Search NASA⌕ Search

SEARCH · Search NASA

Results for “Icing CFD”

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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Toward an Efficient Icing CFD Process Using an Interactive Software Toolkit--SmaggIce 2D

Two-dimensional CFD analysis for iced airfoils can be a labor-intensive task. The software toolkit SmaggIce 2D is being developed to help streamline the CFD process and provide the unique features needed for icing. When complete, it will include a combination of partially automated and fully interactive tools for all aspects of the tasks leading up to the flow analysis: geometry preparation, domain decomposition, block boundary discretization. gridding, and linking with a flow solver. It also includes tools to perform ice shape characterization, an important aid in determining the relationship between ice characteristics and their effects on aerodynamic performance. Completed tools, work-in-progress, and planned features of the software toolkit are presented here.

Vickerman, Mary B.↗
NASA NTRS2001

Toward an Efficient Icing CFD Process Using an Interactive Software Toolkit: Smagglce 2D

Two-dimensional CID analysis for iced airfoils can be a labor-intensive task. The software toolkit SmaggIce 2D is being developed to help streamline the CID process and provide the unique features needed for icing. When complete, it will include a combination of partially automated and fully interactive tools for all aspects of the tasks leading up to the flow analysis: geometry preparation, domain decomposition. block boundary demoralization. gridding, and linking with a flow solver. It also includes tools to perform ice shape characterization, an important aid in determining the relationship between ice characteristics and their effects on aerodynamic performance. Completed tools, work-in-progress, and planned features of the software toolkit are presented here.

Vickerman, Mary B.↗
NASA NTRS2005

A Comparative Study Using CFD to Predict Iced Airfoil Aerodynamics

WIND, Fluent, and PowerFLOW were used to predict the lift, drag, and moment coefficients of a business-jet airfoil with a rime ice (rough and jagged, but no protruding horns) and with a glaze ice (rough and jagged end has two or more protruding horns) for angles of attack from zero to and after stall. The performance of the following turbulence models were examined by comparing predictions with available experimental data. Spalart-Allmaras (S-A), RNG k-epsilon, shear-stress transport, v(sup 2)-f, and a differential Reynolds stress model with and without non-equilibrium wall functions. For steady RANS simulations, WIND and FLUENT were found to give nearly identical results if the grid about the iced airfoil, the turbulence model, and the order of accuracy of the numerical schemes used are the same. The use of wall functions was found to be acceptable for the rime ice configuration and the flow conditions examined. For rime ice, the S-A model was found to predict accurately until near the stall angle. For glaze ice, the CFD predictions were much less satisfactory for all turbulence models and codes investigated because of the large separated region produced by the horns. For unsteady RANS, WIND and FLUENT did not provide better results. PowerFLOW, based on the Lattice Boltzmann method, gave excellent results for the lift coefficient at and near stall for the rime ice, where the flow is inherently unsteady.

Chi, x.↗
NASA NTRS2019

Predicted Ice Shape Formations on a Boundary Layer Ingesting Engine Inlet

Computational ice shapes were generated on the boundary layer ingesting engine nacelle of the D8 Double Bubble aircraft. The computations were generated using LEWICE3D, a well-known CFD icing post processor. A 50-bin global drop diameter discretization was used to capture the collection efficiency due to the direct impingement of water onto the engine nacelle. These discrete results were superposed in a weighted fashion to generate six drop size distributions that span the Appendix C and O regimes. Due to the presence of upstream geometries, i.e. the fuselage nose, the trajectories of the water drops are highly complex. Since the ice shapes are significantly correlated with the collection efficiency, the upstream fuselage nose has a significant impact on the ice accretion on the engine nacelle. These complex trajectories are caused by the ballistic nature of the particles and are thus exacerbated as particle size increases. Shadowzones are generated on the engine nacelle, and due to the curvature of the nose of the aircraft the shadowzone boundary moves from lower inboard to upper outboard as particle size increases. The largest particle impinging one the engine nacelle from the 50-bin discretization was the 47 um drop diameter. As a result, the MVD greater than 40 um Appendix O conditions were characterized by extremely low collection efficiency on the engine nacelle for these direct impingement simulations.

Porter, Christopher↗
NASA NTRSDate not supplied

Comparisons of CFD Simulations of Icing Wind Tunnel Clouds with Experiments Conducted at the NASA Propulsions Systems Laboratory

This paper evaluates simulation predictions against experimental test data of icing clouds that were produced during 2018 ice crystal icing physics tests conducted at the NASA Propulsion Systems Laboratory icing wind tunnel. Aero-thermal and cloud parameters are set and known upstream at the tunnel inlet and spray system, but change as the cloud and air thermodynamically interact as the flowing masses reach the tunnel test section. Utilizing the ANSYS Fluent Discrete Phase Model function, 3D computational fluid dynamics (CFD) simulations were performed, capturing the thermodynamic interactions between the test parameters, and providing predictions of the aero thermal and cloud conditions at the tunnel test section. Simulation predictions were compared with test data that were measured at the tunnel exit plane. Evaluations focused on the cloud concentration (total water content), humidity content, and air temperature. CFD simulation predictions showed areas of agreement and disagreement. Simulations showed that cloud concentration profiles at the test section are strongly related to the initial spray nozzle pattern used at the tunnel inlet. Experimental data suggest that greater dispersion of the cloud occurred as the simulated cloud predicted areas of high and low cloud concentration compared to test data profiles. Simulations, however, captured the magnitude and location of the change in humidity content and the change in air temperature due to the presence of the cloud reasonably well, when compared to test data. This result would suggest that while the ANSYS simulation did not fully predict the spreading of the cloud as measured during experiment, it did capture evaporation and the molecular movements of air and vapor relatively well.

CFD↗
NASA NTRSDate not supplied

Comparisons of CFD Simulations of Icing Wind Tunnel Clouds with Experiments Conducted at the NASA Propulsions Systems Laboratory

This paper evaluates simulation predictions against experimental test data of icing clouds that were produced during 2018 ice crystal icing physics tests conducted at the NASA Propulsion Systems Laboratory icing wind tunnel. Aero-thermal and cloud parameters are set and known upstream at the tunnel inlet and spray system, but change as the cloud and air thermodynamically interact as the flowing masses reach the tunnel test section. Utilizing the ANSYS Fluent Discrete Phase Model function, 3D computational fluid dynamics (CFD) simulations were performed, capturing the thermodynamic interactions between the test parameters, and providing predictions of the aero thermal and cloud conditions at the tunnel test section. Simulation predictions were compared with test data that were measured at the tunnel exit plane. Evaluations focused on the cloud concentration (total water content), humidity content, and air temperature. CFD simulation predictions showed areas of agreement and disagreement. Simulations showed that cloud concentration profiles at the test section are strongly related to the initial spray nozzle pattern used at the tunnel inlet. Experimental data suggest that greater dispersion of the cloud occurred as the simulated cloud predicted areas of high and low cloud concentration compared to test data profiles. Simulations, however, captured the magnitude and location of the change in humidity content and the change in air temperature due to the presence of the cloud reasonably well, when compared to test data. This result would suggest that while the ANSYS simulation did not fully predict the spreading of the cloud as measured during experiment, it did capture evaporation and the molecular movements of air and vapor relatively well.

CFD↗
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

Ice Accretion Software↗
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.

Icing CFD↗
NASA NTRS2005

Verification, Validation, and Solution Quality in Computational Physics: CFD Methods Applied to Ice Sheet Physics

Procedures and methods for veri.cation of coding algebra and for validations of models and calculations used in the aerospace computational fluid dynamics (CFD) community would be ef.cacious if used by the glacier dynamics modeling community. This paper presents some of those methods, and how they might be applied to uncertainty management supporting code veri.cation and model validation for glacier dynamics. The similarities and differences between their use in CFD analysis and the proposed application of these methods to glacier modeling are discussed. After establishing sources of uncertainty and methods for code veri.cation, the paper looks at a representative sampling of veri.cation and validation efforts that are underway in the glacier modeling community, and establishes a context for these within an overall solution quality assessment. Finally, a vision of a new information architecture and interactive scienti.c interface is introduced and advocated.

Thompson, David E.↗
NASA NTRS1996

User Interface Developed for Controls/CFD Interdisciplinary Research

The NASA Lewis Research Center, in conjunction with the University of Akron, is developing analytical methods and software tools to create a cross-discipline "bridge" between controls and computational fluid dynamics (CFD) technologies. Traditionally, the controls analyst has used simulations based on large lumping techniques to generate low-order linear models convenient for designing propulsion system controls. For complex, high-speed vehicles such as the High Speed Civil Transport (HSCT), simulations based on CFD methods are required to capture the relevant flow physics. The use of CFD should also help reduce the development time and costs associated with experimentally tuning the control system. The initial application for this research is the High Speed Civil Transport inlet control problem. A major aspect of this research is the development of a controls/CFD interface for non-CFD experts, to facilitate the interactive operation of CFD simulations and the extraction of reduced-order, time-accurate models from CFD results. A distributed computing approach for implementing the interface is being explored. Software being developed as part of the Integrated CFD and Experiments (ICE) project provides the basis for the operating environment, including run-time displays and information (data base) management. Message-passing software is used to communicate between the ICE system and the CFD simulation, which can reside on distributed, parallel computing systems. Initially, the one-dimensional Large-Perturbation Inlet (LAPIN) code is being used to simulate a High Speed Civil Transport type inlet. LAPIN can model real supersonic inlet features, including bleeds, bypasses, and variable geometry, such as translating or variable-ramp-angle centerbodies. Work is in progress to use parallel versions of the multidimensional NPARC code.

Source record↗
NASA NTRS2009

NASA Iced Aerodynamics and Controls Current Research

This slide presentation reviews the state of current research in the area of aerodynamics and aircraft control with ice conditions by the Aviation Safety Program, part of the Integrated Resilient Aircraft Controls Project (IRAC). Included in the presentation is a overview of the modeling efforts. The objective of the modeling is to develop experimental and computational methods to model and predict aircraft response during adverse flight conditions, including icing. The Aircraft icing modeling efforts includes the Ice-Contaminated Aerodynamics Modeling, which examines the effects of ice contamination on aircraft aerodynamics, and CFD modeling of ice-contaminated aircraft aerodynamics, and Advanced Ice Accretion Process Modeling which examines the physics of ice accretion, and works on computational modeling of ice accretions. The IRAC testbed, a Generic Transport Model (GTM) and its use in the investigation of the effects of icing on its aerodynamics is also reviewed. This has led to a more thorough understanding and models, both theoretical and empirical of icing physics and ice accretion for airframes, advanced 3D ice accretion prediction codes, CFD methods for iced aerodynamics and better understanding of aircraft iced aerodynamics and its effects on control surface effectiveness.

Addy, Gene↗
NASA NTRS2017

Validation Process for LEWICE by Use of a Navier-Stokes Solver

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 the latest LEWICE release, version 3.5. This program differs from previous releases in its ability to model mixed phase and ice crystal conditions such as those encountered inside an engine. It also has expanded capability to use structured grids and a new capability to use results from unstructured grid flow solvers. A quantitative comparison of the results against a database of ice shapes that have been generated in the NASA Glenn Icing Research Tunnel (IRT) has also been performed. This paper will extend the comparison of ice shapes between LEWICE 3.5 and experimental data from a previous paper. Comparisons of lift and drag are made between experimentally collected data from experimentally obtained ice shapes and simulated (CFD) data on simulated (LEWICE) ice shapes. Comparisons are also made between experimentally collected and simulated performance data on select experimental ice shapes to ensure the CFD solver, FUN3D, is valid within the flight regime. The results show that the predicted results are within the accuracy limits of the experimental data for the majority of cases.

large-eddy simulations↗
NASA NTRS2017

A Revised Validation Process for Ice Accretion Codes

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 the latest LEWICE release, version 3.5. This program differs from previous releases in its ability to model mixed phase and ice crystal conditions such as those encountered inside an engine. It also has expanded capability to use structured grids and a new capability to use results from unstructured grid flow solvers. A quantitative comparison of the results against a database of ice shapes that have been generated in the NASA Glenn Icing Research Tunnel (IRT) has also been performed. This paper will extend the comparison of ice shapes between LEWICE 3.5 and experimental data from a previous paper. Comparisons of lift and drag are made between experimentally collected data from experimentally obtained ice shapes and simulated (CFD) data on simulated (LEWICE) ice shapes. Comparisons are also made between experimentally collected and simulated performance data on select experimental ice shapes to ensure the CFD solver, FUN3D, is valid within the flight regime. The results show that the predicted results are within the accuracy limits of the experimental data for the majority of cases.

turbulent heat flux↗
NASA NTRS1992

User's manual for Interactive Data Display System (IDDS)

A computer graphics package for the visualization of three-dimensional flow in turbomachinery has been developed and tested. This graphics package, called IDDS (Interactive Data Display System), is able to 'unwrap' the volumetric data cone associated with a centrifugal compressor and display the results in an easy to understand two-dimensional manner. IDDS will provide the majority of the visualization and analysis capability for the ICE (Integrated CFD and Experiment) system. This document is intended to serve as a user's manual for IDDS in a stand-alone mode. Currently, IDDS is capable of plotting two- or three-dimensional simulation data, but work is under way to expand IDDS so that experimental data can be accepted, plotted, and compared with a simulation dataset of the actual hardware being tested.

Stegeman, James D.↗
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

Utilization of Streamtubes to Analyze the Physical Interaction of a Dispersed Cloud with the CRM65 Hybrid Midspan Model

Recently there have been numerous efforts to identify the relevant icing physics related to the formation of complex three dimensional features, sometimes referred to as ’scallops’, on swept wings. However, much of the physics is still not well understood. This paper computationally investigates the interaction of the icing cloud with the 65 percent Common Research Model (CRM65). Both the interaction with an uniced model, and the interaction with a representative simulated three dimensional ice accretion are analyzed. Preliminary results suggest that the liquid water content increases near the aerodynamic body. For small droplets on the uniced geometry, the particle velocity vector becomes nearly parallel with the aerodynamic model resulting in a low value of collection efficiency. When a three dimensional feature with a length scale much smaller than the leading edge of the airfoil is introduced into the flow, the impingement of small particles can become significantly more perpendicular to the particle velocity vector in the region of these features. Since the liquid water content near the body has increased due to the interaction with the larger scale features of the aerodynamic model, i.e. the leading edge of the swept wing, extremely high collection efficiency is observed. These results suggest that three dimensional features are likely a significant physical driver that should be modeled in some capacity when simulating the impingement of a cloud on an aerodynamic model.

Icing↗
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

Utilization of Streamtubes to Analyze the Physical Interaction of a Dispersed Cloud with the CRM65 Hybrid Midspan Model

Recently there have been numerous efforts to identify the relevant icing physics related to the formation of complex three dimensional features, sometimes referred to as ’scallops’, on swept wings. However, much of the physics is still not well understood. This paper computationally investigates the interaction of the icing cloud with the 65 percent Common Research Model (CRM65). Both the interaction with an uniced model, and the interaction with a representative simulated three dimensional ice accretion are analyzed. Preliminary results suggest that the liquid water content increases near the aerodynamic body. For small droplets on the uniced geometry, the particle velocity vector becomes nearly parallel with the aerodynamic model resulting in a low value of collection efficiency. When a three dimensional feature with a length scale much smaller than the leading edge of the airfoil is introduced into the flow, the impingement of small particles can become significantly more perpendicular to the particle velocity vector in the region of these features. Since the liquid water content near the body has increased due to the interaction with the larger scale features of the aerodynamic model, i.e. the leading edge of the swept wing, extremely high collection efficiency is observed. These results suggest that three dimensional features are likely a significant physical driver that should be modeled in some capacity when simulating the impingement of a cloud on an aerodynamic model.

Icing↗