scholarly journals A modeling approach to ultrasound evaluation of material properties

2010 ◽  
Author(s):  
◽  
Sri Waluyo
Keyword(s):  
Ultrasonic Waves ◽  
Binary Sequences ◽  
Model Verification ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Optimal Model ◽  
Ultrasound Evaluation ◽  
Voigt Model ◽  
Estimation Of Model Parameters ◽  
Linear State

A method for estimating the mechanical properties of a viscoelastic sample from ultrasound measurements was developed. The sample was represented as a mechanical network according to the Kelvin-Voigt model and linear state-space equations were derived to describe the system dynamics. Four parameters can be extracted by comparing the model with measured transmission waves. These parameters can be related to viscoelastic properties of the sample. Broadband pseudo-random binary sequences were designed and used to perturb the sample. The Levenberg-Marquardt method was employed to adjust the model parameters and the least-squares algorithm was used to obtain optimal model parameter estimates. Model verification showed that the algorithm developed could converge to known model parameters. Estimated model parameters showed consistency and reflected known facts about the materials tested. The model could capture the major dynamics of transmitted ultrasonic waves and allow repeatable estimation of model parameters. The model parameters could not only differentiate the materials tested but also follow expected trends of variation. The model parameters were useful for sensory crispness prediction and crispness was more correlated to the elastic modulus than to viscosity, which is consistent with existing research.

Likelihood and Its Applications

2021 ◽  
pp. 125-148
Author(s):  
Timothy E. Essington
Keyword(s):  
Maximum Likelihood ◽  
Dynamic Model ◽  
Likelihood Estimation ◽  
Worked Examples ◽  
Multiple Models ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Multiple Parameters ◽  
Weight Support ◽  
Estimation Of Model Parameters

The chapter “Likelihood and Its Applications” introduces the likelihood concept and the concept of maximum likelihood estimation of model parameters. Likelihood is the link between data and models. It is used to estimate model parameters, judge the degree of precision of parameter estimates, and weight support for alternative models. Likelihood is therefore a crucial concept that underlies the ability to test multiple models. The chapter contains several worked examples that progress the reader through increasingly complex problems, ending at likelihood profiles for models with multiple parameters. Importantly, it illustrates how one can take any dynamic model and data and use likelihood to link the data (random variables) to a probability function that depends on the dynamic model.

scholarly journals A flexible framework for sequential estimation of model parameters in computational hemodynamics

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Christopher J. Arthurs ◽  
Nan Xiao ◽  
Philippe Moireau ◽  
Tobias Schaeffter ◽  
C. Alberto Figueroa
Keyword(s):  
Unscented Kalman Filter ◽  
Three Dimensional ◽  
Synthetic Data ◽  
Sequential Estimation ◽  
Wall Material ◽  
Patient Specific ◽  
Model Parameters ◽  
Computational Framework ◽  
Lumped Parameter ◽  
Estimation Of Model Parameters

AbstractA major challenge in constructing three dimensional patient specific hemodynamic models is the calibration of model parameters to match patient data on flow, pressure, wall motion, etc. acquired in the clinic. Current workflows are manual and time-consuming. This work presents a flexible computational framework for model parameter estimation in cardiovascular flows that relies on the following fundamental contributions. (i) A Reduced-Order Unscented Kalman Filter (ROUKF) model for data assimilation for wall material and simple lumped parameter network (LPN) boundary condition model parameters. (ii) A constrained least squares augmentation (ROUKF-CLS) for more complex LPNs. (iii) A “Netlist” implementation, supporting easy filtering of parameters in such complex LPNs. The ROUKF algorithm is demonstrated using non-invasive patient-specific data on anatomy, flow and pressure from a healthy volunteer. The ROUKF-CLS algorithm is demonstrated using synthetic data on a coronary LPN. The methods described in this paper have been implemented as part of the CRIMSON hemodynamics software package.

scholarly journals Parameter stability and consistency in an alongshore-current model determined with Markov chain Monte Carlo

2008 ◽  
Vol 10 (2) ◽  
pp. 153-162 ◽  
Author(s):  
B. G. Ruessink
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Parameter Stability ◽  
Small Uncertainty ◽  
Best Fit ◽  
Stability And Consistency

When a numerical model is to be used as a practical tool, its parameters should preferably be stable and consistent, that is, possess a small uncertainty and be time-invariant. Using data and predictions of alongshore mean currents flowing on a beach as a case study, this paper illustrates how parameter stability and consistency can be assessed using Markov chain Monte Carlo. Within a single calibration run, Markov chain Monte Carlo estimates the parameter posterior probability density function, its mode being the best-fit parameter set. Parameter stability is investigated by stepwise adding new data to a calibration run, while consistency is examined by calibrating the model on different datasets of equal length. The results for the present case study indicate that various tidal cycles with strong (say, >0.5 m/s) currents are required to obtain stable parameter estimates, and that the best-fit model parameters and the underlying posterior distribution are strongly time-varying. This inconsistent parameter behavior may reflect unresolved variability of the processes represented by the parameters, or may represent compensational behavior for temporal violations in specific model assumptions.

Multi-day flow forecasting using the Kalman filter

1991 ◽  
Vol 18 (2) ◽  
pp. 320-327 ◽  
Author(s):  
Murray A. Fitch ◽  
Edward A. McBean
Keyword(s):  
Kalman Filter ◽  
Prediction Model ◽  
Linear Prediction ◽  
Measured Data ◽  
System Model ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Forecasting Performance ◽  
Optimal Forecasting ◽  
Linear Prediction Model

A model is developed for the prediction of river flows resulting from combined snowmelt and precipitation. The model employs a Kalman filter to reflect uncertainty both in the measured data and in the system model parameters. The forecasting algorithm is used to develop multi-day forecasts for the Sturgeon River, Ontario. The algorithm is shown to develop good 1-day and 2-day ahead forecasts, but the linear prediction model is found inadequate for longer-term forecasts. Good initial parameter estimates are shown to be essential for optimal forecasting performance. Key words: Kalman filter, streamflow forecast, multi-day, streamflow, Sturgeon River, MISP algorithm.

Generalised DOPs with Consideration of the Influence Function of Signal-in-Space Errors

2011 ◽  
Vol 64 (S1) ◽  
pp. S3-S18 ◽  
Author(s):  
Yuanxi Yang ◽  
Jinlong Li ◽  
Junyi Xu ◽  
Jing Tang
Keyword(s):  
Least Squares ◽  
Stochastic Models ◽  
Influence Function ◽  
A Priori ◽  
Functional Model ◽  
Global Navigation Satellite Systems ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Integrated Navigation ◽  
Satellite Systems

Integrated navigation using multiple Global Navigation Satellite Systems (GNSS) is beneficial to increase the number of observable satellites, alleviate the effects of systematic errors and improve the accuracy of positioning, navigation and timing (PNT). When multiple constellations and multiple frequency measurements are employed, the functional and stochastic models as well as the estimation principle for PNT may be different. Therefore, the commonly used definition of “dilution of precision (DOP)” based on the least squares (LS) estimation and unified functional and stochastic models will be not applicable anymore. In this paper, three types of generalised DOPs are defined. The first type of generalised DOP is based on the error influence function (IF) of pseudo-ranges that reflects the geometry strength of the measurements, error magnitude and the estimation risk criteria. When the least squares estimation is used, the first type of generalised DOP is identical to the one commonly used. In order to define the first type of generalised DOP, an IF of signal–in-space (SIS) errors on the parameter estimates of PNT is derived. The second type of generalised DOP is defined based on the functional model with additional systematic parameters induced by the compatibility and interoperability problems among different GNSS systems. The third type of generalised DOP is defined based on Bayesian estimation in which the a priori information of the model parameters is taken into account. This is suitable for evaluating the precision of kinematic positioning or navigation. Different types of generalised DOPs are suitable for different PNT scenarios and an example for the calculation of these DOPs for multi-GNSS systems including GPS, GLONASS, Compass and Galileo is given. New observation equations of Compass and GLONASS that may contain additional parameters for interoperability are specifically investigated. It shows that if the interoperability of multi-GNSS is not fulfilled, the increased number of satellites will not significantly reduce the generalised DOP value. Furthermore, the outlying measurements will not change the original DOP, but will change the first type of generalised DOP which includes a robust error IF. A priori information of the model parameters will also reduce the DOP.

scholarly journals Mixture of Inverse Weibull and Lognormal Distributions: Properties, Estimation, and Illustration

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
K. S. Sultan ◽  
A. S. Al-Moisheer
Keyword(s):  
Information Matrix ◽  
Real Data ◽  
Likelihood Method ◽  
Model Parameters ◽  
Component Mixture ◽  
Data Set ◽  
Hazard Functions ◽  
Lognormal Distributions ◽  
Proposed Model ◽  
Estimation Of Model Parameters

We discuss the two-component mixture of the inverse Weibull and lognormal distributions (MIWLND) as a lifetime model. First, we discuss the properties of the proposed model including the reliability and hazard functions. Next, we discuss the estimation of model parameters by using the maximum likelihood method (MLEs). We also derive expressions for the elements of the Fisher information matrix. Next, we demonstrate the usefulness of the proposed model by fitting it to a real data set. Finally, we draw some concluding remarks.

Estimation of Internal Surface Roughness of Additively Manufactured Components Under Complex Conditions Using Artificial Intelligence and Measurements of Ultrasonic Backscatter

2021 ◽  
Author(s):  
Mohamed Subair Syed Akbar Ali ◽  
Mato Pavlovic ◽  
Prabhu Rajagopal
Keyword(s):  
Artificial Intelligence ◽  
Surface Roughness ◽  
Internal Surface ◽  
Side Surface ◽  
Ultrasonic Waves ◽  
Model Parameters ◽  
Ultrasonic Measurements ◽  
Neural Network Algorithm ◽  
X Ray Computed ◽  
Pulse Echo

Abstract Additive Manufacturing (AM) is increasingly being considered for fabrication of components with complex geometries in various industries such as aerospace and healthcare. Control of surface roughness of components is thus a crucial aspect for more widespread adoption of AM techniques. However, estimating the internal (or ‘far-side’) surface roughness of components is a challenge, and often requires sophisticated techniques such as X-ray computed tomography, which are difficult to implement online. Although ultrasound could potentially offer a solution, grain noise and inspection surface conditions complicate the process. This paper studies the feasibility of using Artificial Intelligence (AI) in conjunction with ultrasonic measurements for rapid estimation of internal surface roughness in AM components, using numerical simulations. In the first models reported here, a pulse-echo configuration is assumed, whereby a specimen sample with rough surfaces is insonified with bulk ultrasonic waves and the backscatter is used to generate A-scans. Simulations are carried out for various combinations of the model parameters, yielding a large number of such A-scans. A neural network algorithm is then created and trained on a subset of the datasets so generated using simulations, and later used to predict the roughness from the rest. The results demonstrate the immense potential of this approach in inspection automation for rapid roughness assessments in AM components, based on ultrasonic measurements.

Influence of Fractional Damping and Time Delay on Maxwell-Voigt Model for Vibration Isolation

2016 ◽  
Author(s):  
Sudhir Kaul
Keyword(s):  
Time Delay ◽  
Dynamic Characteristics ◽  
Vibration Isolation ◽  
Model Parameters ◽  
Fractional Damping ◽  
Vibration Isolators ◽  
Multiple Parameters ◽  
Realistic Representation ◽  
Voigt Model ◽  
Isolation Systems

Models of vibration isolators are very commonly used for the design and analysis of isolation systems. Accurate isolator modeling is critical for a successful prediction of the dynamic characteristics of isolated systems. Isolators exhibit a complex behavior that depends on multiple parameters such as frequency, displacement amplitude, temperature and loading conditions. Therefore, it is important to choose a model that is accurate while adequately representing the relationships with relevant parameters. Recent literature has indicated some inherent advantages of fractional derivatives that can be exploited in the modeling of elastomeric isolators. Furthermore, time delay of damping is also seen to provide a realistic representation of damping. This paper examines the Maxwell-Voigt model with fractional damping and a time delay. This model is compared with the conventional Maxwell-Voigt model (without time delay or fractional damping) and the Voigt model in order to comprehend the influence of fractional damping and time delay on dynamic characteristics. Multiple simulations are performed after identifying model parameters from the data collected for a passive elastomeric isolator. The analysis results are compared and it is observed that the Voigt model is highly sensitive to fractional damping as well as time delay.

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