A Hybrid Approach for 3D Full-Field Measurement on a Closed Slinger Combustor by Hydraulic Simulations

2017 ◽  
Author(s):  
Lichao Jia ◽  
Lili Yang ◽  
Huijing Yuan ◽  
Yongxia Jia ◽  
Yiyang Wang ◽  
...  
Keyword(s):  
Particle Density ◽  
Sampling Rate ◽  
Hybrid Approach ◽  
Least Square ◽  
Calibration Data ◽  
Experimental Conditions ◽  
Match Probability ◽  
Camera System ◽  
Reconstruction Process ◽  
Full Field

This hybrid approach proposed in the present study is a mixture algorithm of the 3D PTV and Tomo-PIV based on a two-camera system, which is able to measure the flow field in a closed system with high refractive without in situ calibration. Knowing the calibration data in the air and refractive indices of different optical media, a simplified multimedia photogrammetry model is established based on the least square method. A new particle matching algorithm using the concept of match probability between the twin image frames has been developed to reconstruct the physical position of the particles. In order to overcome the disadvantage of low particle density, time-resolved PIV is utilized at a sampling rate being 2000 Hz to acquire the instantaneous particle images. Then, the spectrum superposition of the cross-correlation distribution is applied to increase the signal-to-noise ratio in the velocity prediction. The technique proposed here can be used to overcome the difficulty in conventional calibration method for closed measurement objects. Both the computer simulation and some experiments imaging a calibration target reference field are conducted to show the accuracies of the calibration and reconstruction process. The capability of the technique in real experimental conditions is assessed with the measurement of the flow structure in a closed slinger combustor by hydraulic simulations.

Isolation and Characterization of Leishmanial Adenine Aminohydrolase as a Drug target

2020 ◽  
Vol 16 ◽  
Author(s):  
Firdous Butt ◽  
Masoom Yasinzai ◽  
Shaukat Iqbal Malik ◽  
Anum Munir
Keyword(s):  
Kinetic Parameter ◽  
Drug Target ◽  
Product Formation ◽  
Least Square ◽  
Experimental Conditions ◽  
Isolation And Characterization ◽  
Structural And Functional Properties ◽  
Coefficient Corrélation ◽  
Hyperbolic Curve ◽  
Km Value

Background:: Search for new drug targets is becoming imperative these days given that marketed chemotherapeutic drugs have lost their efficacy against harmful agents because of adaptability to climatic changes and co-evolving vectors to new hosts. In the wake of such challenge prominence of biochemical studies is increasing by way of exploring selective enzymes and investigating their structural and functional properties through biochemical kinetic parameter Km for the application of IC50 using designed drugs. Recently discovered Adenine Aminohydrolase [EC 3.5.4.2) in Leishmania has been found to be absent in mammalian purine salvage pathway and thus considered as a promising drug target against infectious agents. Objective:: The objective of this study is to isolate and characterize AAH by learning its kinetic mode of action using preferred substrate Adenine and additives estimated through expected product formation Hypoxanthine. Bioassays designed to measure exact Enzyme kinetic parameter Km value through establishing hyperbolic curve of enzyme reaction with the use of exact values of cellular quantities for IC50 application under experimental conditions devised by presteady state approach for SSA validity. Methods:: Following saturation kinetic, the plot of hyperbolic equilibrium curve developed using initial rates of product formation as a function of [Si] through forward shift under circumstance dG0 the system allows product and reactant favored reactions in relation to[Ef]1≈[E=KM] until complete saturation and estimates Km and Vmax of enzyme system under applied conditions. M-M equation used to assess experimental initial rate data for estimation of Km on excel using Solver and nonlinear least square coefficient correlation “R2”using logarithmic equation for nonlinear curve assessment. Results:: UV/Vis spectrophotometer selectively analyzed reacting components confirming Enzyme characteristic reaction constant Km equal toi15.0 ± 2 μ mol acquired from the Hyperbolic curve developed through use of exact [Si] ranges at selected parameter Km and Vmax. The curve assessed by Michaelis Menten equation provide Km value=14.99 μmol and non-linear least square coefficient correlation “R2” value equal to 0.9895,.along with that optimized lysis buffer formulation. In the docked complexes, the interactive amino acids identified were MSE441, ALA 364, GLN363, MSE518, VAL362, GLY517, ASP538, ALA445, TYR521, and TYR444. 2D interactions revealed hydrophobic and alkyl interactions at non-competitive binding site of the enzyme and therefore recommended as a potential inhibitors against 3ICS protein. Conclusion:: This study encourages biochemical analysis of the novel enzymes with the use of presteady state rationale in association with the computational tools as an effective way of designing drugs in short time against selective enzymes to meet the current challenge efficiently.

scholarly journals Two view NURBS reconstruction based on GACO model

2021 ◽  
Author(s):  
Deepika Saini ◽  
Sanoj Kumar ◽  
Manoj K. Singh ◽  
Musrrat Ali
Keyword(s):  
Optimization Algorithms ◽  
Three Dimensional ◽  
Optimization Procedure ◽  
Least Square ◽  
Ant Colony ◽  
Free Form ◽  
Reconstruction Process ◽  
Nurbs Curve ◽  
Data Points ◽  
Ant Colony Optimization Algorithms

AbstractThe key job here in the presented work is to investigate the performance of Generalized Ant Colony Optimizer (GACO) model in order to evolve the shape of three dimensional free-form Non Uniform Rational B-Spline (NURBS) curve using stereo (two) views. GACO model is a blend of two well known meta-heuristic optimization algorithms known as Simple Ant Colony and Global Ant Colony Optimization algorithms. Basically, the work talks about the solution of NURBS-fitting based reconstruction process. Therefore, GACO model is used to optimize the NURBS parameters (control points and weights) by minimizing the weighted least-square errors between the data points and the fitted NURBS curve. The algorithm is applied by first assuming some pre-fixed values of NURBS parameters. The experiments clearly show that the optimization procedure is a better option in a case where good initial locations of parameters are selected. A detailed experimental analysis is given in support of our algorithm. The implemented error analysis shows that the proposed methodology perform better as compared to the conventional methods.

scholarly journals Material parameter identification using finite elements with time-adaptive higher-order time integration and experimental full-field strain information

2021 ◽  
Author(s):  
Stefan Hartmann ◽  
Rose Rogin Gilbert
Keyword(s):  
Experimental Data ◽  
Finite Elements ◽  
Parameter Identification ◽  
Material Parameter ◽  
Measurement Data ◽  
Least Square ◽  
Image Correlation ◽  
Field Strain ◽  
Material Parameter Identification ◽  
Full Field

AbstractIn this article, we follow a thorough matrix presentation of material parameter identification using a least-square approach, where the model is given by non-linear finite elements, and the experimental data is provided by both force data as well as full-field strain measurement data based on digital image correlation. First, the rigorous concept of semi-discretization for the direct problem is chosen, where—in the first step—the spatial discretization yields a large system of differential-algebraic equation (DAE-system). This is solved using a time-adaptive, high-order, singly diagonally-implicit Runge–Kutta method. Second, to study the fully analytical versus fully numerical determination of the sensitivities, required in a gradient-based optimization scheme, the force determination using the Lagrange-multiplier method and the strain computation must be provided explicitly. The consideration of the strains is necessary to circumvent the influence of rigid body motions occurring in the experimental data. This is done by applying an external strain determination tool which is based on the nodal displacements of the finite element program. Third, we apply the concept of local identifiability on the entire parameter identification procedure and show its influence on the choice of the parameters of the rate-type constitutive model. As a test example, a finite strain viscoelasticity model and biaxial tensile tests applied to a rubber-like material are chosen.

Outlier removal method for the refinement of optical measured displacement field based on critical factor least squares and subdomain division

2021 ◽  
Author(s):  
Bo Wang ◽  
Chen Sun ◽  
Keming Zhang ◽  
Jubing Chen
Keyword(s):  
Least Squares ◽  
Displacement Field ◽  
Critical Factor ◽  
Least Square ◽  
Outlier Removal ◽  
Full Field ◽  
Removal Method ◽  
Strain Estimation ◽  
Data Points ◽  
Measured Displacement

Abstract As a representative type of outlier, the abnormal data in displacement measurement often inevitably occurred in full-field optical metrology and significantly affected the further evaluation, especially when calculating the strain field by differencing the displacement. In this study, an outlier removal method is proposed which can recognize and remove the abnormal data in optically measured displacement field. A iterative critical factor least squares algorithm (CFLS) is developed which distinguishes the distance between the data points and the least square plane to identify the outliers. A successive boundary point algorithm is proposed to divide the measurement domain to improve the applicability and effectiveness of the CFLS algorithm. The feasibility and precision of the proposed method are discussed in detail through simulations and experiments. Results show that the outliers are reliably recognized and the precision of the strain estimation is highly improved by using these methods.

scholarly journals Computer-automated shadow moiré method and applications for 3D surface profiling

2021 ◽  
Author(s):  
Rui Zhao
Keyword(s):  
Least Square ◽  
Automated System ◽  
Image Subtraction ◽  
Full Field ◽  
Surface Profiling ◽  
Shadow Moire ◽  
Shadow Moiré ◽  
3D Surface ◽  
Measurement Resolution ◽  
Moire Method

Among numerous methods for 3D surface profiling, classic shadow moiré method has been kept as the most popular one due to its full-field feature and low cost. This thesis focuses on a computer-vision shadow moiré method with a scope to improve the measurement resolution, accuracy and efficiency. The computer automation is basically realized through the introduction of a phase-shifting technique that is incorporated with a new multi-grid least-square unwrapping algorithm. The method is enhanced by implementing a few additional image processing techniques. These techniques, when implemented, result in improved measurement accuracy and enable easy applications to irregularly shaped surfaces. The study also proposes a new, automated system calibration approach that is based on a real-time image subtraction. A data normalization process is studied to resolve possible confusions in the presentation of the original data. The verification test results show that the modified shadow moiré technique has achieved the initial goal, in that the measurement resolution now reaches a few percentage of the fringe sensititivity.

scholarly journals crestr An R package to perform probabilistic climate reconstructions using fossil proxies

2021 ◽  
Author(s):  
Manuel Chevalier
Keyword(s):  
R Package ◽  
Climate Reconstruction ◽  
Diagnostic Tools ◽  
Calibration Data ◽  
Reconstruction Process ◽  
Climate Reconstructions ◽  
Reconstruction Software ◽  
Modelling Assumptions ◽  
The Many ◽  
Data Collections

Abstract. Statistical climate reconstruction techniques are practical tools to study past climate variability from fossil proxy data. In particular, the methods based on probability density functions (PDFs) are powerful at producing robust results from various environments and proxies. However, accessing and curating the necessary calibration data, as well as the complexity of interpreting probabilistic results, often limit their use in palaeoclimatological studies. To address these problems, I present a new R package (crestr) to apply the CREST method (Climate REconstruction SofTware) on diverse palaeoecological datasets. crestr includes a globally curated calibration dataset for six common climate proxies (i.e. plants, beetles, chironomids, rodents, foraminifera, and dinoflagellate cysts) that enables its use in most terrestrial and marine regions. The package can also be used with private data collections instead of, or in combination with, the provided dataset. It also includes a suite of graphical diagnostic tools to represent the data at each step of the reconstruction process and provide insights into the effect of the different modelling assumptions and external factors that underlie a reconstruction. With this R package, the CREST method can now be used in a scriptable environment, thus simplifying its use and integration in existing workflows. It is hoped that crestr will contribute to producing the much-needed quantified records from the many regions where climate reconstructions are currently lacking, despite the existence of suitable fossil records.

scholarly journals Identification of the Mechanical Properties of Superconducting Windings Using the Virtual Fields Method

2010 ◽  
Vol 24-25 ◽  
pp. 379-384
Author(s):  
J.H. Kim ◽  
F. Nunio ◽  
Fabrice Pierron ◽  
P. Vedrine
Keyword(s):  
Least Square Method ◽  
Numerical Differentiation ◽  
Tensile Tests ◽  
Least Square ◽  
Image Correlation ◽  
Stereo Image ◽  
Correlation Technique ◽  
Virtual Fields Method ◽  
Displacement Fields ◽  
Full Field

Tensile tests were performed in order to identify the stiffness components of superconducting windings in the shape of rings (also called ‘double pancakes’). The stereo image correlation technique was used for full-field displacement measurements. The strain components were then obtained from the measured displacement fields by numerical differentiation. Because differentiation is very sensitive to spatial noise, the displacement maps were fitted by polynomials before differentiation using a linear least-square method. Then, in the orthotropy basis, the four in-plane stiffnesses of the double pancake were determined using the Virtual Fields Method.

Precise hyperacuity estimation of spike timing from calcium imaging

2019 ◽  
Author(s):  
Huu Hoang ◽  
Masa-aki Sato ◽  
Shigeru Shinomoto ◽  
Shinichiro Tsutsumi ◽  
Miki Hashizume ◽  
...  
Keyword(s):  
Sampling Rate ◽  
Time Estimation ◽  
Spike Timing ◽  
Support Vector ◽  
High Temporal Resolution ◽  
Spike Time ◽  
Experimental Conditions ◽  
Two Photon ◽  
Photon Imaging ◽  
Two Photon Imaging

SummaryTwo-photon imaging is a major recording technique in neuroscience, but it suffers from several limitations, including a low sampling rate, the nonlinearity of calcium responses, the slow dynamics of calcium dyes and a low signal-to-noise ratio, all of which impose a severe limitation on the application of two-photon imaging in elucidating neuronal dynamics with high temporal resolution. Here, we developed a hyperacuity algorithm (HA_time) based on an approach combining a generative model and machine learning to improve spike detection and the precision of spike time inference. First, Bayesian inference estimates the calcium spike model by assuming the constancy of the spike shape and size. A support vector machine employs this information and detects spikes with higher temporal precision than the sampling rate. Compared with conventional thresholding, HA_time improved the precision of spike time estimation up to 20-fold for simulated calcium data. Furthermore, the benchmark analysis of experimental data from different brain regions and simulation of a broader range of experimental conditions showed that our algorithm was among the best in a class of hyperacuity algorithms. We encourage experimenters to use the proposed algorithm to precisely estimate hyperacuity spike times from two-photon imaging.

Metamodeling Development for Vehicle Frontal Impact Simulation

2001 ◽  
Author(s):  
R. J. Yang ◽  
N. Wang ◽  
C. H. Tho ◽  
J. P. Bobineau ◽  
B. P. Wang
Keyword(s):  
Response Surface ◽  
Impact Analysis ◽  
Hybrid Approach ◽  
Latin Hypercube Sampling ◽  
Element Model ◽  
Least Square ◽  
Frontal Impact ◽  
Response Surface Methods ◽  
Moving Least Square ◽  
Highly Nonlinear

Abstract Response surface methods or metamodels are commonly used to approximate large engineering systems. This paper presents a new metric for evaluating a response surface method or a metamodeling technique. Five response surface methods are studied: Stepwise Regression, Moving Least Square, Kriging, Multiquadratic, and Adaptive and Interactive Modeling System. A real world frontal impact design problem is used as an example, which is a complex, highly nonlinear, transient, dynamic, large deformation finite element model. The optimal Latin Hypercube Sampling method is used to distribute the sampling points uniformly over the entire design space. The Root Mean Square Error is used as the error indicator to study the accuracy and convergence rate of the metamodels for this vehicle impact analysis. A hybrid approach/strategy for selecting the best metamodels of impact responses is proposed.

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