Relevant bibliographies by topics / Regression straight line

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Regression straight line'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Regression straight line"

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Giordano, José Luis. "On reporting uncertainties of the straight-line regression parameters." European Journal of Physics 20, no. 5 (September 1, 1999): 343–49. http://dx.doi.org/10.1088/0143-0807/20/5/307.

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Schwartz, Lowell M. "Rejection of a deviant point from a straight-line regression." Analytica Chimica Acta 178 (1985): 355–59. http://dx.doi.org/10.1016/s0003-2670(00)86289-3.

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Schaalje, G. Bruce, and Richard A. Butts. "Some Effects of Ignoring Correlated Measurement Errors in Straight Line Regression and Prediction." Biometrics 49, no. 4 (December 1993): 1262. http://dx.doi.org/10.2307/2532270.

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Verrall, R. J. "Graduation by dynamic regression methods." Journal of the Institute of Actuaries 120, no. 1 (1993): 153–70. http://dx.doi.org/10.1017/s002026810003688x.

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AbstractThis paper extends the theory of graduation by parametric formulae to include dynamic estimation methods. This is an application of the Kalman filter and allows the parameters of the curve fitted to vary with age. The amount of variation is determined by the amount of smoothing required, and the method can be regarded as a combination of curve fitting and sequential smoothing, each of which has been used separately for performing graduations. In practice, a dynamic straight line can always be used for the graduation and the method has a sensible logical interpretation.
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Clarke, Allan J., and Stephen Van Gorder. "On Fitting a Straight Line to Data when the “Noise” in Both Variables Is Unknown*." Journal of Atmospheric and Oceanic Technology 30, no. 1 (January 1, 2013): 151–58. http://dx.doi.org/10.1175/jtech-d-12-00067.1.

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Abstract In meteorology and oceanography, and other fields, it is often necessary to fit a straight line to some points and estimate its slope. If both variables corresponding to the points are noisy, the slope as estimated by the ordinary least squares regression coefficient is biased low; that is, for a large enough sample, it always underestimates the true regression coefficient between the variables. In the common situation when the relative size of the noise in the variables is unknown, an appropriate regression coefficient is plus or minus the ratio of the standard deviations of the variables, the sign being determined by the sign of the correlation coefficient. For this case of unknown noise, the authors here obtain the probability density function (pdf) for the true regression coefficient divided by the appropriate regression coefficient just mentioned. For the case when the number of data is very large, a simple analytical expression for this pdf is obtained; for a finite number of data points the relevant pdfs are obtained numerically. The pdfs enable the authors to provide tables for confidence intervals for the true regression coefficient. Using these tables, the end result of this analysis is a simple practical way to estimate the true regression coefficient between two variables given their standard deviations, the sample correlation, and the number of independent data.
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Emancipator, K., and M. H. Kroll. "A quantitative measure of nonlinearity." Clinical Chemistry 39, no. 5 (May 1, 1993): 766–72. http://dx.doi.org/10.1093/clinchem/39.5.766.

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Abstract Quantitative measures of the nonlinearity of an analytical method are defined as follows: the "(dimensional) nonlinearity" of a method is the square root of the mean of the square of the deviation of the response curve from a straight line, where the straight line is chosen to minimize the nonlinearity. The "relative nonlinearity" is defined as the dimensional nonlinearity divided by the difference between the maximum and minimum assayed values. These definitions may be used to develop practical criteria for linearity that are still objective. Calculation of the nonlinearity requires a method of curve-fitting. In this article, we use polynomial regression to demonstrate calculations, but the definition of nonlinearity also accommodates alternative nonlinear regression procedures.
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Jehlička, Vladimír, and Vladimír Mach. "Determination of Estimates of Parameters of Calibration Regression Straight Line with Objective Elimination of Remote Measurements." Collection of Czechoslovak Chemical Communications 60, no. 12 (1995): 2064–73. http://dx.doi.org/10.1135/cccc19952064.

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An alternative algorithm of calculation of estimates of parameters of calibration regression straight line with elimination of remote measurements has been elaborated for a function f(x) composed of linear-nonlinear or nonlinear-linear parts or, as the case may be, of nonlinear-linear-nonlinear parts. This algorithm makes it possible to objectively eliminate remote measurements and determine the interval <x1;x2> in which is located the linear part of dependence of output measured quantity y = f(x) with normal distribution N(f(x), σ2) on the input, i.e., independent variable x. For the procedure used for testing of remoteness of experimental points, a relation has been derived for calculation of the critical value of deviation of the point tested. The algorithm is finished by the calculation of parameters of the corresponding regression straight line and other statistical characteristics. On the basis of the algorithm suggested, a program has been assembled whose reliability was verified on a series of both model and practical examples.
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Wehr, Richard, and Scott R. Saleska. "The long-solved problem of the best-fit straight line: application to isotopic mixing lines." Biogeosciences 14, no. 1 (January 3, 2017): 17–29. http://dx.doi.org/10.5194/bg-14-17-2017.

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Abstract. It has been almost 50 years since York published an exact and general solution for the best-fit straight line to independent points with normally distributed errors in both x and y. York's solution is highly cited in the geophysical literature but almost unknown outside of it, so that there has been no ebb in the tide of books and papers wrestling with the problem. Much of the post-1969 literature on straight-line fitting has sown confusion not merely by its content but by its very existence. The optimal least-squares fit is already known; the problem is already solved. Here we introduce the non-specialist reader to York's solution and demonstrate its application in the interesting case of the isotopic mixing line, an analytical tool widely used to determine the isotopic signature of trace gas sources for the study of biogeochemical cycles. The most commonly known linear regression methods – ordinary least-squares regression (OLS), geometric mean regression (GMR), and orthogonal distance regression (ODR) – have each been recommended as the best method for fitting isotopic mixing lines. In fact, OLS, GMR, and ODR are all special cases of York's solution that are valid only under particular measurement conditions, and those conditions do not hold in general for isotopic mixing lines. Using Monte Carlo simulations, we quantify the biases in OLS, GMR, and ODR under various conditions and show that York's general – and convenient – solution is always the least biased.
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Gritsenko, S. A., and Yu R. Sadyrtdinova. "Association and heritability of traits of milk productivity and blood in the dairy cows." Glavnyj zootehnik (Head of Animal Breeding), no. 12 (December 1, 2020): 16–23. http://dx.doi.org/10.33920/sel-03-2012-02.

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Numerous studies have shown that there is a certain association between the biochemical parameters of blood and milk. It is of big practical importance to determine the heritability of traits involved in the selection process, which gives breeders the opportunity to choose the direction of selection for the fastest solution of the tasks. The purpose of the researches was to establish the association between the indicators of the biochemical composition of milk and blood in the dairy cows, as well as to determine the heritability of the studied traits. It has been found in the course of researches that in most cases the correlations between the biochemical parameters of blood and milk were insignifi cant and unreliable. It can be seen that there is a slight correlation between the main indicators of blood and milk, which should be taken into account by breeders when conducting breeding to improve the quality of milk. The highest regression association has been observed between the content of carotene, calcium and phosphorus in blood and milk. If you increase these indicators in the blood by the corresponding unit, their content in milk will also increase by 0,90, 0,31 and 0,42, respectively. The heritability of milk productivity traits in most cases was at medium and low levels and ranged from 0,03 to 0,66 (straight-line correlation method) and from 0,03 to 0,44 (straight-line regression method). The heritability of hematological traits in most cases was at an average level and ranged from 0,14 to 0,98 (straight-line correlation method) and from 0,11 to 0,76 (straight-line regression method). The total protein content in the blood (h² =0,98 и 0,76) and the number of monocytes (h² =0,82 и 0,60) depended more on the genotypic features of the animal. The average level of heritability coeffi cients has been observed in the blood content of total lipids, phosphorus, eosinophils, young neutrophils, and lymphocytes.
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Rueda, R., L. G. B. Ruiz, M. P. Cuéllar, and M. C. Pegalajar. "An Ant Colony Optimization approach for symbolic regression using Straight Line Programs. Application to energy consumption modelling." International Journal of Approximate Reasoning 121 (June 2020): 23–38. http://dx.doi.org/10.1016/j.ijar.2020.03.005.

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Dissertations / Theses on the topic "Regression straight line"

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Zhang, Ling. "Efficiency Comparison of Distribution-Free Transformations in the Straight-Line Regression Problem." Thesis, Uppsala University, Department of Statistics, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-126667.

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In statistical inference of the distribution-free straight-line regression problem, two common transformations, rank transformation and sign transformation, are used to construct the test statistics. When shall we need to use the transformations and which transformation is more efficient are two common questions met by researchers. In this thesis, we will discuss the comparison of the efficiencies of the statistics before and after the rank transformation or the sign transformation in both theoretical and practical ways. Simulation is also used to compare the efficiencies of the statistics under different distributions. Some recommendations about when to use transformations and which one to choose are put forward associated with the conclusion drawn from the research work we have done.

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Kotrba, Lukáš. "Závislost postavení týmů v žebříčku FIFA na dosažených výsledcích na vrcholných turnajích." Master's thesis, 2015. http://www.nusl.cz/ntk/nusl-335385.

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Title: Dependence of the position of teams in the FIFA rankings on the achievements in top tournaments Objectives: The aim of this work is determining dependency team standings in the FIFA rankings on the achievements in top tournaments. This is the World Cup in 1998, 2002, 2006, 2010 and 2014. Methods: In my thesis I used a method of regression and correlation analysis, correlation coefficient and regression straight line. Results: All results are presented in the analytical part of the work. It was found dependency between the observed data and an increasing trend. The biggest dependence was at the World Cup 2014, which reached high levels. The smallest was at the World Cup 1998 and 2002, where the results reached below average. Keywords: Football, FIFA, FIFA World rankings, FIFA World Cup, correlation analysis, regression analysis, correlation coefficient, regression straight line
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Chang, Yao-kun, and 張耀坤. "Fuzzy regression of straight lines in images." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/54860168409892835798.

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碩士
國立中興大學
資訊科學與工程學系所
99
This study proposes a set of modified masks for fuzzy linear regression to improve the accuracy of estimating the angle of a line in discrete representation.Three different approaches of linear regression including the tradition one, masks of fuzzy and modified masks of fuzzy were analyzed. Our results show that, among the three approaches, the modified masks of fuzzy linear regression has better accuracy if the angle are less than 45 degrees as compared to the traditional linear regression and mask of fuzzy linear regression approaches.
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Books on the topic "Regression straight line"

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Gelman, Andrew, and Deborah Nolan. Linear regression and correlation. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198785699.003.0005.

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This chapter addresses the descriptive treatment of linear regression with a single predictor: straight-line fitting, interpretation of the regression line and standard deviation, the confusing phenomenon of “regression to the mean,” correlation, and conducting regressions on the computer. These concepts are illustrated with student discussions and activities. Many examples are of the sort commonly found in statistics textbooks, but the focus here is on how to work the examples into student-participation activities rather than simply examples to be read or shown on the blackboard. Topics include the following relationships: height and income, height and hand span, world population over time, and exam scores.
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Cheng, Russell. Embedded Model Problem. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0005.

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This chapter introduces embedded models. This is a special case of a parametric model which cannot be obtained simply by setting the parameters to particular values in a simple way. An example is the regression function y = b[1−exp(−ax)], which is always curved when a and b have fixed values. But letting a tend to zero and b tend to infinity simultaneously, whilst keeping ab = c fixed, yields y = cx, a straight-line special case. When this is the true model, fitting the original two-parameter model leads to very unstable and individually meaningless estimates of a and b. Such embedded models are actually very common in the literature, leading to confusion in interpretation of results when undetected. In this chapter, embeddedness is defined and a large number of regression embedded model examples given. Detection and removal of embeddedness by reparametrization is discussed. Two real data numerical examples are given.
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Book chapters on the topic "Regression straight line"

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Gillard, Jonathan. "Regression: Fitting a Straight Line." In Springer Undergraduate Mathematics Series, 103–17. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39561-2_7.

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Bischoff, W., and W. Fieger. "On optimal designs for straight line regression with nonconstant variance." In Hector, 83–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-73576-9_7.

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Strike, P. W. "Regression I: straight-line relationships." In Statistical Methods in Laboratory Medicine, 184–253. Elsevier, 1991. http://dx.doi.org/10.1016/b978-0-7506-1345-3.50011-x.

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"Chapter 8 Straight line regression and calibration." In Data Handling in Science and Technology, 171–230. Elsevier, 1998. http://dx.doi.org/10.1016/s0922-3487(97)80038-x.

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Purcell, Kate. "Article 7(2) and the Special Case of Deltaic Coasts." In Geographical Change and the Law of the Sea, 49–74. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198743644.003.0004.

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This chapter provides a detailed analysis of UNCLOS Article 7(2), which provides that ‘notwithstanding subsequent regression of the low-water line’ straight baselines on deltaic coasts ‘shall remain effective until changed by the coastal State in accordance with [UNCLOS]’. Examining the treaty text, drafting history, and relevant State practice, the chapter challenges the idea that Article 7(2) exceptionally ‘fixes’ baselines that would otherwise shift with the coast. It is suggested that an alternative reading is more consistent with the ordinary meaning of Article 7(2) in context and in light of UNCLOS’s object and purpose. Turning to the drafting history for confirmation, however, reveals a surprising disjunction between (a) the proposals on which Article 7(2) is widely understood to be based, and (b) the meaning and effect of that provision. This has consequences in terms of what Article 7(2) can be said to imply about the fluidity of baselines.
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Hibbert, D. Brynn, and J. Justin Gooding. "Calibration." In Data Analysis for Chemistry. Oxford University Press, 2005. http://dx.doi.org/10.1093/oso/9780195162103.003.0010.

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• To describe the linear calibration model and how to estimate uncertainties in the calibration parameters and test concentrations determined from the model. • To show how to perform calibration calculations using Excel. • To calculate parameters and uncertainties in the standard addition method. • To calculate detection limits from measurements of blanks and uncertainties of the calibration model.… Calibration is at the heart of chemical analysis, and is the process by which the response of an instrument (in metrology called ‘‘indication of the measuring instrument’’) is related to the value of the measurand, in chemistry often the concentration of the analyte. Without proper calibration of instruments measurement results are not traceable, and not even correct. Scales in supermarkets are periodically calibrated to ensure they indicate the correct mass. Petrol pumps and gas and electric meters all must be calibrated and recalibrated at appropriate times. A typical example in analytical chemistry is the calibration of a GC (gas chromatography) analysis. The heights of GC peaks are measured as a function of the concentration of the analyte in a series of standard solutions (‘‘calibration solutions’’) and a linear equation fitted to the data. Before the advent of computers, a graph would be plotted by hand and used for calibration and subsequent measurement. Having drawn the best straight line through the points, the unknown test solution would be measured and the peak height read across to the calibration line then down on to the x-axis to give the concentration (figure 5.1). Nowadays, the regression equation is computed from the calibration data and then inverted to give the concentration of the test solution. Although the graph is no longer necessary to determine the parameters of the calibration equation, it is good practice to plot the graph as a rapid visual check for outliers or curvature. Because we can choose what values the calibration concentrations will take, the concentration is the independent variable, with the instrumental output being the dependent variable (because the output of the instrument depends on the concentration).
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"Early Life History of Fishes in the San Francisco Estuary and Watershed." In Early Life History of Fishes in the San Francisco Estuary and Watershed, edited by Robert G. Titus, Martha C. Volkoff, and William M. Snider. American Fisheries Society, 2004. http://dx.doi.org/10.47886/9781888569599.ch12.

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<em>Abstract.</em>—We compared two approaches to back-calculation with otolith microstructure to develop a method for accurately estimating growth rates of juvenile fall-run Chinook salmon <em>Oncorhynchus tshawytscha </em>in California’s Central Valley. Total otolith width was a strong determinant of fork length (FL) in linear regressions used to determine the <em>y</em>-intercept in the fish size–otolith size relationship in two study groups of Chinook salmon. The Fraser-Lee back-calculation model estimated FL at first feeding in both study groups that did not differ significantly from lengths of first-feeding Chinook salmon in a reference group. In comparison, the biological-intercept method produced back-calculated lengths that were significantly greater in one study group than lengths of first-feeding Chinook salmon in the reference group. Chinook salmon emergence dates, estimated from counts of daily growth increments beyond the first-feeding check, corresponded with observed emergence periods in the river and hatchery populations from which the study groups were sampled. Size-at-age relationships were well described by a power function in both study groups, where mean FL over time approached an apparent asymptote at approximately 80 mm after 90 d postemergence. Growth rate estimates, using back-calculated size from the Fraser-Lee model, averaged 0.50 mm/d in one study group and 0.43 mm/d in the other study group. These estimates fell within the range of previous growth rate estimates for juvenile Chinook salmon in Central Valley riverine, floodplain, and delta environments and were about 2.5 times higher on average than an estimate for the San Francisco Estuary and about 2.3 times lower on average than estimates from the Strait of Georgia. We discuss the utility of otolith microstructure to not only estimate growth rates, but also to reconstruct emergence-date distributions in cohorts of emigrating juvenile Chinook salmon for stock identification purposes.
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"f......... ■ indication of yes/no forced ch. forced ch. yes/no response dilution steps 1.5-23 * 21.6 exposure time <15 sec 10 sec <15 sec 20 sec stimulus interval 15 sec 3 min 1 min 1 min Table 2:Detection method, indication of response and size of stimulus intervals. 4.1 Definition of odor threshold By convention the individual odor threshold is that concentration which is just perceived by the subject in 50% of the cases in which it is presented to him. The group threshold is the concentration that is just perceived by 50% of the panel members. 4.2 Evaluation using the geometric mean The point of change is determined for every series of dilution eva­ luated. It is defined as the geometric mean of the dilution of the last negative and the first positive answer. The arithmetical mean and its standard deviation are calculated frcm the logarithms of the points of change. 4.3 Graphical evaluation The characteristic curve of the odor threshold is used. The rela­ tive cumulative frequency of positive answers is calculated for each odorant concentration and graphically plotted, while for odor concen­ tration a logarithmic scale is used. The odor threshold can be obtained frcm the resulting curve as the 50-percentile and so can the associated 16- and 84-percentiles. 4.4 Probit analysis If the odor sensitivity is normally distributed over the logarithm of the odor concentration, the characteristic curve of the odor threshold is a gaussian curve. This curve is converted into a straight line using the probit transformation. The analyses can be carried out graphically on probability paper or by transformation of the relative cumulative fre­ quency by using a table function and calculating the regression lines. The odor threshold and the 16- and 84-percentiles can be determined frcm the results." In Odour Prevention and Control of Organic Sludge and Livestock Farming, 72. CRC Press, 1986. http://dx.doi.org/10.1201/9781482286311-26.

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Conference papers on the topic "Regression straight line"

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"COEVOLUTIONARY ARCHITECTURES WITH STRAIGHT LINE PROGRAMS FOR SOLVING THE SYMBOLIC REGRESSION PROBLEM." In International Conference on Evolutionary Computation. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0003075100410050.

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Xue, Chuhui, Shijian Lu, and Wei Zhang. "MSR: Multi-Scale Shape Regression for Scene Text Detection." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/139.

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State-of-the-art scene text detection techniques predict quadrilateral boxes that are prone to localization errors while dealing with straight or curved text lines of different orientations and lengths in scenes. This paper presents a novel multi-scale shape regression network (MSR) that is capable of locating text lines of different lengths, shapes and curvatures in scenes. The proposed MSR detects scene texts by predicting dense text boundary points that inherently capture the location and shape of text lines accurately and are also more tolerant to the variation of text line length as compared with the state of the arts using proposals or segmentation. Additionally, the multi-scale network extracts and fuses features at different scales which demonstrates superb tolerance to the text scale variation. Extensive experiments over several public datasets show that the proposed MSR obtains superior detection performance for both curved and straight text lines of different lengths and orientations.
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Wei, Zhigang, Richard C. Rice, Masataka Yatomi, and Kamran M. Nikbin. "The Equivalency-Based Linear Regression Method for Statistical Analysis of Creep/Fatigue Data." In ASME 2010 Pressure Vessels and Piping Division/K-PVP Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/pvp2010-25691.

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Materials scientists and mechanical engineers working on structural integrity are making increasing use of statistical analysis in interpreting creep/fatigue data as they contain an inherent scatter which cannot be substantially reduced even under controlled testing conditions. In practice, in most cases the uniaxial failure or cracking data can be reasonably approximated by a straight line on log-log coordinates, indicating that there is a linear log relationship with the appropriate correlating parameter. Linear regression is the most used method in statistical data analysis and is being recommended in American Society for Testing and Materials (ASTM), British Standards (BS), Det Norske Veritas (DNV) and many other engineering standards. Recently, the current practice on linear regression as adopted by the engineering standards has been critically reviewed, and the shortcomings of these procedures have been clearly demonstrated. A new statistical method based on the equivalency between all variables involved has been proposed for S-N curve analysis. In this paper, a large amount of creep and fatigue data of engineering materials collected from several well known data bases generated in US, Europe, and Japan are systematically analyzed with conventional standard and the new equivalency method. The results are compared and discussed. Finally, a recommendation to improve the fitting parameters taking into account of the scatter in both axes is presented.
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Jeong, David Y. "On Numerical Analyses of Rail Steel Fatigue Crack Growth Data." In 2019 Joint Rail Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/jrc2019-1264.

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For a given material and set of test conditions, fatigue crack propagation behavior can be described by the relationship between cyclic crack-growth rate, da/dN and the fluctuation of stress intensity factor, △K. Such test data are usually displayed in a log-log plot. At intermediate values of △K, fatigue crack-growth data fall along a straight line such that a power-law equation may be used as a curve-fit to the data. Various numerical techniques are applied in order to (1) derive the crack-growth rate and (2) determine the parameters for the power-law equation. Using data from laboratory tests conducted on rail steels, this paper explores the various numerical methods used to characterize fatigue crack-growth behavior. Tests were conducted using two different fracture-mechanics specimens (a standard compact tension specimen and a non-standard single edge notch specimen). Three different numerical techniques were applied to determine the fatigue crack-growth rate, da/dN from test data measuring crack length, a versus number of fatigue cycles, N: (1) secant method, (2) modified secant method, and (3) incremental polynomial method. Four different least squares regression analyses were then applied to determine the parameters for the power law. Moreover, the outcome of these analyses is to determine the combination of numerical techniques which yields the least amount of error when the crack-growth rate equation is integrated and compared to the original a versus N data. Fatigue life calculations performed by integrating the crack-growth rate equation demonstrate the sensitivity of predicted growth rates to the power-law parameters derived from the different regression analyses. This paper explores the various numerical methods and techniques employed to analyze fatigue crack growth data using test data on rail steels.
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Bowman, Anthony J., and Hyunjae Park. "Investigation and Development of Proposed General Pressure Drop and Heat Transfer Correlations for Laminar Flow in a Toroidal Coiled Tube System." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-59872.

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In this paper, the laminar flow pressure drop and heat transfer correlations published and applied to plain, coiled tube heat exchanger systems are extensively investigated. It was found that most correlations obtained for toroidal geometric systems have been applied to the analysis of helical and spiral tube systems. While toroidal (and helical) coils have a constant radius of curvature about the coil center-point (and center-line), spiral coils have a continuously varying radius of curvature, in which the flow does not reach a typical fully developed flow condition. The centrifugal forces, arising from the curved flow path, contribute to the enhancement of heat transfer (at the cost of additional pressure drop) over straight tube heat exchangers of the same length. In this paper, using published correlations and available experimental test data for pressure drop and heat transfer in toroidal tube systems, the proposed general correlations are developed by using a filtered-mean multiple regression method. The Coiling Influence Factors for the friction factor and heat transfer, CIFf and CIFh, respectively; defined and used in the authors’ previous works [1,2,3] it was found that the deviations between the proposed and published correlations are within about 3% for friction factor and 5–20% for heat transfer, depending on working fluid. In order to assess the validity of applying the generalized correlations developed in this work for toroidal tube systems, onto other curved tube systems, a numerical analysis of toroidal coil systems, using the commercially available CFD package (Fluent 6) has been explicitly performed. A comparison is made between the CFD result for average heat transfer (CIFh) with that predicted by the proposed general correlation for toroidal coils and available experimental data. As an extension of this work, a comparison of curved tube over straight tube heat exchanger effectiveness is made to highlight its use as a design optimization parameter and motivation for additional coiled tube heat exchanger research.
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Lai, Heather L., and Brian Hamilton. "Quantifying Non-Linearity in Early Decay Curves of Measured and Computer-Modeled Room Impulse Responses of a Highly Non-Diffuse Room Exhibiting Flutter Echo." In ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-24348.

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Abstract This paper investigates the use of two room acoustics metrics designed to evaluate the degree to which the linearity assumptions of the energy density curves are valid. The study focuses on measured and computer-modeled energy density curves derived from the room impulse response of a space exhibiting a highly non-diffuse sound field due to flutter echo. In conjunction with acoustical remediation, room impulse response measurements were taken before and after the installation of the acoustical panels. A very dramatic decrease in the reverberation time was experienced due to the addition of the acoustical panels. The two non-linearity metrics used in this study are the non-linearity parameter and the curvature. These metrics are calculated from the energy decay curves computed per octave band, based on the definitions presented in ISO 3382-2. The non-linearity parameter quantifies the deviation of the EDC from a straight line fit used to generated T20 and T30 reverberation times. Where the reverberation times are calculated based on a linear regression of the data relating to either −5 to −25 dB for T20 or −5 to −35 dB for T30 reverberation time calculations. This deviation is quantified using the correlation coefficient between the energy decay curve and the linear regression for the specified data. In order to graphically demonstrate these non-linearity metrics, the energy decay curves are plotted along with the linear regression curves for the T20 and T30 reverberation time for both the measured data and two different room acoustics computer-modeling techniques, geometric acoustics modeling and finite-difference wave-based modeling. The intent of plotting these curves together is to demonstrate the relationship between these metrics and the energy decay curve, and to evaluate their use for quantifying degree of non-linearity in non-diffuse sound fields. Observations of these graphical representations are used to evaluate the accuracy of reverberation time estimations in non-diffuse environments, and to evaluate the use of these non-linearity parameters for comparison of different computer-modeling techniques or room configurations. Using these techniques, the non-linearity parameter based on both T20 and T30 linear regression curves and the curvature parameter were calculated over 250–4000 Hz octave bands for the measured and computer-modeled room impulse response curves at two different locations and two different room configurations. Observations of these calculated results are used to evaluate the consistency of these metrics, and the application of these metrics to quantifying the degree of non-linearity of the energy decay curve derived from a non-diffuse sound field. These calculated values are also used to evaluate the differences in the degree of diffusivity between the measured and computer-modeled room impulse response. Acoustical computer modeling is often based on geometrical acoustics using ray-tracing and image-source algorithms, however, in non-diffuse sound fields, wave based methods are often able to better model the characteristic sound wave patterns that are developed. It is of interest to study whether these improvements in the wave based computer-modeling are also reflected in the non-linearity parameter calculations. The results showed that these metrics provide an effective criteria for identifying non-linearity in the energy decay curve, however for highly non-diffuse sound fields, the resulting values were found to be very sensitive to fluctuations in the energy decay curves and therefore, contain inconsistencies due to these differences.
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7

Velukkudi Santhanam, Senthil Kumar, Ganesh Pasupathy, and Padmanabhan Kuppuswamy Anantha. "Determination of Superplastic Material Properties for Parent Material and Friction Stir Welded Joint of Al-Alloy AA6061-T6." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-51368.

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Superplastic forming (SPF) takes the advantage of the metallurgical phenomenon of superplasticity (SP) to form complex and highly intricate bulk and sheet metal parts. SP refers to the extraordinary formability of certain metals and alloys, ceramics, composites (both metallic- and ceramic-based), dispersion strengthened materials, nanostructured materials and bulk metallic glasses, which allows them to suffer elongations of several hundred percent under the action of tensile forces. The superplastic forming characteristics of materials like aluminium, titanium and magnesium alloys have been clearly identified in order to produce complicated near-net shapes. These materials are used in the aeronautical manufacturing industry and automotive manufacturing industries due to the significant weight (by ∼ 30%) and cost (by ∼ 50%) saving that is possible. Some research work has proved superplastic forming of friction stir welded (FSW) joints also. The FSW joint efficiencies have been characterized by mechanical and metallurgical examination. Studies are also available on the behavior of FSW joints of similar and dissimilar metals. Information on the performance of friction stir welded joints during superplastic forming is rather limited, but it is important to achieve excellent properties in the friction stir welded joints also during superplastic forming. FSP (friction stir processing) – SPF (superplastic forming) is presently being promoted as a very viable near-net shape technology for making very large and complicated sheet metal products. To achieve this superplastic material parameters are much required in industry to develop new shapes. One has to understand the flow rule relationship and mechanics involved during sheet metal forming at high temperature to select the material and forming tool with selected process parameters. This paper deals with the determination of superplastic material properties of non-superplastic aluminum alloy AA6061-T6. The superplastic material properties like strain rate sensitivity index, flow stress and strain rate were determined for both the selected material and friction stir welded sheets at various tool rotation speeds. The superplastic free blow forming experiments were performed for various constant temperatures and pressure for the parent material. Similarly the superplastic free blow forming experiments were performed for the friction stir welded joint for various tool rotation speed at constant temperature. The methods were used to determine the material properties are straight line fit method and polynomial regression method. The superplastic forming height is significantly high in case of the FSW specimens at 2000 rpm, the initial forming rate is faster and the strain rate sensitivity index obtained is also higher when compared to the parent material properties. The strain rate sensitivity index obtained for friction stir welded specimen during superplastic forming was foundto have improved when compared to the parent material.
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Cunha, Sérgio B. "Forecasting the Consequences of Pipeline Failures." In 2014 10th International Pipeline Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/ipc2014-33036.

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The consequences of the accidental release of petroleum based liquids or natural gas from onshore pipelines are studied. Damage to property, environment and society are considered. Property damage and environmental reparation costs are evaluated directly from publicly available data. Straight forward regression models are proposed to quantify these types of consequence, considering the released fluid and the characteristics of the environment. Societal impact, taken as the number of casualties, is evaluated by combining approximated fire models, heat vs. mortality correlations, population density and the statistical value of life. For gas, a jet fire model is employed; the heat flux is parameterized by the pressure and the failure size. For liquid releases, either pool or jet fire model might be employed, according to the size of the hole. The heat flux of the pool fire model depends on the size of the pool, which is determined by a correlation between released volume and affected area. On the jet fire model the heat flux is parameterized by the release rate and the heat of combustion. This study may serve as basis for the estimation of the consequences of failure in the evaluation of the risk of operating hazard liquids and natural gas pipelines.
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9

Suharto, K. S. "Structural Health Monitoring of An Offshore Platform Trend of Corrosion and Marine Growth With Predictive Maintenance." In Digital Technical Conference. Indonesian Petroleum Association, 2020. http://dx.doi.org/10.29118/ipa20-se-424.

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Nowadays, there are a lot of oil and gas exploration activities all over the world, especially offshore. Oil and gas production itself has many supporting factors in the production process, of which one of them is the offshore platform. Over time, the offshore platform will experience a degradation in strength from the initial design, therefore offshore platforms need to carry out appropriate inspection, maintenance and repair (IMR) programs so that the offshore platform does not reach critical conditions and disrupt the oil and gas production process, resulting in significant losses and not achieving the planned production rates. The offshore platform design is a combination of steel structure and other materials, methods, and loads that are initially worked at the port, such as wave loads, currents, and several other parameters such as gravity, wind, and earthquake. Most of the offshore platforms that currently exist (worldwide) were made in the period of oil investment that developed between 1970 and 1980, thus, the platform's age has now reached 40 to 50 years (El-Reedy, 2012). This research data based on a platform in the Makassar strait between the islands of Kalimantan and Sulawesi. The method used in this research is the platform's design, data observation, data analysis, data processing, and statistical multilinear regression analysis. This research is investigating the degradation trend of the offshore platform and predicting the future of potential failure because of corrosion and marine growth. After the platform encounters degradation over several years, it will decrease the service life before the degradation starts to matter. The results show after data processing that, offshore platforms encounter degradation over several years, and it is presented by a graph containing the condition trend over a multi-year condition because of corrosion and marine growth, and its effect towards unity check (UC) that define structure health. Unity check value produced from allowable stress versus capacity. In this research, structural health monitoring is predicting the potential failure of the platform towards its UC effect of corrosion and marine growth, with a max of the UC value is 0.8 because its a critical value. The failure of the structure possibly would have an affect on the cost-effectiveness. Therefore it is necessary to predict when the structure will fail so that the costs used are effective. Unfortunately, there is no core data for the environmental changes every year at the platform, so the model's equation does not include environmental conditions.
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