Adaptive Aerospace Systems

Pushing the Boundaries with High-Fidelity Computational Optimization

Morphorm - April 30, 2025

Morphorm Advances Morphing Aero-Structure Design

Morphorm, in partnership with the Air Force Research Laboratory (AFRL), is advancing a new frontier in computational design for morphing aerostructures. This collaboration centers on the development of a GPU-accelerated multiphysics optimization platform for the design of self-actuating, finite-thickness surfaces capable of achieving seamless shape transitions under complex thermomechanical loading conditions.

Traditional morphing mechanisms rely on discrete hinges and bulky actuators, incurring significant Size, Weight, and Power (SWaP) penalties. These limitations are particularly acute in high-speed regimes, where extreme thermal gradients and aerodynamic loads fundamentally alter structural behavior. To overcome these barriers, Morphorm’s platform integrates large-deformation mechanics, heat transfer, and embedded actuation into a unified design optimization framework.

Technical Highlights

Co-Design of Internal Architecture and Actuation Strategy

By coupling structural topology optimization with embedded actuator placement, Morphorm’s approach enables intelligent material and architecture distributions that drive large, controllable shape changes while maintaining structural integrity.

Integrated Multiphysics Objectives and Constraints

The platform features objective functions that jointly consider shape fidelity across multiple configurations, compliance under thermomechanical loading, and global system-level performance metrics. These are coupled with fabrication-aware constraints to ensure manufacturability and robustness.

Finite Deformation Optimization Formulations

To model the large, nonlinear deformations required for effective morphing, the system incorporates advanced variational formulations and sensitivity analysis tools, enabling accurate prediction and efficient gradient-based optimization.

A key innovation lies in the exploration of design bifurcations – qualitative shifts in topology and mechanism behavior triggered by changes in performance targets or constraints. This enables deeper insight into actuation strategies, energy budgets, and the trade space of morphing efficacy versus system complexity.

Software and Deployment

The computational framework is being developed with a focus on GPU acceleration, enabling scalable, high-resolution simulations of coupled thermal, structural, and actuation phenomena. The project deliverables include a robust software package, detailed documentation, and a curated dataset to support future research and development.

This effort strengthens Morphorm’s leadership in adaptive structures and design automation, positioning its technology to support next-generation hypersonic systems and mission-adaptive airframes.

Figure 1. Computational domain for the multi-level topology optimization study. Displacement boundary conditions are applied as follows: all translational degrees of freedom (X, Y, Z) are fixed on the green surface at Y = 2.5, as well as along the pink edge. The red surface at X = 0.0 has fixed X displacements only. Actuating forces (N) are applied at nodes on the yellow and blue surfaces, with their magnitudes determined via optimization. Nodes on the yellow and blue surfaces, along with those on the bottom surface, are designated as non-optimizable - material addition or removal is not permitted at these locations.

Example Problem: Multi-Level Topology Optimization of Morphing Structures

As part of the AFRL collaboration, Morphorm developed and demonstrated a cutting-edge multi-level topology optimization framework to co-design internal material architecture and embedded actuation strategies for morphing aero-structures.

This example problem focuses on designing a lightweight, finite-thickness structure that morphs to a desired shape using distributed actuation. The challenge lies in simultaneously determining the optimal material layout and the actuation forces required to achieve the target deformation, while balancing competing design criteria: mass, stiffness, and shape-matching accuracy.

The model domain, shown in Figure 1, is defined with fixed displacement boundary conditions along specific surfaces and edges, while actuating forces are applied at designated non-optimizable surfaces. The material is modeled as linear elastic with realistic mechanical properties.

Optimization Formulation

The optimization problem is framed as a multi-level design task:

Outer-level optimization uses Bayesian optimization to efficiently search the high-dimensional space of actuation force parameters. This data-efficient strategy intelligently balances exploration and exploitation, enabling rapid convergence toward promising design regions.
Inner-level optimization employs gradient-based topology optimization to compute the optimal material layout for each candidate actuation input. This fast, high-resolution method ensures precise structural adaptation under actuation.

This unique combination of global Bayesian optimization with local gradient-based topology optimization enables the discovery of non-intuitive, high-performing designs that would be extremely difficult to identify using traditional methods alone.

Objectives include minimizing deformation misfit, reducing structural mass, and satisfying stiffness constraints. To ensure physically meaningful solutions, the formulation includes volume budgets, compliance constraints, and manufacturing-aware minimum feature sizes.

Figure 2. Displacement field overlaid on the optimized material layout obtained from the multi-level optimization study. The plot illustrates the deformation response of the structure under the applied boundary conditions and optimized loading configuration. The visualized displacement field highlights regions of high and low deformation, providing insight into the mechanical performance of the optimized topology. — Figure 2. *Displacement field overlaid on the optimized material layout obtained from the multi-level optimization study.* The plot illustrates the deformation response of the structure under the applied boundary conditions and optimized loading configuration. The visualized displacement field highlights regions of high and low deformation, providing insight into the mechanical performance of the optimized topology.

Results and Insights

An initial design exploration evaluated 35 candidate designs. The best-performing solution achieved a 70% mass reduction and minimized the shape mismatch to a normalized error of 1.29%, requiring actuation forces of 17.4 kN and 7.9 kN, see Figure 2. In contrast, the worst solution needed significantly higher forces and resulted in greater error.

A follow-on study further refined the material layout using the best actuation inputs. This led to a 20% improvement in shape-matching accuracy, reducing the deformation error to 1.02%, while maintaining the same mass reduction target.

This example demonstrates how Morphorm’s hybrid optimization approach delivers breakthrough solutions - combining the global search power of Bayesian methods with the precision of gradient-based topology optimization. The result is an efficient, intelligent design framework ideally suited for next-generation adaptive aerospace structures.

Looking Ahead

This AFRL collaboration marks a critical step in Morphorm’s mission to redefine digital engineering with real-time, simulation-driven design tools. By expanding the boundaries of computational design under real-world operational constraints, Morphorm continues to deliver transformative technologies for aerospace and defense applications.

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