Introduction to the Glenn Icing Computational Environment (GlennICE)

National Aeronautics and Space Administration

Introduction to the Glenn Icing

Computational Environment ( GlennICE )

Thomas Ozoroski

NASA Glenn Research Center – Icing Branch Thermal Fluids Analysis Workshop, Cleveland, OH 8/26/2024 www.nasa.gov National Aeronautics and Space Administration Agenda • Background and Introduction • GlennICE Workflow Overview • ONERA M6 Example Case Walkthrough • CRM - HL Example Case • ARIES - I Example Case • Future Developments • Additional Projects and Research • How To Obtain GlennICE www.nasa.gov National Aeronautics and Space Administration What I Hope to Convey and Showcase Today • Provide a high - level overview of aircraft icing • Discuss what sort of research we are conducting in the Icing Branch at NASA Glenn • Highlight some of our previous research and showcase a few of our facilities • Provide an overview of our new computational tool GlennICE • Walk you through an example case using GlennICE • Highlight some research we are actively engaged with and some examples • Talk about where we are investing money in tool development www.nasa.gov

National Aeronautics and Space Administration

BACKGROUND AND INTRODUCTION

www.nasa.gov National Aeronautics and Space Administration

Introduction to Icing

Background • Ice formation and its resulting performance impact is a complex problem that is interdisciplinary by nature.

• Due to this complexity, icing tools are only validated over a narrow range of vehicle geometries and icing conditions.

Goals • Improve the state of the art of icing tools to be applicable over a larger range of conditions, including high - lift configurations, novel vehicle designs, Freezing Drizzle (FZDZ), Freezing Rain (FZRA), and Ice Crystals (IC) .

Impact • Enable icing tools to be utilized earlier in the design process to reduce design cost, as well as increasing safety and efficiency.

• Note that novel vehicle and engine designs have an unknown risk to the newly codified Appendix D and O icing conditions (FZRA, FZDZ and IC), and carry a regulatory burden that existing tube and wing designs with a proven flight history do not carry.

The various icing certification environments and the altitude at which they occur.

• Absent of advancements in validated simulation techniques for icing, the costs associated with proving the safety of novel vehicle and engine designs can be extremely large or even economically prohibitive for market entry.

www.nasa.gov See Appendix B History of Icing Certification Rules of AC 25 - 28 for more detail.

National Aeronautics and Space Administration

Icing Research Tunnel (IRT)

Recirculating Icing Wind Tunnel 6 x 9 ft (1.8 x 2.7 m) test section Constructed in 1943 Underwent numerous upgrades in its history Heavily utilized by NASA, industry, DoD, … Designed for super cooled water icing Now some limited ice crystal capability https://www.nasa.gov/directorates/armd/aetc/icing - research - tunnel/ www.nasa.gov National Aeronautics and Space Administration

PSL: Propulsion Systems Lab

Engine test stand with altitude capability Icing capability enhancement started ~2009 Ice crystals and supercooled water Completed icing tests between 2012 - 2018: 3 full engine test campaigns 1 driven rig 2 fundamental physics tests Looking to restart icing testing ~2026 https://www.nasa.gov/directorates/armd/aetc/propulsion - systems - laboratory - psl - facility/ www.nasa.gov National Aeronautics and Space Administration

NASA Ice Crystal Icing Tools

COMDES - MELT TADICE Mean - line compressor analysis code Icing wind tunnel simulation tool (1D) that coupled with an ice crystal thermodynamic models the thermodynamic interactions state code, which is used as a turbofan between the water/ice particles of an icing engine icing risk analysis tool cloud and air as it flows down the tunnel.

Blockage Growth Rate https://software.nasa.gov/software/LEW - 19874 - 1 https://software.nasa.gov/software/LEW - 20027 - 1 www.nasa.gov National Aeronautics and Space Administration

NASA Icing Tools

LEWICE3D (1993 – 2015 ) LEWICE (1980s – 2006) Simulates ice accretion in quasi - 3D on Simulates 2D ice accretion using super - user - specified cut planes using full 3D cooled droplets impinging on a body.

droplet trajectories.

Experiment LEWICE Clean airfoil https://www1.grc.nasa.gov/aeronautics/icing/software/ www.nasa.gov National Aeronautics and Space Administration

Why We Are Developing GlennICE

• Historically, icing codes have relied on integral boundary layer methods and stepping back from the stagnation/attachment line • Our historical codes are not fully 3D – LEWICE is 2D – LEWICE3D is a quasi - 3D code • LEWICE3D uses a CFD flow solution and computes ice growth along selected cut planes – This procedure typically involves lofting 2D ice shapes into a 3D ice shape • LEWICE3D relied heavily on empirical data and was tuned for transport aircraft – The type of problems encountered in new and novel designs really pushes LEWICE3D • The need was identified for NASA to develop a new fully 3D icing solver • GlennICE was designed from the ground up using – Modern coding practices – Designed to accrete ice in a fully 3D workflow – Removing empirical reliance to focus on modeling underlying physics from first - principle www.nasa.gov National Aeronautics and Space Administration

GlennICE Overview

• Fully 3D icing simulation tool • CFD post processor • Can predict droplet impingement & resulting ice growth on aircraft surfaces • Lagrangian droplet tracking with adaptive refinement for efficient solution convergence • Highly efficient parallelization schemes built for modern HPCs • Built using modern coding practices and standards from the ground up • Designed and focused on tackling problems relevant to government, industry, and academia GlennICE As of 2018,GlennICE is the foundational code through External Icing which NASA will develop and evaluate physical models Rotational Icing associated with ice accretion Engine Icing www.nasa.gov National Aeronautics and Space Administration

The Problems We Are Tackling

With an icing code capable of accreting ice on an arbitrary 3D configuration, we

can analyze and improve configurations earlier in the design phase

Fuel Efficient Concepts Enduring Safety Emerging Markets www.nasa.gov

National Aeronautics and Space Administration

G lenn ICE WORKFLOW OVERVIEW

www.nasa.gov National Aeronautics and Space Administration

Standard GlennICE Workflow

www.nasa.gov National Aeronautics and Space Administration

Impingement Computation: Lagrangian vs Eulerian

Eulerian Lagrangian Pros Pros • Domain decomposition. • Accuracy of solution is decoupled from underlying • Ability to couple with fluid flow. CFD mesh.

• Ease of use as a post processing tool for arbitrary CFD solutions.

Cons • Susceptible to numerical diffusion Cons • Cloud is viewed as a continuum… i.e. unable to physically capture tangled trajectories • Problem is akin to those seen in ray tracing.

• Difficult to decompose the domain for parallel problems.

• Many trajectories are required to compute smooth collection for naïve schemes.

www.nasa.gov National Aeronautics and Space Administration

Lagrangian Approach: Node Centered Streamtube Definition

• GlennICE discretizes the streamtube area on the release plane and associates a scalar quantity of water flow rate with each individual trajectory • Allows for trajectory refinement on curved surfaces • The streamtube area is defined by the equation: # 𝑜𝑓 𝑓𝑎𝑐𝑒𝑠 ෠ ത 𝑆𝑡𝑟𝑒𝑎𝑚𝑡𝑢𝑏𝑒 𝐴𝑟𝑒𝑎 = ෍ 𝑊 𝐴 𝑉 ∙ ො 𝑛 𝑖 𝑖 𝑖 𝑖 = 1 • From streamtube area, a water flow rate is assigned to the trajectory Schematic depicting the computation of stream tube associated to a trajectory on a non - planar inlet www.nasa.gov National Aeronautics and Space Administration

Feature Finding and Automatic Refinement

• GlennICE currently computes wall distance internally based on the Fast Distance method of Wigton [1].

• Users only have to specify the CFD surface they are interested in targeting, and GlennICE will adaptively refine the trajectory Minimum release to target these surfaces.

Wall Distance • Adaptively releasing trajectories allows for immense time savings when trying to converge a solution www.nasa.gov [1] Wigton, Larry “Final Report for first year of prime Contract NAS2 - 14090 “Research in Computational Aeroscience Applications Implemented on Advance Parallel Computing Systems” 1996 National Aeronautics and Space Administration

Lagragian Approach - Impinging Water

• A fully generic approach is costly.

• Metrics can be created to identify regions where continuum assumptions are likely valid.

• Only need to refine trajectories that hit near the edge of a CFD face • Using these assumptions, unnecessary work can be avoided, greatly improving efficiency.

Schematic of the Impinging Water Methodology • The desired result is a hybrid approach that is : • Robust in regions where the continuum assumption breaks down.

• Efficient in regions where the continuum assumption is valid.

Depiction of the trajectory and trajectory connectivity at refinement iteration 20 at various levels of zoom. Note that the location of the zoomed images are identified by the colored boxes.

www.nasa.gov Porter, Christopher E. "A Comparison of Trajectory Refinement Schemes for GlennICE." AIAA AVIATION 2022 Forum. 2022.

National Aeronautics and Space Administration

Static vs. Dynamic Scheduling

• In a static scheduling process, the trajectory routines are ordered

to distribute an equal amount of trajectories between the total

number of processors.

P3 P3 P3 P4 P4 P4

P2 P2 P2

P1 P1 P1

• In a dynamic scheduling process, the distribution of

Number of Trajectories Static Dynamic

trajectories is not predetermined but is managed at

Processor Scheduling Scheduling

runtime based on the current workload and

1 3 -- 2 3 6 processor availability.

3 3 2 Initial Subset 4 3 4

P1

P2 P3 P4 P2 P4 P3 P2 P2 P4 P2 P4 P2

www.nasa.gov National Aeronautics and Space Administration

Dynamic Scheduling and Load Management

• An important part of the Trajectories Nearly GlennICE software is the Freestream Missing the Surface efficiency we have built into the trajectory routine • A computation on a semi - span aircraft can be upwards of 50M+ trajectories • Efficient computation of these trajectories is a key part of integration into a production workflow • To achieve this, we manage runtime based on the current workload and processor availability Trajectories Hits the Surface www.nasa.gov National Aeronautics and Space Administration

Speedup and Efficiency

Time 𝟏 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐨𝐫 Speedup Speedup = Efficiency = Time 𝑥 Processors Number of Processors www.nasa.gov National Aeronautics and Space Administration

Roughness Augmentation and Heat Transfer

• LEWICE and LEWCE3D relied heavily on 1D empirical data – Tuned to transport aircraft – Based on IBL methods – Not applicable for new concepts or expanding the design envelope • HTC is a crucial component because it dictates what the ice shape will be • When a liquid droplet impacts the surface, it releases heat when it freezes • If there is not enough convective heat transfer, only a portion of the droplet will freeze – If there is unfrozen water, it moves along the surface until freezing or running off • Our three - dimensional methods still rely on empirical assumptions – We increase the convective heat transfer to account for the influence of icing roughness – Current methods in GlennICE work, but reliance on them is not a long - term strategy – We still need to develop methods that are informed by first - principle www.nasa.gov

National Aeronautics and Space Administration

ONERA M6 EXAMPLE CASE

www.nasa.gov National Aeronautics and Space Administration

Case Setup

• Coupling between GlennICE and the CFD software is Geometry Description: entirely one - way • GlennICE generates a discretized flow field based on a • ONERA M6 geometry CFD solution – . szplt or . fvuns • Grid generated using Heldenmesh • Two methodologies exist for providing solutions: – 955,027 nodes • The 1 - Solution – user to provides heat transfer coefficient (HTC) + and Adiabatic Wall Temperature (T ) aw – y ≤ 1 • The 2 - Solution – requires two CFD solutions run with different • Chord length of 1.0 m with a span of roughly 1.5 m isothermal wall temperatures and GlennICE computes HTC and T aw internally • Cantilevered off of a symmetry plane • We accept tetrahedral and mixed - element unstructured solutions www.nasa.gov National Aeronautics and Space Administration

CFD Analysis

• Using NASA’s FUN3D solver to generate a steady RANS CFD solution • SA - neg turbulence model • Perfect - gas finite - volume discretization • Imposing a nondimensional wall temperature of W = 0.96 and W = 1.04 as a boundary T,1 T,2 condition • W = 0.96 → W = 0.96 * T → W = 0.96 * 267.15 K→ W = 256.464 K T,1 T,1 s,∞ T,1 T,1 • Generate a . szplt file for the volume and boundary for each wall temperature • 2 - Soln Method requires a ‘heating’ variable, can also be named: Wall Heat Flux, surface heat flux, QWall www.nasa.gov National Aeronautics and Space Administration

Single - Bin Ice Accretion

• We have our CFD solutions, what now?

• Let us conduct a single - shot accretion of a single droplet diameter

• We choose a spray time of 2700s (equal to 45 minutes)

• Main parameters in the glennice.nml file

– Convert all CFD solution variables to m - k - s units inside the submission file – Input freestream icing conditions shown below – Tag the appropriate surfaces for what the inlet and icing surfaces are – Specifying convergence metrics

• Typically, we run jobs on NASA Advanced Supercomputing

– Job parameters through the glennice.nml file – Job submission through a .pbs job file (can also be run serially) Properties of interest for our analysis www.nasa.gov National Aeronautics and Space Administration

What This Actually Looks Like

glennice.nml Input File Parameters: Command Line Execution: www.nasa.gov National Aeronautics and Space Administration

Single - Bin Ice Accretion

• So we computed our first ice shape: what do we look at?

• Two main files are output – The surface data . szplt file – The ice shape . stl file • For a typical user, the key variables are often – Collection efficiency ( beta_total ) – Computed ice shape • Collection efficiency will show us where we get water impinging and how much • We also compute and print out – Convergence metrics – Ice Mass, Volume, & Thickness – Hit counts – Augmentation values – Freezing fraction – Many more www.nasa.gov National Aeronautics and Space Administration

Changing Convergence Metrics With a Refinement Restart

• Within GlennICE we have different ways to determine whether a job can be considered ‘converged’ • Our recommended procedure is setting a value for pct_converged_limit – This allows for the code to stop once we have achieved this limit • For our first job, we set pct_converged_limit at 80% – What happens when we switch this value to be 90% instead • The knowledge of what this variable actually represents is a little more technical • A user can think about this as the percentage of all the faces that achieve a criteria that has been set in the glennice.nml file • In a Eulerian solver, you have a known residual that defines convergence – This does not exist for a Lagrangian scheme www.nasa.gov National Aeronautics and Space Administration

What Our Standard Output Will Show

We change the pct_converged_limit to 90% and utilize a refinement restart We see our original metric of 80% was met We achieve a fraction_contained_tol above our 90% threshold in the next iteration www.nasa.gov National Aeronautics and Space Administration

Convergence Metric Change Using a Refinement Restart

• So we changed our pct_converged_limit from 80% to 90%, what happened to our results?

• If we look at the ice shape, very little seems to have changed • Take a slice at Y = 0.75 m – We see that collection efficiency is very similar • So, it ‘appears’ that nothing discernable happened, so let us take a look at what changed between runs Minimal differences compared to 80% www.nasa.gov National Aeronautics and Space Administration

Convergence Metric Change Using a Refinement Restart

• Our metrics for convergence added trajectories at the impingement limits • The number of trajectory hits on the surface increased significantly in this region • Our convergence metrics improved in the impingement limit region These metrics and the results will not always behave the same for every configuration www.nasa.gov National Aeronautics and Space Administration

Multi - Bin Ice Accretion

• In reality, droplets are not encountered at a single diameter but in a distribution of droplets • We can approximate these distributions through binning the diameters and how much of the total volume of water they take up • We base our accretion parameters off the median volume diameter (MVD) and what we will call the mass fraction • Our mass fraction says what fraction of the total mass each droplet contributes – If a single diameter accretes 800 g of mass and their mass fraction is 0.1, they will end up contributing 80 g of mass to the solution How this is specified within the glennice.nml file: The distribution we will assume for this problem: Langmuir D MVD = 20μm Distribution IRT 7 - Bin Distribution for an MVD = 24.9 μm www.nasa.gov National Aeronautics and Space Administration

Multi - Bin Ice Accretion

• When you run multiple bins, each droplet diameter will behave and converge differently – The path of a large droplet will behave differently than a small – Lighter particles move with the flow and turn more easily than heavier particles • This means that you have to converge each diameter individually • In the end, we combine all of the individual diameters and account for the combined influence of each www.nasa.gov National Aeronautics and Space Administration

Multi - Bin Ice Accretion

• We see in our slice that a multi - bin accretion has ice forming further back on the airfoil – The green line moves further back in the streamwise direction • We see that the peak water loading on the leading edge is slightly smaller – Looking at peak collection efficiency the diameter = 20μm is larger than MVD = 20μm • We can see the larger ice shape is being driven by the amount of water impinging beyond the diam = 20μm profile shown in orange • These differences drive the requirements for ice protection systems – How big, how far back, how much energy is required, etc.

www.nasa.gov National Aeronautics and Space Administration

Temperature and Icing

• In icing there is never one condition that you have to certify your aircraft or IPS to

– it is always multiple

• Different icing temperatures result in different ice shapes: – Warmer temperatures result in glaze or mixed ice shapes (pilots call this clear ice) – Colder temperatures result in rime ice shapes – Correctly predicting what location on the airfoil and at what temperature is challenging

• Glaze ice shapes tend to form large horns (like what we showed previously)

– Dominated by water run back and the migration of unfrozen water

• Rime ice shapes tend to form more aerodynamic shapes

– Dominated by droplets freezing immediately upon impact

• Sizing an ice protection system has to account for all situations

– Not abating all of the accreted ice can produce ridge ice – unmelted or refrozen ice aft of the IPS which can result in accretion where it cannot be mitigated www.nasa.gov National Aeronautics and Space Administration

Four Groups of Ice Shapes

If we classify based primarily on flowfield physics: • Roughness (often earlier accretion times) – Ice Roughness is larger than the local boundary layer.

Height – Effects determined by height, concentration, surface location.

θ Chord • Horn ice (often from glaze conditions) Line – Characterized by large flow separation.

s/c – Horn size, location, and angle are key parameters with roughness and the detailed cross - section geometry having little effect.

• Streamwise ice (often from rime conditions) – Forms streamlined shape on the leading edge, with localized flow separation.

Geometry k – Surface roughness can have significant effect on aerodynamics.

s Chord • Spanwise - ridge ice (often from SLD, reimpingement , refrozen ice) Line – Obstacle in the flow since “airfoil” boundary layer has time to develop.

– Location and height are key parameters, but ridge geometry and airfoil geometry are also important.

www.nasa.gov Adapted from Andy Broeren , NASA Glenn, 7.1.1 and 7.1.2 Icing Workshop, Ice Formation and Effects Part 2 National Aeronautics and Space Administration

Temperature Change

• For this example, we will examine how to change the temperature and generate a new ice shape for a cold condition • We currently make the assumption that changing the temperature is independent of impingement characteristics – The trajectories and impingements for a diam = 20μm particle at - 3˚C is the same as one for - 20˚C – This assumption has shown to be valid when doing a single - shot approach • What we do is make changes in the mass and energy balance – How much freezes on impact – How much unfrozen water gets migrated further back • This temperature change can be analyzed using previously computed trajectories – Efficiently conduct trade studies for an entire aircraft – Save time by not recomputing trajectories – Easy automation of the procedure • Looking at a temperature sweep in this way is common in industry – This method helps to identify what the critical ice shape is for certification www.nasa.gov National Aeronautics and Space Administration

Temperature Changes

• We will demonstrate this by decreasing the temperature from T = 267.15K to T = 257.15K • Will leverage the multi - bin analysis produced previously and perform a surface restart with GlennICE • We can see that the new ice shape no longer produces horns • The new ice shape is formed based upon an increased amount of water that is freezing upon impact • Let us see how this changes some of the surface properties of interest T = 267.15 K ice shape T = 257.15 K ice shape Decreasing Temperature www.nasa.gov National Aeronautics and Space Administration

Temperature Change Using a Surface Restart

• Utilizing the slice location of

Y = 0.75m as before

• Freezing fraction is describing the

amount of water that is freezing on

each face

• It shows that we have significantly

changed the location where ice is

freezing

– Horns are no longer being generated – Less incomplete freezing of particles – More ice is being formed on the leading edge www.nasa.gov National Aeronautics and Space Administration

Trajectory Visualization

• Particle trajectories differ from simple streamline

– Particles have a mass and size to them – Gravity and other forces can influence trajectories – These effects are related to the Stokes number or the modified inertia parameter

• For this example, we will release a rake of trajectories with a size of

20μm for the configuration we have been working on

• This capability is often used for visualization and debugging and is not

looked at in a large - scale situation

• It is important to keep in mind that different particles will behave

3 2

differently since mass with scale with r and drag will scale with r

www.nasa.gov National Aeronautics and Space Administration

Trajectory Visualization

Particles that impinge on the geometry terminate, while those that do not

continue traveling out of the domain

www.nasa.gov

National Aeronautics and Space Administration

IRT TRAJECTORY VISUALIZATION

www.nasa.gov National Aeronautics and Space Administration

Trajectory Visualization – IRT

• In the IRT, we have spray nozzles that produce our icing cloud – Note: These droplets are not being released at the same location – Note: The droplets are not the Larger Particles same size or distribution • Highlights the different behavior of differently sized particles • Can easily see the influence gravity has Smaller Particles www.nasa.gov National Aeronautics and Space Administration

Trajectory Visualization – IRT

Side View Iso View Top View www.nasa.gov

National Aeronautics and Space Administration

CRM - HL EXAMPLE CASE

www.nasa.gov National Aeronautics and Space Administration

Case Background

• The High Lift Common Research Model (CRM - HL) has been a research platform that NASA and others have used for extensive experimental and computational analyses • The goal is to be able to develop tools for predicting CLmax for relevant configurations • Part of improved CLmax prediction is doing so with the inclusion of artificial ice shapes • GlennICE has been used along with LEWICE3D to compute ice shapes on the CRM - HL for experimental analyses while showcasing refinement techniques www.nasa.gov National Aeronautics and Space Administration

GlennICE Results

Full Aircraft Accretion Wing and Wing Tip Accretion Nacelle Accretion www.nasa.gov National Aeronautics and Space Administration

GlennICE Results

nTraj = 2,335 nTraj = 158 nTraj = 22,417,553 nTraj = 94,266 www.nasa.gov National Aeronautics and Space Administration

GlennICE Results

nTraj = 22,417,553 nTraj = 94,266 nTraj = 22,417,553 nTraj = 94,266 www.nasa.gov

National Aeronautics and Space Administration

ARIES - I EXAMPLE CASE

www.nasa.gov National Aeronautics and Space Administration

ARIES - I Example Case

Simplified Configuration • A simplified axisymmetric configuration of the NASA ARIES - I geometry • Demonstrate the rotational reference frame capabilities in GlennICE • Allows for an analysis of propellor configurations typical of UAM/UAV configurations • Currently utilizing an algebraic model to transfer unfrozen Experimental Geometry [1] water – Only able to analyze rime conditions Computational Domain www.nasa.gov [1] P.V. Hardenberg, et al., “Ice Shape Analysis of an eVTOL Propeller in Forward Flight at the NASA Glenn Icing Research Tun nel ”, Aviation 2024 National Aeronautics and Space Administration

CFD Results

Front View of the Configuration Side View of the Configuration www.nasa.gov National Aeronautics and Space Administration Rotational Reference Frame Particle Trajectory

GlennICE Analysis

• Current regulations limit UAM configurations to minimal icing encounters • Will help to enable UAM manufacturers to size IPS systems • Will help to enable new technology, designs, and increase aviation safety • Capable of translating this to next generation propulsion systems like open rotor Spinner Hub Comparison at MVD = 80μm [1] Propellor Blade Comparison at MVD = 80μm [1] www.nasa.gov [1] D. Rigby, P.V Hardenberg, “ GlennICE Simulation of a 24, 28, and 36 inch Diameter eVOTL Propellers in Forward Flight”, Aviation 2024

National Aeronautics and Space Administration

FUTURE DEVELOPMENT

www.nasa.gov National Aeronautics and Space Administration

Future Technologies and Development

Future Developments Active Developments Shallow Water Roughness SLD – Mixing Planes Multi - shot Ice Crystal Icing Method (SWIM) Heat Transfer Splashing • Heavily rely on • Allows for • Can account • More tightly • Secondary • Enable empirical radial for Coriolis coupling impingement melting and assumptions distribution of and centrifugal GlennICE and of particles refreezing of • Most methods particle forces a CFD solver and particle accretion in an available for release breakup engine • Enables • Grow ice for a roughness • Enables rotational icing certain • High interest • Cause of modeling are internal capabilities at amount of by industry for engine - out not applicable analysis of glaze time, enabling new situations to icing engines conditions recompute technologies during flight • Strongly flowfield, and influences • Support • Enabling • Of interest to resultant ice recompute ice certification of design and technology for engine shapes shape new platforms certification of open - manufacturers • Will enable large bypass fan/open - rotor, • Generate • Crucial path • Experimental more accurate ratio engines UAM more complex forward for data through computational propellors, ice shapes aircraft safety • Will compare SIDRAM solutions and engine and to data available configurations sustainability obtained in PSL www.nasa.gov

National Aeronautics and Space Administration

ADDITIONAL WORK

www.nasa.gov National Aeronautics and Space Administration

High Lift Common Research Model

Clean Description of Technical Content • Generation of an experimental database that fills two Iced specific gaps in data, high lift and/or flight Reynolds number • Subsequent usage of this data to benchmark computational tools to replace/reduce wind tunnel usage, lowering the cost to design and certify new vehicles • In addition, there have been a variety of recent papers utilizing Wall Modeled Large Eddy Simulation on existing stowed straight and swept wing icing performance data Performance data of the Clean and Iced CRM - HL configurations at the cross - facility Reynolds Numbers Schematic of the artificial ice shapes (Red) on the High Lift Common Research Model Usage of Ultraviolet (UV) light and UV sensitive paint for ice shape identification www.nasa.gov National Aeronautics and Space Administration

Transonic Truss Braced Wing Icing

➢ Provide validated capability to assess

impact of icing

➢ Assess potential impact of icing on fuel burn

objective

➢ Develop potential materials that are both

durable and icephobic

➢ Targeting IRT tests in FY24 and FY25

www.nasa.gov National Aeronautics and Space Administration

Engine Ice Accretion Testing – SIDRM

Objectives: Top - down view • Gather data to develop & validate computational icing tools for ice crystal (IC) icing • Developed SIDRM Model Simulated Inter - compressor • Simulates inner compressor duct & strut region Duct Research Model (SIDRM) • Ice crystal accretion using a heated surface www.nasa.gov https://doi.org/10.4271/2023 - 01 - 1399 or https://ntrs.nasa.gov/citations/20230003642 Project Sponsor: AATT

National Aeronautics and Space Administration

HOW DO I OBTAIN G lenn ICE ?

www.nasa.gov National Aeronautics and Space Administration Coming Soon to the NASA Software Store

We will be releasing on the Software Store in the

next couple months

Will be available for US Citizens and Companies https://www.software.nasa.gov User Manual available at: https://ntrs.nasa.gov/citations/20240008077 www.nasa.gov

National Aeronautics and Space Administration

Acknowlegdements

Thank you to the additiona l GlennICE development team members: Christopher Porter, Eric Galloway, David Rigby, William Wright, and Zaid Sabri.

NASA supported this research through the Transformative Tools and Technology project along with the The Advanced Air Transport Technology Project These individuals and project are thanked for their contributions.

www.nasa.gov National Aeronautics and Space Administration

QUESTIONS?

Feel free to contact me at: thomas.ozoroski@nasa.gov www.nasa.gov