Bayesian Methods for Updating Dynamic Models

2011 ◽  
Vol 64 (1) ◽  
Author(s):  
Ka-Veng Yuen ◽  
Sin-Chi Kuok
Keyword(s):  
Response Time ◽  
Bayesian Methods ◽  
Dynamic Models ◽  
Model Updating ◽  
Time History ◽  
Recent Decade ◽  
Model Complexity ◽  
Matching Process ◽  
Wide Range ◽  
Time Histories

Model updating of dynamical systems has been attracting much attention because it has a very wide range of applications in aerospace, civil, and mechanical engineering, etc. Many methods were developed and there has been substantial development in Bayesian methods for this purpose in the recent decade. This article introduces some state-of-the-art work. It consists of two main streams of model updating, namely model updating using response time history and model updating using modal measurements. The former one utilizes directly response time histories for the identification of uncertain parameters. In particular, the Bayesian time-domain approach, Bayesian spectral density approach and Bayesian fast Fourier transform approach will be introduced. The latter stream utilizes modal measurements of a dynamical system. The method introduced here does not require a mode matching process that is common in other existing methods. Afterwards, discussion will be given about the relationship among model complexity, data fitting capability and robustness. An application of a 22-story building will be presented. Its acceleration response time histories were recorded during a severe typhoon and they are utilized to identify the fundamental frequency of the building. Furthermore, three methods are used for analysis on this same set of measurements and comparison will be made.

Time history analysis-based nonlinear finite element model updating for a long-span cable-stayed bridge

2020 ◽  
pp. 147592172096386
Author(s):  
Kaiqi Lin ◽  
You-Lin Xu ◽  
Xinzheng Lu ◽  
Zhongguo Guan ◽  
Jianzhong Li
Keyword(s):  
Finite Element ◽  
Response Time ◽  
Finite Element Model ◽  
Model Updating ◽  
Time History ◽  
Element Model ◽  
Nonlinear Finite Element ◽  
Long Span ◽  
Time Histories ◽  
Nonlinear Finite Element Model

Accurate finite element models play significant roles in the design, health monitoring and life-cycle maintenance of long-span bridges. However, due to uncertainties involved in finite element modelling, updating of the finite element model to best represent the real bridge is inevitable. This is particularly true after a long-span bridge experiences a moderate or severe earthquake and suffers some damage. This study thus proposes a time history analysis-based nonlinear finite element model updating method for long-span cable-stayed bridges. Special efforts are made to (1) establish the response time history-based objective functions and associated acceptance criteria, (2) conduct comprehensive sensitivity analyses to select appropriate nonlinear updating parameters and (3) develop a highly efficient cluster computing-aided optimization algorithm. A scaled structure of the Sutong cable-stayed bridge in China is adopted as a case study. Three nonlinear test cases performed in the shake table tests of the scaled bridge are used to validate the feasibility and accuracy of the proposed method. A good agreement is observed between the simulated response time histories and the measured response time histories for the scaled bridge under both moderate and strong ground motions. The proposed method could provide an accurate nonlinear finite element model for better performance assessment, damage detection and life-cycle maintenance of long-span cable-stayed bridges.

Fuel-Air Ratio Determination From Cylinder Pressure Time Histories

1985 ◽  
Vol 107 (4) ◽  
pp. 252-257 ◽  
Author(s):  
J. C. Gilkey ◽  
J. D. Powell
Keyword(s):  
Time History ◽  
Operating Conditions ◽  
Cylinder Pressure ◽  
Pressure Time ◽  
Wide Range ◽  
Pressure Trace ◽  
Time Histories ◽  
High Bandwidth ◽  
Ratio Determination ◽  
Microprocessor Technology

Determining fuel-air ratio quickly over a wide range of engine operating conditions is desirable for better transient engine control. This paper describes a method based on cylinder pressure time history pattern recognition which has potential for providing such a high bandwidth measurement. The fact that fuel-air ratio has an effect on the shape of the cylinder pressure trace is well-known. It should therefore be possible to obtain the fuel-air ratio of an engine by examining the pressure trace if the engine speed, load, and EGR are known. The difficulty lies in separating the effects of unknown engine load, speed, and EGR from the fuel-air ratio effects. An algorithm was developed using a wide range of steady state experimental data from a single cylinder engine. Application of the algorithm requires the calculation of first, second and third moments of the cylinder pressure time history. Verification of the algorithm showed that the root mean square error in estimates were about 5 percent for fuel-air ratio and 3 percent for a combination of fuel-air and EGR. These results were obtained using a single pressure trace which yields a response time of 1.5 engine revolutions. The algorithm was also found to be relatively insensitive to the use of different fuels, errors in spark advance, and variations in relative humidity. Research is continuing to verify the accuracy under transient engine conditions. An operational count shows that this algorithm should be well within the limits of present microprocessor technology.

Accuracy and sensitivity of different Bayesian methods for genomic prediction using simulation and real data

2020 ◽  
Vol 19 (3) ◽  
Author(s):  
Saheb Foroutaifar
Keyword(s):  
Data Analysis ◽  
Bayesian Methods ◽  
Milk Fat ◽  
Prediction Accuracy ◽  
Real Data ◽  
Fat Percentage ◽  
Real Data Analysis ◽  
Wide Range ◽  
High Heritability ◽  
Qtl Effects

AbstractThe main objectives of this study were to compare the prediction accuracy of different Bayesian methods for traits with a wide range of genetic architecture using simulation and real data and to assess the sensitivity of these methods to the violation of their assumptions. For the simulation study, different scenarios were implemented based on two traits with low or high heritability and different numbers of QTL and the distribution of their effects. For real data analysis, a German Holstein dataset for milk fat percentage, milk yield, and somatic cell score was used. The simulation results showed that, with the exception of the Bayes R, the other methods were sensitive to changes in the number of QTLs and distribution of QTL effects. Having a distribution of QTL effects, similar to what different Bayesian methods assume for estimating marker effects, did not improve their prediction accuracy. The Bayes B method gave higher or equal accuracy rather than the rest. The real data analysis showed that similar to scenarios with a large number of QTLs in the simulation, there was no difference between the accuracies of the different methods for any of the traits.

scholarly journals A Review on Biosensors and Recent Development of Nanostructured Materials-Enabled Biosensors

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1109
Author(s):  
Varnakavi. Naresh ◽  
Nohyun Lee
Keyword(s):  
High Surface ◽  
Three Dimensional ◽  
Biological Response ◽  
Volume Ratio ◽  
Recent Decade ◽  
Disease Diagnosis ◽  
Electrical Signal ◽  
Detection Sensitivity ◽  
Wide Range ◽  
Color Tunability

A biosensor is an integrated receptor-transducer device, which can convert a biological response into an electrical signal. The design and development of biosensors have taken a center stage for researchers or scientists in the recent decade owing to the wide range of biosensor applications, such as health care and disease diagnosis, environmental monitoring, water and food quality monitoring, and drug delivery. The main challenges involved in the biosensor progress are (i) the efficient capturing of biorecognition signals and the transformation of these signals into electrochemical, electrical, optical, gravimetric, or acoustic signals (transduction process), (ii) enhancing transducer performance i.e., increasing sensitivity, shorter response time, reproducibility, and low detection limits even to detect individual molecules, and (iii) miniaturization of the biosensing devices using micro-and nano-fabrication technologies. Those challenges can be met through the integration of sensing technology with nanomaterials, which range from zero- to three-dimensional, possessing a high surface-to-volume ratio, good conductivities, shock-bearing abilities, and color tunability. Nanomaterials (NMs) employed in the fabrication and nanobiosensors include nanoparticles (NPs) (high stability and high carrier capacity), nanowires (NWs) and nanorods (NRs) (capable of high detection sensitivity), carbon nanotubes (CNTs) (large surface area, high electrical and thermal conductivity), and quantum dots (QDs) (color tunability). Furthermore, these nanomaterials can themselves act as transduction elements. This review summarizes the evolution of biosensors, the types of biosensors based on their receptors, transducers, and modern approaches employed in biosensors using nanomaterials such as NPs (e.g., noble metal NPs and metal oxide NPs), NWs, NRs, CNTs, QDs, and dendrimers and their recent advancement in biosensing technology with the expansion of nanotechnology.

scholarly journals Discovering governing equations from data by sparse identification of nonlinear dynamical systems

2016 ◽  
Vol 113 (15) ◽  
pp. 3932-3937 ◽  
Author(s):  
Steven L. Brunton ◽  
Joshua L. Proctor ◽  
J. Nathan Kutz
Keyword(s):  
Dynamical Systems ◽  
Nonlinear Dynamical Systems ◽  
Measurement Data ◽  
Climate Science ◽  
Model Complexity ◽  
Governing Equations ◽  
Canonical Systems ◽  
Nonlinear Dynamical ◽  
Balance Accuracy ◽  
Wide Range

Extracting governing equations from data is a central challenge in many diverse areas of science and engineering. Data are abundant whereas models often remain elusive, as in climate science, neuroscience, ecology, finance, and epidemiology, to name only a few examples. In this work, we combine sparsity-promoting techniques and machine learning with nonlinear dynamical systems to discover governing equations from noisy measurement data. The only assumption about the structure of the model is that there are only a few important terms that govern the dynamics, so that the equations are sparse in the space of possible functions; this assumption holds for many physical systems in an appropriate basis. In particular, we use sparse regression to determine the fewest terms in the dynamic governing equations required to accurately represent the data. This results in parsimonious models that balance accuracy with model complexity to avoid overfitting. We demonstrate the algorithm on a wide range of problems, from simple canonical systems, including linear and nonlinear oscillators and the chaotic Lorenz system, to the fluid vortex shedding behind an obstacle. The fluid example illustrates the ability of this method to discover the underlying dynamics of a system that took experts in the community nearly 30 years to resolve. We also show that this method generalizes to parameterized systems and systems that are time-varying or have external forcing.

CO and H2O Time-Histories in Shock-Heated Blends of Methane and Ethane for Assessment of a Chemical Kinetics Model

2017 ◽  
Vol 139 (12) ◽  
Author(s):  
O. Mathieu ◽  
C. R. Mulvihill ◽  
E. L. Petersen ◽  
Y. Zhang ◽  
H. J. Curran
Keyword(s):  
Chemical Kinetics ◽  
Laminar Flame ◽  
Combustion Process ◽  
Time History ◽  
Good Marker ◽  
Ignition Delay Times ◽  
Depletion Rate ◽  
Time Histories ◽  
Main Components ◽  
Detailed Kinetics

Methane and ethane are the two main components of natural gas and typically constitute more than 95% of it. In this study, a mixture of 90% CH4/10% C2H6 diluted in 99% Ar was studied at fuel lean (equiv. ratio = 0.5) conditions, for pressures around 1, 4, and 10 atm. Using laser absorption diagnostics, the time histories of CO and H2O were recorded between 1400 and 1800 K. Water is a final product from combustion, and its formation is a good marker of the completion of the combustion process. Carbon monoxide is an intermediate combustion species, a good marker of incomplete/inefficient combustion, as well as a regulated pollutant for the gas turbine industry. Measurements such as these species time histories are important for validating and assessing chemical kinetics models beyond just ignition delay times and laminar flame speeds. Time-history profiles for these two molecules were compared to a state-of-the-art detailed kinetics mechanism as well as to the well-established GRI 3.0 mechanism. Results show that the H2O profile is accurately reproduced by both models. However, discrepancies are observed for the CO profiles. Under the conditions of this study, the CO profiles typically increase rapidly after an induction time, reach a maximum, and then decrease. This maximum CO mole fraction is often largely over-predicted by the models, whereas the depletion rate of CO past this peak is often over-estimated for pressures above 1 atm.

CO and H2O Time-Histories in Shock-Heated Blends of Methane and Ethane for Assessment of a Chemical Kinetics Model

2017 ◽  
Author(s):  
O. Mathieu ◽  
C. Mulvihill ◽  
E. L. Petersen ◽  
Y. Zhang ◽  
H. J. Curran
Keyword(s):  
Gas Turbine ◽  
Chemical Kinetics ◽  
Laminar Flame ◽  
Combustion Process ◽  
Practical Interest ◽  
Time History ◽  
Good Marker ◽  
Ignition Delay Times ◽  
Time Histories ◽  
Detailed Kinetics

Methane and ethane are the two main components of natural gas and typically constitute more than 95% of it. In this study, a mixture of 90% CH4 /10% C2H6 diluted in 99% Ar was studied at fuel lean (ϕ = 0.5) conditions, for pressures around 1, 4, and 10 atm. Using laser absorption diagnostics, the time histories of CO and H2O were recorded between 1400 and 1800 K. Water is a final product from hydrocarbon combustion, and following its formation is a good marker of the completion of the combustion process. Carbon monoxide is an intermediate combustion species, a good marker of incomplete/inefficient combustion, as well as a regulated pollutant for the gas turbine industry. Measurements such as these species time histories are important for validating and assessing chemical kinetics models beyond just ignition delay times and laminar flame speeds. Time-history profiles for these two molecules measured herein were compared to a modern, state-of-the-art detailed kinetics mechanism as well as to the well-established GRI 3.0 mechanism. Results show that the H2O profile is accurately reproduced by both models. However, discrepancies are observed for the CO profiles. Under the conditions of this study, the measured CO profiles typically increase rapidly after an induction time, reach a maximum and then decrease. This maximum CO mole fraction is often largely over-predicted by the models, whereas the depletion rate of CO past this peak is often over-estimated by the models for pressures above 1 atm. This study demonstrates the need to improve on the accuracy of the HCCO reactions involved in CO formation for pressures of practical interest for the gas turbine industry.

scholarly journals Protein dynamics revealed by NMR relaxation methods

2018 ◽  
Vol 2 (1) ◽  
pp. 93-105 ◽  
Author(s):  
Fa-An Chao ◽  
R. Andrew Byrd
Keyword(s):  
Protein Dynamics ◽  
Structural Biology ◽  
Dynamic Models ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Data Bank ◽  
Biological Macromolecules ◽  
Relaxation Methods ◽  
Genomic Databases ◽  
Wide Range

Structural biology often focuses primarily on three-dimensional structures of biological macromolecules, deposited in the Protein Data Bank (PDB). This resource is a remarkable entity for the worldwide scientific and medical communities, as well as the general public, as it is a growing translation into three-dimensional space of the vast information in genomic databases, e.g. GENBANK. There is, however, significantly more to understanding biological function than the three-dimensional co-ordinate space for ground-state structures of biomolecules. The vast array of biomolecules experiences natural dynamics, interconversion between multiple conformational states, and molecular recognition and allosteric events that play out on timescales ranging from picoseconds to seconds. This wide range of timescales demands ingenious and sophisticated experimental tools to sample and interpret these motions, thus enabling clearer insights into functional annotation of the PDB. NMR spectroscopy is unique in its ability to sample this range of timescales at atomic resolution and in physiologically relevant conditions using spin relaxation methods. The field is constantly expanding to provide new creative experiments, to yield more detailed coverage of timescales, and to broaden the power of interpretation and analysis methods. This review highlights the current state of the methodology and examines the extension of analysis tools for more complex experiments and dynamic models. The future for understanding protein dynamics is bright, and these extended tools bring greater compatibility with developments in computational molecular dynamics, all of which will further our understanding of biological molecular functions. These facets place NMR as a key component in integrated structural biology.

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