Inverse Simulation Under Uncertainty by Optimization

Du, Xiaoping

doi:10.1115/1.4023859

Cited by 13 publications

(4 citation statements)

References 27 publications

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“…A simulation collision model 43 of two rigid bodies is shown in Figure 8. In the model, the velocities before the collision

v_{A 0}

and

v_{B 0}

are required to be identified, where

v_{A 0}

is the horizontal velocity of the ball

A

toward the right, and

v_{B 0}

is the velocity of the slider

B

in the downward direction along the inclined plane as shown in Figure 4.…”

Section: Numerical Examples and Discussionmentioning

confidence: 99%

Novel solution framework for inverse problem considering interval uncertainty

Tang

et al. 2021

Numerical Meth Engineering

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This article proposes a novel solution framework for the inverse problem considering interval uncertainty in structural or systemic responses, which provides an efficient tool for the uncertain inverse problems of nonlinear structures or systems. Interval is used to model and characterize the uncertainty, and the bounds of uncertain structural responses are only required. In each iterative step, the approximate deterministic inverse problem is constructed according to the interval analysis results, and then the identified intervals of inputs are updated by solving the approximate deterministic inverse problem. Therefore, the interval inverse problem is decoupled into a series of interval analyses and deterministic inverse problems that are alternately solved, which dramatically promotes the computational efficiency of the interval inverse problem. Besides, an iterative mechanism is proposed to ensure the convergence of the whole procedure. Finally, two numerical examples and an engineering application are investigated to demonstrate the accuracy and efficiency of the proposed method.

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“…A simulation collision model 43 of two rigid bodies is shown in Figure 8. In the model, the velocities before the collision

v_{A 0}

and

v_{B 0}

are required to be identified, where

v_{A 0}

is the horizontal velocity of the ball

A

toward the right, and

v_{B 0}

is the velocity of the slider

B

in the downward direction along the inclined plane as shown in Figure 4.…”

Section: Numerical Examples and Discussionmentioning

confidence: 99%

Novel solution framework for inverse problem considering interval uncertainty

Tang

et al. 2021

Numerical Meth Engineering

View full text Add to dashboard Cite

show abstract

“…Generally, it is necessary to conduct the uncertain sampling for modeling parameters, and then estimate the influence of the uncertainties in on the identified parameters according to the deterministic inverse results under each sampling point. Du [20] estimated the probability distributions of the unknown input variables by using the first order second moment reliability analysis method and the inverse simulation computation. Liu et al [21] employed the point estimation method to transform the uncertain inverse problem into the deterministic inverse problem, and thus effectively evaluating the statistical moments of unknown parameters.…”

Section: Introductionmentioning

confidence: 99%

An efficient inverse method for structural identification considering modeling and response uncertainties

Cao

Liu

et al. 2021

Preprint

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The inverse problem analysis method provides an effective way for the structural parameter identification. Due to the coupling of multi-source uncertainties in the measured responses and the modeling parameters, the inverse of unknown structural parameter will face the challenges in the solving mechanism and the computational cost. In this paper, an uncertain inverse method based on convex model and dimension reduction decomposition is proposed to realize the interval identification of unknown structural parameter according to the uncertain measured responses and modeling parameters. Firstly, the polygonal convex set model is established to quantify the uncertainties of modeling parameters. Afterwards, a space collocation method based on dimension reduction decomposition is proposed to transform the inverse problem considering multi-source uncertainties into a few interval inverse problems considering response uncertainty. The transformed interval inverse problem involves the two-layer solving process including interval propagation and optimization updating. In order to solve the interval inverse problems considering response uncertainty, an efficient interval inverse method based on the high dimensional model representation and affine algorithm is further developed. Through the coupling of the above two methods, the proposed uncertain inverse method avoids the time-consuming multi-layer nested calculation procedure, and then effectively realize the inverse uncertainty quantification of unknown structural parameters. Finally, two engineering examples are provided to verify the effectiveness of the proposed uncertain inverse method.

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“…The problem can be tackled by the help of uncertainty analysis, through which the interval of a reconstructed result can be calculated according to those input interval traces. 3,4 Though there are many methods for analyzing uncertainty of accident reconstruction results, [5][6][7][8] many methods cannot be used in analyzing uncertainty of simulation results because of the accident reconstruction model in a simulation is implicit or too complicated. Under such condition, a methodology named response surface methodology (RSM) was proposed, cases showed that the methodology can work well under any condition and results are reasonable.…”

Section: Introductionmentioning

confidence: 99%

Design of experiment in analyzing uncertainty of simulation results in accident reconstruction

Zou

Hua

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part D:

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In order to obtain a more accurate interval of an accident reconstruction result, two modified experiment design methods were proposed. The first is based on the orthogonal design (OD) and it is named as the orthogonal-based design (OBD), while the other is based on the uniform design (UD) and it is named as the multi-response surface-uniform design (MUD). Furthermore, some conclusions are given according to seven numerical cases: results obtained from the Latin hypercube sampling design (LHS) are the worst, while those obtained from the UD are the last but one, and all are worse than the results obtained from the OBD and the MUD. Furthermore, some conclusions about the number of simulation runs of each design of experiment (DOE) can be given: if the second order response surface model is selected, the number of simulation runs of the LHS is the same as the UD, and all are lesser than the number of simulation runs of the OBD and the MUD in most cases. As for the OBD and the MUD, the number of simulation runs of the OBD is lesser than the number of simulation runs of the MUD, but the MUD can be applied more widely than the OBD. Finally, a true vehicle-pedestrian accident case is given, Pc-Crash was used to reconstruct the accident based on all traces provided by the police, and the interval of the impact velocity of the vehicle was calculated using the OBD, results showed that the research is meaningful in practice.

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Inverse Simulation Under Uncertainty by Optimization

Cited by 13 publications

References 27 publications

Novel solution framework for inverse problem considering interval uncertainty

Novel solution framework for inverse problem considering interval uncertainty

An efficient inverse method for structural identification considering modeling and response uncertainties

Design of experiment in analyzing uncertainty of simulation results in accident reconstruction

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