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This presentation is an update on the Ice Crystal Icing Research that has been conducted at the NASA Glenn Research Center in recent years. This presentation highlights key findings from fundamental ice crystal icing tests and from a fully instrumented research engine test. All material in the presentation is publicly available information.
In-situ cloud data from three international flight campaigns are compared to the Federal Aviation Administration Title 14 Code of Federal Regulations Part 33 Appendix D mixed-phase/glaciated environmental envelope, and the corresponding identical European Aviation Safety Agency CS-25 Appendix P envelope. The appendices consist of a temperature-altitude envelope, a 99th percentile total water content envelope at the 17.4 Nm distance scale, a distance factor for estimation at other distance scales, ice crystal median mass diameter, and recommended liquid water content levels in mixed-phase icing conditions. The data were collected during 54 flights out of one subtropical and two tropical locations, with 472 runs from about 17,000’ to 39,000’ in approximately 115 clouds. The campaigns provide about 29,600 Nm of in situ data in deep convection over four targeted temperature intervals: -10, -30, -40, and -50, all ± 5 C. The dataset is a modern and unique documentation of the ice crystal icing environment, and results described in this article will contribute to regulatory and industry assessment of Appendices D and P.
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NASA is developing traffic flow management strategies to reduce the impact of aviation on climate and improve efficiency of aircraft routes in the presence of limited airport and airspace capacity constraints, which are partly due to convective weather and natural calamities. Development of Strategic Traffic Flow Management (STFM) requires models of aircraft emissions and contrails, and models of their effect on climate. There is extensive literature on the modeling of contrails and the Radiative Forcing (RF) associated with contrails. This report captures the latest NASA developments in this research area and describes the integrated modeling, analysis, and software development to support STFM. Aircraft contrails are long, thin and often linear clouds triggered by aircraft engine exhausts in the high-altitude ice-saturated atmosphere. Contrails, similar to that of natural cirrus clouds, can impact global climate by reflecting shortwave radiation and trapping longwave radiation. Recent studies from the Intergovernmental Panel on Climate Change (IPCC) have shown that aircraft contrails are estimated to have greater impact on global warming than aircraft CO 2 emissions. The Ames Contrail Simulation Model (ACSM) presented in this report simulates the full life cycle of aircraft contrails, including their formation, dynamic evolution, and dissipation, and calculates the associated RF with actual meteorological and air traffic data. ACSM combines models from previous studies that focus on contrail formation and persistence based on the Schmidt-Appleman theoretical criteria, and it incorporates elements of cloud dynamics, microphysics, and climate modeling found in other surveyed contrail models, while also making adjustment for improved computational efficiency. In addition, ACSM is integrated with NASA's state-of-the-art flight simulation software for rapid assessment of aircraft contrail impacts. Applications include assessments of long-term global climate impact resulting from aviation-induced contrails and the design of optimal contrail-mitigation aircraft operation strategies.
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A new ice removal system developed by Ames Research Center (ARC) and refined and marketed by DNE Technologies, Inc., employs a technique known as expulsive ice shedding. Expulsive separation "blankets" are applied to airplane wings, helicopter rotors, engine inlets, etc. A strong direct current pulse is passed through conductors embedded in the blanket, causing an explosive expansion of the blanket that ejects the ice. The original system has been significantly improved by the company. The power supply weighed nearly 40 pounds for eight square feet of blanket; the advanced system's power supply drives 16 square feet of blanket and weighs less than 10 pounds.
Despite exceptional recent advances in tailoring the wettability of surfaces, to date, no engineered surface can passively suppress the in-plane growth of frost that invariably occurs in humid, subfreezing environments. Here, we show that up to 90% of a surface can exhibit passive antifrosting by using chemical or physical wettability patterns to template “ice stripes” across the surface. As ice exhibits a depressed vapor pressure relative to liquid water, these sacrificial ice stripes siphon the supersaturated water vapor to keep the intermediate surface areas dry from dew and frost. Further, we show that when these sacrificial ice stripes are elevated atop microfins, they diffusively coarsen in a suspended state above the surface. As a result, the suspended state of the coarsening ice results in a diffusive growth rate an order of magnitude slower than frost coarsening directly on a solid substrate and should also minimize its adhesive strength to the surface.
None. This is raw data from a data set taken during the CRRELEX94 experiment. The data are polarimetric C-band radar measurements of a saline ice sheet grown in the outdoor Geophysical Research Facility at the Cold Regions Research and Engineering Lab. See references for other descriptions of data.
In the wake of the Space Shuttle Columbia disaster every effort is being made to determine the susceptibility of Space Shuttle elements to debris impacts. Ice and frost debris is formed around the aft heat shield closure of the orbiter and liquid hydrogen feedlines. This debris has been observed to liberate upon lift-off of the shuttle and presents potentially dangerous conditions to the Space Shuttle Main Engine. This paper describes the testing done to determine the impact tolerance of the Space Shuttle Main Engine nozzle coolant tubes to ice strikes originating from the launch pad or other parts of the shuttle.
The present conference on remote sensing instrumentation considers topics in water resources research, planetary remote sensing and mathematical geophysics, sea ice behavior in view of the Cold Regions Research and Engineering Laboratories pond measurements, Shuttle Imaging Radar-B (SIR-B) system performance and calibration results, geological applications of remote sensing, and microwave scattering from vegetation. Further attention is given to atmospheric probing with lasers, image processing for remote sensing, forest inventory and condition assessment, atmospheric remote sensing, SIR-B geological results, the reflectance, emission, and scattering characteristics of vegetation, sea ice sensing, multipolarization SAR results, the theoretical modeling of surfaces and volumes, crop condition assessment and productivity estimates, and SIR-B results for vegetation cover. Also considered are the microwave remote sensing of soil moisture, advanced sensors, advanced information extraction, wind and wave remote observations, results from the ESA Remote Sensing satellite, large area crop and land cover statistics, SIR-B results for directional ocean wave spectra, seasonal snow cover, ice sheets, and lake ice, fundamental research in terrain remote sensing, image classification methods, vegetation stress detection, remote sensing of coastal processes, SAR systems and calibration, electromagnetic geophysical methods and signal processing, image processing techniques, and SAR internal wave measurements.
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Internal combustion engine fueled by carbon-free hydrogen (H 2 -ICE) offers a promising alternative for sustainable transportation. Herein, we report a facile and universal strategy through the physical mixing of Pt catalyst with zeolites to significantly improve the catalytic performance in the selective catalytic reduction of nitrogen oxides (NO x ) with H 2 (H 2 -SCR), a process aiming at NO x removal from H 2 -ICE. Via the physical mixing of Pt/TiO 2 with Y zeolite (Pt/TiO 2 + Y), a remarkable enhancement of NO x reduction activity and N 2 selectivity was simultaneously achieved. The incorporation of Y zeolite effectively captured the in-situ generated water, fostering a water-rich environment surrounding the Pt active sites. This environment weakened the NO adsorption while concurrently promoting the H 2 activation, leading to the strikingly elevated H 2 -SCR activity and N 2 selectivity on Pt/TiO 2 + Y catalyst. This study provides a unique, easy and sustainable physical mixing approach to achieve proficient heterogeneous catalysis for environmental applications.
The range of hydrodynamic operating conditions to which the turbine is exposed results in significant pressure fluctuations on both the pressure and suction sides of the blades. Understanding these dynamic pressures has a range of applications. Structurally, the resulting dynamic loads are significant in understanding the design life and maintenance schedule of the bearing, shafts and runner components. The pulsing pressures have also been seen to have a detrimental effect on the surface condition of the blades. Biologically, the pressure gradients and pressure extremes are the primary driver of barotrauma for fish passing through hydroturbines. Improvements in computational fluid dynamics (CFD) can be used to simulate such unsteady pressures in the regions of concern. High frequency model scale and prototype measurements of pressures at the blade are important in the validation of these models. Experimental characterization of pressure fields over hydroturbine blades has been demonstrated by a number of studies which using multiple pressure transducers to map the pressure contours on the runner blades. These have been performed at both model and prototype scales, often to validate computational models of the pressure and flow fields over the blades. This report provides a review of existing studies in which the blade pressure was measured in situ. The report assesses the technology for both model and prototype scale testing. The details of the primary studies in this field are reported and used to inform the types of hardware required for similar experiments based on the Ice Harbor Dam owned by the US Corps of Engineers on the Snake River, WA, USA. Such a study would be used to validate the CFD performed for the BioPA.
Lunar Ice Cube, scheduled to be launched on ARTEMIS I in late 2021, is a deep space cubesat mission with the goalsof demonstrating 1) a cubesat-scale instrument (BIRCHES) capable of addressing NASA HEOMD Strategic KnowledgeGaps related to lunar volatile distribution (abundance, location, and transportation physics of water ice), and 2) cubesatpropulsion, via the Busek BIT 3 RF Ion engine. The mission will also demonstrate the AIM/IRIS microcryocooler for thefirst time in deep space. BIRCHES integration is nearly complete, with several changes made to the thermal design toimprove detector performance. Final preflight instrument testing and calibration, our ongoing concern to be emphasizedhere, have been delayed due to the mandated closure rules of NASA facilities. Lunar Ice Cube, along with two othercubesats deployed from ARTEMIS I, Lunar Flashlight and LunaH-Map, will be the first deep cubesat missions to deliverscience data to the Planetary Data System.
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Increased electrification of aircraft systems, a growing trend in the aircraft industry, affects subsystems that have historically been pneumatically driven by the engine. Optimized design of propulsion and power systems must account for the requirements of these subsystems. One such subsystem, the wing ice protection system, is the focus of this paper. A preliminary computational icing analysis has been conducted using the LEWICE and ANSYS FENSAP-ICE software to characterize the requirements of a thermal ice protection system for three More Electric Aircraft concepts. This paper summarizes the methodology, simulation execution, and icing conditions used for the analysis as well as discusses the results, observed trends, and impacts to system design. This initial analysis indicates that the computed heat requirements for the wing ice protection systems are not within the capabilities of known traditional systems. A discussion of potential methods for reducing the heat flow requirement by the ice protection system is included. Potential refinements to the analysis to improve the accuracy are also discussed.
There are numerous hazards associated with air travel. One of the most serious dangers to the pilot and passengers safety is the result of flying into conditions which are conducive to the formation of ice on the surface of an aircraft. Being a pilot myself I am very aware of the dangers that Icing can pose and the effects it can have on an airplane. A couple of the missions of the Icing branch is to make flying safer with more research to increase our knowledge of how ice effects the aerodynamics of an airfoil, and to increase are knowledge of the weather for better forecasting. The Icing Branch uses three different tools to determine the aerodynamic affects that icing has on a wing. The Icing research tunnel is an efficient way to test various airfoils in a controlled setting. To make sure the data received from the wind tunnel is accurate the Icing branch conducts real flight tests with the DHC-6 Twin Otter. This makes sure that the methods used in the wind tunnel accurately model what happens on the actual aircraft. These two tools are also compared to the LEWICE code which is a program that models the ice shape that would be formed on an airfoil in the particular weather conditions that are input by the user. One benefit of LEWICE is that it is a lot cheaper to run than the wind tunnel or flight tests which make it a nice tool for engineers designing aircraft that don t have the money to spend on icing research. Using all three of these tools is a way to cross check the data received from one and check it against the other two. industries, but it is also looked at by weather analysts who are trying to improve forecasting methods. The best way to avoid the troubles of icing encounters is to not go into it in the first place. By looking over the flight data the analyst can determine which conditions will most likely lead to an icing encounter and then this information will aid forecasters when briefing the pilots on the weather conditions. am looking at the weather data from certain flights and analyzing the type of precipitation that the plane is flying through. During flight tests there is a probe on the bottom of the aircraft that gathers information on the size and shape of the particles that it is flying through. The data can then be viewed on a computer. After grouping the weather into certain groups we can then pick certain groups which we think should be analyzed farther. The goal is to remove all the ice particles because they do not contribute to the icing on an aircraft. We use a 2D analyzer which measures the droplet size and categorizes the drops into bins of certain sizes. We can then look at what the characteristics of the weather that we were flying through such as the temperature and dew point and compare this with the size of the drops that the 2D analyzer measured. We can then look at what type and shape of ice that formed on the wing during this time period. Having this data will help us to reproduce these conditions using LEWICE and the wind tunnel. Having consistency among the tests will make things more accurate. With respect to weather forecasting we will be able to learn which conditions can lead to icing. Better accuracy in weather reporting will lead to fewer run-ins with icing which will also lead to fewer accidents.
The majority of research on efficient and scalable algorithms in computational science and engineering has focused on the forward problem : given parameter inputs, solve the governing equations to determine output quantities of interest. In contrast, in this paper, we consider the broader question: given a (large-scale) model containing uncertain parameters, (possibly) noisy observational data, and a prediction quantity of interest, how do we construct efficient and scalable algorithms to (1) infer the model parameters from the data (the deterministic inverse problem ), (2) quantify the uncertainty in the inferred parameters (the Bayesian inference problem), and (3) propagate the resulting uncertain parameters through the model to issue predictions with quantified uncertainties (the forward uncertainty propagation problem )? We present efficient and scalable algorithms for this end-to-end, data-to-prediction process under the Gaussian approximation and in the context of modeling the flow of the Antarctic ice sheet and its effect on loss of grounded ice to the ocean. The ice is modeled as a viscous, incompressible, creeping, shear-thinning fluid. The observational data come from satellite measurements of surface ice flow velocity, and the uncertain parameter field to be inferred is the basal sliding parameter, represented by a heterogeneous coefficient in a Robin boundary condition at the base of the ice sheet. The prediction quantity of interest is the present-day ice mass flux from the Antarctic continent to the ocean. We show that the work required for executing this data-to-prediction process—measured in number of forward (and adjoint) ice sheet model solves—is independent of the state dimension, parameter dimension, data dimension, and the number of processor cores. The key to achieving this dimension independence is to exploit the fact that, despite their large size, the observational data typically provide only sparse information on model parameters. This property can be exploited to construct a low rank approximation of the linearized parameter-to-observable map via randomized SVD methods and adjoint-based actions of Hessians of the data misfit functional.