PROJECTED CMMC LEVEL REQUIREMENT
Level 2 (Self)
TECHNOLOGY AREAS
None
MODERNIZATION PRIORITIES
Advanced Computing and Software
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Hypersonics
|
Trusted AI and Autonomy
KEYWORDS
Hypersonics; Multidisciplinary Design, Analysis, and Optimization; MDAO; Computational Fluid Dynamics; Artificial Intelligence / Machine Learning; AI/ML; Software Tools; Aerodynamics
OBJECTIVE
Develop and demonstrate an integrated multidisciplinary design, analysis, and optimization (MDAO) framework for hypersonic boost-glide weapons that enables concurrent optimization of vehicle geometry, mission trajectory, and control strategy by leveraging existing modeling tools, incorporating reduced-order models, applying artificial intelligence and machine learning (AI/ML) to accelerate design and reduce computational cost, and providing early insights into system cost estimation, manufacturability, and technology development roadmaps.
ITAR
The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with section 3.5 of the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws.
DESCRIPTION
The Department of the Navy (DON) requires advanced simulation and optimization capabilities to accelerate the conceptual design and mission planning of hypersonic boost-glide weapons. These systems must deliver long-range strike capabilities, survive extreme thermal and structural environments, and maintain maneuverability for terminal effectiveness against defended targets. Designing such vehicles is highly complex due to the strong coupling between aerodynamic heating, structural loading, control authority, system mass, and mission trajectory.
Conventional design approaches treat these disciplines in isolation and in sequence, often resulting in suboptimal performance, prolonged development timelines, and increased costs. MDAO methods offer a more integrated approach, enabling concurrent consideration of key factors and improved trade space exploration. However, coupling high-fidelity models across multiple domains creates significant computational challenges. Practical MDAO frameworks must incorporate reduced-order models, surrogate approximations, and robust optimization techniques that balance computational efficiency and modeling accuracy [Refs 1, 3, 4].
This SBIR topic seeks innovative tools and methods that support an integrated MDAO framework for the design and optimization of hypersonic boost-glide weapons. Solutions should enable concurrent optimization of vehicle geometry, mission trajectory, and control strategies while accounting for launch platform constraints such as volume, mass, interface requirements, and environmental loads. The framework should also address internal system considerations such as payload integration, guidance and control subsystems, and thermal protection. The capability should support conceptual-level design and deliver outputs that inform system cost estimation, manufacturability, technology development roadmaps, and risk reduction strategies.
Proposals should demonstrate capabilities in the following areas:
Aerodynamic and trimmed flight analysis to predict forces and moments over a broad range of Mach numbers, including control surface deflection effects and geometric deformation. Integration with existing computational fluid dynamics tools is encouraged.
Aerothermal modeling to estimate heating loads and surface temperatures, including convective and radiative heat transfer and thermal protection system behavior.
Structural analysis to evaluate stresses, strains, and deformation under combined aerodynamic and thermal loads, with support for high-temperature materials and composite structures.
Mass properties and internal system layout to optimize placement of payloads, sensors, power systems, and thermal subsystems while maintaining center-of-gravity control and packaging feasibility.
Trajectory and control optimization to evaluate and enhance flight performance while meeting constraints on range, maneuverability, survivability, and terminal accuracy.
System-level integration into an existing or proposed MDAO architecture such as ADAPT [Ref 2] orMDAO, with support for geometry parameterization, solver coupling, multi-objective optimization, and design variable management.
Uncertainty quantification and robust optimization to evaluate sensitivity to variations in input parameters, models, or environmental conditions, and to ensure resilient design outcomes.
AI/ML methods to accelerate convergence, construct reduced-order models, support adaptive sampling, and enable data-driven design exploration. Solutions may also include optimizing the objective function itself by learning weighting factors for multi-objective problems, generating surrogate models for expensive simulations, discovering improved formulations via symbolic regression, or adaptively refining the objective function as new information becomes available.
Proposed solutions should leverage existing tools, frameworks, and prior government investments wherever feasible. The resulting toolset should support traceability between design inputs and mission-level measures of effectiveness, helping guide early-phase trade studies and enabling faster transition to detailed design and development.
PHASE I
Develop a prototype MDAO framework for hypersonic boost-glide weapons. Integrate key modules and demonstrate coupling with existing architectures such as ADAPT orMDAO. Apply the framework to optimize a representative boost-glide vehicle, capturing control surface deflection effects and geometric deformations over a notional trajectory. Evaluate computational efficiency, model fidelity, and extensibility. Prepare a Phase II plan.
PHASE II
Develop a fully integrated MDAO framework that enables co-design of vehicle geometry, trajectory, and control strategies for hypersonic boost-glide weapons. Incorporate launch platform constraints and model in-flight geometric deformations, control surface deflections, and effects such as ablation. Demonstrate manufacturability and cost-informed design on a non-canonical configuration. Leverage AI/ML to accelerate optimization, support surrogate modeling, and enable adaptive, data-driven design exploration. Validate the framework on realistic scenarios and implement workflow automation to support repeatable, efficient design cycles.
PHASE III DUAL USE APPLICATIONS
Transition the MDAO framework and supporting modules to practical applications within the Department of War and commercial aerospace sectors. Conduct extensive validation and optimization across a broad range of hypersonic vehicle configurations and flight conditions. Support integration into existing design and analysis workflows to enable use within operational design environments. Collaborate with industry and DOW stakeholders to ensure compliance with deployment standards. Develop comprehensive training materials, user documentation, and technical support resources to enable adoption by both expert and non-expert users.
REFERENCES
Coulter, B.G., Huang, D. and Wang, Z. "Geometric design of hypersonic vehicles for optimal mission performance with high-fidelity aerodynamic models." Journal of Aircraft, 60(3), 2023, pp.870-882. https://arc.aiaa.org/doi/10.2514/1.C036980
Field, A. J., Lickenbrock, M., McGough, W. and Syfrett, P. "An Overview of HPCMP CREATE-AVTM ADAPT." AIAA SCITECH 2022 Forum, p. 1317. https://arc.aiaa.org/doi/abs/10.2514/6.2022-1317
Tsuchiya, T., Takenaka, Y. and Taguchi, H. "Multidisciplinary design optimization for hypersonic experimental vehicle." AIAA journal, 45(7), 2007, pp.1655-1662. https://arc.aiaa.org/doi/10.2514/1.26668
Lock, A., Oberman, G., Jahn, I. H., van der Heide, C., Bone, V., Dower, P. M. and Manzie, C. "Hypersonic glide vehicle shape and trajectory co-design." AIAA SCITECH 2025 Forum, p. 1337. https://arc.aiaa.org/doi/10.2514/6.2025-1337
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