SEARCH · Search NASA

Results for “ice growth”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 181 records · Page 10

All sources NASA NTRS DOE OSTI

NASA NTRSDate not supplied

Three Dimensional Surface Redefinition Method for Computational Ice Accretion Solvers

Computational tools have been increasing in maturity and are thus commonly used in the engineering design process. Advancements in NASA’s current state of the art computational ice accretion tool are required to tackle the icing challenges of tomorrow. GlennICE, a next generation ice accretion solver, is under development at NASA to tackle these challenges. One of the elements of this migration is transitioning from a quasi-three dimensional strip theory based ice accretion methodology to a fully three dimensional methodology. This requires construction of a method to redefine or extrude a discretized or tessellated surface geometry based on the predicted volumetric ice growth for each tessellated surface triangle. This paper describes the methodology that is employed in the GlennICE software, and assesses the performance of the method in replicating a well defined analytical test case.

Computational↗

NASA NTRS2008

Coating Reduces Ice Adhesion

The Shuttle Ice Liberation Coating (SILC) has been developed to reduce the adhesion of ice to surfaces on the space shuttle. SILC, when coated on a surface (foam, metal, epoxy primer, polymer surfaces), will reduce the adhesion of ice by as much as 90 percent as compared to the corresponding uncoated surface. This innovation is a durable coating that can withstand several cycles of ice growth and removal without loss of anti-adhesion properties. SILC is made of a binder composed of varying weight percents of siloxane(s), ethyl alcohol, ethyl sulfate, isopropyl alcohol, and of fine-particle polytetrafluoroethylene (PTFE). The combination of these components produces a coating with significantly improved weathering characteristics over the siloxane system alone. In some cases, the coating will delay ice formation and can reduce the amount of ice formed. SILC is not an ice prevention coating, but the very high water contact angle (greater than 140 ) causes water to readily run off the surface. This coating was designed for use at temperatures near -170 F (-112 C). Ice adhesion tests performed at temperatures from -170 to 20 F (-112 to -7 C) show that SILC is a very effective ice release coating. SILC can be left as applied (opaque) or buffed off until the surface appears clear. Energy dispersive spectroscopy (EDS) and x-ray photoelectron spectroscopy (XPS) data show that the coating is still present after buffing to transparency. This means SILC can be used to prevent ice adhesion even when coating windows or other objects, or items that require transmission of optical light. Car windshields are kept cleaner and SILC effectively mitigates rain and snow under driving conditions.

Smith, Trent↗

NASA NTRS2014

Intercomparison of Large-Eddy Simulations of Arctic Mixed-Phase Clouds: Importance of Ice Size Distribution Assumptions

Large-eddy simulations of mixed-phase Arctic clouds by 11 different models are analyzed with the goal of improving understanding and model representation of processes controlling the evolution of these clouds. In a case based on observations from the Indirect and Semi-Direct Aerosol Campaign (ISDAC), it is found that ice number concentration, Ni, exerts significant influence on the cloud structure. Increasing Ni leads to a substantial reduction in liquid water path (LWP), in agreement with earlier studies. In contrast to previous intercomparison studies, all models here use the same ice particle properties (i.e., mass-size, mass-fall speed, and mass-capacitance relationships) and a common radiation parameterization. The constrained setup exposes the importance of ice particle size distributions (PSDs) in influencing cloud evolution. A clear separation in LWP and IWP predicted by models with bin and bulk microphysical treatments is documented and attributed primarily to the assumed shape of ice PSD used in bulk schemes. Compared to the bin schemes that explicitly predict the PSD, schemes assuming exponential ice PSD underestimate ice growth by vapor deposition and overestimate mass-weighted fall speed leading to an underprediction of IWP by a factor of two in the considered case. Sensitivity tests indicate LWP and IWP are much closer to the bin model simulations when a modified shape factor which is similar to that predicted by bin model simulation is used in bulk scheme. These results demonstrate the importance of representation of ice PSD in determining the partitioning of liquid and ice and the longevity of mixed-phase clouds.

clouds↗

NASA NTRS2006

Further Refinement of the LEWICE SLD Model

A research project is underway at NASA Glenn Research Center to produce computer software that can accurately predict ice growth for any meteorological conditions for any aircraft surface. This report will present results from version 3.2 of this software, which is called LEWICE. This version differs from previous releases in that it incorporates additional thermal analysis capabilities, a pneumatic boot model, interfaces to external computational fluid dynamics (CFD) flow solvers and has an empirical model for the supercooled large droplet (SLD) regime. An extensive comparison against the database of ice shapes and collection efficiencies that have been generated in the NASA Glenn Icing Research Tunnel (IRT) has also been performed. The complete set of data used for this comparison will eventually be available in a contractor report. This paper will show the differences in collection efficiency and ice shape between LEWICE 3.2 and experimental data. This report will first describe the LEWICE 3.2 SLD model. A semi-empirical approach was used to incorporate first order physical effects of large droplet phenomena into icing software. Comparisons are then made to every two-dimensional case in the water collection database and the ice shape database. Each collection efficiency condition was run using the following four assumptions: 1) potential flow, no splashing; 2) potential flow, with splashing; 3) Navior-Stokes, no splashing; 4) Navi r-Stokes, with splashing. All cases were run with 21 bin drop size distributions and a lift correction (angle of attack adjustment). Quantitative comparisons are shown for impingement limit, maximum water catch, and total collection efficiency. Due to the large number of ice shape cases, comprehensive comparisons were limited to potential flow cases with and without splashing. Quantitative comparisons are shown for horn height, horn angle, icing limit, area, and leading edge thickness. The results show that the predicted results for both ice shape and water collection are within the accuracy limits of the experimental data for the majority of cases.

Wright, William B.↗

NASA NTRS1999

Validation Results for LEWICE 2.0

A research project is underway at NASA Lewis to produce a computer code which can accurately predict ice growth under any meteorological conditions for any aircraft surface. This report will present results from version 2.0 of this code, which is called LEWICE. This version differs from previous releases due to its robustness and its ability to reproduce results accurately for different spacing and time step criteria across computing platform. It also differs in the extensive amount of effort undertaken to compare the results in a quantified manner against the database of ice shapes which have been generated in the NASA Lewis Icing Research Tunnel (IRT). The results of the shape comparisons are analyzed to determine the range of meteorological conditions under which LEWICE 2.0 is within the experimental repeatability. This comparison shows that the average variation of LEWICE 2.0 from the experimental data is 7.2% while the overall variability of the experimental data is 2.5%.

Wright, William B.↗

NASA NTRS1998

A Summary of Validation Results for LEWICE 2.0

A research project is underway at NASA Lewis to produce a computer code which can accurately predict ice growth under any meteorological conditions for any aircraft surface. This report will present results from version 2.0 of this code, which is called LEWICE. This version differs from previous releases due to its robustness and its ability to reproduce results accurately for different point spacing, and time step criteria across general computing platforms. It also differs in the extensive amount of effort undertaken to compare the results in a quantifiable manner against the database of ice shapes which have been generated in the NASA Lewis Icing, Research Tunnel (IRT), The complete set of data used for this comparison is available in a recent contractor report . The result of this comparison shows that the difference between the predicted ice shape from LEWICE 2.0 and the average of the experimental data is 7.2% while the variability of the experimental data is 2.5%.

Wright, William B.↗

NASA NTRS2005

Validation Results for LEWICE 3.0

A research project is underway at NASA Glenn to produce computer software that can accurately predict ice growth under any meteorological conditions for any aircraft surface. This report will present results from version 3.0 of this software, which is called LEWICE. This version differs from previous releases in that it incorporates additional thermal analysis capabilities, a pneumatic boot model, interfaces to computational fluid dynamics (CFD) flow solvers and has an empirical model for the supercooled large droplet (SLD) regime. An extensive comparison of the results in a quantifiable manner against the database of ice shapes and collection efficiency that have been generated in the NASA Glenn Icing Research Tunnel (IRT) has also been performed. The complete set of data used for this comparison will eventually be available in a contractor report. This paper will show the differences in collection efficiency between LEWICE 3.0 and experimental data. Due to the large amount of validation data available, a separate report is planned for ice shape comparison. This report will first describe the LEWICE 3.0 model for water collection. A semi-empirical approach was used to incorporate first order physical effects of large droplet phenomena into icing software. Comparisons are then made to every single element two-dimensional case in the water collection database. Each condition was run using the following five assumptions: 1) potential flow, no splashing; 2) potential flow, no splashing with 21 bin drop size distributions and a lift correction (angle of attack adjustment); 3) potential flow, with splashing; 4) Navier-Stokes, no splashing; and 5) Navier-Stokes, with splashing. Quantitative comparisons are shown for impingement limit, maximum water catch, and total collection efficiency. The results show that the predicted results are within the accuracy limits of the experimental data for the majority of cases.

Wright, William B.↗

NASA NTRS1999

User Manual for the NASA Glenn Ice Accretion Code LEWICE: Version 2.0

A research project is underway at NASA Glenn to produce a computer code which can accurately predict ice growth under a wide range of meteorological conditions for any aircraft surface. This report will present a description of the code inputs and outputs from version 2.0 of this code, which is called LEWICE. This version differs from previous releases due to its robustness and its ability to reproduce results accurately for different spacing and time step criteria across computing platform. It also differs in the extensive effort undertaken to compare the results against the database of ice shapes which have been generated in the NASA Glenn Icing Research Tunnel (IRT) 1. This report will only describe the features of the code related to the use of the program. The report will not describe the inner working of the code or the physical models used. This information is available in the form of several unpublished documents which will be collectively referred to as a Programmers Manual for LEWICE 2 in this report. These reports are intended as an update/replacement for all previous user manuals of LEWICE. In addition to describing the changes and improvements made for this version, information from previous manuals may be duplicated so that the user will not need to consult previous manuals to use this code.

Wright, William B.↗

NASA NTRS1995

Convective Heat Transfer from Castings of Ice Roughened Surfaces in Horizontal Flight

A technique was developed to cast frozen ice shapes that had been grown on a metal surface. This technique was applied to a series of ice shapes that were grown in the NASA Lewis Icing Research Tunnel on flat plates. Eight different types of ice growths, characterizing different types of roughness, were obtained from these plates, from which aluminum castings were made. Test strips taken from these castings were outfitted with heat flux gages, such that when placed in a dry wind tunnel, they could be used to experimentally map out the convective heat transfer coefficient in the direction of flow from the roughened surfaces. The effects on the heat transfer coefficient for parallel flow, which simulates horizontal flight, were studied. The results of this investigation can be used to help size heaters for wings, helicopter rotor blades, jet engine intakes, etc., or de-icing for anti-icing applications where the flow is parallel to the iced surface.

Dukhan, Nihad↗

NASA NTRS2000

Southern Ocean Response to NADW Changes

The possibility of North Atlantic Deep Water (NADW) changes in both past and future climates has raised the issue of how the Southern Ocean would respond. Recent experiments with the GISS coupled atmosphere-ocean model have shown that a "bipolar see-saw" between NADW production and Antarctic Bottom Water (AABW) production in the Weddell Sea can occur in conjunction with freshening of the North Atlantic. However, this effect operates not through a slow ocean response but via a rapid atmospheric mechanism. As NADW reduces, colder temperatures in the North Atlantic, and Northern Hemisphere in general, are associated with higher surface pressure (increased atmospheric mass). Reduced mass in the Southern Hemisphere occurs in response, with lower pressure over the South Pole (an EOF #1 effect, the "high phase" of the Antarctic Oscillation).The lower pressure is associated with stronger west winds that generate an intensified Antarctic Circumpolar Current (ACC), which leads to longitudinal heat divergence in the South Atlantic (and heat convergence in the Southern Indian Ocean). Colder temperatures in the Weddell Sea region lead to sea ice growth, increased salinity and surface water density, and greater Weddell Sea Bottom Water production. Increased poleward transport of heat occurs in the South Atlantic in conjunction with increased bottom water production, but its convergence at high latitudes is not sufficient to offset the longitudinal heat divergence due to the intensified ACC. The colder temperatures at high latitudes in the South Atlantic increase the latitudinal temperature gradient, baroclinic instability, eddy energy and eddy poleward transport of momentum, helping to maintain the lower pressure over the pole in an interactive manner. The heat flux convergence in the Indian Ocean provides a warming tendency in that region, and overall global production of AABW remains unchanged. These results have implications for the interpretation of the ice core records of the last deglaciation, but may also be relevant for changes during the Holocene and perhaps even in response to increased CO2 forcing,

Rind, David↗

DOE OSTI2021

Analysis of the microphysical properties of snowfall using scanning polarimetric and vertically pointing multi-frequency Doppler radars

Radar dual-wavelength ratio (DWR) measurements from the Stony Brook Radar Observatory Ka-band scanning polarimetric radar (KASPR, 35 GHz), a W-band profiling radar (94 GHz), and a next-generation K-band (24 GHz) micro rain radar (MRRPro) were exploited for ice particle identification using triple-frequency approaches. The results indicated that two of the radar frequencies (K and Ka band) are not sufficiently separated; thus, the triple-frequency radar approaches had limited success. On the other hand, a joint analysis of DWR, mean Doppler velocity (MDV), and polarimetric radar variables indicated potential in identifying ice particle types and distinguishing among different ice growth processes and even in revealing additional microphysical details. We investigated all DWR pairs in conjunction with MDV from the KASPR profiling measurements and differential reflectivity (Z DR ) and specific differential phase (K DP ) from the KASPR quasi-vertical profiles. The DWR-versus-MDV diagrams coupled with the polarimetric observables exhibited distinct separations of particle populations attributed to different rime degrees and particle growth processes. In fallstreaks, the 35–94 GHz DWR pair increased with the magnitude of MDV corresponding to the scattering calculations for aggregates with lower degrees of riming. The DWR values further increased at lower altitudes while Z DR slightly decreased, indicating further aggregation. Particle populations with higher rime degrees had a similar increase in DWR but a 1–1.5 m s –1 larger magnitude of MDV and rapid decreases in K DP and Z DR . The analysis also depicted the early stage of riming where Z DR increased with the MDV magnitude collocated with small increases in DWR. This approach will improve quantitative estimations of snow amount and microphysical quantities such as rime mass fraction. The study suggests that triple-frequency measurements are not always necessary for in-depth ice microphysical studies and that dual-frequency polarimetric and Doppler measurements can successfully be used to gain insights into ice hydrometeor microphysics.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2021

Mass Balance of the Antarctic Ice Sheet 1992–2016: Reconciling Results from GRACE Gravimetry With ICESat, ERS1/2 and Envisat Altimetry

GRACE and ICESat Antarctic mass-balance differences are resolved utilizing their dependencies on corrections for changes in mass and volume of the same underlying mantle material forced by ice-loading changes. Modeled gravimetry corrections are 5.22 times altimetry corrections over East Antarctica (EA) and 4.51 times over West Antarctica (WA), with inferred mantle densities 4.75 and 4.11 g/cu. cm. Derived sensitivities (Sg, Sa) to bedrock motion enable calculation of motion (δB0) needed to equalize GRACE and ICESat mass changes during 2003–08. For EA, δB0 is −2.2 mm/a subsidence with mass matching at 150 Gt/a, inland WA is −3.5 mm/a at 66 Gt/a, and coastal WA is only −0.35 mm/a at −95 Gt/a. WA subsidence is attributed to low mantle viscosity with faster responses to post-LGM deglaciation and to ice growth during Holocene grounding-line readvance. EA subsidence is attributed to Holocene dynamic thickening. With Antarctic Peninsula loss of −26 Gt/a, the Antarctic total gain is 95 ± 25 Gt/a during 2003–08, compared to 144 ± 61 Gt/a from ERS1/2 during 1992–2001. Beginning in 2009, large increases in coastal WA dynamic losses overcame long-term EA and inland WA gains bringing Antarctica close to balance at −12 ± 64 Gt/a by 2012–16.

Antarctic glaciology↗

NASA NTRSDate not supplied

Analysis of Supercooled Large Drop Velocity Measurement in the NASA Icing Research Tunnel

An experiment was conducted in the Icing Research Tunnel (IRT) at the NASA Glenn Research Center to measure the velocity of supercooled large drops (SLD) in the test section of the tunnel. Previous experiments in the IRT suggested that supercooled large drops passing through the test section of the tunnel do not move at the same velocity as the surrounding air flow. The difference between drop velocity and tunnel air velocity is called slip velocity. The slip velocity is important for determining the exact nature of SLD icing simulation in the IRT. It can impact the ice growth process because of its effect on drop cooling rate during transit from the spray bars to the test section. It can also affect the amount of splashing that occurs upon impact. Slip velocity is an important flow parameter to determine how far the current facility capabilities can be extended into the SLD regime. Initial measurement data analysis of the drop velocities indicates that drops with diameter larger than about 100 to 200 µm experience velocity slip.

SLD↗

NASA NTRSDate not supplied

A Study of Parallel Scalability and Dynamic Workload Balancing in GlennICE

The Glenn Icing Computational Environment (GlennICE) is a computational tool designed to calculate ice growth on complex three-dimensional geometries. It utilizes user-supplied computational fluid dynamics solutions for the geometry of interest. Key developments include advancements in convergence of collection efficiency, trajectory optimization, and refinement methodology. These improvements have significantly enhanced GlennICE’s efficiency for practical engineering applications. A recent study focused on benchmarking GlennICE’s scalability in a parallel environment using static scheduling. Findings indicated a potential twofold increase in efficiency through workload balance enhancements. This paper presents an analysis of the solver’s new workload balancing improvements, incorporating shared memory and dynamic scheduling routines. Results demonstrate a highly efficient and consistent algorithm across high-performance computing clusters.

Computational Icing↗

NASA NTRSDate not supplied

A Study of Parallel Scalability and Dynamic Workload Balancing in GlennICE

Computational Icing↗

NASA NTRSDate not supplied

Scalability of GlennICE in a Parallel Environment

GlennICE (Glenn Icing Computational Environment) is a comptational tool designed to calculate ice growth on complex three- dimensional geometries using the input from a user-supplied computational fluid dynamics (CFD) solution for the geometry of interest. The most significant developments in the advancement of GlennICE have been investigating the convergence of the collection efficiency, efficiently finding trajectories, and improving the refinement methodology. Such developments have increased the efficiency of GlennICE for tractability in a practical engineering application. Although studies have demonstrated a reduction in the amount of work (memory footprint) required, research has yet to systematically investigate the effects of scaling GlennICE. This paper sets out to benchmark the scalability of GlennICE within a parallel environment and investigate if an increase in the number of processors result in a linear speed up.

Computational Icing↗

NASA NTRSDate not supplied

Scalability of GlennICE in a Parallel Environment

The Glenn Icing Computational Environment (GlennICE) is a computational tool designed to calculate ice growth on complex three-dimensional geometries using the input from a user-supplied computational fluid dynamics (CFD) solution for the geometry of interest. The most significant developments in the advancement of GlennICE have been investigating the convergence of the collection efficiency, efficiently finding trajectories, and improving the refinement methodology. Such developments have increased the efficiency of GlennICE for practical engineering application. With the increasing demand for applying GlennICE for more memory-intensive problems, the scalability of GlennICE has yet to be investigated. This paper is aimed at presenting a method to benchmark the scalability of GlennICE utilizing a relevant engineering problem within a parallel environment. This leads to the final goal of investigating whether an increase in the number of processors utilized results in a linear speedup of the GlennICE software.

Computational Icing, Icing, CFD, MPI↗

NASA NTRS2017

Viscous Three-Dimensional Simulation of Flow in an Axial Low Pressure Compressor at Engine Icing Operating Points

Viscous three-dimensional simulations of the Honeywell ALF502R-5 low pressure compressor (sometimes called a booster) using the NASA Glenn code GlennHT have been carried out. A total of ten simulations were produced. Five operating points are investigated, with each point run with two different wall thermal conditions. These operating points are at, or near, points where engine icing has been determined to be likely. In the future, the results of this study will be used for further analysis such as predicting collection efficiency of ice particles and ice growth rates at various locations in the compressor. A mixing plane boundary condition is used between each blade row, resulting in convergence to steady state within each blade row. The k-omega turbulence model of Wilcox, combined with viscous grid spacing near the wall on the order of one, is used to resolve the turbulent boundary layers. For each of the operating points, heat transfer coefficients are generated on the blades and walls. The heat transfer coefficients are produced by running the operating point with two different wall thermal conditions and then solving simultaneously for the heat transfer coefficient and adiabatic wall temperature at each point. Average Nusselt numbers are calculated for the most relevant surfaces. The values are seen to scale with Reynolds number to approximately a power of 0.7. Additionally, images of surface distribution of Nusselt number are presented. Qualitative comparison between the five operating points show that there is relatively little change in the character of the distribution. The dominant observed effect is that of an overall scaling, which is expected due to Reynolds number differences. One interesting aspect about the Nusselt number distribution is observed on the casing (outer diameter) downstream of the exit guide vanes (EGVs). The Nusselt number is relatively high between the pairs of EGVs, with two lower troughs downstream of each EGV trailing edge. This is of particular interest since rather complex ice shapes have been observed in that region.

compressor↗