Quantification of Uncertainty in Structural Dynamic Models

Hasselman, T. K.

doi:10.1061/(asce)0893-1321(2001)14:4(158)

Cited by 35 publications

(11 citation statements)

References 13 publications

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“…There has been substantial work in the literature on uncertainty quantification and propagation (UQ, UP), but little on the reverse propagation that is needed for establishment of the desired metric. Hasselman has published a great deal in this area, including papers on structural dynamics model accuracy [2] and on UQ, focusing on quantifying and propagating structural dynamic model uncertainty using modal test [3], [4]. His UQ papers describe a rigorous methodology using the system frequency response functions as well as mass and stiffness matrices, and the use of a proprietary software code for implementation.…”

Section: Literature Surveymentioning

confidence: 99%

Quantification of Dynamic Model Validation Metrics Using Uncertainty Propagation from Requirements

Brown

Peck

Stewart

2018

Model Validation and Uncertainty Quantification, Volume 3

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The Space Launch System, NASA's new large launch vehicle for long range space exploration, is presently in the final design and construction phases, with the first launch scheduled for 2019. A dynamic model of the system has been created and is critical for calculation of interface loads and natural frequencies and mode shapes for guidance, navigation, and control (GNC). Because of the program and schedule constraints, a single modal test of the SLS will be performed while bolted down to the Mobile Launch Pad just before the first launch. A Monte Carlo and optimization scheme will be performed to create thousands of possible models based on given dispersions in model properties and to determine which model best fits the natural frequencies and mode shapes from modal test. However, the question still remains as to whether this model is acceptable for the loads and GNC requirements. An uncertainty propagation and quantification (UP and UQ) technique to develop a quantitative set of validation metrics that is based on the flight requirements has therefore been developed and is discussed in this paper. There has been considerable research on UQ and UP and validation in the literature, but very little on propagating the uncertainties from requirements, so most validation metrics are "rules-of-thumb;" this research seeks to come up with more reason-based metrics. One of the main assumptions used to achieve this task is that the uncertainty in the modeling of the fixed boundary condition is accurate, so therefore that same uncertainty can be used in propagating the fixedtest configuration to the free-free actual configuration. The second main technique applied here is the usage of the limit-state formulation to quantify the final probabilistic parameters and to compare them with the requirements. These techniques are explored with a simple lumped spring-mass system and a simplified SLS model. When completed, it is anticipated that this requirements-based validation metric will provide a quantified confidence and probability of success for the final SLS dynamics model, which will be critical for a successful launch program, and can be applied in the many other industries where an accurate dynamic model is required.

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Section: Literature Surveymentioning

confidence: 99%

Quantification of Dynamic Model Validation Metrics Using Uncertainty Propagation from Requirements

Brown

Peck

Stewart

2018

Model Validation and Uncertainty Quantification, Volume 3

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“…Various forms of uncertainties arise which depend on the nature of geological formation or construction method, the site investigation, the type and the accuracy of design calculations etc. In recent years there has been considerable interest amongst engineers and researchers in the issues related to quantification of uncertainty as it affects safety, design as well as the cost of projects [1]- [4].…”

Section: Introductionmentioning

confidence: 99%

Elastic Stress Predictor for Stochastic Finite Element Problems

Drakos¹

2015

WJM

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The paper presents a new algorithm of elastic stress predictor in non linear stochastic finite element method using the Generalized Polynomial Chaos. The statistical moments of strains calculated based on the displacement Polynomial Chaos expansion. To descretise the stochastic process of material the Karhunen-Loeve Expansion was used and it is presented. Using the strains and the material Karhunen-Loeve Expansion the stress components are calculated. A numerical example of shallow foundation was carried out and the results of stress and strain of the new algorithm were compared with those raised from Monte Carlo method which is treated as the exact solution. A great accuracy was presented.

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“…They then outlined a procedure for including the uncertainty in the frequency response analysis, employing metrics based on the correlation of modal analysis and test data. This approach was covered in detail in a more recent work by Hasselman [167].…”

Section: Principal Components Analysismentioning

confidence: 99%

Mid-Frequency Dynamics of Complex Structural Systems: Assessing the State of the Art and Defining Future Research Directions

Pierre¹,

Castanier²

2002

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searchi AUTHOR(S)PROFESSOR CHRISTOPHER PIERRE Approved for public rsleas3 ; distribution unlinätsd, PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)8 ABSTRACT (Maximum 200 words)One of the last frontiers of structural dynamics is mid-frequency vibration analysis of complex structures. In the low-frequency range, finite element analysis (FEA) is well established as the standard method, However, as the frequency of vibration increases, the cost of FEA becomes prohibitive due to the necessary refinement of the finite element mesh to capture the shorter wavelength of vibration. Furthermore, the system response becomes sensitive to small parameter variations at higher frequencies, which means that a statistical analysis should be employed to make confident response predictions. In the high-frequency range, statistical energy analysis (SEA) is popular. However, SEA provides only averaged response predictions and cannot capture the resonant behavior in the response that becomes evident as frequency decreases. ABSTRACTOne of the last frontiers of structural dynamics is mid-frequency vibration analysis of complex structures. In the low-frequency range, finite element analysis (FEA) is well established as the standard method. However, as the frequency of vibration increases, the cost of FEA becomes prohibitive due to the necessary refinement of the finite element mesh to capture the shorter wavelength of vibration. Furthermore, the system response becomes sensitive to small parameter variations at higher frequencies, which means that a statistical analysis should be employed to make confident response predictions. In the high-frequency range, statistical energy analysis (SEA) is popular. However, SEA provides only averaged response predictions and cannot capture the resonant behavior in the response that becomes evident as frequency decreases. Thus, there exists a midfrequency range in which there is no established analysis technique analogous to FEA or SEA. The goal of this research is to produce a review paper on mid-frequency vibration analysis that will provide a survey of the relevant literature, identify the key technical challenges, formulate an assessment of the state of the art, and propose directions for future research. OBJECTIVESThe main objectives of this research are as follows: 1. Perform a comprehensive literature search on mid-frequency vibration analysis and related topics, and collect an extensive set of references 2. Review the relevant literature and summarize the mid-frequency approaches and techniques that have been developed to date 3. Assess the state of the art by determining the major research challenges in the field and drawing conclusions on research progress to date, including applications to realistic complex structures 4. Based on these findings, propose general directions and specific topics for future research 5. Write a review paper that p...

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Quantification of Uncertainty in Structural Dynamic Models

Cited by 35 publications

References 13 publications

Quantification of Dynamic Model Validation Metrics Using Uncertainty Propagation from Requirements

Quantification of Dynamic Model Validation Metrics Using Uncertainty Propagation from Requirements

Elastic Stress Predictor for Stochastic Finite Element Problems

Mid-Frequency Dynamics of Complex Structural Systems: Assessing the State of the Art and Defining Future Research Directions

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