Influence Of Experimental Errors Research Articles

Study on the Test Error of Silt Dynamic Characteristic and Its Influence on the Peak Ground Acceleration

In order to grasp the nonlinear experimental errors of dynamic shear modulus ratio (DSMR) and damping ratio (DR), as well as the current level of resonant column testing, the GZ-1 resonant column instrument was used to study the probability statistical indicators, basic laws, and the impact of experimental errors on peak acceleration of DSMR and DR of silt under 8 typical shear strains. The results show that, firstly, the DSMR and DR of silt under different shear strains obey normal distributions. Secondly, there is no significant difference between the reference range of the standard deviation of the DSMR and DR of silt and the outer envelope line. This result indicates that the dispersion of experimental errors for the same person is very small. Thirdly, in medium to hard sites, the influence of experimental errors in DSMR and DR on peak acceleration can be ignored. And the impact of DR test errors on peak acceleration should not be ignored on soft ground with a probability range exceeding 95%. Overall, the testing accuracy of the testing personnel proved to generally meet the requirements, while in other special cases, it is necessary to increase the number of parallel tests and improve the testing technology. Otherwise, it will cause certain risks to the estimation of seismic input for engineering structures. Doi: 10.28991/HIJ-2023-04-01-05 Full Text: PDF

GenX 3: the latest generation of an established tool.

Since its publication more than 15 years ago the GenX software has been continuously developed and has established itself as a standard package for analyzing X-ray and neutron reflectometry data. The evolution of the software during the last two major revisions is reported here. This includes a simplified model builder for beginners, simple samples, additional sample models, statistical error analysis and the use of just-in-time compilation modules for the reflectometry kernel to achieve higher performance. In addition, the influence of experimental errors on the reflectivity curve is discussed, and new features are described that allow the user to include these in the error statistics to improve the fitting and uncertainty estimation.

Viscoelastic Property of an LDPE Melt in Triangular- and Trapezoidal-Loop Shear Experiment.

The time-dependent viscoelastic behaviors of a low-density polyethylene melt (LDPE) in a triangular- and trapezoidal-loop shear experiment reported previously are described here by an integral-type Rivlin–Sawyers (RS) constitutive equation. The linear viscoelasticity of the melt was obtained through a dynamic frequency sweep experiment at a small strain and fitted by a relaxation spectrum. The nonlinear viscoelasticity was characterized by viscosity. All the experimental viscoelastic behaviors of the melt can be divided into two types in terms of the predictions of the RS model: (1) predictable time-dependent viscoelastic behaviors at low shear rates or during short-term shear, and (2) unpredictable shear weakening behavior occurring at the high shear rate of 3–5 s−1 during long-term shear with the characteristic time interval of about 40–100 s. The influence of experimental error caused possibly by inhomogeneous samples on the viscoelasticity of the melt was analyzed, and the large relative error in the experiment is about 10–30%.

Toward a Distinct and Quantitative Validation Method for Predictive Process Modelling—On the Example of Solid-Liquid Extraction Processes of Complex Plant Extracts

Physico-chemical modelling and predictive simulation are becoming key for modern process engineering. Rigorous models rely on the separation of different effects (e.g., fluid dynamics, kinetics, mass transfer) by distinct experimental parameter determination on lab-scale. The equations allow the transfer of the lab-scale data to any desired scale, if characteristic numbers like e.g., Reynolds, Péclet, Sherwood, Schmidt remain constant and fluid-dynamics of both scales are known and can be described by the model. A useful model has to be accurate and therefore match the experimental data at different scales and combinations of process and operating parameters. Besides accuracy as one quality attribute for the modelling depth, model precision also has to be evaluated. Model precision is considered as the combination of modelling depth and the influence of experimental errors in model parameter determination on the simulation results. A model is considered appropriate if the deviation of the simulation results is in the same order of magnitude as the reproducibility of the experimental data to be substituted by the simulation. Especially in natural product extraction, the accuracy of the modelling approach can be shown through various studies including different feedstocks and scales, as well as process and operating parameters. Therefore, a statistics-based quantitative method for the assessment of model precision is derived and discussed in detail in this paper to complete the process engineering toolbox. Therefore a systematic workflow including decision criteria is provided.

A novel method of evaluating the lift force on the bluff body based on Noca’s flux equation

The influence of experimental error on lift force evaluated by Noca’s flux equation is studied based on adding errors into the direct numerical simulation data for flow past cylinder at Re = 100. As Noca suggested using the low-pass filter to get rid of the high-frequency noise in the evaluated lift force, we verify that his method is inapplicable for dealing with the dataset of 1% experimental error, although the precision is acceptable in practice. To overcome this defect, a novel method is proposed in this paper. The average of the lift forces calculated by using multiple control volume is taken as the evaluation before applying the low-pass filter. The method is applied to an experimental data for flow past a cylinder at approximately Re = 900 to verify its validation. The results show that it improves much better on evaluating the lift forces.

Langmuir–Hinshelwood kinetic study for palm oil catalytic cracking over Al-MCM-41

Palm oil catalytic cracking over a mesoporous aluminosilicate material (Al-MCM-41) containing 5 % alumina was studied in order to evaluate the Langmuir–Hinshelwood (LH) kinetic parameters. The Al-MCM-41 catalyst was prepared by the sol–gel technique and was characterized by X-ray diffraction and nitrogen adsorption techniques. The Brunauer–Emmett–Teller surface area of the catalyst was found to be 1,278 m2g−1. A 400 mL stirred batch autoclave reactor was used for catalytic cracking of 100 g refined palm oil and 1 g catalyst at a reaction temperature ranging from 573 to 673 K. The pressure–time data at different reaction temperatures were analyzed statistically in order to minimize experimental errors in the recorded pressures, whereas the statistically predicted pressure data were used to calculate the kinetic parameters. It was found that the fitting quality of the statistical model data using the LH model is similar to that of the raw experimental data. However, the values of the predicted parameters are significantly different. The estimated activation energy from LH kinetics was found to be 87 and 112 kJ mol−1 calculated from statistical model data and raw experimental data, respectively. The predicted parameters obtained from statistical model data are found to be more accurate as the influence of experimental error is minimized prior to data analysis.

粮食筛选系统中双通道带通滤光膜的研制

Optical sorting of grain,is using different characteristics of near infrared absorption spectra of impurity inferior food and high quality food,and based on the difference of feature band which reflects the test components in the spectrum to do the sieving grain.Before the light beam into the sensor,it needs to pass through the optical filter,filtering the multiple test band and reducing the noise effect.A dual bandpass optical filter was developed which is the core parts in the system,it is based on the film system design theory,adopt Smith method and the film system design software basing on the combination of period and non period multilayer coatings to design the dual band-pass filter coating,.Utilize electron beam evaporation and ion assisted deposition method to prepare and mathematical modeling to analyze,which has reduced the influence of experimental error to the spectrum and prepared the filter coating.The average transmittance of which is more than 97%in 1 200 nm and 1 450 nm band and less than 1%in 1 000~1 130 nm and 1 130~2 000 nm band.The film meets the use requirements through the test system and evaluation.

The influence of sulfur on chemisorption of gold by carbonaceous matter at 200–400°C and P total = 1 kbar

pyrite. Experiments were performed in autoclave fur� naces at 200, 300, and 400°С and Ptotal = 1 kbar. Walls of golden ampoules, in which solid phases were loaded, were used as a source of gold. Pyrite and CM were put in separate nonhermetical ampoules. The amount of bidistilled water added was estimated by the P–V–T relation under concrete run conditions. Asphaltene obtained through fractionation of brown coal from the Pavlovsk deposit in Primor’e was used as CM. Preliminary analysis did not register gold in its composition. We used elementary sulfur (10 mg) or pyrite (30 mg) in runs. Run series without sulfur in the C–O–H–Au system was performed for comparison. All three run series on each isotherm were developed and analyzed simultaneously to minimize the influ� ence of experimental error during comparison of the results.

Neural networks for modelling of kinetic reaction data applicable to catalyst scale up and process control and optimisation in the frame of combinatorial catalysis

This work describes an application of artificial neural networks (ANNs) for modelling the kinetics of catalytic reactions using methods not based on fundamental knowledge. Thus, neural networks have been used to model the behaviour of different reactions under different reactor conditions. The modelling of catalytic reactions by neural networks has been demonstrated, and the influence of experimental error in input data has been estimated. In addition, a novel methodology for modelling catalytic data employing already-trained neural networks has been systematically studied by using experimental results from the catalytic hydroisomerization of different n-paraffins. It can be then expected that a new reaction system can be rapidly analysed with a small number of experiments if a library of well-trained neural networks which represent a series of different reaction networks is constructed.

Maximum likelihood estimation with nonlinear regression in polarographic and potentiometric studies

In this work we review some aspects of maximum likelihood nonlinear modeling in polarographic and potentiometric techniques. Different algorithms, namely the Levenberg–Marquardt and the “error-in-variables” methods in parametric and Monte-Carlo nonparametric estimation are used. Conclusions are drawn upon the influence of experimental errors and error correlation, introduced via statistical weighting, in the accuracy and precision of the estimated parameters. Several of the tested alternatives, including regression on the signal variable alone with a global error weighting function, are shown to provide adequate representation of the experimental data.

Determination of SNP allele frequencies in pooled DNAs by primer extension genotyping and denaturing high-performance liquid chromatography

By testing DNA pools rather than single samples the number of tests for a case–control association study can be decreased to only two for each marker: one on the patient and one on the control pool. A fundamental requirement is that each pool represents the frequency of the markers in the corresponding population beyond the influence of experimental errors. Consequently the latter must be carefully determined. To this aim, we prepared pools of different size (49–402 individuals) with accurately quantified DNAs, estimated the allelic frequencies in the pools of two SNPs by primer extension genotyping followed by DHPLC analysis and compared them with the real frequencies determined in the single samples. Our data show that (1) the method is highly reproducible: the standard deviation of repeated determinations was ±0.014; (2) the experimental error (i.e., the discrepancy between the estimated and real frequencies) was ±0.013 (95% C.I.: 0.0098–0.0165). The magnitude of this error was not correlated to the pool size or to the type of SNP. The effect of the observed experimental error on the power of the association test was evaluated. We conclude that this method constitutes an efficient tool for high-throughput association screenings provided that the experimental error is low. We therefore recommend that before a pool is used for extensive association studies, its quality, i.e., the experimental error, is verified by determining the difference between estimated and real frequencies for at least one marker.

A new method of the determination of significant factors with factor analysis in AES

An important step of the factor analysis method is the determination of the number of significant factors. Common techniques as Malinowski indicator function provide perhaps uncertain results in spectroscopic cases depending on signal to noise ratio, the overlap of spectra, sample misalignment and concentration of the factors. ¶A new method for the determination of the number of components in Auger depth profiling is established. The new method of correlation of abstract basic spectra (CABS) takes into consideration the influence of experimental error especially noise. Simulations were carried out, to prove the CABS method. The results are compared with those obtained by established methods. Auger depth profiles of SiC and Si3N4-layers on Si(111) were analyzed by means of the CABS method and Target Factor Analysis. Aspects of data pretreatment as filtering are discussed.

ANALYSIS OF THE DECONVOLUTION OF X-RAY DIFFRACTION PROFILE AND SIMULTANEOUS CALCULATION OF BRAGG ANGLE

To eliminate the influence of experimental error, analytical function is adopted to model the experimental profiles. Much attention has been paid to the analysis of the effects of such factors as Kα doublet, broadening and alignment of profiles etc. on the Fourier coefficients and deconvoluted profile obtained from the Stokes correction.The reliability of the separation of size-and strain-broadening from Fourier coefficients or deconvoluted profile is estimated. A method is proposed for the simultaneous refinement of accurate Bragg angle from the deconvolution procedure.

The influence of experimental errors during laboratory evaluation of FCC catalysts

The well-known microactivity test (MAT) ASTM D 3907-92, has been used worldwide in laboratory scale testing to determine the activity and selectivity of fluid catalytic cracking (FCC) catalysts. To assess catalytic selectivities by interpolation, either a kinetic model or functional (empirical) relationships between product yields and conversion should be used. Experimental errors may significantly affect analysis and erroneous conclusions might arise, justifying application of statistical methods. Although these methods are well established, they are frequently neglected or oversimplified in catalysis. The aim of the present work is to evaluate the influence of typical experimental errors from MAT units on interpolated yields, based on previously tested empirical functions and data from the literature, and from FCC catalyst samples from PETROBRAS Research Center, with and without a vanadium trap additive. Statistical criteria to identify possible outliers and the confidence intervals for predicted values are discussed. It is shown that the experimental errors on the independent variable, sometimes disregarded, drastically decrease the discrimination capacity of the MAT test concerning iso-conversion and iso-coke evaluations. Concentrating experiments near the conversion region of interest and improvement of MAT repeatability could mitigate this unavoidable problem. These conclusions can be generalized to selectivity analyses related to the reaction systems other than catalytic cracking.

Regularisation techniques for dynamic model updating using input residual

The updating process to correct finite element models using experimental data is generally an ill conditioned problem. The method here presented is based on the use of the input residual, that presents some important advantages over other proposed techniques. The actions required to limit the solution instability due to ill conditioning are discussed. The importance of using regularisation techniques to minimise the influence of experimental errors is demonstrated. Several procedures are analysed through simulated and experimental tests, finally showing that the most reliable results are obtained using a priori information and the singular values truncation technique based on the minimisation of the output residual.

Influence of experimental errors on the determination of flux control coefficients from transient metabolite concentrations

The influence of experimental errors on the determination of flux control coefficients from transient metabolite concentrations with the method proposed by Delgado and Liao [(1992) Biochem. J. 282, 919-927] has been investigated by using Monte Carlo simulations. The method requires least-squares fitting of the transient metabolite concentrations. Three different fitting methods have been evaluated. Simulated metabolite concentrations of a fictive metabolic pathway were scattered randomly, emulating experimental errors, before performing the fits. This was repeated a large number of times; the mean values and standard deviations of the resulting control coefficients are reported. The results show that the proposed method for determining control coefficients is too sensitive to experimental errors to be practicable, with theoretically justified fitting methods. This is in particular due to the high degree of correlation between the concentrations. An alternative ad hoc fitting method produced biased mean values of the estimates of the control coefficients, but with remarkably low standard deviations.

Activation energy spectra for stress-induced ordering in amorphous materials calculated using Fourier techniques

A method for calculating the activation energy spectrum from isothermal data using Fourier techniques is used for studying the deformation processes in amorphous metals. The influence of experimental error on the calculated spectrum is discussed. The activation energy spectrum derived from the anelastic deformation of Fe40Ni40B20 amorphous metallic ribbon is presented. The spectrum obtained involves three well-separated peaks, each of which represents a different material transport mechanism.

Estimating the Bending Characteristics of Fabric by a Nonlinear Least-squares-method.

A simulation method is presented for the estimation of the parameters which characterize the forcedbending vibration of a strip of textile fabric, such as the constants related to the hysteresis and damping; a nolinear least-squares-method is adopted to consider the nonlinearity inherent to the phenomena, and the stability of estimation as well as the influence of experimental error on the estimation are examined. The amplitude-fre-quency dependency obtained by experiment could be sufficiently reproduced from the parameters estimated by this method. In the estimation of the whole curve of hysteresis, the accuracy was not so good but could be fairly improved if the rigidity data at higher curvatures were incorporated as auxiliary information.

An Estimation of the Dynamic Constitutive Equations by using the Method of Nonlinear Least Squares.

An estimation method of the dynamic constitutive equations using the method of least squares is described. In this paper, the unknown parameters in the constitutive equations of which the functional form is known were estimated. The estimation is performed by using the method of nonlinear least squares utilizing the Gauss-Newton method because the constitutive equations are nonlinear. Numerical simulation were done such that the unknown parameters in the constitutive equation were estimated from the value of the strain wave in a long bar subjected to longitudinal impact. The stability of the estimation and influence of experimental error on the estimated parameters were examined. As a result, it was concluded that the present method is practically useful.

Sensitivity Analysis of the Deal‐Grove Model Parameters: Problem Definition and Strategy Considerations

In this work the Deal‐Grove (DG) oxidation model, being the key ingredient in many current oxidation models, has been examined in detail from the physical and numerical point of view. It is clearly proven that for a rather broad range of (B/A, B) combinations the fitting error is smaller than the experimental error. So there exists a wide range of allowable parameters of the Deal‐Grove model to satisfy different physical explanations. This means that a good fit alone is not a sufficient criterion to validate a possible physical model. It is also demonstrated that the existence of breakpoints in the temperature behavior could be seen as an artifact of the model parameters extraction procedure normally used. Therefore, a new and efficient parameter extraction strategy, which carefully considers the influence of experimental errors, is suggested in this work. As an example, a physically acceptable expression for the parameters in the is found as a substitute for the look‐up table used in SUPREM‐3.