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All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Analysis of the Honeywell Uncertified Research Engine with Ice Crystal Cloud Ingestion at Simulated Altitudes

The Honeywell Uncertified Research Engine (HURE), a research version of a turbofan engine that never entered production, was tested in the NASA Propulsion System Laboratory (PSL), an altitude test facility at the NASA Glenn Research Center. The PSL is a facility that is equipped with water spray bars capable of producing an ice cloud consisting of ice particles, having a controlled particle diameter and concentration in the air flow. To develop the test matrix of the HURE, numerical analysis of flow and ice particle thermodynamics was performed on the compression system of the turbofan engine to predict operating conditions that could potentially result in a risk of ice accretion due to ice crystal ingestion. The goal of the test matrix was to provide operating conditions such that ice would accrete in either the fan-stator through the inlet guide vane region of the compression system or within the first stator of the high pressure compressor. The predictive analyses were performed with the mean line compressor flow modeling code (COMDESMELT) which includes an ice particle model. The HURE engine was tested in PSL with the ice cloud over the range of operating conditions of altitude, ambient temperature, simulated flight Mach number, and fan speed with guidance from the analytical predictions. The engine was fitted with video cameras at strategic locations within the engine compression system flow path where ice was predicted to accrete, in order to visually confirm ice accretion when it occurred. In addition, traditional compressor instrumentation such as total pressure and temperature probes, static pressure taps, and metal temperature thermocouples were installed in targeted areas where the risk of ice accretion was expected. The current research focuses on the analysis of the data that was obtained after testing the HURE engine in PSL with ice crystal ingestion. The computational method (COMDES-MELT) was enhanced by computing key parameters through the fan-stator at multiple span wise locations, in order to increase the fidelity with the current mean-line method. The Icing Wedge static wet-bulb temperature thresholds were applied for determining the risk of ice accretion in the fan-stator, which is thought to be an adiabatic region. At some operating conditions near the splitter-lip region, other sources of heat (non-adiabatic walls) were suspected to be the cause of accretion, and the Icing Wedge was not applied to predict accretion at that location. A simple order-of-magnitude heat transfer model was implemented into the COMDES-MELT code to estimate the wall temperature minimum and maximum thresholds that support ice accretion, as observed by video confirmation. The results from this model spanned the range of wall temperatures measured on a previous engine that experienced ice accretion at certain operating conditions. The goal of this study is to show that the computational process developed on earlier engine icing tests can be used to provide an icing risk assessment in adiabatic regions for other engines.

Turbofan engine↗
NASA NTRS1993

Polarimetric backscattering from sea ice

Polarimetric backscattering from sea ice is presented. Theoretical models for backscattering are first presented for various ice types. Then, theoretical results are compared with experimental data for new thin ice, first-year ice, and multi-year ice. Sea ice is modeled as a layer medium containing random scatterers and rough interfaces. For multi-year sea ice with snow cover, the sea ice layer is modeled as an ice background with embedded spheroidal air bubbles and the snow layer as air with ice grains. The hummocky topography on multi-year ice is characterized by a Gaussian distribution which has an averaging effect on backscattering coefficients. First-year sea ice is described by an ice medium hosting ellipsoidal brine inclusions. These inclusions are oriented preferentially in the vertical direction due to the columnar structure of first-year sea ice. Azimuthally, the orientation of the brine pockets are random corresponding to the random c-axes in sea ice, unless the axes are oriented by sea currents. For thin ice in newly opened leads, it has been observed that there exists a thin brine layer with very high salinity on the top surface of the new ice. This brine layer is depicted as a medium with high permittivity which can significantly affect electromagnetic scattering signatures from the lower thin ice layer, with a higher fractional volume of brine inclusions due to higher salinity as compared to thick first-year sea ice. The rough medium interfaces are described as Gaussian rough surfaces characterized by root-mean-square heights and surface correlation lengths. The contribution from rough surface, calculated under the Kirchhoff approximation or small perturbation method, is assumed to be independent from volume scattering. The total loss including absorption and scattering losses in the scattering media is represented by the imaginary part of effective permittivity obtained from the strong fluctuation theory. The polarimetric scattering coefficients for different ice types are then derived under the distorted Born approximation.

Nghiem, S. V.↗
NASA NTRS2012

Simulation Tools Model Icing for Aircraft Design

Here s a simple science experiment to try: Place an unopened bottle of distilled water in your freezer. After 2-3 hours, if the water is pure enough, you will notice that it has not frozen. Carefully pour the water into a bowl with a piece of ice in it. When it strikes the ice, the water will instantly freeze. One of the most basic and commonly known scientific facts is that water freezes at around 32 F. But this is not always the case. Water lacking any impurities for ice crystals to form around can be supercooled to even lower temperatures without freezing. High in the atmosphere, water droplets can achieve this delicate, supercooled state. When a plane flies through clouds containing these droplets, the water can strike the airframe and, like the supercooled water hitting the ice in the experiment above, freeze instantly. The ice buildup alters the aerodynamics of the plane - reducing lift and increasing drag - affecting its performance and presenting a safety issue if the plane can no longer fly effectively. In certain circumstances, ice can form inside aircraft engines, another potential hazard. NASA has long studied ways of detecting and countering atmospheric icing conditions as part of the Agency s efforts to enhance aviation safety. To do this, the Icing Branch at Glenn Research Center utilizes a number of world-class tools, including the Center s Icing Research Tunnel and the NASA 607 icing research aircraft, a "flying laboratory" for studying icing conditions. The branch has also developed a suite of software programs to help aircraft and icing protection system designers understand the behavior of ice accumulation on various surfaces and in various conditions. One of these innovations is the LEWICE ice accretion simulation software. Initially developed in the 1980s (when Glenn was known as Lewis Research Center), LEWICE has become one of the most widely used tools in icing research and aircraft design and certification. LEWICE has been transformed over the years from strictly a research tool to one used routinely by industry and other government agencies. Glenn contractor William Wright has been the architect of this development, supported by a team of researchers investigating icing physics, creating validation data, and ensuring development according to standard software engineering practices. The program provides a virtual simulation environment for determining where water droplets strike an airfoil in flight, what kind of ice would result, and what shape that ice would take. Users can enter geometries for specific, two-dimensional cross sections of an airfoil or other airframe surface and then apply a range of inputs - different droplet sizes, temperatures, airspeeds, and more - to model how ice would build up on the surface in various conditions. The program s versatility, ease of use, and speed - LEWICE can run through complex icing simulations in only a few minutes - have contributed to it becoming a popular resource in the aviation industry.

Source record↗
NASA NTRSDate not supplied

Estimating the Arctic Cloud-Sea Ice Feedback With Observations during the EOS Period

Arctic sea ice responds to and drives Arctic climate change. The interactions between Arctic sea ice and clouds represent a mechanism through which sea ice can drive climate change. We composite active remote sensing satellite cloud properties for ice-free, marginal ice zone (MIZ), and ice-covered surfaces during MIZ crossing events to investigate the influence of the transition from an ice-covered to an ice-free surface on low-level clouds. We demonstrate that the event-based methodology controls for large-scale meteorological factors and isolates the sea ice effect on clouds. We find larger cloud fraction and total water content below ~1.5 km over ice-free relative to ice-covered surfaces during non-summer months, indicating a low-level cloud sensitivity to Arctic sea ice decline. During summer, results show larger cloud fraction and water content over ice-free surfaces, however the differences are statistically indistinguishable. Evidence is provided that atmospheric thermodynamic profile differences cause the cloud property differences, namely that ice-free footprints are warmer, moister, have more positive surface turbulent fluxes and are less stable than their ice-covered counterparts. Ice-free and ice-covered surface cloud property differences scale with surface temperature differences such that cloud property differences are only found in the presence of a surface temperature difference. We conclude that surface temperature differences modulate the cloud response to sea ice loss through influences on surface turbulent fluxes and lower tropospheric stability. The results imply a positive non-summer sea ice-cloud feedback and that up to a 0.02 cloud fraction and 0.05 g m 3 total water content increase in fall are due to the observed sea ice decline.

Patrick C. Taylor↗
NASA NTRSDate not supplied

Simultaneous Ice Water Content Measurements at Multiple Locations on the NASA DC-8 Aircraft during the 2018 HIWC RADAR Flight Campaign

Ice water content measurements were made simultaneously at three locations on the NASA DC-8 in natural, glaciated conditions during the 2018 High Ice Water Content RADAR flight campaign. The purpose of these measurements was to further evaluate efficiency factors of hot-wire total water content probes in glaciated conditions and investigate the enhancement of ice crystal concentrations near fuselage surfaces due to flow field inertial effects, and ice crystals impacting the nose, breaking up, and flowing downstream. The total water content measurements were made using either Science Engineering Associates Ice Crystal Detectors or Robust Probes. Three common sensors were mounted on an underwing canister considered to be in near free-flow conditions, a standoff from a fuselage window, and the nose of the fuselage near the pitot probes. The Ice Crystal Detector total water content sensor and Robust Probe sensor collection and retention efficiencies were evaluated through comparisons with the underwing Ice Crystal Detector and Robust Probe measurements to the IKP2 isokinetic evaporator probe, which provided the reference ice water content measurement. Local ice water content at the nose position was evaluated by comparing ratios of the nose and underwing ice crystal detectors to the IKP2. Local ice water content at the window-standoff location was also evaluated by comparing total water content sensor measurements from the window ice crystal detector to the measurements made with the underwing and nose Ice Crystal Detectors The key findings were: 1) the Ice Crystal Detector total water content sensor ice water content efficiency factor was similar to previous estimates, but reduced with increased ice crystal median mass diameter; 2) the ice water content at the fuselage nose location near the DC-8 pitot probes was approximately 2.5 times the freestream values – although this estimate is affected by a higher probe efficiency factor due to smaller particles in the debris cloud from impacts upstream of the probe; and 3) the ice water content at the 17” standoff from the port window varied from about 50% to 3 times freestream values in a complicated manner. Similar measurement locations are not uncommon on cloud research aircraft, where ice particle measurements may be subject to similar uncertainties.

Aircraft Icing↗
NASA NTRSDate not supplied

Simultaneous Ice Water Content Measurements at Multiple Locations on the NASA DC-8 Aircraft during the 2018 HIWC RADAR Flight Campaign

Ice water content measurements were made simultaneously at three locations on the NASA DC-8 in natural, glaciated conditions during the 2018 High Ice Water Content RADAR flight campaign. The purpose of these measurements was to further evaluate efficiency factors of hot-wire total water content probes in glaciated conditions and investigate the enhancement of ice crystal concentrations near fuselage surfaces due to flow field inertial effects, and ice crystals impacting the nose, breaking up, and flowing downstream. The total water content measurements were made using either Science Engineering Associates Ice Crystal Detectors or Robust Probes. Three common sensors were mounted on an underwing canister considered to be in near free-flow conditions, a standoff from a fuselage window, and the nose of the fuselage near the pitot probes. The Ice Crystal Detector total water content sensor and Robust Probe sensor collection and retention efficiencies were evaluated through comparisons with the underwing Ice Crystal Detector and Robust Probe measurements to the IKP2 isokinetic evaporator probe, which provided the reference ice water content measurement. Local ice water content at the nose position was evaluated by comparing ratios of the nose and underwing ice crystal detectors to the IKP2. Local ice water content at the window-standoff location was also evaluated by comparing total water content sensor measurements from the window ice crystal detector to the measurements made with the underwing and nose Ice Crystal Detectors The key findings were: 1) the Ice Crystal Detector total water content sensor ice water content efficiency factor was similar to previous estimates, but reduced with increased ice crystal median mass diameter; 2) the ice water content at the fuselage nose location near the DC-8 pitot probes was approximately 2.5 times the freestream values – although this estimate is affected by a higher probe efficiency factor due to smaller particles in the debris cloud from impacts upstream of the probe; and 3) the ice water content at the 17” standoff from the port window varied from about 50% to 3 times freestream values in a complicated manner. Similar measurement locations are not uncommon on cloud research aircraft, where ice particle measurements may be subject to similar uncertainties.

o Aircraft Icing↗
NASA NTRS2017

Remote Sensing of Crystal Shapes in Ice Clouds

Ice crystals in clouds exist in a virtually limitless variation of geometries. The most basic shapes of ice crystals are columnar or plate-like hexagonal prisms with aspect ratios determined by relative humidity and temperature. However, crystals in ice clouds generally display more complex structures owing to aggregation, riming and growth histories through varying temperature and humidity regimes. Crystal shape is relevant for cloud evolution as it affects microphysical properties such as fall speeds and aggregation efficiency. Furthermore, the scattering properties of ice crystals are affected by their general shape, as well as by microscopic features such as surface roughness, impurities and internal structure. To improve the representation of ice clouds in climate models, increased understanding of the global variation of crystal shape and how it relates to, e.g., location, cloud temperature and atmospheric state is crucial. Here, the remote sensing of ice crystal macroscale and microscale structure from airborne and space-based lidar depolarization observations and multi-directional measurements of total and polarized reflectances is reviewed. In addition, a brief overview is given of in situ and laboratory observations of ice crystal shape as well as the optical properties of ice crystals that serve as foundations for the remote sensing approaches. Lidar depolarization is generally found to increase with increasing cloud height and to vary with latitude. Although this variation is generally linked to the variation of ice crystal shape, the interpretation of the depolarization remains largely qualitative and more research is needed before quantitative conclusions about ice shape can be deduced. The angular variation of total and polarized reflectances of ice clouds has been analyzed by numerous studies in order to infer information about ice crystal shapes from them. From these studies it is apparent that pristine crystals with smooth surfaces are generally inconsistent with the data and thus crystal impurity, distortion or surface roughness is prevalent. However, conclusions about the dominating ice shapes are often inconclusive and contradictory and are highly dependent on the limited selection of shapes included in the investigations. Since ice crystal optical properties are mostly determined by the aspect ratios of the crystal components and their microscale structure, it is advised that remote sensing applications focus on the variation of these ice shape characteristics, rather than on the macroscale shape or habit. Recent studies use databases with nearly continuous ranges of crystal component aspect ratio and-or roughness levels to infer the variation of ice crystal shape from satellite and airborne remote sensing measurements. Here, the rationale and results of varying strategies for the remote sensing of ice crystal shape are reviewed. Observed systematic variations of ice crystal geometry with location, cloud height and atmospheric state suggested by the data are discussed. Finally, a prospective is given on the future of the remote sensing of ice cloud particle shapes.

clouds↗
NASA NTRS1999

Variability of Arctic Sea Ice as Determined from Satellite Observations

The compiled, quality-controlled satellite multichannel passive-microwave record of polar sea ice now spans over 18 years, from November 1978 through December 1996, and is revealing considerable information about the Arctic sea ice cover and its variability. The information includes data on ice concentrations (percent areal coverages of ice), ice extents, ice melt, ice velocities, the seasonal cycle of the ice, the interannual variability of the ice, the frequency of ice coverage, and the length of the sea ice season. The data reveal marked regional and interannual variabilities, as well as some statistically significant trends. For the north polar ice cover as a whole, maximum ice extents varied over a range of 14,700,000 - 15,900,000 sq km, while individual regions experienced much greater percent variations, for instance, with the Greenland Sea having a range of 740,000 - 1,110,000 sq km in its yearly maximum ice coverage. In spite of the large variations from year to year and region to region, overall the Arctic ice extents showed a statistically significant, 2.80% / decade negative trend over the 18.2-year period. Ice season lengths, which vary from only a few weeks near the ice margins to the full year in the large region of perennial ice coverage, also experienced interannual variability, along with spatially coherent overall trends. Linear least squares trends show the sea ice season to have lengthened in much of the Bering Sea, Baffin Bay, the Davis Strait, and the Labrador Sea, but to have shortened over a much larger area, including the Sea of Okhotsk, the Greenland Sea, the Barents Sea, and the southeastern Arctic.

Parkinson, Claire L.↗
NASA NTRS2001

Studies of Antarctic Sea Ice Concentrations from Satellite Data and Their Applications

Large changes in the sea ice cover have been observed recently. Because of the relevance of such changes to climate change studies it is important that key ice concentration data sets used for evaluating such changes are interpreted properly. High and medium resolution visible and infrared satellite data are used in conjunction with passive microwave data to study the true characteristics of the Antarctic sea ice cover, assess errors in currently available ice concentration products, and evaluate the applications and limitations of the latter in polar process studies. Cloud-free high resolution data provide valuable information about the natural distribution, stage of formation, and composition of the ice cover that enables interpretation of the large spatial and temporal variability of the microwave emissivity of Antarctic sea ice. Comparative analyses of co-registered visible, infrared and microwave data were used to evaluate ice concentrations derived from standard ice algorithms (i.e., Bootstrap and Team) and investigate the 10 to 35% difference in derived values from large areas within the ice pack, especially in the Weddell Sea, Amundsen Sea, and Ross Sea regions. Landsat and OLS data show a predominance of thick consolidated ice in these areas and show good agreement with the Bootstrap Algorithm. While direct measurements were not possible, the lower values from the Team Algorithm results are likely due to layering within the ice and snow and/or surface flooding, which are known to affect the polarization ratio. In predominantly new ice regions, the derived ice concentration from passive microwave data is usually lower than the true percentage because the emissivity of new ice changes with age and thickness and is lower than that of thick ice. However, the product provides a more realistic characterization of the sea ice cover, and are more useful in polar process studies since it allows for the identification of areas of significant divergence and polynya activities. Also, heat and salinity fluxes are proportionately increased in these areas compared to those from the thicker ice areas. A slight positive trend in ice extent and area from 1978 through 2000 is observed consistent with slight continental cooling during the period. However, the confidence in this result is only moderate because the overlap period for key instruments is just one month and the sensitivity to changes in sensor characteristics, calibration and threshold for the ice edge is quite high.

Comiso, Josefino C.↗
NASA NTRS2009

Life in Ice: Implications to Astrobiology

During the 2008 Tawani International Expedition Schirmacher Oasis/Lake Untersee Antarctica Expedition, living and instantly motile bacteria were found in freshly thawed meltwater from ice of the Schirmacher Oasis Lakes, the Anuchin Glacier ice and samples of the that perennial ice sheet above Lake Untersee. This phenomenon of living bacteria encased in ice had previously been observed in the 32,000 year old ice of the Fox Tunnel. The bacteria found in this ice included the strain FTR1T which was isolated and published as valid new species (Carnobacterium pleistocenium) the first validly published living Pleistocene organism still alive today. Living bacteria were also extracted from ancient ice cores from Vostok, Antarctica. The discovery that many strains of bacteria are able to survive and remain alive while frozen in ice sheets for long periods of time may have direct relevance to Astrobiology. The abundance of viable bacteria in the ice sheets of Antarctica suggests that the presence of live bacteria in ice is common, rather than an isolated phenomenon. This paper will discuss the results of recent studies at NSSTC of bacteria cryopreserved in ice. This paper advances the hypothesis that cryopreserved cells, and perhaps even viable bacterial cells, may exist today--frozen in the water-ice of lunar craters, the Polar Caps or craters of Mars; or in the permafrost of Mars; ice and rocks of comets or water bearing asteroids; or in the frozen crusts of the icy moons of Jupiter and Saturn. The existence of bacterial life in ice suggests that it may not be necessary to drill through a thick ice crust to reach liquid water seas deep beneath the icy crusts of Europa, Ganymede and Enceladus. The presence of viable bacteria in the ice of the Earth s Polar Caps suggests that the possibility that cryo-panspermia (i.e., the trans-planetary transfer of microbial life by impact ejection/spallation of bacteria-rich polar ice masses) deserves serious consideration and study as a possible natural phenomenon of the solar system that may have played a profoundly important role in the Origin of Life on Earth and the Distribution of Life in the Cosmos. The paper concludes with a consideration of the protective properties of ice by absorption of UV-B, UV-C, h-rays, gamma-rays and the high energy proton environment of the Jupiter Radiation Belt. A proposed instrument that may provide additional data on the potential survivability of microbial extremophiles encased in ice and subjected to the simulated space environment will be briefly described.

Hoover, Richard B.↗
NASA NTRSDate not supplied

The Influence of Sea ice on Arctic Cloud Properties: What Can We Learn by Applying a In Situ Observational Strategy to Satellite Data?

Sea ice is declining because of anthropogenic climate change. This change alters many aspects of the Arctic climate system, including the way that the surface and atmosphere interact. Atmosphere-surface coupling processes represent an important cloud feedback mechanism that can alter the Arctic surface energy budget. For the Arctic, it has been hypothesized that a reduction in sea ice cover could lead to an increase in clouds. If this process were to occur as originally hypothesized, sea ice loss in all seasons would lead to an increase in clouds. Recent observational studies find a cloud response to sea ice loss in non-summer months and no cloud response in summer months. However, previous studies rely on inter-annual variability and reanalysis to control for the influence of meteorology, reducing the confidence in the resulting conclusions. We adopt a phenomenological, event-based approach that does not need to use meteorological reanalysis. The approach analyzes cloud properties derived from CALIPSO-CloudSat over sea ice and adjacent ice-free footprints by compositing individual satellite ground tracks that cross the Arctic sea ice edge. The underlying assumption, which we verify, is that footprints that are close to each other in space and time experience similar large-scale meteorological conditions. Our results show larger cloud fraction and more cloud liquid water over ice-free than over sea ice footprints and provide additional evidence for a seasonal dependence of cloud-sea ice coupling that is in line with previous work. We find a different result where the maximum cloud property differences between sea ice and ice-free ocean occurs in spring, not fall as earlier studies suggest. We argue that these cloud differences between sea ice and ice-free ocean are primarily caused by the influence of the surface type on the thermodynamic stability of the lower troposphere and not principally from an increase in surface evaporation. In addition, we explore the sensitivity of these results to marginal ice zone width, season, Atlantic vs. Pacific sector, and the cloud property dependence on the distance from the sea ice edge. Overall, our results provide further evidence that cloud-sea ice coupling processes are not offsetting the observed surface energy budget perturbation due to sea ice loss in summer; thus, the cloud response to declining sea ice appears to contribute to amplified Arctic warming.

Patrick C Taylor↗
DOE OSTI2024

Sensitivity of Arctic Clouds to Ice Microphysical Processes in the NorESM2 Climate Model

Abstract Ice formation remains one of the most poorly represented microphysical processes in climate models. While primary ice production (PIP) parameterizations are known to have a large influence on the modeled cloud properties, the representation of secondary ice production (SIP) is incomplete and its corresponding impact is therefore largely unquantified. Furthermore, ice aggregation is another important process for the total cloud ice budget, which also remains largely unconstrained. In this study, we examine the impact of PIP, SIP, and ice aggregation on Arctic clouds, using the Norwegian Earth System Model, version 2 (NorESM2). Simulations with both prognostic and diagnostic PIP show that heterogeneous freezing alone cannot reproduce the observed cloud ice content. The implementation of missing SIP mechanisms (collisional breakup, drop shattering, and sublimation breakup) in NorESM2 improves the modeled ice properties, while improvements in liquid content occur only in simulations with prognostic PIP. However, results are sensitive to the description of collisional breakup. This mechanism, which dominates SIP in the examined conditions, is very sensitive to the treatment of the sublimation correction factor, a poorly constrained parameter that is included in the utilized parameterization. Finally, variations in ice aggregation treatment can also significantly impact cloud properties, mainly through their impact on collisional breakup efficiency. Overall, enhancement in ice production through the addition of SIP mechanisms and the reduction in ice aggregation (in line with radar observations of shallow Arctic clouds) result in enhanced cloud cover and decreased TOA radiation biases, compared to satellite measurements, especially during the cold months. Significance Statement Arctic clouds remain a large source of uncertainty in projections of the future climate due to the poor representation of the microphysical processes that govern their life cycle. Ice formation is among the least understood processes. While it is widely recognized that better constraints on primary ice production (PIP) are needed to improve existing parameterizations, we show that secondary ice production (SIP) and ice aggregation can have also a significant impact on ice number concentrations. Constraining ice formation through the addition of missing SIP mechanisms and reducing ice aggregation can improve the representation of the cloud macrophysical properties and enhance total cloud cover in the Arctic region, which in turn contributes to decreased TOA radiation biases in the cold months.

Meteorology & Atmospheric Sciences↗
NASA NTRS2004

Preparing and Analyzing Iced Airfoils

SmaggIce version 1.2 is a computer program for preparing and analyzing iced airfoils. It includes interactive tools for (1) measuring ice-shape characteristics, (2) controlled smoothing of ice shapes, (3) curve discretization, (4) generation of artificial ice shapes, and (5) detection and correction of input errors. Measurements of ice shapes are essential for establishing relationships between characteristics of ice and effects of ice on airfoil performance. The shape-smoothing tool helps prepare ice shapes for use with already available grid-generation and computational-fluid-dynamics software for studying the aerodynamic effects of smoothed ice on airfoils. The artificial ice-shape generation tool supports parametric studies since ice-shape parameters can easily be controlled with the artificial ice. In such studies, artificial shapes generated by this program can supplement simulated ice obtained from icing research tunnels and real ice obtained from flight test under icing weather condition. SmaggIce also automatically detects geometry errors such as tangles or duplicate points in the boundary which may be introduced by digitization and provides tools to correct these. By use of interactive tools included in SmaggIce version 1.2, one can easily characterize ice shapes and prepare iced airfoils for grid generation and flow simulations.

Vickerman, Mary B.↗
NASA NTRS2019

Experimental Aerodynamic Simulation of Glaze Ice Accretion on a Swept Wing

Aerodynamic assessment of icing effects on swept wings is an important component of a larger effort to improve three-dimensional icing simulation capabilities. An understanding of ice-shape geometric fidelity and Reynolds and Mach number effects on iced-wing aerodynamics is needed to guide the development and validation of ice-accretion simulation tools. To this end, wind-tunnel testing was carried out for 8.9% and 13.3% scale semispan wing models based upon the Common Research Model airplane configuration. Various levels of geometric fidelity of an artificial ice shape representing a realistic glaze-ice accretion on a swept wing were investigated. The highest fidelity artificial ice shape reproduced all of the three-dimensional features associated with the glaze ice accretion. The lowest fidelity artificial ice shapes were simple, spanwise-varying horn ice geometries intended to represent the maximum ice thickness on the wing upper surface. The results presented in this paper show that changes in Reynolds and Mach number have only a small effect on the iced-wing aerodynamics relative to the clean-wing configuration. Furthermore, the addition of grit roughness to some lower-fidelity artificial ice shapes resulted in favorable lift and pitching moment comparisons to the wing with the highest fidelity artificial ice shape. For the wing with simple horn ice shapes, the dependence of maximum lift coefficient on horn height and angle are generally consistent with the trends observed for similar experiments conducted on iced airfoils in past research. In terms of usable lift however, the horn height did have a significant effect even for lower horn angles. This could be an important finding since usable lift may be more indicative of the impending iced-swept wing stall and need for additional pitch control than maximum lift coefficient.

Broeren, Andy P.↗
NASA NTRS1995

(abstract) Variations in Polarimetric Backscatter of Saline Ice Grown Under Diurnal Thermal Cycling Condition

An experiment was carried out in January 1994 at the Geophysical Research Facility in the Cold Regions Research and Engineering Laboratory. To investigate effects on polarimetric scattering signatures of sea ice growth under diurnal temperature variations, an ice sheet was grown for 2.5 days for the thickness of 10 cm and a polarimetric radar operating at C-band was used to obtain backscattering data in conjunction with ice-characterization measurements. The ice sheet was grown in the late morning of January 19, 1994. The initial growth rate was slow due to high insolation and temperature. As the air temperature dropped during the night, the growth rate increased significantly. The air temperature changed drastically from about -10(deg)C to -35(deg)C between day and night. The temperature cycle was repeated during the next day and the growth rate varied in the same manner. The surface of the ice was partially covered by frost flowers and the areal coverage increased as the ice became thicker. Throughout the ice growth duration of 2.5 days, polarimetric backscatter data were collected at roughly every centimeter of ice growth. For each set of radar measurements of saline ice, a set of calibration measurements was carried out with trihedrial corner reflectors and a metallic sphere. Measured polarimetric backscattering coefficients of the ice sheet reveal a strong correlation between radar data and temperature variations. As the temperature increased (decreased), the backscatter increased (decreased) correspondingly. From the ice-characterization data, temperatures of the air, at the ice-air interface, and in the ice layer had the same variation trend. Another interesting experimental observation is that the salinity measured as a function of ice depth from a sample of 10-cm thich ice indicated that the salinity variations had a similar cycle as the temperature; i.e., the salinity profile recorded the history of the temperature variations. Characterization data of the ice sheet are used in a theoretical model for scattering from saline ice with frost cover to explain the observed polarimetric signatures.

characterization polarimetric radar salinity tempe↗
NASA NTRS2018

The Arctic Sea Ice Cover of 2016: A Year of Record-Low Highs and High-Than-Expected Lows

The Arctic sea ice cover of 2016 was highly noteworthy, as it featured record low monthly sea ice extents at the start of the year but a summer (September) extent that was higher than expected by most seasonal forecasts. Here we explore the 2016 Arctic sea ice state in terms of its monthly sea ice cover, placing this in the context of the sea ice conditions observed since 2000. We demonstrate the sensitivity of monthly Arctic sea ice extent and area estimates, in terms of their magnitude and annual rankings, to the ice concentration input data (using two widely used datasets) and to the averaging methodology used to convert concentration to extent (daily or monthly extent calculations). We use estimates of sea ice area over sea ice extent to analyse the relative “compactness†of the Arctic sea ice cover, highlighting anomalously low compactness in the summer of 2016 which contributed to the higher-than-expected September ice extent. Two cyclones that entered the Arctic Ocean during August appear to have driven this low-concentration/compactness ice cover but were not sufficient to cause more widespread melt-out and a new record-low September ice extent. We use concentration budgets to explore the regions and processes (thermodynamics/ dynamics) contributing to the monthly 2016 extent/area estimates highlighting, amongst other things, rapid ice intensification across the central eastern Arctic through September. Two different products show significant early melt onset across the Arctic Ocean in 2016, including record-early melt onset in the North Atlantic sector of the Arctic. Our results also show record-late 2016 freeze-up in the central Arctic, North Atlantic and the Alaskan Arctic sector in particular, associated with strong sea surface temperature anomalies that appeared shortly after the 2016 minimum (October onwards). We explore the implications of this low summer ice compactness for seasonal forecasting, suggesting that sea ice area could be a more reliable metric to forecast in this more seasonal, "New Arctic", sea ice regime.

sea ice↗
NASA NTRS2019

Independent Effects of Reynolds Number and Mach Number on the Aerodynamics of an Iced Swept Wing

Aerodynamic assessment of icing effects on swept wings is an important component of a larger effort to improve three-dimensional (3D) icing simulation capabilities. An understanding of ice-shape geometric fidelity and Reynolds and Mach number effects on the iced-wing aerodynamics is needed to guide the development and validation of ice-accretion simulation tools. To this end, wind tunnel testing was carried out for a 13.3-percent-scale semispan wing based upon the Common Research Model airplane configuration. The wind tunnel testing was conducted at the Office National d’Etudes et de Recherches Aérospatiales (ONERA) F1 pressurized wind tunnel with Reynolds numbers of 1.6 x 10(exp 6) to 11.9 x 10(exp 6 ) and Mach numbers of 0.09 to 0.34. Five different configurations were investigated using fully 3D, high-fidelity artificial ice shapes that maintain nearly all of the 3D ice-accretion features documented in prior icing wind tunnel tests. These large, leading-edge ice shapes were nominally based upon airplane holding in icing conditions scenarios. For three of these configurations, lower fidelity simulations were also built and tested. The results presented in this paper show that while Reynolds and Mach number effects are important for quantifying the clean-wing performance, there is very little to no effect for an iced wing with 3D, high-fidelity artificial ice shapes or 3D smooth ice shapes with grit roughness. These conclusions are consistent with the large volume of past research on iced airfoils. However, some differences were also noted for the associated stalling angle of the iced swept wing and for various lower fidelity versions of the leading-edge ice accretion. More research is planned to further investigate the key features of ice-accretion geometry that must be simulated in lower fidelity versions in order to capture the essential aerodynamics.

Aircraft icing↗
NASA NTRS2014

Icing Analysis of a Swept NACA 0012 Wing Using LEWICE3D Version 3.48

Icing calculations were performed for a NACA 0012 swept wing tip using LEWICE3D Version 3.48 coupled with the ANSYS CFX flow solver. The calculated ice shapes were compared to experimental data generated in the NASA Glenn Icing Research Tunnel (IRT). The IRT tests were designed to test the performance of the LEWICE3D ice void density model which was developed to improve the prediction of swept wing ice shapes. Icing tests were performed for a range of temperatures at two different droplet inertia parameters and two different sweep angles. The predicted mass agreed well with the experiment with an average difference of 12%. The LEWICE3D ice void density model under-predicted void density by an average of 30% for the large inertia parameter cases and by 63% for the small inertia parameter cases. This under-prediction in void density resulted in an over-prediction of ice area by an average of 115%. The LEWICE3D ice void density model produced a larger average area difference with experiment than the standard LEWICE density model, which doesn't account for the voids in the swept wing ice shape, (115% and 75% respectively) but it produced ice shapes which were deemed more appropriate because they were conservative (larger than experiment). Major contributors to the overly conservative ice shape predictions were deficiencies in the leading edge heat transfer and the sensitivity of the void ice density model to the particle inertia parameter. The scallop features present on the ice shapes were thought to generate interstitial flow and horse shoe vortices which enhance the leading edge heat transfer. A set of changes to improve the leading edge heat transfer and the void density model were tested. The changes improved the ice shape predictions considerably. More work needs to be done to evaluate the performance of these modifications for a wider range of geometries and icing conditions

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