D unwrapped RDE geometry using the open-source CFD library “OpenFOAM” employing an unsteady pressure-based compressible reactive flow solver with a <em>k</em>–<em>ε</em> turbulence model in a structured rectangular grid system. Both reduced and detailed chemical kinetic models have been used to assess the effect of the chemistry on the detonation wave characteristics and the underlying flow features. A systematic grid sensitivity study has been conducted with various grid sizes to quantify the weakly stable overdriven detonation on a coarse mesh and oscillating features at fine mesh resolutions.The main focus of the current study is to investigate the effects of operating injection pressure on detonation wave characteristics of an air-breathing Rotating Detonation Engine (RDE) fueled with natural gas-hydrogen fuel blends. Wave speeds, peak pressures and temperatures, and dominant frequencies have been computed from the time histories. The flow structures were then visualized using 2D contours of temperature and species concentration.

Saha, Pankaj↗
NASA NTRS1997

Image discrimination models predict detection in fixed but not random noise

By means of a two-interval forced-choice procedure, contrast detection thresholds for an aircraft positioned on a simulated airport runway scene were measured with fixed and random white-noise masks. The term fixed noise refers to a constant, or unchanging, noise pattern for each stimulus presentation. The random noise was either the same or different in the two intervals. Contrary to simple image discrimination model predictions, the same random noise condition produced greater masking than the fixed noise. This suggests that observers seem unable to hold a new noisy image for comparison. Also, performance appeared limited by internal process variability rather than by external noise variability, since similar masking was obtained for both random noise types.

NASA Center ARC↗
DOE OSTI2022

Large-eddy simulation of helical- and straight-bladed vertical-axis wind turbines in boundary layer turbulence

Here, turbulent wake flows behind helical- and straight-bladed vertical axis wind turbines (VAWTs) in boundary layer turbulence are numerically studied using the large-eddy simulation (LES) method combined with the actuator line model. Based on the LES data, systematic statistical analyses are performed to explore the effects of blade geometry on the characteristics of the turbine wake. The time-averaged velocity fields show that the helical-bladed VAWT generates a mean vertical velocity along the center of the turbine wake, which causes a vertical inclination of the turbine wake and alters the vertical gradient of the mean streamwise velocity. Consequently, the intensities of the turbulent fluctuations and Reynolds shear stresses are also affected by the helical-shaped blades when compared with those in the straight-bladed VAWT case. The LES results also show that reversing the twist direction of the helical-bladed VAWT causes the spatial patterns of the turbulent wake flow statistics to be reversed in the vertical direction. Moreover, the mass and kinetic energy transports in the turbine wakes are directly visualized using the transport tube method, and the comparison between the helical- and straight-bladed VAWT cases show significant differences in the downstream evolution of the transport tubes.

17 WIND ENERGY↗
DOE OSTI2020

A Clustering-Based Scenario Generation Framework for Power Market Simulation with Wind Integration

A critical step in stochastic optimization models of power system analysis is to select a set of appropriate scenarios and significant numbers of scenario generation methods exist in the literature. This paper develops a clustering based scenario generation method, which aims to improve the performance of existing scenario generation techniques by grouping a set of correlated wind sites into clusters according to their cross-correlations. Copula based models are utilized to model spatiotemporal correlations and the Gibbs sampling is then used to generate scenarios for day-ahead markets. Our results show that the generated scenarios based on clustered wind sites outperform existing approaches in terms of reliability and sharpness and can reduce the total computational time for scenario generation and reduction significantly. The clustering-based framework can therefore provide a better support for real-world market simulations with high wind penetration.

data visualization↗
DOE OSTI2013

Simulation of Wind Turbine Performance and Loading Patterns using a Coupled Meso-scale/Micro-scale Flow Analysis Model

HELIOS simulations were conducted for the WPS-30 turbine from the Sexbierum experimental site. The MMCI module was used to include the effects of the atmospheric boundary layer using the strategy described in the methodology section. In Figure 1 , the thrust and power variation for the WRF/Helios (Meso-scale/micro-scale simulation) is compared with measured power and stand-alone Helios computations (uniform inflow). The unsteady variations in power and thrust caused by the turbulent inflow conditions is clearly evident from these figures. Note that the mean hub height velocity could not be exactly matched to the uniform inflow values of 8 m/s because of constraints in adjusting the geostrophic wind and roughness length in Meso-scale WRF simulations. The offset in mean power and thrust notable in the figures are because of the small difference in the mean wind speed. Nonetheless, these results provide proof-of-concept of the integrated Meso-scale/Micro-scale simulation capability developed as part of this work. To further substantiate the effects of turbulent inflow, flow visualizations (iso-surfaces of vorticity overlaid on contours of velocity magnitude) are shown in Figure 2 . Meandering of the vortex wake and eventual breakdown are observed in the flow visualizations. Vortex breakdown is observed to occur between 1.5 and 2.5 diameter distance from the turbine. In uniform inflow (not shown here), the vortex wake persists to much longer distance of about 5 diameters before beginning to breakdown. It is expected that the vortex breakdown will be strongly correlated with the turbulence intensity of the inflow. The capability developed herein provides enabling technology for analyzing and characterizing such effects.

17 WIND ENERGY↗
DOE OSTI2014

Overview of New Tools to Perform Safety Analysis: BWR Station Black Out Test Case

Dynamic Probabilistic Risk Assessment (DPRA) methodologies couple system simulator codes (e.g., RELAP, MELCOR) with simulation controller codes (e.g., RAVEN, ADAPT). While system simulator codes accurately model system dynamics deterministically, simulation controller codes introduce both deterministic (e.g., system control logic, operating procedures) and stochastic (e.g., component failures, parameter uncertainties) elements into the simulation. Typically, a DPRA is performed by: 1) sampling values of a set of parameters from the uncertainty space of interest (using the simulation controller codes), and 2) simulating the system behavior for that specific set of parameter values (using the system simulator codes). For complex systems, one of the major challenges in using DPRA methodologies is to analyze the large amount of information (i.e., large number of scenarios ) generated, where clustering techniques are typically employed to allow users to better organize and interpret the data. In this paper, we focus on the analysis of a nuclear simulation dataset that is part of the Risk Informed Safety Margin Characterization (RISMC) Boiling Water Reactor (BWR) station blackout (SBO) case study. We apply a software tool that provides the domain experts with an interactive analysis and visualization environment for understanding the structures of such high-dimensional nuclear simulation datasets. Our tool encodes traditional and topology-based clustering techniques, where the latter partitions the data points into clusters based on their uniform gradient flow behavior. We demonstrate through our case study that both types of clustering techniques complement each other in bringing enhanced structural understanding of the data.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2013

Aerodynamic Characterization of a Thin, High-Performance Airfoil for Use in Ground Fluids Testing

The FAA has worked with Transport Canada and others to develop allowance times for aircraft operating in ice-pellet precipitation. Wind-tunnel testing has been carried out to better understand the flowoff characteristics and resulting aerodynamic effects of anti-icing fluids contaminated with ice pellets using a thin, high-performance wing section at the National Research Council of Canada Propulsion and Icing Wind Tunnel. The objective of this paper is to characterize the aerodynamic behavior of this wing section in order to better understand the adverse aerodynamic effects of anti-icing fluids and ice-pellet contamination. Aerodynamic performance data, boundary-layer surveys and flow visualization were conducted at a Reynolds number of approximately 6.0×10(exp 6) and a Mach number of 0.12. The clean, baseline model exhibited leading-edge stall characteristics including a leading-edge laminar separation bubble and minimal or no separation on the trailing edge of the main element or flap. These results were consistent with expected 2-D aerodynamics and showed no anomalies that could adversely affect the evaluation of anti-icing fluids and ice-pellet contamination on the wing. Tests conducted with roughness and leading-edge flow disturbances helped to explain the aerodynamic impact of the anti-icing fluids and contamination. The stalling characteristics of the wing section with fluid and contamination appear to be driven at least partially by the effects of a secondary wave of fluid that forms near the leading edge as the wing is rotated in the simulated takeoff profile. These results have provided a much more complete understanding of the adverse aerodynamic effects of anti-icing fluids and ice-pellet contamination on this wing section. This is important since these results are used, in part, to develop the ice-pellet allowance times that are applicable to many different airplanes.

Broeren, Andy P.↗
NASA NTRS2013

Aerodynamic Characterization of a Thin, High-Performance Airfoil for Use in Ground Fluids Testing

The FAA has worked with Transport Canada and others to develop allowance times for aircraft operating in ice-pellet precipitation. Wind-tunnel testing has been carried out to better understand the flowoff characteristics and resulting aerodynamic effects of anti-icing fluids contaminated with ice pellets using a thin, high-performance wing section at the National Research Council of Canada Propulsion and Icing Wind Tunnel. The objective of this paper is to characterize the aerodynamic behavior of this wing section in order to better understand the adverse aerodynamic effects of anti-icing fluids and ice-pellet contamination. Aerodynamic performance data, boundary-layer surveys and flow visualization were conducted at a Reynolds number of approximately 6.0 x 10(exp 6) and a Mach number of 0.12. The clean, baseline model exhibited leading-edge stall characteristics including a leading-edge laminar separation bubble and minimal or no separation on the trailing edge of the main element or flap. These results were consistent with expected 2-D aerodynamics and showed no anomalies that could adversely affect the evaluation of anti-icing fluids and ice-pellet contamination on the wing. Tests conducted with roughness and leading-edge flow disturbances helped to explain the aerodynamic impact of the anti-icing fluids and contamination. The stalling characteristics of the wing section with fluid and contamination appear to be driven at least partially by the effects of a secondary wave of fluid that forms near the leading edge as the wing is rotated in the simulated takeoff profile. These results have provided a much more complete understanding of the adverse aerodynamic effects of anti-icing fluids and ice-pellet contamination on this wing section. This is important since these results are used, in part, to develop the ice-pellet allowance times that are applicable to many different airplanes.

Broeren, Andy P.↗
DOE OSTI2023

Λ Baryon Production in ν¯µ Interactions in the MicroBooNE Detector

The Cabibbo suppressed production of $\Lambda$ baryons in anti-neutrino interactions with nuclei is a rare process that is yet to be measured with a modern neutrino detector with automated reconstruction. The cross section for this process is sensitive to a number of unique nuclear effects, most notably the secondary interactions of the produced hyperon while attempting to escape from the nucleus. Other interactions within the nuclear remnant can impact the estimation of neutrino energy in oscillation measurements, and thus an accurate description of the nuclear environment is required. The strangeness violating hyperon production process is only available to anti-neutrinos. The model of this interaction is implemented into the NuWro neutrino interaction Monte Carlo simulation, and some predictions are presented, focusing on the role of nuclear effects. This model introduces a hyperon-nucleus potential, which calculations from hypernuclear theory permit to be strongly repulsive in th e case of $\Sigma$ baryons. The presence of this potential is found to sculpt the shape of the differential cross section in some variables. The MicroBooNE detector will be described, followed by a description of a measurement of the flux averaged, restricted phase space cross section of Cabibbo suppressed $\Lambda$ baryon production. A sophisticated event selection is employed, as a very large quantity of background neutrino interactions must be removed to perform the measurement with any sensitivity. This selection introduces some novel techniques such as the island finding method, and achieves a background reduction of $\sim 10^6$, with an efficiency of around 7\%. The calculation of the systematic uncertainties will be explained, including two procedures explored to handle sources of background with extremely poor simulation statistics: an in-situ constraint using data from sidebands, and a visual inspection of the data and simulation to remove the troublesome background events. The sensitivity to the $\Lambda$ baryon production cross section is calculated in the form of Bayesian posterior probability distributions, combining the systematic uncertainties with data and simulation statistical uncertainties. As a rare process, the statistical uncertainties are highly non-Gaussian, and the Bayesian approach is applied to include the full shapes of these uncertainties. Data corresponding to $2.2 \times 10^{20}$ protons on target of neutrino mode running and $4.9 \times 10^{20}$ protons on target of anti-neutrino running is analysed. When the data was unblinded, five $\Lambda$ production candidates were selected from the data, consistent with the MC simulation prediction of $5.3 \pm 1.1$ events. The final estimated cross section is $1.8^{+2.0}_{-1.6} \times 10^{-40}$cm$^2/$Ar when employing the sideband constraint procedure. A similar result of $2.0^{+2.2}_{-1.8} \times 10^{-40}$cm$^2/$Ar is obtained when performing the visual scan instead. The methods used in t his analysis are intended to be easily exported to other LArTPC detectors such as the Short Baseline Near Detector.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2022

Coupling Carbon Oxidation and Surface Recession in Direct-Simulation Monte Carlo Code, SPARTA

Ablative thermal protection system (TPS) materials for spacecraft are composites that are often made out of carbon-based reinforcement and a polymeric matrix. They endure high-temperature oxidation and surface recession when re-entering Earth’s atmosphere. Ablation is the result of many coupled and competing thermal, mechanical, and chemical phenomena, and it is difficult to isolate the role of each on the overall degradation of the TPS. Here we develop an ablation model for material recession coupled explicitly to finite rate carbon oxidation in complex microstructures. In this work, Stochastic PArallel Rarified-gas Time-accurate Analyzer (SPARTA), a direct-simulation Monte Carlo (DSMC) code, is modified to allow oxidation-driven ablation of implicitly defined carbon surfaces. In SPARTA, implicit surfaces are generated from the grid corner point values via a marching cubes algorithm, therefore creating a new set of surface elements every time ablation is performed. The finite-rate oxidation model developed by Gopalan et. al, was adapted to tally surface reactions and other surface data on a per-grid cell basis. The ablation functionality was also adjusted so once the reactions have occurred, the number of reactions leading to CO formation can be converted to corner point reduction values; therefore, carbon removal is directly proportional to surface recession. We also develop robust algorithms which handle the evolution of the flow cells and solid material regions, including split cells (flow cell divided in two by a solid surface). Finally, we demonstrate our implicit chemistry model for 2D and 3D geometries by producing reaction statistics and detailed visualization of oxidation-induced material recession at the microscale.

V Arias↗
DOE OSTI2024

Full-field quantitative visualization of shock-driven pore collapse and failure modes in PMMA

The dynamic collapse of pores under shock loading is thought to be directly related to hot spot generation and material failure, which is critical to the performance of porous energetic and structural materials. However, the shock compression response of porous materials at the local, individual pore scale is not well understood. This study examines, quantitatively, the collapse phenomenon of a single spherical void in PMMA at shock stresses ranging from 0.4 to 1.0 GPa. Using a newly developed internal digital image correlation technique in conjunction with plate impact experiments, full-field quantitative deformation measurements are conducted in the material surrounding the collapsing pore for the first time. The experimental results reveal two failure mode transitions as shock stress is increased: (i) the first in situ evidence of shear localization via adiabatic shear banding and (ii) dynamic fracture initiation at the pore surface. Numerical simulations using thermo-viscoplastic dynamic finite element analysis provide insights into the formation of adiabatic shear bands (ASBs) and stresses at which failure mode transitions occur. Further numerical and theoretical modeling indicates the dynamic fracture to occur along the weakened material inside an adiabatic shear band. Finally, analysis of the evolution of pore asymmetry and models for ASB spacing elucidate the mechanisms for the shear band initiation sites, and elastostatic theory explains the experimentally observed ASB and fracture paths based on the directions of maximum shear.

42 ENGINEERING↗
NASA NTRS2017

Effect of Ice Shape Fidelity on Swept-Wing Aerodynamic Performance

Low-Reynolds number testing was conducted at the 7 ft. x 10 ft. Walter H. Beech Memorial Wind Tunnel at Wichita State University to study the aerodynamic effects of ice shapes on a swept wing. A total of 17 ice shape configurations of varying geometric detail were tested. Simplified versions of an ice shape may help improve current ice accretion simulation methods and therefore aircraft design, certification, and testing. For each configuration, surface pressure, force balance, and fluorescent mini-tuft data were collected and for a selected subset of configurations oil-flow visualization and wake survey data were collected. A comparison of two ice shape geometries and two configurations with simplified geometric detail for each ice shape geometry is presented in this paper.

Scale models↗