Austin A. Phoenix

Thermal morphing anisogrid smart space structures: thermal isolation design and linearity evaluation

To meet the requirements for the next generation of space missions, a paradigm shift is required from current structures that are static, heavy and stiff, toward innovative structures that are adaptive, lightweight, versatile, and intelligent. A novel morphing structure, the thermally actuated anisogrid morphing boom, can be used to meet the design requirements by making the primary structure actively adapt to the on-orbit environment. The anisogrid structure is able to achieve high precision morphing control through the intelligent application of thermal gradients. This active primary structure improves structural and thermal stability performance, reduces mass, and enables new mission architectures. This effort attempts to address limits to the authors previous work by incorporating the impact of thermal coupling that was initially neglected. This paper introduces a thermally isolated version of the thermal morphing anisogrid structure in order to address the thermal losses between active members. To evaluate the isolation design the stiffness and thermal conductivity of these isolating interfaces need to be addressed. This paper investigates the performance of the thermal morphing system under a variety of structural and thermal isolation interface properties.

Thermal modeling and design of the anisogrid morphing structure for a modular optical telescope concept

Abstract. To meet the requirements for the next generation of optical space telescopes, a paradigm shift is required from current structures that are static, heavy, and stiff toward innovative structures that are adaptive, lightweight, versatile, and intelligent. A morphing or adaptive structure, the thermally actuated anisogrid morphing boom, can be used to meet the design requirements by making the primary structure actively adapt to the on-orbit environment. The adaptive anisogrid structure is actuated through the intelligent application of thermal gradients. This active primary structure improves structural and thermal stability performance, reduces mass, and enables mission architectures. This effort expands on the author’s previous work by incorporating the impact of thermal coupling and demonstrating an updated architecture. This paper introduces a thermally isolated version of the thermal morphing anisogrid structure to enable control of the thermal losses between active members. To evaluate the isolation design, the stiffness and thermal conductivity of these isolating interfaces is addressed. This paper determines that the applied morphing error remains below 5% across all stiffnesses if the joint thermal conductivity is below 0.2  W/(mK). This paper investigates the performance of the thermal morphing system under a variety of structural and thermal isolation interface properties and determines the linear operational regime.

Optimal Parameter Identification for Model Correlation Using Model Reduction Methods

Classically, to achieve correlation between a dynamic test and a Finite Element Model (FEM), an experienced engineer chooses a small subset of input parameters and uses a model updating technique or engineering judgment to update the parameters until the error between the FEM and the test article is acceptable. To reduce the intricacy and difficulty of model correlation, model reduction methods such as the Discrete Empirical Interpolation Method (DEIM), and dime are implemented to reduce the scale of the problem by reducing the number of FEM parameters to its most critical ones. These model reduction methods serve to identify the critical parameters required to develop an accurate model with reduced engineering effort and computational resources. The insight gained using these methods is critical to develop an optimal, reduced parameter set that provides high correlation with minimal iterative costs. This can be seen as a particular approach to sensitivity analysis in the model updating community. The parameter set rankings derived from each method are evaluated by correlating each parameter set on five simulated test geometries. The methodology presented highlights the most valuable parameters for correlation, enabling a straightforward and computationally efficient model correlation approach.

Thermal morphing anisogrid smart space structures: Part 1. Introduction, modeling, and performance of the novel smart structural application:

To meet the requirements for the next generation of space missions, a paradigm shift is required from current structures that are static, heavy, and stiff to innovative structures that are adaptive, lightweight, versatile, and intelligent. The largest benefit provided by this new structural concept is in the ability to deliver high precision position stability. The conventional high precision structural design uses two decoupled systems to achieve positional stability. First, a high mass structure delivers the effectively infinite stiffness and thermal stability so that no deformations occur under all operational loading conditions. Second, to meet the morphing and on-orbit positional requirements, supplementary mechanisms provide the nano, micro, and macro displacement control required. This paper proposes the use of a novel morphing structure, the thermally actuated anisogrid morphing boom, to meet the design requirements through actively morphing the primary structure in order to adapt to the on-orbit environment and meet both requirements in a consolidated structure. The proposed concept achieves the morphing control through the use of thermal strain to actuate the individual helical members in the anisogrid structure. Properly controlling the temperatures of multiple helical members can introduce six degree of freedom morphing control. This system couples the use of low coefficient of thermal expansion materials with precise thermal control to provide the high precision morphing capability. This concept has the potential to provide substantial mass reductions relative to current methods and meet the high precision displacement requirements of spacecraft systems. This paper will detail the concept itself, demonstrate the modeling procedure, and investigate the design space to quantify the potential of the thermally morphing anisogrid smart structure.

Improved model correlation through optimal parameter ranking using model reduction algorithms: Augmenting engineering judgment

As the complexity and scales of dynamic models increase, novel and efficient model correlation methodologies are vital to the development of accurate models. Classically, to correlate a Finite Element Model (FEM) such that it matches a dynamic test, an experienced engineer chooses a small subset of input parameters that are surmised to be crucial, sensitive and/or possibly erroneous. The operator will then use engineering judgment, or a model updating technique to update the selected subset of parameters until the error between the FEM and the test article is reduced to within a set bound. To reduce the intricacy and difficulty of model correlation, a methodology is proposed to provide a quantitative parameter importance ranking using a model reduction algorithm applied to a parameter sensitivity analysis. Four model reduction algorithms are studied in this effort, the Discrete Empirical Interpolation Method (SVD-DEIM), Q-DEIM, Projection Coefficient and finally Weighted Projection Coefficient. These model reduction methods identify and rank critical parameters, enabling the selection of a minimum set of critical correlation parameters. This reduced set of parameters results in reduced computational resources and engineering effort required to generate a correlated model. The insight gained using these methods is essential in developing an optimal, reduced parameter set that provides high correlation capability with minimal iterative costs. To evaluate the proposed parameter selection methodology, a representative set of academic and industry experts provided their engineering judgment for comparison with the methodology presented. A comprehensive investigation of the robustness of this methodology is performed on a simple cantilever beam for demonstration. The scale of the model has expressly been chosen to allow for all potential ranking variations to be evaluated so that these ranking methods can be understood relative to the true optimal ranking. The ranking robustness to incorrect engineering judgment, resulting in uncertainty in the assumed size of the design space and, therefore, the error bounds, is investigated. The methodology presented identifies the most useful parameters for correlation, enabling a straightforward and computationally efficient model correlation approach as compared with other methods. To quantify the ranking quality, a metric, the Correlation Norm Error, is developed. For the problem discussed, blind random assessments result in a Correlation Norm Error of 413.3. Engineering judgment has been shown to improve upon blind random assessments, reducing the Correlation Norm Error to 334.3. The best performing model reduction method, Q-DEIM using 10 FEM runs as the input, was able to identify the optimal ranking correctly, reducing the Correlation Norm Error to zero.

Thermal morphing anisogrid smart space structures part 2: Ranking of geometric parameter importance, trust region optimization, and performance evaluation

As future space mission structures are required to achieve more with scarcer resources, new structural configurations and modeling capabilities will be needed to meet the next generation space structural challenges. A paradigm shift is required away from the current structures that are static, heavy, and stiff, to innovative lightweight structures that meet requirements by intelligently adapting to the environment. As the complexity of these intelligent structures increases, the computational cost of the modeling and optimization efforts become increasingly demanding. Novel methods that identify and reduce the number of parameters to only those most critical considerably reduce these complex problems, allowing highly iterative evaluations and in-depth optimization efforts to be computationally feasible. This parameter ranking methodology will be demonstrated on the optimization of the thermal morphing anisogrid boom. The proposed novel morphing structure provides high precision morphing through the use of thermal strain as the sole actuation mechanism. The morphing concept uses the helical members in the anisogrid structure to provide complex constrained actuations that can achieve the six degree of freedom morphing capability. This structure provides a unique potential to develop an integrated structural morphing system, where the adaptive morphing capability is integrated directly into the primary structure. To identify parameters of interest, the Q-DEIM model reduction algorithm is implemented to rank the model parameters based on their impact on the morphing performance. This parameter ranking method provides insight into the system and enables the optimal allocation of computational and engineering resources to the most critical areas of the system for optimization. The methodology, in conjunction with a singular value decomposition (SVD), provides a ranking and identifies parameters of relative importance. The SVD is used to truncate the nine parameters problem at two locations, generating a five parameter optimization problem and a three parameter optimization problem. To evaluate the ranking, a parameter sweep in conjunction with a simple minimum cost function search algorithm will compare all 120 five parameter ranking orders to the Q-DEIM ranking. This reduced parameter set significantly reduces the parameter complexity and the computational cost of the model optimization. This paper will present the methodology to define the resulting performance of the optimal thermal morphing anisogrid structure, minimum morphing control, and the systems frequency response capability as a function of available power.