Optimal Parameter Identification for Model Correlation Using Model Reduction Methods

Phoenix, Austin A.; Bales, Dustin; Sarlo, Rodrigo; Pham, Thanh Son; Tarazaga, Pablo A.

doi:10.1007/978-3-319-30249-2_25

Cited by 5 publications

(8 citation statements)

References 9 publications

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“…The critical technical challenges to be addressed include morphing accuracy, power requirements, workspace quality, and thermal control accuracy requirements. These challenges associated with the thermal morphing anisogrid smart structure are to be addressed through a reduced optimization methodology (Phoenix et al., 2016, in press a) used to define minimum morphing performance. The intent of this investigation is to contribute an alternate morphing concept to address the smart structural challenges in future space missions.…”

Section: Introductionmentioning

confidence: 99%

“…This paper looks to develop methods that reduce the overall complexity of the model optimization while maintaining accurate behavior through identifying and allocating resources to the most critical input parameters. The model reduction methodology used herein was initially developed by the authors in Phoenix et al. (2016, in press a) to reduce the number of parameters used for a complex model correlation processes.…”

Section: Introductionmentioning

confidence: 99%

“…This is addressed by implementing a method to reduce the number of optimization parameters. The method chosen to reduce the optimization is a parameter ranking method using the model reduction method Q-DEIM (Sorensen and Embree, 2014) to generate a parameter ranking as detailed in Phoenix and Tarazaga (in press b) and Phoenix et al. (2016).…”

Section: Introductionmentioning

confidence: 99%

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

Phoenix

Borggaard

Tarazaga

2017

Journal of Vibration and Control

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Phoenix

Borggaard

Tarazaga

2017

Journal of Vibration and Control

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“…These methods provide insight into the parameters of importance. Work by the authors (Phoenix et al., 2016) has provided some basic understating on the possible use of newly developed reduction methods, such as the Discrete Empirical Interpolation Method (SVD-DEIM) and Q-DEIM, for parametric sensitivity analysis. These methods have shown significant promise for the identification of optimal input sets for other applications such as optimal sensor placement (Bales et al., 2016).…”

Section: Introductionmentioning

confidence: 99%

“…The methodology developed in this paper has been shown, via examples, to be computationally efficient in identifying and ranking the important parameters in complex correlation efforts (Phoenix et al., 2016). The approach was shown to remove low-impact and redundant parameters to generate an efficient reduced set for optimization (Phoenix et al., 2017).…”

Section: Introductionmentioning

confidence: 99%

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

Phoenix

Bales

Sarlo

et al. 2017

Journal of Vibration and Control

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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.

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Mach Five to Ten Morphing Waverider: Control Point Study

Phoenix

Rogers

Maxwell

et al. 2019

Journal of Aircraft

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Optimal Parameter Identification for Model Correlation Using Model Reduction Methods

Cited by 5 publications

References 9 publications

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

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

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

Mach Five to Ten Morphing Waverider: Control Point Study

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