Automated, Fast Computational Fluid Dynamics (CFD) Solver Technologies for Hypersonics

Navy SBIR 25.1- Topic N251-060
Office of Naval Research (ONR)
Pre-release 12/4/24   Opens to accept proposals 1/8/25   Closes 2/5/25 12:00pm ET    [ View Q&A ]

N251-060 TITLE: Automated, Fast Computational Fluid Dynamics (CFD) Solver Technologies for Hypersonics

OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Advanced Computing and Software;Hypersonics;Trusted AI and Autonomy

OBJECTIVE: Develop automated and fast computational fluid dynamics (CFD) solver technologies for accurately predicting laminar hypersonic base flows in thermo-chemical non-equilibrium, significantly reducing the dependency on user expertise and computational costs during the early design phases of hypersonic vehicles.

DESCRIPTION: Boundary layer transition (BLT) is critically important for the design and performance of hypersonic weapons. The transition from laminar to turbulent flow significantly impacts the heating rates experienced by the vehicle. Laminar flow heating rates are 4 to 7 times lower than those in fully turbulent flow, which reduces the requirements for Thermal Protection Systems (TPS) and insulation [Ref 1]. Additionally, BLT affects the aerodynamic performance of slender high lift-to-drag (L/D) ratio vehicles, where a significant increase in drag due to turbulent flow can lead to a reduced range. Therefore, assessing BLT early in the design phase is essential to optimize vehicle performance and ensure the effectiveness of hypersonic weapons.

Significant progress has been made in the computation of hypersonic boundary layer instabilities, which are crucial for predicting BLT. Advanced methods such as quiet and forced Direct Numerical Simulation (DNS) [Ref 2] and Planar Parabolized Stability Equations (PSE) [Ref 3] have enhanced our understanding and compute flow instabilities. Input-Output analysis [Ref 4], One-Way Navier-Stokes [Ref 5], and Adaptive Mesh Refinement Wavepacket Tracking (AMR-WPT) [Ref 6] techniques further contribute to accurate predictions. Examples like instability computations on the fin-cone [Ref 7], BOLT [Ref 8] and HyTRV [Ref 9] illustrate these advancements.

Accurate prediction of the BLT process requires a high-quality laminar base flow, which depends on user-generated computational grids and chosen numerical schemes. A key challenge for obtaining a high-quality laminar base flow at high Mach numbers is maintaining low noise levels to avoid premature transition and using steady-state marching techniques to avoid disturbance amplification. Obtaining base flows at Mach numbers high enough to produce thermal and chemical non-equilibrium also provides significant challenges.

Incorporating realistic features into hypersonic boundary layer stability analysis remains challenging. Simulations are complicated by factors such as thermo-chemical nonequilibrium, ablation, steps and gaps, surface roughness, realistic wall temperature distribution with spatiotemporal variations, and surface deformations or Outer Mold Line (OML) morphing. These elements are critical for accurate modeling but increase the complexity and computational cost.

Performing reliable and fast stability analysis on complex geometries presents several challenges: Generating high-quality grids for these simulations requires significant time. Achieving convergence can be problematic. Robustness of the methods is often an issue. The overall cost of obtaining accurate solutions is high. These challenges hinder the timely and efficient design of hypersonic systems.

Emerging approaches show promise in improving solution time and robustness for hypersonic simulations. High-order, low-dissipation numerical methods can enhance accuracy while reducing computational costs. Adaptive Mesh Refinement (AMR) focuses computational resources on critical areas, improving efficiency. Implicit shock tracking techniques can handle complex shock interactions more effectively. Additionally, leveraging efficient computing architectures such as graphics processing units (GPUs) can significantly reduce computation time, making high-fidelity simulations more practical for hypersonic vehicle design.

Integrating data-driven methods like Artificial Intelligence (AI), Machine Learning (ML), and neural networks can significantly enhance stability analysis and system optimization. However, the training costs for these models are prohibitive. Developing automated fast CFD solvers can enable the rapid training of ML models for reduced-order modeling. This integration can facilitate BLT analyses earlier in the design cycle within a Multi-Disciplinary Analysis and Optimization (MDAO) framework, enhancing the overall efficiency and effectiveness of hypersonic weapon development. Automating grid-generation and solver parameter selection is crucial to reducing the sensitivity of predictions to user expertise and shortening design cycles, while ensuring the tools can run efficiently on both existing and emerging high-performance computing architectures (Central Processing Unit [CPU]/GPU).

This SBIR topic aims to implement fully automated fast CFD solvers. The target requirements are:

• Order of Magnitude Improvement: Achieve at least 10X improvement in solver efficiency and time to solution on heterogeneous computing platforms, ensuring platform-independent performance gains.

• Complex Configuration Simulation: Ability to simulate realistic, complex hypersonic vehicle configurations along a flight trajectory, including the effects of surface roughness, thermo-chemical nonequilibrium, steps, gaps, wall temperature distribution, and other relevant physical phenomena.

• Automated Integration: Provide automated solver interface BLT prediction tools and MDAO frameworks.

• Pre- and Post-Processing Automation: Automate pre-processing (solver parameters setup and grid generation) and post-processing tasks to minimize user intervention and expertise requirements.

The objective is to achieve operational readiness and integration into existing design and analysis workflows.

Preference will be given to approaches that do not require large HPC systems and can run on affordable GPU hardware.

PHASE I: Develop a prototype CFD solver for automated grid-generation and grid-adaptation for hypersonic laminar flows. Demonstrate this approach on canonical problems, including both sharp and blunt leading edges, using existing experimental data. Showcase the accuracy and computational cost of the proposed automated method for a 3D problem. Highlight a path forward for platform-independent computation on existing and emerging high-performance computing architectures (CPU/GPU).

PHASE II: Implement a fully integrated automated simulation approach for computing hypersonic base flows for transition prediction. Key requirements include the ability to automatically track shocks, employ low-dissipation numerics, adaptively mesh to track relevant flow features, and include reacting flow and ablation capabilities. Ensure efficient utilization of computational resources on both existing and emerging high-performance computing architectures. The solver should compute hypersonic flow fields with minimal user interaction and be operable by non-expert users through an effective user interface. Demonstrate the solver technology on realistic, non-canonical hypersonic flow scenarios, including non-equilibrium effects, steps and gaps and efficient ablation simulation. Preference will be given to approaches that do not require large HPC systems and can run on affordable GPU hardware.

PHASE III DUAL USE APPLICATIONS: Transition the developed solver technology to practical applications within the Department of Defense (DoD) and commercial sectors. Perform extensive validation and optimization of the solver for a broad range of hypersonic vehicle configurations and flight conditions. Achieve operational readiness and integration into existing design and analysis workflows. Collaborate with industry partners and DoD agencies to ensure the solver meets the required standards for deployment. Additionally, develop comprehensive training programs and documentation to facilitate widespread adoption and use by non-expert users.

REFERENCES:

1. Schneider, S. P. "Hypersonic Laminar-Turbulent Transition on Circular Cones and Scramjet Forebodies." Progress in Aerospace Sciences, 40(1-2), February 2004, pp. 150. https://www.sciencedirect.com/science/article/abs/pii/S037604210300112X

2. Hader, C. and Fasel, H. F. "Towards simulating natural transition in hypersonic boundary layers via random inflow disturbances." Journal of Fluid Mechanics 847 (R3), 29 May 2018. https://www.cambridge.org/core/journals/journal-of-fluid-mechanics/article/abs/towards-simulating-natural-transition-in-hypersonic-boundary-layers-via-random-inflow-disturbances/72D9DE83B10F69929F4E171E78E07F36

3. Paredes, P.;V Theofilis, V.; Rodriguez, D. and Tendero, J. A. "The PSE-3D instability analysis methodology for flows depending strongly on two and weakly on the third spatial dimension." AIAA Paper 2011-3752, 6th AIAA Theoretical Fluid Mechanics Conference, 27-30 June 2011, Honolulu, Hawaii. https://arc.aiaa.org/doi/10.2514/6.2011-3752

4. Nichols, J.W. and Candler, G. V. "Input-Output analysis of complex hypersonic boundary layers." AIAA Paper 2019-1383. AIAA Scitech 2019 Forum, San Diego, California, 7-11 January 2019. https://arc.aiaa.org/doi/10.2514/6.2019-1383

5. Kamal, O.; Rigas, G.; Lakebrink, M. and Colonius, T. "Application of the One-Way Navier-Stokes (OWNS) equations to hypersonic boundary layers." AIAA Paper 2020-2986, AIAA Aviation 2020 Forum, June 15-19, 2020. https://arc.aiaa.org/doi/abs/10.2514/6.2020-2986

6. Browne, O. M. F.; Haas, A. P.; Fasel, H. F. and Brehm, C. "An efficient linear wavepacket tracking method for hypersonic boundary-layer stability prediction." Journal of Computational Physics, 380, 2019, pp. 243-268. https://www.sciencedirect.com/science/article/abs/pii/S0021999118307721

7. Peck, M. M.; Groot, K. J. and Reed, H. L. "Boundary-layer instabilty on a highly swept fin on a cone at Mach 6." Journal of Fluid Mechanics 987(A13), 2024. doi:10.1017/jfm.2024.299 https://www.cambridge.org/core/journals/journal-of-fluid-mechanics/article/boundarylayer-instability-on-a-highly-swept-fin-on-a-cone-at-mach-6/C8ADFCB8F17679611163269B7DB20CB7

8. Johnston, Z. M. and Candler, G. V. "Hypersonic boundary layer transition of the BoLT-2 flowfield at flight conditions." AIAA Paper 2023-84, Eleventh International Conference on Computational Fluid Dynamics (ICCFD11), Maui, Hawaii, USA, July 11-15, 2022.

https://www.iccfd.org/iccfd11/assets/pdf/papers/ICCFD11_Paper-2701.pdf

9. Dong, S.; Yu, M.; Tong, F.; Wang, Q. and Yuan, X. "Hypersonic turbulent boundary layer over the windward side of a lifting body." Journal of Fluid Mechanics 2024; 988:A29. doi:10.1017/jfm.2024.434

KEYWORDS: Hypersonic Flows, Boundary Layer Transition (BLT), Computational Fluid Dynamics, CFD Solver, Thermo-Chemical Nonequilibrium, Automated Grid Generation, Adaptive Mesh Refinement (AMR), High-Performance Computing (HPC), Laminar Base Flows, Ablation, Multi-Disciplinary Analysis and Optimization (MDAO)

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The DoD issued its Navy 25.1 SBIR Topics pre-release on December 4, 2024 which opens to receive proposals on January 8, 2025, and closes February 5, 2025 (12:00pm ET).

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Topic Q & A

1/9/25  Q. 1. Reduction of User Expertise Dependency
  • Reducing user expertise dependency is a key goal. Should the process be fully automated, or should we focus on optimizing specific steps or prioritizing the most computationally intensive aspects?
2. Scalability of Adaptive Mesh Refinement (AMR)
  • AMR is highly effective for localized computations, but has it been validated for full vehicle systems with complex geometries? Will access to References 4 and 5 or similar literature be provided at the beginning of the contract? Are there benchmarks or strategies recommended for addressing scalability challenges?
3. Integration of Nonlinear Effects
  • While robust for linear stability analysis, how effectively can the methodology address scenarios dominated by nonlinear effects? Would extensions of AMR through targeted refinement algorithms or coupling with nonlinear solvers be recommended? Are there ongoing efforts in this direction?
4. Thermo-Chemical Non-Equilibrium
  • Simulating flows under thermo-chemical non-equilibrium is inherently complex. What assumptions or approximations are currently used to balance accuracy and computational cost? Does ONR have predefined tolerances for these trade-offs? Would incorporating machine learning models for rapid estimation of equilibrium states be a viable enhancement?
5. Transition Prediction Limitations
  • Transition processes from laminar to turbulent flow are challenging. What levels of accuracy and precision are required, and does the current methodology meet these standards? Would hybrid turbulence models or machine learning-based rapid estimation techniques be a suitable direction for improvement?
6. Wave Packet Approach
  • The wave packet tracking methodology is intriguing. How does it address interactions between multiple wave packets or external perturbations? Would validating these aspects against experimental or high-fidelity numerical data be beneficial? Are there benchmarks or plans to expand validation efforts?
7. Mesh Refinement Limitations
  • AMR can be resource-intensive. Are there predefined thresholds for memory or processing power that dictate its applicability to large domains or rapidly transitioning flows? Would adaptive coarsening techniques be beneficial?
8. Boundary Condition Challenges
  • The overset mesh strategy relies heavily on accurate boundary conditions. How are these conditions derived and validated, particularly in high-complexity scenarios? Would integrating data-driven boundary condition models be worth exploring?
9. Predefined Marching Directions
  • Predefined marching directions can be limiting if wave propagation deviates. Has the AMR-WPT methodology been tested under such conditions? Are there validation results available?
10. Transition from Laminar to Turbulent Flow
  • Predicting turbulent boundary layers remains a significant hurdle. Are specific computational challenges being prioritized for this extension? Would modular frameworks for seamless integration of turbulence models be recommended?
   A. 1. Reduction of User Expertise Dependency
  • The Navy is seeking a fully automated process, including grid generation, refinement, and solver parameter selection (e.g., time step, CFL ramp), to maximize robustness and efficiency.
2. Scalability of Adaptive Mesh Refinement (AMR)
  • AMR is an emerging technology for full-scale hypersonic vehicle simulation and requires additional research to fully meet the objectives of this SBIR topic.
  • References 4 and 5 are available from AIAA.
  • There are no recommended benchmarks or strategies for addressing scalability challenges. Offerors are expected to develop and implement effective strategies to tackle scalability issues, including defining benchmarks and metrics to evaluate the performance of their AMR approach.
3. Integration of Nonlinear Effects
  • The topic focuses on developing an automated and efficient reacting compressible Navier-Stokes solver suitable for high-quality base flow simulations, including thermo-chemical nonequilibrium. Addressing nonlinear effects is a necessary component of this effort.
  • AMR appears to be a viable approach to meet the topic objectives, but the Navy is not endorsing any specific solution to the problem.
  • The Navy is not aware of any ongoing efforts in this area. Offerors are expected to develop and propose their own approaches to integrating nonlinear effects into their methodology.
4. Thermo-Chemical Non-Equilibrium
  • The Navy encourages performers to review current literature to assess the state of the art (SOA). The current SOA for hypersonic CFD includes the use of two-temperature models to account for thermal non-equilibrium.
  • The Navy is interested in innovative strategies to enhance the efficiency of reacting flow simulations while maintaining accuracy and robustness for high-quality base flow predictions.
5. Transition Prediction Limitations
  • The topic focuses on automated solvers capable of providing the basic state for boundary layer stability analysis (e.g., LST, PSE, forced DNS and AMR-WPT). The development of turbulence models is outside the scope of this SBIR topic.
6. Wave Packet Approach
  • The Wave Packet approach (AMR-WPT) requires a base flow (basic state). This topic focuses on the automated and efficient generation of base flows, not on the improvement of AMR-WPT itself.
  • This topic is not focused on developing analysis tools for transition prediction but rather on automated solvers capable of providing the basic state for boundary layer stability analysis.
  • Validation of the basic state can leverage existing experimental data from the literature, such as BOLT and fin-cone geometries (see relevant references).
7. Mesh Refinement Limitations
  • The Navy does not have predefined thresholds for memory or processing power beyond the requirements stated in the topic. In Phase I: “Highlight a path forward for platform-independent computation on existing and emerging high-performance computing architectures (CPU/GPU).” In Phase II: “Preference will be given to approaches that do not require large HPC systems and can run on affordable GPU hardware.”
8. Boundary Condition Challenges
  • The Navy does not prescribe specific validation approaches or methodologies for boundary condition implementation.
9. Predefined Marching Directions
  • This question is not relevant to the topic, as the focus is on the automated generation of base flows for boundary layer stability analysis, not on the detailed testing or validation of AMR-WPT under predefined marching direction conditions.
10. Transition from Laminar to Turbulent Flow
  • The development of turbulence models is outside the scope of the current topic. Please refer to the topic objective. However, ensuring the solver is compatible with modular frameworks that could support such extensions in future applications may be considered beneficial, provided it does not detract from the primary objectives.


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