Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Ice”

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.

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

At least 1,567 records · Page 87

The Formation of Organic Compounds of Astrobiological Interest by the Irradiation Processing of Astrophysical Ices

Many environments in space contain very low temperature mixed molecular ices that are exposed to ionizing radiation in the form of cosmic rays and high-energy photons. While traditional chemistry would not be expected to occur at the temperatures typical of these ices (T < 50 K), ionizing radiation can break bonds in the original molecules in the ices to form highly reactive ions and radicals. These ions and radicals are subsequently free to react despite the low temperatures of the original ices. Laboratory experiments, many of them carried out at the Astrochemistry Laboratory at NASA-Ames, show that the irradiation of ices made of even simple molecules like H2O, CO, CO2, CH4, NH3, etc. can result in the robust formation of large numbers of far more complex organic compounds. Many of these new products are of direct interest to astrobiology. For example, the irradiation of mixed molecular ices has been shown to produce amino acids, amphiphiles, quinones, sugars, heterocyclic compounds, and nucleobases, all molecular building blocks used by terrestrial life. Insofar as the presence of these materials plays a role in the origin of life on planets, this has profound implications for the potential abundance of life in the universe since these experiments simulate universal conditions that are expected to be found wherever new stars and planets form.

Astrobiology↗

Preliminary Flight Deck Observations During Flight in High Ice Water Content Conditions

In 2006, Mason et al. identified common observations that occurred in engine power-loss events attributed to flight in high concentrations of ice crystals. Observations included light to moderate turbulence, precipitation on the windscreen (often reported as rain), aircraft total temperature anomalies, lack of significant airframe icing, and no flight radar echoes at the location and altitude of the engine event. Since 2006, Mason et al. and others have collected information from pilots who experienced engine power-loss events via interviews and questionnaires to substantiate earlier observations and support event analyses. In 2011, Mason and Grzych reported that vertical acceleration data showed increases in turbulence prior to engine events, although the turbulence was usually light to moderate and not unique to high ice water content (HIWC) clouds. Mason concluded that the observation of rain on the windscreen was due to melting of ice high concentrations of ice crystals on the windscreen, coalescing into drops. Mason also reported that these pilot observations of rain on the windscreen were varied. Many pilots indicated no rain was observed, while others observed moderate rain with unique impact sounds. Mason concluded that the variation in the reports may be due to variation in the ice concentration, particle size, and temperature.

Wind Tunnel Tests↗

Revisiting the Potential of Melt Pond Fraction as a Predictor for the Seasonal Arctic Sea Ice Extent Minimum

The rapid change in Arctic sea ice in recent decades has led to a rising demand for seasonal sea ice prediction. A recent modeling study that employed a prognostic melt pond model in a stand-alone sea ice model found that September Arctic sea ice extent can be accurately predicted from the melt pond fraction in May. Here we show that satellite observations show no evidence of predictive skill in May. However, we find that a significantly strong relationship (high predictability) first emerges as the melt pond fraction is integrated from early May to late June, with a persistent strong relationship only occurring after late July. Our results highlight that late spring to mid summer melt pond information is required to improve the prediction skill of the seasonal sea ice minimum. Furthermore, satellite observations indicate a much higher percentage of melt pond formation in May than does the aforementioned model simulation, which points to the need to reconcile model simulations and observations, in order to better understand key mechanisms of melt pond formation and evolution and their influence on sea ice state.

sea ice↗

Arctic Sea Ice Simulation in the PlioMIP Ensemble

Eight general circulation models have simulated the mid-Pliocene warm period (mid-Pliocene, 3.264 to 3.025 Ma) as part of the Pliocene Modelling Intercomparison Project (PlioMIP). Here, we analyse and compare their simulation of Arctic sea ice for both the pre-industrial period and the mid-Pliocene. Mid-Pliocene sea ice thickness and extent is reduced, and the model spread of extent is more than twice the pre-industrial spread in some summer months. Half of the PlioMIP models simulate ice-free conditions in the mid-Pliocene. This spread amongst the ensemble is in line with the uncertainties amongst proxy reconstructions for mid-Pliocene sea ice extent. Correlations between mid-Pliocene Arctic temperatures and sea ice extents are almost twice as strong as the equivalent correlations for the pre-industrial simulations. The need for more comprehensive sea ice proxy data is highlighted, in order to better compare model performances.

simulation↗

Validation of Quasi-Invariant Ice Cloud Radiative Quantities with MODIS Satellite-Based Cloud Property Retrievals

Similarity relations applied to ice cloud radiance calculations are theoretically analyzed and numerically validated. If t(1v) and t(1vg) are conserved where t is optical thickness, v the single-scattering albedo, and g the asymmetry factor, it is possible that substantially different phase functions may give rise to similar radiances in both conservative and non-conservative scattering cases, particularly in the case of large optical thicknesses. In addition to theoretical analysis, this study uses operational ice cloud optical thickness retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) Level 2 Collection5 (C5) and Collection 6 (C6) cloud property products to verify radiative similarity relations. It is found that, if the MODIS C5 and C6 ice cloud optical thickness values are multiplied by their respective (1wg)factors, the resultant products referred to as the effective optical thicknesses become similar with their ratio values around unity. Furthermore, the ratios of the C5 and C6 ice cloud effective optical thicknesses display an angular variation pattern similar to that of the corresponding ice cloud phase function ratios. The MODIS C5 and C6 values of ice cloud similarity parameter, defined as [(1w)(1(exp. 1/2)wg)]12, also tend to be similar.

Radiative transfer↗

Effect of Ice Shape Fidelity on Swept-Wing Aerodynamic Performance

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

Scale models↗

Southern Ocean Heat Storage, Reemergence, and Winter Sea Ice Decline Induced by Summertime Winds

The observational record shows a substantial 40-yr upward trend in summertime westerly winds over the Southern Ocean, as characterized by the southern annular mode (SAM) index. Enhanced summertime westerly winds have been linked to cold summertime sea surface temperature (SST) anomalies. Previous studies have suggested that Ekman transport or upwelling is responsible for this seasonal cooling. Here, another process is presented in which enhanced vertical mixing, driven by summertime wind anomalies, moves heat downward, cooling the sea surface and simultaneously warming the subsurface waters. The anomalously cold SSTs draw heat from the atmosphere into the ocean, leading to increased depth-integrated ocean heat content. The subsurface heat is returned to the surface mixed layer during the autumn and winter as the mixed layer deepens, leading to anomalously warm SSTs and potentially reducing sea ice cover. Observational analyses and numerical experiments support our proposed mechanism, showing that enhanced vertical mixing produces subsurface warming and cools the surface mixed layer. Nevertheless, the dominant driver of surface cooling remains uncertain; the relative importance of advective and mixing contributions to the surface cooling is model dependent. Modeling results suggest that sea ice volume is more sensitive to summertime winds than sea ice extent, implying that enhanced summertime westerly winds may lead to thinner sea ice in the following winter, if not lesser ice extent. Thus, strong summertime winds could precondition the sea ice cover for a rapid retreat in the following melt season.

Southern Ocean↗

A first constraint on basal melt-water production of the Greenland ice sheet

The Greenland ice sheet has been one of the largest sources of sea-level rise since the early 2000s. However, basal melt has not been included explicitly in assessments of ice-sheet mass loss so far. Here, we present the first estimate of the total and regional basal melt produced by the ice sheet and the recent change in basal melt through time. We find that the ice sheet’s present basal melt production is 21.4 +4.4/−4.0 Gt per year, and that melt generated by basal friction is responsible for about half of this volume. We estimate that basal melting has increased by 2.9 ± 5.2 Gt during the first decade of the 2000s. As the Arctic warms, we anticipate that basal melt will continue to increase due to faster ice flow and more surface melting thus compounding current mass loss trends, enhancing solid ice discharge, and modifying fjord circulation.

Greenland Ice Sheet↗

Novel Parameterization of Ice Cloud Effective Diameter from Collocated CALIOP-IIR and CloudSat Retrievals

Satellite-based measurements of global ice cloud microphysical properties are sampled to develop a novel set of physical parameterizations, relating to cloud layer temperature and effective diameter D(e), that can be implemented for two separate applications: in numerical weather prediction models and lidar-based cloud radiative forcing studies. Ice cloud optical properties (i.e., spectral scattering and absorption) are estimated based on the effective size and habit mixture of the cloud particles. Historically, the ice cloud D(e) has been parameterized from aircraft in situ measurements. However, aircraft-based parameterizations are opportunistic in that they only represent specific types of clouds (e.g., convective anvil, tropopause-topped cirrus) in the regions in which they were sampled and, in some cases, are limited in fully resolving the entire vertical cloud layer. Breaking away from the aircraft-based parameterization paradigm, this study is the first of its kind to attempt a parameterization of D(e) as a function of temperature, ice water content (IWC), and lidar-derived extinction from satellite-based global oceanic measurements of ice clouds. Data from both active and passive remote sensing sensors from two of NASA’s A-Train satellites, CloudSat and CALIPSO, are collected to guide development of globally robust parameterizations of all ice cloud types and one exclusively for cirrus clouds.

Ice Cloud↗

A Model for Ice Accretion Roughness Evolution and Spatial Variations

Over the past decade, multiple investigations of ice accretion roughness and spatial variations have been performed in the Icing Research Tunnel (IRT) at the NASA Glenn Research Center. The early investigations used models of NACA 0012 airfoils with different chord sizes and focused on temporal scaling and primary cloud scaling parameters such as stagnation point collection efficiency. Subsequent investigations included the effects of model sweep, airfoil shape, airfoil lifting condition, and freestream static temperature. To develop a predictive model for roughness evolution in generalized icing situations, the maximum roughness values for the airfoils and conditions used in the angle of attack and freestream temperature investigations were scaled to eliminate the temporal variations. LEWICE simulations were employed to identify the local collection efficiency at the locations of maximum roughness, and a two-dimensional panel-method code was used to identify the local static pressure at the location of the maximum roughness. Because of the stochastic nature of ice accretion roughness, a physics-directed approach was employed to develop a multi-dimensional correlation based on the local pressure coefficient, the local total temperature, the cloud properties, and the cloud exposure time. The resulting correlation predictions are compared to ice shapes measured in the IRT for both a 21-in. NACA 0012 airfoil model and a 60-in. HAARP-II model. The resulting predictions indicate that the local freezing fraction must be included in the roughness predictive model. Implications regarding icing heat transfer predictions using the resulting roughness model are also discussed.

Icing↗

Aircraft Icing Analysis of Alternatives

This paper provides a description of a recent comprehensive Analysis of Alternatives (AoA) for icing research. A team of NASA icing subject matter experts (SMEs) was assembled from NASA’s Glenn and Langley Research Centers, which began by gathering inputs from icing stakeholders in industry, Government, and academia. Those inputs, which were grouped into a number of themes, were used by the SMEs to identify needed icing technical elements. Each of these high-level technical elements included the icing physics, modeling capabilities, and experimental capabilities needed to address a particular element. A multivariable analysis technique called TOPSIS (Technique for Order Preference by Similarity to Ideal Solutions) was then applied to prioritize the technical elements for several potential future realities. A large number of icing research needs were identified and analyzed to determine the highest priorities for NASA key focus areas and those that will endure into the future. The primary outcome of the AoA is the identification of legacy and newly identified capability areas in priority order to guide future investment.

Icing↗

Aircraft Icing Analysis of Alternatives

This paper provides a description of a recent comprehensive Analysis of Alternatives (AoA) for icing research. A team of NASA icing subject matter experts (SMEs) was assembled from NASA’s Glenn and Langley Research Centers, which began by gathering inputs from icing stakeholders in industry, Government, and academia. Those inputs, which were grouped into a number of themes, were used by the SMEs to identify needed icing technical elements. Each of these high-level technical elements included the icing physics, modeling capabilities, and experimental capabilities needed to address a particular element. A multivariable analysis technique called TOPSIS (Technique for Order Preference by Similarity to Ideal Solutions) was then applied to prioritize the technical elements for several potential future realities. A large number of icing research needs were identified and analyzed to determine the highest priorities for NASA key focus areas and those that will endure into the future. The primary outcome of the AoA is the identification of legacy and newly identified capability areas in priority order to guide future investment

Icing, Rotorcraft↗

The Footprints of 16 Year Trends of Arctic Springtime Cloud and Radiation Properties on September Sea Ice Retreat

The most prominent September Arctic sea ice decline over the period of 2000–2015 occurs over the Siberian Sea, Laptev Sea, and Kara Sea. The satellite observed and retrieved sea ice concentration (SIC) and cloud/radiation properties over the Arctic (70°–90°N) have been used to investigate the impact of springtime cloud and radiation properties on September SIC variation. Positive trends of cloud fractions, cloud water paths, and surface downward longwave flux at the surface over the September sea ice retreat areas are found over the period of 1 March to 14 May, while negative trends are found over the period of 15 May to 28 June. The spatial distributions of correlations between springtime cloud/radiation properties and September SIC have been calculated, indicating that increasing cloud fractions and downward longwave flux during springtime tend to enhance sea ice melting due to strong cloud warming effect. Surface downward and upward shortwave fluxes play an important role from May to June when the onset of sea ice melting occurs. The comparison between linearly detrended and nondetrended of each parameter indicates that significant impact of cloud and radiation properties on September sea ice retreat occurs over the Chukchi/Beaufort Sea at interannual time scale, especially over the period of 31 March to 29 April, while strongest climatological trends are found over the Laptev/Siberian Sea.

Arctic sea ice trend↗

NASA Icing Update

This NASA Icing Update was prepared for presentation to the SAE International AC-9C Inflight Icing Technology Committee. This update includes the following topics: recently completed propeller icing test in the NASA Icing Research Tunnel; recently completed Icing Research Tunnel Capability Untilization Study; planning for propulsor icing research; and status of the NASA Propulsion Systems Laboratory.

Icing, Propellers, Scaling↗

Sea Ice Concentration and Extent

Among the most seasonal and most dynamic parameters on the surface of the Earth is sea ice which at any one time covers about 3-6% of the planet. In the Northern Hemisphere, sea ice grows in extent from about 6 x 10(exp 6) sq km to 16 x 10(exp 6) sq km, while in the Southern Hemisphere, it grows from about 3 x 10(exp 6) sq km to about 19 x 10(exp 6) sq km (Comiso, 2010; Gloersen et al., 1992). Sea ice is up to about 2-3 m thick in the Northern Hemisphere and about 1 m thick in the Southern Hemisphere (Wadhams, 2002), and compared to the average ocean depth of about 3 km, it is a relatively thin, fragile sheet that can break due to waves and winds or melt due to upwelling of warm water. Being constantly advected by winds, waves, and currents, sea ice is very dynamic and usually follows the directions of the many gyres in the polar regions. Despite its vast expanse, the sea ice cover was previously left largely unstudied and it was only in recent years that we have understood its true impact and significance as related to the Earths climate, the oceans, and marine life.

sea ice extent↗

Fragment Size Distribution for Ice Particle Impacts on a Glass Plate

This work presents the results of an experimental study of ice particle impacts on a flat glass plate. The experiment was conducted at the Ballistics Impact Laboratory of NASA Glenn Research Center. The main objective of the experiment was to gain understanding about the modifications needed to the experimental configuration for a future parametric study at a larger range of values for particle diameters and other parameters. This was achieved by studying the effect of the velocity of an impacting ice particle on the post-impact fragment size and distribution for a reduced range of impacting particle diameters. Pre-impact particle diameter and velocity data were captured with a high-speed side camera. Post-impact fragment data were captured in a single frame with a 29-megapixel camera located above and normal to the target. Repeat runs were conducted for ice particles with diameters ranging from 1.7 to 2.9 millimeters, impacting at velocities between 39 and 98 meters per second. The fragment areas were measured, and the corresponding equivalent diameters and histogram distributions were calculated. Analysis of the data showed that the average equivalent diameter for the fragments in a run was an order of magnitude smaller than the diameter of the impacting ice particle. The histograms for equivalent diameter distribution were nonnormal with long tails, with most of the fragments having equivalent diameters concentrated toward the minimum value of the fragment size that could be resolved. Factors affecting the accuracy of the data during the digital imaging analysis were identified. Needed modifications to the setup to handle small size ice particles and other testing conditions were also identified.

engine icing↗

NASA Icing Update – May 2024

This presentation provides a status update on select NASA icing research activities for the SAE AC-9C Icing Technical Committee Meeting on May 6, 2024. The updates include the following topics: (1) Propulsion Systems Lab (PSL), (2) Adaptive Icing Tunnel (AIT), (3) Ice Adhesion / Deformed Skin Adhesion Test, (4) GlennICE, (5) Efficient Quiet Integrated Propulsor, (6) Icing Research Tunnel (IRT) CFD Characterization, and (7) Supercooled Large Droplet (SLD) Research.

Icing↗

Arctic Mixed-Phase Cloud Base Ice Precipitation Properties During the M-PACE Field Campaign

Cloud-climate feedbacks are still the greatest source of uncertainty in current climate projections. Arctic clouds, which are predominantly stratiform and supercooled, often long-lived, and nearly-continuously precipitate ice particles, contribute roughly 10% of the uncertainty attributed to the global cloud feedback. This Arctic cloud uncertainty is driven by incomplete observational and theoretical knowledge required to estimate and explain the state and active processes occurring in those clouds. A focus on ice precipitation properties at Arctic cloud base rather than the surface deconfounds the product of cloud condensate sink processes from the influence of the atmospheric thermodynamic state below cloud base, rendering cloud-base properties a more appealing target for inference and evaluation of model simulations. This dataset provides a set of 25 samples from the M-PACE field campaign, all of which were retrieved using the synthesis of ARM radar and lidar measurements. The retrieved ice precipitation variables in this dataset include, among others, the ice number concentration, water content, PSD parameters, precipitation rate, mass-weighted fall velocity, vertical air motion, and effective radius, all of which are highly valuable for model evaluation and a general understanding of polar cloud sink processes. Each variable sample includes its mean value and associated uncertainty. Additional variables based on ARM measurements (liquid layer statistics, etc.) are included in this dataset as well. The retrieval algorithm and analysis of this dataset are described in Silber (JGR, 2023, https://doi.org/10.1029/2022JD038202).

54 ENVIRONMENTAL SCIENCES↗