scholarly journals Test Verification and Application of a Longitudinal Temperature Force Testing Method for Long Seamless Rails Using FBG Strain Sensor

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Ping Wang ◽  
Kaize Xie ◽  
Rong Chen ◽  
Liyang Shao ◽  
Lianshan Yan ◽  
...  
Keyword(s):  
Temperature Variation ◽  
Relative Error ◽  
Test Specimen ◽  
Single Point ◽  
Maximum Error ◽  
Sensitivity Coefficient ◽  
Maximum Relative Error ◽  
Longitudinal Temperature ◽  
Temperature Force ◽  
Theoretical Results

In order to evaluate the health status of continuous welded rail accurately, a deduction on the FBG sensing principle has been made with regard to the temperature variation of test specimens under different constraint conditions. A long seamless rail testing solution and its on-site application are designed based on this deduction. According to the verification experiments of sensing principle inside, the effect of the reference temperature on the FBG temperature and strain sensitivity coefficient within −30°C~30°C is not higher than 0.05%; the maximum relative error of single point between the tested and theoretical results of test specimen under constrained condition is 3.2%; and the maximum relative error of slopes of fitted straight lines based on the tested and theoretical results within the entire test temperature range is 2.3%, verifying the deduced FBG sensing principle with regard to the test specimen under constrained condition. The maximum error of the longitudinal temperature force between the on-site tested results and calculated results in long seamless rails is only 6.1 kN, the corresponding rail temperature variation is 0.3°C, and the accumulated error is controllable within 5%.

Development of a Predictive Equation for Ventilation in a Wall-Solar Chimney System

2017 ◽  
Vol 139 (3) ◽  
Author(s):  
David Park ◽  
Francine Battaglia
Keyword(s):  
Mathematical Model ◽  
Relative Error ◽  
Natural Ventilation ◽  
Thermal Conditions ◽  
Ambient Air ◽  
Maximum Error ◽  
Scale Model ◽  
Small Scale ◽  
Maximum Relative Error ◽  
Solar Chimney

A solar chimney is a natural ventilation technique that has potential to save energy consumption as well as to maintain the air quality in a building. However, studies of buildings are often challenging due to their large sizes. The objective of this study was to determine the relationships between small- and full-scale solar chimney system models. Computational fluid dynamics (CFD) was employed to model different building sizes with a wall-solar chimney utilizing a validated model. The window, which controls entrainment of ambient air for ventilation, was also studied to determine the effects of window position. A set of nondimensional parameters were identified to describe the important features of the chimney configuration, window configuration, temperature changes, and solar radiation. Regression analysis was employed to develop a mathematical model to predict velocity and air changes per hour, where the model agreed well with CFD results yielding a maximum relative error of 1.2% and with experiments for a maximum error of 3.1%. Additional wall-solar chimney data were tested using the mathematical model based on random conditions (e.g., geometry, solar intensity), and the overall relative error was less than 6%. The study demonstrated that the flow and thermal conditions in larger buildings can be predicted from the small-scale model, and that the newly developed mathematical equation can be used to predict ventilation conditions for a wall-solar chimney.

scholarly journals NUMERICAL EXPERIMENTS WITH AN ALGORITHM FOR PROCESSING EXPERIMENTAL DATA TO DETERMINE THE PARAMETERS OF A RHEOLOGICAL MODEL OF A LIQUID WITH THE EFFECT OF «SOLIDIFICATION»

2021 ◽  
pp. 206-211
Author(s):  
В.Н. Колодежнов ◽  
А.В. Колтаков ◽  
С.С. Капранчиков ◽  
А.С. Веретенников
Keyword(s):  
Experimental Data ◽  
Polyethylene Glycol ◽  
Calcium Carbonate ◽  
Relative Error ◽  
Rheological Model ◽  
Numerical Experiments ◽  
Maximum Error ◽  
Viscoplastic Fluid ◽  
Maximum Relative Error ◽  
Hardening Effect

Предложена методика обработки экспериментальных данных и алгоритм для ее реализации по определению параметров реологической модели вязкопластической жидкости, которая демонстрирует проявление эффекта «отвердевания». С целью проверки работоспособности алгоритма проведены численные эксперименты с наборами генерируемых случайным образом “псевдоэкспериментальных” данных с заранее заданной величиной максимальной относительной погрешности. Проведен анализ влияния максимальной относительной погрешности исходных “псевдоэкспериментальных” данных на величину относительной погрешности определяемых в ходе численных экспериментов параметров реологической модели. По итогам проведенных экспериментов показано, что относительная погрешность определения параметров реологической модели соизмерима с максимальной погрешностью генерируемых “псевдоэкспериментальных” данных. Рассмотрен пример обработки экспериментальных данных для суспензии частиц карбоната кальция на основе полиэтиленгликоля. A technique for processing experimental data and an algorithm for its implementation to determine the parameters of a rheological model of a viscoplastic fluid, which demonstrates the manifestation of the "hardening" effect, are proposed. In order to test the algorithm's operability, numerical experiments were carried out with sets of randomly generated "pseudo-experimental" data with a predetermined maximum relative error. The analysis of the influence of the maximum relative error of the initial “pseudo-experimental” data on the value of the relative error of the parameters of the rheological model determined during numerical experiments was carried out. Based on the results of the conducted experiments, it is shown that the relative error in determining the parameters of the rheological model is commensurate with the maximum error of the generated “pseudo-experimental” data. An example of processing experimental data for a suspension of calcium carbonate particles based on polyethylene glycol is considered.

Robustness of a Neural Network Model for Power Peak Factor Estimation in Protection Systems

2006 ◽  
Author(s):  
Rose Mary G. P. Souza ◽  
Joa˜o M. L. Moreira
Keyword(s):  
Neural Network ◽  
Relative Error ◽  
Safety Margin ◽  
Maximum Error ◽  
Maximum Relative Error ◽  
Peak Factor ◽  
Protection Systems ◽  
Power Peak ◽  
Input Variables ◽  
Factor Estimation

This work presents results of robustness verification of artificial neural network correlations that improve the real time prediction of the power peak factor for reactor protection systems. The input variables considered in the correlation are those available in the reactor protection systems, namely, the axial power differences obtained from measured ex-core detectors, and the position of control rods. The correlations, based on radial basis function (RBF) and multilayer perceptron (MLP) neural networks, estimate the power peak factor, without faulty signals, with average errors between 0.13%, 0.19% and 0.15%, and maximum relative error of 2.35%. The robustness verification was performed for three different neural network correlations. The results show that they are robust against signal degradation, producing results with faulty signals with a maximum error of 6.90%. The average error associated to faulty signals for the MLP network is about half of that of the RBF network, and the maximum error is about 1% smaller. These results demonstrate that MLP neural network correlation is more robust than the RBF neural network correlation. The results also show that the input variables present redundant information. The axial power difference signals compensate the faulty signal for the position of a given control rod, and improves the results by about 10%. The results show that the errors in the power peak factor estimation by these neural network correlations, even in faulty conditions, are smaller than the current PWR schemes which may have uncertainties as high as 8%. Considering the maximum relative error of 2.35%, these neural network correlations would allow decreasing the power peak factor safety margin by about 5%. Such a reduction could be used for operating the reactor with a higher power level or with more flexibility. The neural network correlation has to meet requirements of high integrity software that performs safety grade actions. It is shown that the correlation is a very simple algorithm that can be easily codified in software. Due to its simplicity, it facilitates the necessary process of validation and verification.

Error-aware Design Procedure to Implement Energy-efficient Approximate Squaring Hardware

2020 ◽  
Vol 10 (4) ◽  
pp. 471-477
Author(s):  
Merin Loukrakpam ◽  
Ch. Lison Singh ◽  
Madhuchhanda Choudhury
Keyword(s):  
Energy Saving ◽  
Relative Error ◽  
Energy Efficient ◽  
Piecewise Linear ◽  
Arithmetic Operation ◽  
Design Procedure ◽  
Digital Signal ◽  
Maximum Relative Error ◽  
Square Function ◽  
Efficient Design

Background:: In recent years, there has been a high demand for executing digital signal processing and machine learning applications on energy-constrained devices. Squaring is a vital arithmetic operation used in such applications. Hence, improving the energy efficiency of squaring is crucial. Objective:: In this paper, a novel approximation method based on piecewise linear segmentation of the square function is proposed. Methods: Two-segment, four-segment and eight-segment accurate and energy-efficient 32-bit approximate designs for squaring were implemented using this method. The proposed 2-segment approximate squaring hardware showed 12.5% maximum relative error and delivered up to 55.6% energy saving when compared with state-of-the-art approximate multipliers used for squaring. Results: The proposed 4-segment hardware achieved a maximum relative error of 3.13% with up to 46.5% energy saving. Conclusion:: The proposed 8-segment design emerged as the most accurate squaring hardware with a maximum relative error of 0.78%. The comparison also revealed that the 8-segment design is the most efficient design in terms of error-area-delay-power product.

scholarly journals Back protection of canvas paintings

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Tim Padfield ◽  
Nicolas Padfield ◽  
Daniel Sang-Hoon Lee ◽  
Anne Thøgersen ◽  
Astrid Valbjørn Nielsen ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Relative Humidity ◽  
Temperature Variation ◽  
Room Temperature ◽  
Single Point ◽  
Confined Space ◽  
Constant Wall Temperature ◽  
Back Plate ◽  
Inner Surface

Abstract In this paper different scenarios for back protection of a canvas painting and their effect on the stability of the relative humidity behind the painting are tested. A painting on canvas, stretched on a wooden frame, was fitted with various styles of back protection and then exposed to a cycle of temperature variation at the back, with the front exposed to a constant room temperature. The painting was also exposed to a constant wall temperature and varying room temperature. The space between the canvas and the back board was fitted with temperature and relative humidity (RH) sensors. The sensors were used to provide the essential single-point data of temperature and RH at the given locations. For more comprehensive understanding of the rather confined space, further numerical simulation (computational fluid dynamics) was adopted as part of the investigation. The computational fluid dynamics was used to understand the natural convection within the microclimate through the depictions of temperature distribution, as well as the corresponding airflow. The unprotected painting suffered a large RH variation at its back, because of the varying canvas temperature interacting with the constant room air moisture content. Effective stabilisation of the RH behind the canvas against temperature variation was provided by a shiny aluminium alloy sheet sealed against the frame. The non-absorbent back board experienced a strong variation in RH, because of humidity buffering of the space by the painting canvas at a different temperature. Either a space or insulation between this back plate and the wall reduced the risk of condensation on the inner surface of the back plate. Insulation will however increase the risk of condensation on the wall surface behind the painting. An absorbent back board de-stabilised the RH at the painting canvas surface by providing a competing humidity buffer at a different temperature. To provide protection against moisture exchange with an unsuitable room RH, extra humidity buffer was placed 3 mm behind the painting canvas, kept close to the painting temperature by insulation between this buffer and the back board. This stabilised RH at the canvas surface but increased both the temperature and the RH variation at the back board and thus increased the risk of condensation on the inner surface of the back board. The RH and the temperature in the narrow spaces between the painting canvas and the wooden stretcher frame were always more nearly constant than in the open canvas area, which suggests an explanation for the widely observed better condition of the areas of canvas paintings which lie close over the support structure. Our conclusion is that a non-absorbent, impermeable back plate gives good RH stability against a changing temperature gradient between wall and canvas painting surface.

A Meshless Local Petrov-Garlerkin Method for Solving the Biharmonic Equation

2014 ◽  
Vol 931-932 ◽  
pp. 1488-1494
Author(s):  
Supanut Kaewumpai ◽  
Suwon Tangmanee ◽  
Anirut Luadsong
Keyword(s):  
Relative Error ◽  
Biharmonic Equation ◽  
Interpolation Method ◽  
Step Function ◽  
Special Treatment ◽  
Maximum Relative Error ◽  
Heaviside Step Function ◽  
Treatment Techniques ◽  
Point Interpolation Method ◽  
Point Interpolation

A meshless local Petrov-Galerkin method (MLPG) using Heaviside step function as a test function for solving the biharmonic equation with subjected to boundary of the second kind is presented in this paper. Nodal shape function is constructed by the radial point interpolation method (RPIM) which holds the Kroneckers delta property. Two-field variables local weak forms are used in order to decompose the biharmonic equation into a couple of Poisson equations as well as impose straightforward boundary of the second kind, and no special treatment techniques are required. Selected engineering numerical examples using conventional nodal arrangement as well as polynomial basis choices are considered to demonstrate the applicability, the easiness, and the accuracy of the proposed method. This robust method gives quite accurate numerical results, implementing by maximum relative error and root mean square relative error.

scholarly journals Evaluation of the Undrained Shear Strength of Organic Soils from a Dilatometer Test Using Artificial Neural Networks

2018 ◽  
Vol 8 (8) ◽  
pp. 1395 ◽  
Author(s):  
Zbigniew Lechowicz ◽  
Masaharu Fukue ◽  
Simon Rabarijoely ◽  
Maria Sulewska
Keyword(s):  
Neural Networks ◽  
Shear Strength ◽  
Artificial Neural Networks ◽  
Relative Error ◽  
Undrained Shear Strength ◽  
Organic Soils ◽  
Maximum Relative Error ◽  
Empirical Methods ◽  
Pressure Reading ◽  
Undrained Shear

The undrained shear strength of organic soils can be evaluated based on measurements obtained from the dilatometer test using single- and multi-factor empirical correlations presented in the literature. However, the empirical methods may sometimes show relatively high values of maximum relative error. Therefore, a method for evaluating the undrained shear strength of organic soils using artificial neural networks based on data obtained from a dilatometer test and organic soil properties is presented in this study. The presented neural network, with an architecture of 5-4-1, predicts the normalized undrained shear strength based on five independent variables: the normalized net value of a corrected first pressure reading (po − uo)/σ′v, the normalized net value of a corrected second pressure reading (p1 − uo)/σ′v, the organic content Iom, the void ratio e, and the stress history indictor (oc or nc). The neural model presented in this study provided a more reliable prediction of the undrained shear strength in comparison to the empirical methods, with a maximum relative error of ±10%.

Quantitative Retrieval of Tidal Flat Moisture Based on TM Images

2013 ◽  
Vol 448-453 ◽  
pp. 1066-1071
Author(s):  
Li Jun Yang ◽  
Ming Fei Wu ◽  
Yun Hong Zhu
Keyword(s):  
Remote Sensing ◽  
Relative Error ◽  
Tidal Flat ◽  
Spectral Characteristics ◽  
Atmospheric Correction ◽  
Maximum Error ◽  
Exponential Model ◽  
Wave Band ◽  
Gradient Distribution ◽  
Distribution Features

Based on spectrometry, the remote sensing inversion researches of the surface tidal flat moisture are conducted in combination with spectral values measured in the field and moisture measured in the laboratory. Firstly, the remote sensing images are preprocessed, including geometric correction, atmospheric correction and image enhancement. Then, the spectral characteristics of typical ground objects are analyzed to partition the whole image and separate the bare tidal flats. At last, TM5 wave band and exponential model are determined to be the best wave band and optimal model for the inversion of the bare tidal flat moisture. The experiment shows: (1)This method can help to improve the accuracy of the surface tidal flat moisture inversion, with the maximum error of moisture inversion is 3%, the relative error is 7.1% and the average relative error is 6.5%. (2)The surface tidal flat moisture is of evident gradient distribution features, which can be used as basis of tidal flat topographic survey.

scholarly journals Chaotic prediction of vibration performance degradation trend of rolling element bearing based on Weibull distribution

2019 ◽  
Vol 103 (1) ◽  
pp. 003685041989219
Author(s):  
Li Cheng ◽  
Xintao Xia ◽  
Liang Ye
Keyword(s):  
Weibull Distribution ◽  
Relative Error ◽  
High Reliability ◽  
Prediction Method ◽  
Performance Degradation ◽  
Rolling Element Bearing ◽  
Maximum Relative Error ◽  
Rolling Element Bearings ◽  
Rolling Element ◽  
Vibration Performance

Rolling element bearings are used in all rotating machinery, and the degradation performance of rolling element bearings directly affects the performance of the machine. Therefore, high reliability prediction of the performance degradation trend of rolling element bearings has become an urgent research problem. However, the degradation characteristics of the rolling element bearings vibration time series are difficult to extract, and the mechanism of performance degradation is very complicated. The accurate physical model is difficult to establish. In view of the above reasons, based on the vibration performance data of rolling element bearings, a model of bearing performance degradation trend parameter based on wavelet denoising and Weibull distribution is established. Then, the phase space reconstruction of the series of bearing performance degradation trend parameter is carried out, and the prognosis is obtained by the improved adding weighted first-order local prediction method. The experimental results show that the bearing vibration performance degradation parameter can accurately depict the degradation trend of the bearing, and the reliability level is 91.55%; and the prediction of bearing performance degradation trend parameter is satisfactory: the mean relative error is only 0.0053% and the maximum relative error is less than 0.03%.

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