Large Eddy Simulation (LES) Flow Solver Suitable for Modeling Conjugate Heat Transfer
Navy STTR 2019.B - Topic N19B-T027
NAVAIR - Ms. Donna Attick - email@example.com
Opens: May 31, 2019 - Closes: July 1, 2019 (8:00 PM ET)
TECHNOLOGY AREA(S): Air Platform
ACQUISITION PROGRAM: PMA234 Airborne Electronic Attack Systems
OBJECTIVE: Develop computationally efficient, Computational Fluid Dynamics (CFD), flow modeling toolsets suitable for modeling 3D time-resolved conjugate heat transfer (CHT) for use in predicting thermal behavior of aircraft parts including gas turbine (GT) engines.
DESCRIPTION: The Navy currently lacks a detailed 3D heat rejection system design or analysis capability. Most aircraft platforms have a substantial need to develop a better understanding of heat rejection / heat transfer in Navy systems. The science of modeling heat transfer in the presence of high-speed turbulent flow is crucial to Navy systems. Current CFD practice is to heavily simplify the airflow modeling, which removes important physics phenomena. Wall Modeled Large Eddy Simulation (WMLES)-based CFD toolset do an outstanding job of flow modeling turbulent flow, particularly in separated and poorly behaved flow cases. With improvements to the heat transfer modeling, particularly in the boundary layer, the state of the art will be improved, allowing improved design efforts, benefitting warfighter customers.
One of the most important factors determining performance, reliability and safety in aircraft systems is the thermal
state of the many subsystems. Incorrectly modeled or estimated thermal effects can lead to premature degradation of components, which increases both maintenance costs and safety risk. This topic is aimed at providing to the NAVAIR Internal Flow Modeling Team (NIF-T) a toolset that will allow them to accurately solve conjugate heat transfer (CHT) problems for the benefit of the Navy fleet and program office aircraft propulsion and power customers.
Fully detailed 3D CHT modeling has long been difficult to accomplish. Its complexities are commonly side-stepped in favor of simpler (often over-simplified), far less accurate, approaches. This is especially true in current CFD state of the art toolsets, where heat transfer is often modeled using simplified flow models, using over-simplified (uniform) temperature or uniform heat flux boundary conditions. Currently available solvers, such as ANSYS Icepak [Ref 1], used commercially for analysis of CHT problems, typically focus on modeling the cooling of consumer electronics. This has been shown to be an effective tool for convection or fan forced cooling in electronics applications. However, it lacks significantly in modeling the physics of high speed / transonic / supersonic flow such as in military GT engines. The flow field in electronics is typically modeled acceptably using either direct numerical simulation (DNS) or Reynolds-Averaged Navier-Stokes (RANS) simulations. RANS solvers are often used by industry in GT engine analyses as well due to their less computationally expensive nature. When used with care, RANS can give reasonable accuracy in well-behaved flow cases. The RANS formulation relies on turbulence models to model boundary layer stress, but do not work as well with separated, poorly-behaved turbulent flows.
Large Eddy Simulation (LES) is a well-known approach to flow modeling that has been implemented in many commercial CFD toolsets. It is very good at accurately modeling time-varying turbulent flow of features at grid scale and above, while moderating the computational cost as compared to DNS. LES, while providing increased accuracy over RANS, comes at the expense of increased (5-10X) computational resource requirements (when modeling high speed flow around solid objects where an accurate representation of the flow behavior in the boundary layer is desired) as compared to RANS methods. To mitigate the non-linear growth of computational requirements with increasing flow complexity, the state of the art in LES has recently advanced with the introduction of wall modeling of boundary layers [Ref 2]. This allows for substantial grid size reduction which greatly reduces the computational requirements for accurate LES solutions. However, the various optional boundary layer turbulence / shear assumptions of the many different Wall Modeled Large Eddy Simulation (WMLES) approaches have, in most cases, not been carefully studied in order to develop accurate heat transfer predictions through this simplified boundary layer region. Many flow analysis organizations have come to rely on the outstanding unsteady flow predictions of WMLES toolsets.
This STTR topic seeks to further develop and improve the accuracy of the CHT predictions of WMLES toolsets, both with and without transpiration. It seeks to improve wall modeling assumptions such that CHT analysis results accurately predict the physics of heat transfer in turbulent boundary layers. We are seeking to develop toolsets that accurately predict CHT in selected sample cases with known results that are (threshold) at least as accurate as RANS model CHT results, with the objective being that the use of wall modeling produces CHT results that are equally as accurate as fully resolved (not wall modeled) LES analyses.
The proposed toolset must be able to accurately model the heat transfer between the fluid and the bounding structures within the respective domains of each. These toolsets must work in a computationally efficient manner, with the objective that accurate CHT results take no more computing resources than current WMLES analyses. The Threshold would be that any changes to the Wall Modeling features would result in WMLES models with accurate CHT calculations that use less computing resources that fully resolved LES models while the CHT features are active.
Any proposed toolset would be evaluated primarily on physical accuracy for both flow properties and CHT in wall regions, and secondarily on computational efficiency. Physical accuracy would need to be demonstrated against experimental results of the physics being modeled, and other well accepted WMLES tool results. Experimental comparisons are to be chosen by the proposing organization to demonstrate accuracy of the toolset when given arbitrary problems as well as established problems, while following simple modeling guidelines. Computational efficiency of the toolset would be demonstrated with representative and real-world flow problems being simulated on the DoD high-performance computers [Ref 7] (or functional equivalent) with threshold and objective measures as described above.
Note that the task / cost of developing new wall modeling features in an LES toolset that lacks wall modeling is outside the scope of this topic. Preference will be given in the Phase II selection process for proposing entities working with an already implemented LES toolset with wall modeling for accurate boundary layer flow prediction. However, an approach that selects an LES toolset currently without wall modeling would be allowed as long as the proposal commits to delivering, at the end of Phase II, a fully functional WMLES toolset that meets the objectives stated herein. Two known open source LES toolsets are High Fidelity Large Eddy Simulation (HiFiLES) [Ref 3] and OpenFOAM [Ref 4].
PHASE I: Demonstrate an in-depth knowledge of the physics and modeling issues involved in modeling turbulent air flow and making CHT predictions, with and without transpiration using both RANS and WMLES toolsets.
Select one currently available WMLES toolset to be updated / demonstrated for this effort, and one RANS tool for use in flow and CHT results comparison for this effort.
Define and describe a plan for confirming / improving / demonstrating the CHT features of the selected WMLES toolset, to be carried out in Phase II.
Develop a detailed plan (to be carried out in Phase II) to conduct one or more proof-of-concept demonstrations of the predictive power and accuracy of the proposed resulting toolset that must include measures of its computational efficiency.
Provide a risk analysis of the proposed Phase II effort, identifying key areas of technical risk, and provide a risk mitigation plan for each identified risk. (Note: Technical Risk is defined here as: “issues arising from, or aspects related to the contractor selected approach that could result in a less than satisfactory result, based on the measures of success in this solicitation”.)
PHASE II: Carry out the contractor’s development and demonstration plan for improved CHT modeling with the selected WMLES toolset (as defined in Phase I above). Enhance and / or demonstrate accurate CHT modeling performance (as described above) of the selected WMLES toolset. Demonstrate scalability, universality and applicability of the solver, including its computational efficiency for use in real-world, GT propulsion relevant flows. Evaluate, document, and demonstrate the CHT predictive power of the toolset using a contractor-obtained test data set, selected by agreement with the NAVAIR TPOC. By the end of the Phase II effort, deliver and install a working prototype version of the resulting enhanced WMLES toolset on the DoD High Performance Computer Systems (DoD HPC). Obtain and provide to the Navy all needed licenses and enabling tools for input of model data and output of results.
PHASE III DUAL USE APPLICATIONS: Perform testing and then any further development of the toolset to address any identified deficiencies to provide a commercially viable and well accepted CHT / WMLES toolset to be utilized by the developing organization as consultants and / or sold or licensed to other organizations. Deliver and install the final working version of the enhanced WMLES toolset on the DoD High Performance Computer Systems (DoD HPC), including all needed licenses and enabling tools for input of model data, and output of results. Train and assist up to 15 members of the NAVAIR NIF-T team in the use of the final WMLES toolset.
The toolset developed here are expected to have far-reaching uses for all DoD branches and many private sector companies. GT design as well as aircraft design in general would benefit from robust 3D flow-based heat transfer analysis, especially with regard to component reliability, performance, and efficiency of propulsion and cooling systems. Gas turbine engines are currently in use for land-based electrical power generation, ship power plants, land vehicles, and most aircraft. Beyond GT engines, accurate heat transfer calculations in an accurate WMLES flow modeling tool would have benefits for use in the design of gasoline and diesel engines, heat exchangers of all types, the refrigeration industry, nuclear reactor original equipment manufacturers (OEM), and design of general purpose heating, ventilation and air conditioning (HVAC). However, the high-speed flow inherent in GT engines would perhaps most benefit from the combination of WMLES with accurate CHT. Potential uses exist for any industry where accurate CHT analysis would enhance design features, such as the refrigeration industry, automotive and surface vehicle, nuclear, HVAC etc.
1. “ANSYS Icepack: Electronics Cooling Simulation.” ANSYS Inc., 2018. https://www.ansys.com/products/electronics/ansys-icepak
2. Bose, Sanjeeb T., and Park, George Ilhwan. “Wall-Modeled Large-Eddy Simulation for Complex Turbulent Flows.” Annual Review of Fluid Mechanics, 2018, Volume 50:535-61. https://doi.org/10.1146/annurev-fluid- 122316-045241
3. "HiFiLES - High Fidelity Large Eddy Simulation." Aerospace Computing Laboratory (ACL), Department of Aeronautics and Astronautics, Stanford University, 2014. https://hifiles.stanford.edu
4. "OpenFOAM." OpenCFD Ltd. (ESI Group), 2018. https://www.openfoam.com
5. Duchaine, F., Maheu, N., Moureau, V., Balarac, G., and Moreau S. “Large eddy simulation and conjugate heat transfer around a low-mach turbine blade.” Journal of Turbomachinery, American Society of Mechanical Engineers, Paper No: TURBO-13-1092, 136(5), 051015. http://turbomachinery.asmedigitalcollection.asme.org/article.aspx?articleid=1761870
6. Gourdain, N., et al. “Large eddy simulation of flows in industrial compressors: a path from 2015 to 2035.” Philosophical Transactions of the Royal Society, A 2017 372 20130223, 2014. https://royalsocietypublishing.org/doi/pdf/10.1098/rsta.2013.0323
7. Department of Defense High Performance Computing Modernization Program, 2018, https://centers.hpc.mil
KEYWORDS: Conjugate Heat Transfer; Heat Exchangers; Transpiration Cooling; High-Temperature Turbines; Large Eddy Simulation, LES; Wall Modeled LES; Gas Turbine Engines;