Results for “recession” — Page 2

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↗

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 NTRS1973

The nonuniform recession of the south polar cap of Mars

The nature of the irregular springtime recession of the Martian polar caps is investigated, with particular reference to the southern polar cap. Our current knowledge about the composition of the caps is outlined, and the historical record of their springtime recession is reviewed. An attempt is made to correlate the irregularities of the recession pattern of the southern polar cap with the features of the terrain revealed by Mariner 9 photography at a time when the southern cap was at its minimum extent. The results are interpreted in terms of the physical and meteorological processes active in the polar regions.

Veverka, J.↗

NASA NTRS1992

Laminar flow in a recess of a hydrostatic bearing

The flow in a recess of a hydrostatic journal bearing is studied in detail. The Navier-Stokes equations for the laminar flow of an incompressible liquid are solved numerically in a two-dimensional plane of a typical bearing recess. Pressure- and shear-induced flows, as well as a combination of these two flow conditions, are analyzed. Recess friction, pressure-ram effects at discontinuities in the flow region, and film entrance pressure loss effects are calculated. Entrance pressure loss coefficients over a forward-facing step are presented as functions of the mean flow Reynolds number for pure-pressure and shear-induced laminar flows.

San Andres, Luis A.↗

NASA NTRS1999

SiC and Si3N4 Recession Due to SiO2 Scale Volatility Under Combustor Conditions

SiC and Si3N4 materials were tested under various turbine engine combustion environments, chosen to represent either conventional fuel-lean or fuel-rich mixtures proposed for high speed aircraft. Representative CVD, sintered, and composite materials were evaluated in both furnace and high pressure burner rig exposure. While protective SiO2 scales form in all cases, evidence is presented to support paralinear growth kinetics, i.e. parabolic growth moderated simultaneously by linear volatilization. The volatility rate is dependent on temperature, moisture content, system pressure, and gas velocity. The burner tests were used to map SiO2 volatility (and SiC recession) over a range of temperature, pressure, and velocity. The functional dependency of material recession (volatility) that emerged followed the form: exp(-QIRT) * P(exp x) * v(exp y). These empirical relations were compared to rates predicted from the thermodynamics of volatile SiO and SiO(sub x)H(sub Y) reaction products and a kinetic model of diffusion through a moving, boundary layer. For typical combustion conditions, recession of 0.2 to 2 micron/h is predicted at 1200- 1400C, far in excess of acceptable long term limits.

Smialek, James L.↗

NASA NTRS1999

SiC and Si3N4 Recession Due to SiO2 Scale Volatility Under Combustor Conditions

Silicon carbide (SiC) and Si3N4 materials were tested in various turbine engine combustion environments chosen to represent either conventional fuel-lean or fuel-rich mixtures proposed for high-speed aircraft. Representative chemical vapor-deposited (CVD), sintered, and composite materials were evaluated by furnace and high-pressure burner rig exposures. Although protective SiO2 scales formed in all cases, the evidence presented supports a model based on paralinear growth kinetics (i.e., parabolic growth moderated simultaneously by linear volatilization). The volatility rate is dependent on temperature, moisture content, system pressure, and gas velocity. The burner tests were thus used to map SiO2 volatility (and SiC recession) over a range of temperatures, pressures, and velocities. The functional dependency of material recession (volatility) that emerged followed the form A[exp(-Q / RT)](P(sup x)v(sup y). These empirical relations were compared with rates predicted from the thermodynamics of volatile SiO and SiOxHy reaction products and a kinetic model of diffusion through a moving boundary layer. For typical combustion conditions, recession of 0.2 to 2 micrometers/hr is predicted at 1200 to 1400 C, far in excess of acceptable long-term limits.

Smialek, James L.↗

NASA NTRS2007

Finite Element Development and Specifications of a Patched, Recessed Nomex Core Honeycomb Panel for Increased Sound Transmission Loss

This informal report summarizes the development and the design specifications of a recessed nomex core honeycomb panel in fulfillment of the deliverable in Task Order 13RBE, Revision 10, Subtask 17. The honeycomb panel, with 0.020-inch thick aluminum face sheets, has 0.016-inch thick aluminum patches applied to twenty-five, 6 by 6 inch, quarter inch thick recessed cores. A 10 dB higher transmission loss over the frequency range 250 - 1000 Hz was predicted by a MSC/NASTRAN finite element model when compared with the transmission loss of the base nomex core honeycomb panel. The static displacement, due to a unit force applied at either the core or recessed core area, was of the same order of magnitude as the static displacement of the base honeycomb panel when exposed to the same unit force. The mass of the new honeycomb design is 5.1% more than the base honeycomb panel. A physical model was constructed and is being tested.

Grosveld, Ferdinand W.↗

NASA NTRSDate not supplied

Measurement of material recession and shock standoff in plasma windtunnel using neural nets

Arcjets are plasma wind tunnels used to test the performance of heatshield materials for spacecraft atmospheric entry. These facilities present an extremely harsh flow environment with heat fluxes up to 10^9 W/m^2 for up to 30 minutes. The plasma is low-temperature (~1 eV) but high pressure (> 10 kPa) creating high-enthalpy supersonic flows similar to atmospheric entry conditions. Typically, material samples are measured before and after a test to characterize the total recession. However, this does not capture time-dependent effects such as material expansion and non-linear recession. This work will present new analysis of arcjet test videos which measure both the time-dependent 2D recession of the material samples and the shock standoff distance. New results showing non-linear material erosion rates will be highlighted. The material and shock edges are extracted from the videos by training and applying a convolutional neural network. Due to the consistent camera settings, the machine learning model achieves high accuracy (~99%) on new data with only a small number of training frames (~80). The new results will be discussed in the context of temperature dependent plasma-surface interaction.

Magnus A Haw↗

NASA NTRSDate not supplied

Measurement of Material Recession and Shock Standoff in Plasma Windtunnel using Neural Nets

Arcjets are plasma wind tunnels used to test the performance of heatshield materials for spacecraft atmospheric entry. These facilities present an extremely harsh flow environment with heat fluxes up to 109 W/m2 for up to 30 minutes. The plasma is low-temperature (∼1 eV) but high pressure (> 10 kPa) creating high-enthalpy supersonic flows similar to atmospheric entry conditions. Typically, material samples are measured before and after a test to characterize the total recession. However, this does not capture time-dependent effects such as material expansion and non-linear recession. This work will present new analysis of arcjet test videos which measure both the time-dependent 2D recession of the material samples and the shock standoff distance. The results show non-linear time-dependent effects are present for some conditions. The material and shock edges are extracted from the videos by training and applying a convolutional neural network. Due to the consistent camera settings, the machine learning model achieves high accuracy (± 2 px) relative to manually segmented images with only a small number of training frames (80).

Neural network↗

NASA NTRSDate not supplied

The Effect of Drag Model on Heatshield Recession due to Particle Impacts for Martian Spacecraft

A spacecraft entering the Martian atmosphere during a dust storm may experience recession to the heatshield due to dust particle impacts. Aerodynamic drag is the primary force that determines the trajectory of the dust particles through the shock layer. This paper examines the effect of particle drag model on the heatshield recession. Three particle drag models are assessed including two that are intended to be applicable over a wide range of particle flow conditions. Particle trajectories are computed in conditions measured during the 2007 major global dust storm. It was found that accounting for Knudsen number and compressibility effects made a large difference in the estimated particle impact velocity. The two drag models that were valid for transitional, compressible particle flow environments predicted only slightly-different amounts of heatshield recession due to dust particle impacts. A brief description of the effects of non-spherical particles on drag coefficients is provided.

Heatshield↗

NASA NTRSDate not supplied

Numerical Simulations of A Conceptual MSR-EES Shoulder Recession

The present study demonstrates our in-house material response solver, Icarus's capability to simulate the ablative conditions, including pyrolysis effects due to the interaction between hypersonic boundary layers and the thermal protection system (TPS). A conceptual aeroshell shoulder design, which undergoes mission-relevant flow and material conditions, is selected for the demonstration purpose. LAURA, a structured flow solver, is used to solve flow around the shoulder at several trajectory points of a flight path. The aerothermal dataset obtained from LAURA is used to enforce boundary conditions on the aeroshell wall to simulate ablation processes. The two-layered material system is stacked with HEEET (TPS) and Aluminum (actual material). A set of base parameters, such as the angle of material orientation with respect to the flow direction, convective heat-transfer co-efficient, and aerothermal boundary conditions on the rear end of the HEEET material layer, is selected to conduct a base-case simulation. A small amount of recession was observed, indicating that the design should be fine to go through the selected flow and material conditions. Furthermore, the parameters mentioned above are individually altered and are observed to affect the shoulder design's recession compared to the base case. Verification of our Icarus setup was also carried out using a one-dimensional grid and FIAT, a one-dimensional material-response solver, to build credibility for our results. As the current work is an uncoupled fluid-material-response simulation, a local sharp mesh deformation in the recessed surface near the shoulder corner is observed.

Prakash Shrestha↗

NASA NTRSDate not supplied

The Effect of Drag Model on Heatshield Recession due to Particle Impacts for Martian Spacecraft

A spacecraft entering the Martian atmosphere during a dust storm may experience recession to the heatshield due to dust particle impacts. Aerodynamic drag is the primary force that determines the trajectory of the dust particles through the shock layer. This paper examines the effect of particle drag model on the heatshield recession. Three particle drag models are assessed including two that are intended to be applicable over a wide range of particle flow conditions. Particle trajectories are computed in conditions measured during the 2007 major global dust storm. It was found that accounting for Knudsen number and compressibility effects made a large difference in the estimated particle impact velocity. The two drag models that were valid for transitional, compressible particle flow environments predicted only slightly-different amounts of heatshield recession due to dust particle impacts. A brief description of the effects of non-spherical particles on drag coefficients is provided.

Heatshield↗

NASA NTRSDate not supplied

A Parallelized Oxidation-Driven Surface Recession Framework in DSMC Code, SPARTA

Spacecrafts rely on ablative thermal protection systems (TPS) made of composites consisting of a carbon-based reinforcement and a polymeric matrix. These materials are designed to withstand high-temperature oxidation and surface recession during re-entry into the Earth's atmosphere. However, ablation occurs due to a complex interplay of thermal, mechanical, and chemical factors, making it challenging to determine the individual impact of each on the TPS's overall degradation. In this study, we have developed an ablation model that can leverage a finite rate carbon oxidation model to predict material recession and surface states more accurately. 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 can perform both gas-surface and pure-surface reactions and is now adapted to tally 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 briefly discuss some unique challenges associated with parallelizing this dynamic surface state and geometry. Finally, we analyze the performance of this parallelized implicit chemistry model with simple 2D and 3D benchmark cases by producing surface state statistics, area changes over time, and visualization across a range of surface temperatures and processors with and without load-balancing.

DSMC↗

NASA NTRS1975

Surface recession characteristics of a cryogenic insulation subjected to arc-tunnel heating

Specimens of a cryogenic insulation, proposed for use on the space shuttle external tank, were tested in an arc tunnel over a range of heating rates, pressures, and enthalpies corresponding to the shuttle ascent environment. A regression analysis was used to correlate the test data. Correlation equations involving surface recession rate as a function of heating rate, pressure, and enthalpy were developed. These equations can be used to make total surface recession predictions for shuttle ascent flight environments.

Pittman, C. M.↗

NASA NTRS1981

A simulation study of the recession coefficient for antecedent precipitation index

The antecedent precipitation index (API) is a useful indicator of soil moisture conditions for watershed runoff calculations and recent attempts to correlate this index with spaceborne microwave observations have been fairly successful. It is shown that the prognostic equation for soil moisture used in some of the atmospheric general circulation models together with Thornthwaite-Mather parameterization of actual evapotranspiration leads to API equations. The recession coefficient for API is found to depend on climatic factors through potential evapotranspiration and on soil texture through the field capacity and the permanent wilting point. Climatologial data for Wisconsin together with a recently developed model for global isolation are used to simulate the annual trend of the recession coefficient. Good quantitative agreement is shown with the observed trend at Fennimore and Colby watersheds in Wisconsin. It is suggested that API could be a unifying vocabulary for watershed and atmospheric general circulation modelars.

Choudhury, B. J.↗