Relevant bibliographies by topics / Count data modelling

Academic literature on the topic 'Count data modelling'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Count data modelling"

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Safari-Katesari, Hadi, S. Yaser Samadi, and Samira Zaroudi. "Modelling count data via copulas." Statistics 54, no. 6 (November 1, 2020): 1329–55. http://dx.doi.org/10.1080/02331888.2020.1867140.

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Snell, Joyce, and J. K. Lindsey. "Modelling Frequency and Count Data." Journal of the Royal Statistical Society. Series A (Statistics in Society) 159, no. 1 (1996): 188. http://dx.doi.org/10.2307/2983489.

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Lindsey, J. K. "Modelling Frequency and Count Data." Biometrics 54, no. 1 (March 1998): 397. http://dx.doi.org/10.2307/2534035.

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Owen, Willis L. "Modelling Frequency and Count Data." Technometrics 38, no. 3 (August 1996): 290–91. http://dx.doi.org/10.1080/00401706.1996.10484515.

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Saei, Ayoub, and Ray Chambers. "MODELLING TRUNCATED AND CLUSTERED COUNT DATA." Australian New Zealand Journal of Statistics 47, no. 3 (September 2005): 339–49. http://dx.doi.org/10.1111/j.1467-842x.2005.00399.x.

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Shaddick, G., L. L. Choo, and S. G. Walker. "Modelling correlated count data with covariates." Journal of Statistical Computation and Simulation 77, no. 11 (November 2007): 945–54. http://dx.doi.org/10.1080/10629360600851974.

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Ullah, Shahid, Caroline F. Finch, and Lesley Day. "Statistical modelling for falls count data." Accident Analysis & Prevention 42, no. 2 (March 2010): 384–92. http://dx.doi.org/10.1016/j.aap.2009.08.018.

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Wearden, John H. "Modelling Chronometric Counting." Timing & Time Perception 4, no. 3 (October 20, 2016): 271–98. http://dx.doi.org/10.1163/22134468-00002070.

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Participants performed on a temporal generalization task with standard durations being either 4 or 8 s, and comparison durations ranging from 2.5 to 5.5, or 5 to 11 s. They were required to count during all stimulus presentations, and counts were recorded as spacebar presses. Generalization gradients around both standard values peaked at the standard, but the gradient from the 8-s condition was steeper. Measured counts had low variance, both within trials and between trials, and a start process, which was different from the counting sequence, could also be identified in data. A computer model assuming that a comparison duration was identified as the standard when the count value for the comparison was one that had previously occurred for a standard fitted the temporal generalization gradients well. The model was also applied to some published data on temporal reproduction with counting, and generally fitted data adequately. The model makes a distinction between the variance of the count unit from one trial to another, and the counts within the trial, and this distinction was related to the overall variance of behaviours resulting from counting, and the ways in which variability of timing measures change with the duration timed.
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Reboussin, Beth. "Book Review: Modelling frequency and count data." Statistical Methods in Medical Research 7, no. 3 (June 1998): 321. http://dx.doi.org/10.1177/096228029800700309.

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Chee, Chew-Seng. "Modelling of count data using nonparametric mixtures." AStA Advances in Statistical Analysis 100, no. 3 (September 18, 2015): 239–57. http://dx.doi.org/10.1007/s10182-015-0255-7.

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Dissertations / Theses on the topic "Count data modelling"

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Quoreshi, Shahiduzzaman. "Modelling high frequency financial count data /." Umeå : Umeå University, 2005. http://swopec.hhs.se/umnees/abs/umnees0656.htm.

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Hellström, Jörgen. "Count data modelling and tourism demand." Doctoral thesis, Umeå universitet, Institutionen för nationalekonomi, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-82168.

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This thesis consists of four papers concerning modelling of count data and tourism demand. For three of the papers the focus is on the integer-valued autoregressive moving average model class (INARMA), and especially on the ENAR(l) model. The fourth paper studies the interaction between households' choice of number of leisure trips and number of overnight stays within a bivariate count data modelling framework. Paper [I] extends the basic INAR(1) model to enable more flexible and realistic empirical economic applications. The model is generalized by relaxing some of the model's basic independence assumptions. Results are given in terms of first and second conditional and unconditional order moments. Extensions to general INAR(p), time-varying, multivariate and threshold models are also considered. Estimation by conditional least squares and generalized method of moments techniques is feasible. Monte Carlo simulations for two of the extended models indicate reasonable estimation and testing properties. An illustration based on the number of Swedish mechanical paper and pulp mills is considered. Paper[II] considers the robustness of a conventional Dickey-Fuller (DF) test for the testing of a unit root in the INAR(1) model. Finite sample distributions for a model with Poisson distributed disturbance terms are obtained by Monte Carlo simulation. These distributions are wider than those of AR(1) models with normal distributed error terms. As the drift and sample size, respectively, increase the distributions appear to tend to T-2) and standard normal distributions. The main results are summarized by an approximating equation that also enables calculation of critical values for any sample and drift size. Paper[III] utilizes the INAR(l) model to model the day-to-day movements in the number of guest nights in hotels. By cross-sectional and temporal aggregation an INARMA(1,1) model for monthly data is obtained. The approach enables easy interpretation and econometric modelling of the parameters, in terms of daily mean check-in and check-out probability. Empirically approaches accounting for seasonality by dummies and using differenced series, as well as forecasting, are studied for a series of Norwegian guest nights in Swedish hotels. In a forecast evaluation the improvements by introducing economic variables is minute. Paper[IV] empirically studies household's joint choice of the number of leisure trips and the total night to stay on these trips. The paper introduces a bivariate count hurdle model to account for the relative high frequencies of zeros. A truncated bivariate mixed Poisson lognormal distribution, allowing for both positive as well as negative correlation between the count variables, is utilized. Inflation techniques are used to account for clustering of leisure time to weekends. Simulated maximum likelihood is used as estimation method. A small policy study indicates that households substitute trips for nights as the travel costs increase.

Härtill 4 uppsatser.


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Leonte, Daniela School of Mathematics UNSW. "Flexible Bayesian modelling of gamma ray count data." Awarded by:University of New South Wales. School of Mathematics, 2003. http://handle.unsw.edu.au/1959.4/19147.

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Bayesian approaches to prediction and the assessment of predictive uncertainty in generalized linear models are often based on averaging predictions over different models, and this requires methods for accounting for model uncertainty. In this thesis we describe computational methods for Bayesian inference and model selection for generalized linear models, which improve on existing techniques. These methods are applied to the building of flexible models for gamma ray count data (data measuring the natural radioactivity of rocks) at the Castlereagh Waste Management Centre, which served as a hazardous waste disposal facility for the Sydney region between March 1978 and August 1998. Bayesian model selection methods for generalized linear models enable us to approach problems of smoothing, change point detection and spatial prediction for these data within a common methodological and computational framework, by considering appropriate basis expansions of a mean function. The data at Castlereagh were collected in the following way. A number of boreholes were drilled at the site, and for each borehole a gamma ray detector recorded gamma ray emissions at different depths as the detector was raised gradually from the bottom of the borehole to ground level. The profile of intensity of gamma counts can be informative about the geology at each location, and estimation of intensity profiles raises problems of smoothing and change point detection for count data. The gamma count profiles can also be modelled spatially, to inform the geological profile across the site. Understanding the geological structure of the site is important for modelling the transport of chemical contaminants beneath the waste disposal area. The structure of the thesis is as follows. Chapter 1 describes the Castlereagh hazardous waste site and the geophysical data, which motivated the methodology developed in this research. We summarise the principles of Gamma Ray (GR) logging, a method routinely employed by geophysicists and environmental engineers in the detailed evaluation of hazardous site geology, and detail the use of the Castlereagh data in this research. In Chapter 2 we review some fundamental ideas of Bayesian inference and computation and discuss them in the context of generalised linear models. Chapter 3 details the theoretical basis of our work. Here we give a new Markov chain Monte Carlo sampling scheme for Bayesian variable selection in generalized linear models, which is analogous to the well-known Swendsen-Wang algorithm for the Ising model. Special cases of this sampling scheme are used throughout the rest of the thesis. In Chapter 4 we discuss the use of methods for Bayesian model selection in generalized linear models in two specific applications, which we implement on the Castlereagh data. First, we consider smoothing problems where we flexibly estimate the dependence of a response variable on one or more predictors, and we apply these ideas to locally adaptive smoothing of gamma ray count data. Second, we discuss how the problem of multiple change point detection can be cast as one of model selection in a generalized linear model, and consider application to change point detection for gamma ray count data. In Chapter 5 we consider spatial models based on partitioning a spatial region of interest into cells via a Voronoi tessellation, where the number of cells and the positions of their centres is unknown, and show how these models can be formulated in the framework of established methods for Bayesian model selection in generalized linear models. We implement the spatial partition modelling approach to the spatial analysis of gamma ray data, showing how the posterior distribution of the number of cells, cell centres and cell means provides us with an estimate of the mean response function describing spatial variability across the site. Chapter 6 presents some conclusions and suggests directions for future research. A paper based on the work of Chapter 3 has been accepted for publication in the Journal of Computational and Graphical Statistics, and a paper based on the work in Chapter 4 has been accepted for publication in Mathematical Geology. A paper based on the spatial modelling of Chapter 5 is in preparation and will be submitted for publication shortly. The work in this thesis was collaborative, to a smaller or larger extent in its various components. I authored Chapters 1 and 2 entirely, including definition of the problem in the context of the CWMC site, data gathering and preparation for analysis, review of the literature on computational methods for Bayesian inference and model selection for generalized linear models. I also authored Chapters 4 and 5 and benefited from some of Dr Nott's assistance in developing the algorithms. In Chapter 3, Dr Nott led the development of sampling scheme B (corresponding to having non-zero interaction parameters in our Swendsen-Wang type algorithm). I developed the algorithm for sampling scheme A (corresponding to setting all algorithm interaction parameters to zero in our Swendsen-Wang type algorithm), and performed the comparison of the performance of the two sampling schemes. The final discussion in Chapter 6 and the direction for further research in the case study context is also my work.
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Jansakul, Naratip. "Some aspects of modelling overdispersed and zero-inflated count data." Thesis, University of Exeter, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364435.

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Kaskasamkul, Panicha. "Capture-recapture estimation and modelling for one-inflated count data." Thesis, University of Southampton, 2018. https://eprints.soton.ac.uk/424742/.

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Capture-recapture methods are used to estimate the unknown size of a target population whose size cannot be reasonably enumerated. This thesis proposes the estimators and the models specifically designed to estimate the size of a population for one-inflated capture-recapture count data allowing for heterogeneity. These estimators can assist with overestimation problems occurring from one-inflation that can be seen in several areas of researches. The estimators are developed under three approaches. The first approach is based on a modification by truncating singletons and applying the conventional Turing and maximum likelihood estimation approach to the one-truncated geometric data for estimating the parameter p0. These p0 are applied to the Horvitz-Thompson approach for the modified Turing estimator (T_OT) and the modified maximum likelihood estimator (MLE_OT). The second approach is the model-based approach. It focuses on developing a statistical model that describes the mechanism to generate the extra of count ones. The new estimator MLE_ZTOI is developed from a maximum likelihood approach by using the nested EM algorithm based upon the zero-truncated one-inflated geometric distribution. The last approach focuses on modifying a classical Chao’s estimator to involve the frequency of counts of twos and threes instead of the frequency of counts of ones and twos. The modified Chao estimator (MC) is asymptotic unbiased estimator for a power series distribution with and without one-inflation and provides a lower bound estimator under a mixture of power series distributions with and without one-inflation. The three bias-correction versions of the modified Chao estimator have been developed to reduce the bias when the sample size is small. A variance approximation of MC and MC3 are also constructed by using a conditioning technique. All of the proposed estimators are assessed through simulation studies. The real data sets are provided for understanding the methodologies.
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Anan, Orasa. "Capture-recapture modelling for zero-truncated count data allowing for heterogeneity." Thesis, University of Southampton, 2016. https://eprints.soton.ac.uk/402562/.

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Capture-recapture modelling is a powerful tool for estimating an elusive target population size. This thesis proposes four new population size estimators allowing for population heterogeneity. The first estimator is developed under the zero-truncated of generalised Poisson distribution (ZTGP), called the MLEGP. The two parameters of the ZTGP are estimated by using a maximum likelihood with the Expectation-Maximisation algorithm (EM algorithm). The second estimator is the population size estimator under the zero-truncated Conway-Maxwell-Poisson distribution (ZTCMP). The benefits of using the Conway-Maxwell-Poisson distribution (CMP) are that it includes the Bernoulli, Poisson and geometric distribution as special cases. It is also flexible for over- and under-dispersions relative to the original Poisson model. Moreover, the parameter estimates can be achieved by a simple linear regression approach. The uncertainty in estimating variances of the unknown population size under new estimator is studied with analytic and resampling approaches. The geometric distribution is one of the nested models under the Conway-Maxwell-Poisson distribution, the Turing and the Zelterman estimators are extended for the geometric distribution and its related model, respectively. Variance estimation and confidence intervals are constructed by the normal approximation method. An uncertainty of variance estimation of population size estimators for single marking capture-recapture data is studied in the final part of the research. Normal approximation and three resample approaches of variance estimation are compared for the Chapman and the Chao estimators. All of the approaches are assessed through simulations, and real data sets are provided as guidance for understanding the methodologies.
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Glaser, Stephanie [Verfasser], and Robert [Akademischer Betreuer] Jung. "Modelling of spatial effects in count data / Stephanie Glaser ; Betreuer: Robert Jung." Hohenheim : Kommunikations-, Informations- und Medienzentrum der Universität Hohenheim, 2017. http://d-nb.info/1129779661/34.

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Fiaccone, Rosemeire L. "Modelling multivariate binary and count data, with application to infant diarrhoea in Brazil." Thesis, Lancaster University, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.440384.

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Quoreshi, Shahiduzzaman. "Time series modelling of high frequency stock transaction data." Doctoral thesis, Umeå : Department of Economics, Umeå universitet, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-757.

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Maloney, Alan. "Optimal (Adaptive) Design and Estimation Performance in Pharmacometric Modelling." Doctoral thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-182284.

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The pharmaceutical industry now recognises the importance of the newly defined discipline of pharmacometrics. Pharmacometrics uses mathematical models to describe and then predict the performance of new drugs in clinical development. To ensure these models are useful, the clinical studies need to be designed such that the data generated allows the model predictions to be sufficiently accurate and precise. The capability of the available software to reliably estimate the model parameters must also be well understood.  This thesis investigated two important areas in pharmacometrics: optimal design and software estimation performance. The three optimal design papers progressed significant areas of optimal design research, especially relevant to phase II dose response designs. The use of exposure, rather than dose, was investigated within an optimal design framework. In addition to using both optimal design and clinical trial simulation, this work employed a wide range of metrics for assessing design performance, and was illustrative of how optimal designs for exposure response models may yield dose selections quite different to those based on standard dose response models. The investigation of the optimal designs for Poisson dose response models demonstrated a novel mathematical approach to the necessary matrix calculations for non-linear mixed effects models. Finally, the enormous potential of using optimal adaptive designs over fixed optimal designs was demonstrated. The results showed how the adaptive designs were robust to initial parameter misspecification, with the capability to "learn" the true dose response using the accruing subject data. The two estimation performance papers investigated the relative performance of a number of different algorithms and software programs for two complex pharmacometric models. In conclusion these papers, in combination, cover a wide spectrum of study designs for non-linear dose/exposure response models, covering: normal/non-normal data, fixed/mixed effect models, single/multiple design criteria metrics, optimal design/clinical trial simulation, and adaptive/fixed designs.
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Books on the topic "Count data modelling"

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Modelling frequency and count data. Oxford: Clarendon Press, 1995.

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Tan, Min. Extended Poisson process modelling of count data. Birmingham: University of Birmingham, 2003.

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Maiti, Raju. Modelling and coherent forecasting of zero-inflated time series count data. Ahmedabad: Indian Institute of Management, 2013.

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Zimmermann, Klaus F., and Ralph Rotte. Count Data Modelling: An Intro. Elgar Publishing Limited, Edward, 1997.

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Lindsey, James K. Modelling Frequency and Count Data. Oxford Statistical Science Series. Oxford University Press, 1995.

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Book chapters on the topic "Count data modelling"

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Blundell, R., R. Griffith, and F. Windmeijer. "Dynamics and Correlated Responses in Longitudinal Count Data Models." In Statistical Modelling, 35–42. New York, NY: Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-0789-4_5.

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Knape, Jonas, Niclas Jonzén, Martin Sköld, and Leonid Sokolov. "Multivariate State Space Modelling of Bird Migration Count Data." In Modeling Demographic Processes In Marked Populations, 59–79. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-78151-8_4.

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Ng, Shu Kay, Liming Xiang, and Kelvin Kai Wing Yau. "Mixture of Generalized Linear Models for Count or Categorical Data." In Mixture Modelling for Medical and Health Sciences, 77–103. Boca Raton : CRC Press, Taylor & Francis Group, 2019.: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429195181-4.

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Ronning, Gerd, and Robert C. Jung. "Estimation of a First Order Autoregressive Process with Poisson Marginals for Count Data." In Advances in GLIM and Statistical Modelling, 188–94. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2952-0_29.

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Boucher, Jean-Philippe, Michel Denuit, and Montserrat Guillén. "Modelling of Insurance Claim Count with Hurdle Distribution for Panel Data." In Advances in Mathematical and Statistical Modeling, 45–59. Boston: Birkhäuser Boston, 2008. http://dx.doi.org/10.1007/978-0-8176-4626-4_4.

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Liesenfeld, Roman, Ingmar Nolte, and Winfried Pohlmeier. "Modelling financial transaction price movements: a dynamic integer count data model." In High Frequency Financial Econometrics, 167–97. Heidelberg: Physica-Verlag HD, 2008. http://dx.doi.org/10.1007/978-3-7908-1992-2_8.

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Quicke, Donald, Buntika A. Butcher, and Rachel Kruft Welton. "More generalized linear modelling." In Practical R for biologists: an introduction, 171–86. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789245349.0015.

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Abstract This chapter employs generalized linear modelling using the function glm when we know that variances are not constant with one or more explanatory variables and/or we know that the errors cannot be normally distributed, for example, they may be binary data, or count data where negative values are impossible, or proportions which are constrained between 0 and 1. A glm seeks to determine how much of the variation in the response variable can be explained by each explanatory variable, and whether such relationships are statistically significant. The data for generalized linear models take the form of a continuous response variable and a combination of continuous and discrete explanatory variables.
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Quicke, Donald, Buntika A. Butcher, and Rachel Kruft Welton. "More generalized linear modelling." In Practical R for biologists: an introduction, 171–86. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789245349.0171.

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Abstract This chapter employs generalized linear modelling using the function glm when we know that variances are not constant with one or more explanatory variables and/or we know that the errors cannot be normally distributed, for example, they may be binary data, or count data where negative values are impossible, or proportions which are constrained between 0 and 1. A glm seeks to determine how much of the variation in the response variable can be explained by each explanatory variable, and whether such relationships are statistically significant. The data for generalized linear models take the form of a continuous response variable and a combination of continuous and discrete explanatory variables.
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Bellier, Edwige, Pascal Monestiez, and Christophe Guinet. "Geostatistical Modelling of Wildlife Populations: A Non-stationary Hierarchical Model for Count Data." In Quantitative Geology and Geostatistics, 1–12. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-2322-3_1.

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MacKenzie, Gilbert. "Regression Models for Repeated Medical Random Counts." In Advances in Stochastic Modelling and Data Analysis, 177–85. Dordrecht: Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-017-0663-6_11.

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Conference papers on the topic "Count data modelling"

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Abdullah, M. Asrul Affendi, Siti Afiqah Muhamad Jamil, and Y. M. Tengku Mardhiah Tengku Jalal. "Modelling count data: An application to a breast cancer data in Malaysia." In INTERNATIONAL CONFERENCE ON MATHEMATICS, ENGINEERING AND INDUSTRIAL APPLICATIONS 2016 (ICoMEIA2016): Proceedings of the 2nd International Conference on Mathematics, Engineering and Industrial Applications 2016. Author(s), 2016. http://dx.doi.org/10.1063/1.4965123.

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Pekina, Anastasia, Yulia Maslennikova, and Vladimir Bochkarev. "Modelling of Magnetic Storms Count Data Using Nonlinear Poisson Regression." In 2019 Russian Open Conference on Radio Wave Propagation (RWP). IEEE, 2019. http://dx.doi.org/10.1109/rwp.2019.8810365.

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Jurgensen, Jan Henning, Lars Nordstrom, Patrik Hilber, Elin Andreasson, Anna Lilly Brodersson, and Lalit Goel. "Modelling of Recurrent Circuit Breaker Failures with Regression Models for Count Data." In 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). IEEE, 2018. http://dx.doi.org/10.1109/pmaps.2018.8440209.

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Dular, Matevzˇ, and Olivier Coutier-Delgosha. "Numerical Modelling of Cavitation Erosion." In ASME 2008 Fluids Engineering Division Summer Meeting collocated with the Heat Transfer, Energy Sustainability, and 3rd Energy Nanotechnology Conferences. ASMEDC, 2008. http://dx.doi.org/10.1115/fedsm2008-55034.

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The goal of the work is to develop an expert system for monitoring and control of cavitation in hydraulic machines and to research the possibility of cavitation erosion prediction using CFD tools only. The geometry in question is a simple single hydrofoil, which is exposed to the developed cavitating flow at different flow conditions. The work was divided in more parts: numerical simulation of cavitating flow, experimental evaluation of the simulation, measurements of cavitation erosion, development of cavitation erosion model and finally the prediction of cavitation erosion using solely CFD. A study of erosion effects of cavitation on simple single hydrofoil configurations in a cavitation tunnel was made. A thin copper foil, applied to the surface of the hydrofoils, was used as an erosion sensor. A pit-count method was used to evaluate the damage. The cavitation phenomenon on hydrofoils at different flow conditions (system pressure, flow velocity) was observed. The erosion model is based on the physical description of different phenomena (cavitation cloud implosion, pressure wave emission and its attenuation, micro-jet formation and finally pit formation), which are involved in the process of pit formation. The cavitating flow was simulated using an “in house” CFD code which uses barotropic state law. The code was previously tested on numerous experiments. For the present case the predictions of velocity profiles and pressure evolutions in the vicinity of the hydrofoil were compared to experimentally measured data. In all cases a very good correlation was obtained. The erosion model was implemented into the code. It used values of local pressure, local void fraction and flow velocity to determine the magnitude of damage at a certain point. The results of prediction were compared to the experimentally measured damage on the hydrofoil and it was shown that it is possible, for this simple case, to use solely CFD tools to predict cavitation erosion evolution in time, final extent and final magnitude with a very good accuracy.
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Ragab, Kasem E., and Lamyaa El-Gabry. "Heat Transfer Analysis of the Surface of Nonfilm-Cooled and Film-Cooled Nozzle Guide Vanes in Transonic Annular Cascade." In ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/gt2017-64982.

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One of the approaches adopted to improve turbine efficiency and increase power to weight ratio is reducing vane count. In the current study, numerical analysis was performed for the heat transfer over the surface of nozzle guide vanes under the condition of reduced vane count using three dimensional computational fluid dynamics (CFD) models. The investigation has taken place in two stages: the baseline nonfilm-cooled nozzle guide vane, and the film-cooled nozzle guide vane. A finite volume based commercial code (ANSYS CFX 15) was used to build and analyze the CFD models. The investigated annular cascade has no heat transfer measurements available; hence in order to validate the CFD models against experimental data, two standalone studies were carried out on the NASA C3X vanes, one on the nonfilm-cooled C3X vane and the other on the film-cooled C3X vane. Different modelling parameters were investigated including turbulence models in order to obtain good agreement with the C3X experimental data, the same parameters were used afterwards to model the industrial nozzle guide vanes. Three Shear Stress Transport (SST) turbulence model variations were evaluated, the SST with Gamma-Theta transition model was found to yield the best agreement with the experimental results; model capabilities were demonstrated when the laminar to turbulent transition took place.
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Kolbjørnsen, Odd, Erik Hammer, Stefano Pruno, Peter Wellsbury, and Malgorzata Kusak. "Norwegian Released Wells Project: Study Design, Material Preparation, Measurements, and Data Analysis." In 2022 SPWLA 63rd Annual Symposium. Society of Petrophysicists and Well Log Analysts, 2022. http://dx.doi.org/10.30632/spwla-2022-0126.

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The Released Wells Initiative is a joint industry project administrated and organized by the Norwegian Oil and Gas Association and funded by the majority of companies operating on the Norwegian Continental Shelf (NCS). The project is unique on a worldwide scale, as it will analyze every drill cutting sample from every exploration and appraisal well in the Norwegian national archive. The archive contains about 700,000 samples of unwashed ditch cuttings from more than 1900 wells. Each sample is washed and dried according to a consistent automated procedure, and preserved both in a digital format, using high-resolution white light (WL) and UV (UV) photography and X Ray Fluorescence (XRF) analysis. The cleaned samples from all the wells are available for future analysis. The eighty most recently released wells were subjected to an extended suite of measurements: X-Ray Diffraction (XRD), automated mineralogy (QEMSCAN), Infrared spectroscopy (IRS) and total organic carbon measurement (TOC). We present details of the study design, sample preparation and analysis process. The extended set of observations are not selected based on the results of other measurements. The data set is therefore an independent source of information void of conditional dependencies between measurement types. We discuss the possibilities that the dataset offers and present results from analysis that have been made. In a set of repeated measurements, we find a high degree of consistency. The correlation between the two samples sets is found to be above 0.95 for XRF, XRD, QEMSCAN, IRS, and for the average photon count in white light images. For the UV images the correlation is lower, however typically these UV images have a low photon count. In a case example we show how the XRF data contribute to understanding the provenance of the Brent Group in a region of the North Sea. We show how the study design enables methods of advanced analytics, where the extended measurement set can be used to train predictive models. In our data analysis we utilize boosting threes to predict e.g., XRD mineralogy from XRF data. We report an out of sample error of 4.5%-6.9%, for quartz, total clay and carbonates. We further discuss opportunities and challenges with the dataset. The dataset provides opportunities to aid interpretation and future decision making, with impacts on drilling, completion, geological interpretation, modelling, production and future projects (including carbon capture and storage). The project is also unique in terms of openness as the complete data set will be released to the public in 2024.
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7

de la Calzada, P., and J. Ferna´ndez-Castan˜eda. "Investigation of Numerical and Physical Modelling Effects on the CFD Simulation of the Unsteady Flow in a HPT Stage." In ASME Turbo Expo 2003, collocated with the 2003 International Joint Power Generation Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/gt2003-38796.

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In order to investigate the unsteady flow behaviour in an HPT stage and the effects on the CFD solution of some numerical and physical modelling assumptions usually undertaken by the engineering community, an ITP in-house unsteady CFD code called Mu2s2T is first validated and then run under different configurations. The code is a fully unstructured code which solves the Reynolds averaged Navier-Stokes equations with a k-ω turbulence model. Hybrid meshes are used by having semi-structured meshes along the profile wall and fully unstructured triangular meshes on the inviscid region. The VKI Brite Euram transonic turbine stage experimental test case is used for the investigation. This turbine is representative of a state of the art HPT and presents high potential interaction due to the vane shock waves. After validating the code in this case, the influence of typical engineering assumptions is investigated. First the influence of the rotor stagger angle is analysed, resulting in a high sensitivity of the predicted pressure level at the front part of the blade and a better matching with the experimental data when an opening of 1° is applied. The influence in the solution of applying an integer airfoil count ratio compared with the solution with exact number off computed by means of phase lagged boundary conditions is also investigated. Additional Euler and Navier-Stokes computations are presented and the influence of the viscous effects is discussed. Finally a simulation including vane trailing edge cooling is performed so that conclusions about the influence of the cooling can be drawn.
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Mahrouqi, Bashara, Ahmed AL Salehi, AL Khansa Aadi, Pierre Olivier, Liali Qasmi, Sakharin Suwannathatsa, and Khalfan Mahrazy. "Rock Types Modelling Impact on Field Development and Volumes; A Case Study from Lower Shuaiba Carbonate Reservoir in Field WU." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211650-ms.

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Abstract Objective/Scope Facies distribution in the static model of carbonate reservoirs provides a framework for the distribution the other petrophysical properties. Lower Shuaiba (LSH) reservoir model in Field WU is aiming to provide improved facies distribution to improve STOIIP calculation and well placement/count. Lower Shuaiba reservoir is characterised by 5 lithostratigraphic cycles with different facies and rock types. These facies are consistent in the lower zones, however they are more variable in the top layers 1A (in the oil window). This layer is dominated by skeletal packstones, skeletal wackestones and thin beds of Rudist floatstones/rudstones as characterised from core data. These facies are comparable in their log responses and therefore cannot be differentiated with logs alone. However, they have different properties (Por, Perm) which have a great impact on STOIIP and well placement. Moreover, the Rudist-rich rock type is interpreted as an imbibed layer which adds more complexity to the reservoir model. Methods, Procedures, Process Two model scenarios were built based on the different facies distribution methods: (1) core-based method, which is limited for few wells and assuming lateral continuity of rock types between the wells; (2) capillary-based method, which uses the Saturation height function to predict the facies. This method uses the idea that facies differ in their capillary entry-height and thus will have different saturation responses. Whenever possible, these were then compared with core data. Results, Observations, Conclusions This new capillary-based model improved the volume estimation compared with the layer-cake model because of the improved mapping of the imbibed Rudist facie. The model allows ranking the proposed wells based on their facies distribution to maximise well production and reduce early water breakthrough. It also explains the anomalies in saturation/production (caused by facies distribution) in some of the existing wells. The study found that the anomalous high peak resistivity is associated with the imbibed Rudist layer (confirmed by log and production test) and should be avoided while drilling the horizontal section. It also highlighted the important of continuous appraisal in the area to add control points for the facies model. Novel or Additive Information The paleo Free Water Level (FWL) for Lower Shuaiba formation was determined based on the best fit between a core-based (drainage) saturation-height models and Archie log saturation. The FWL could not be identified on pressure plot due to the large scatter of pressure points which is typical for this low permeability /low mobility reservoir. Other factors such as wettability heterogeneities, fluid fill history and hysteresis makes it difficult to define the oil gradient and hence FWL. This FWL and saturation height function (SHF) were then used to predict the rock types, based on best match iterations between saturation log and saturation calculated by SHF.
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BEILICCI, Robert, and Erika Beata Maria BEILICCI. "Advance Hydraulic Modelling of Barzava River, Romania, Caras Severin County." In Air and Water – Components of the Environment 2021 Conference Proceedings. Casa Cărţii de Ştiinţă, 2021. http://dx.doi.org/10.24193/awc2021_17.

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Study case is situated in Caras Severin county. To solve theoretical problems of movement of water in the river Barzava, it requires modelling of water flow in this case. Numerical modelling was performed using the program MIKE11. Advanced computational modules are included for description of flow over hydraulic structures, including possibilities to describe structure operation. The Hydrodynamic (HD) module is the nucleus of the MIKE 11 modelling system and forms the basis for most modules including Flood Forecasting, Advection-Dispersion, Water Quality and Non-cohesive sediment transport modules. The MIKE 11 HD module solves the vertically integrated equations for the conservation of mass and momentum, i.e. the Saint-Venant equations. The input data are: area plan with location of cross sections; cross sections topographical data and roughness of river bed; flood discharge hydrograph. Advanced computational modules are included for description of flow over hydraulic structures, including possibilities to describe structure operation. After simulation with MIKE 11 result the water level in each cross section.
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Li, Yaqiong, Xuhui Fan, Ling Chen, Bin Li, Zheng Yu, and Scott A. Sisson. "Recurrent Dirichlet Belief Networks for interpretable Dynamic Relational Data Modelling." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/342.

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The Dirichlet Belief Network~(DirBN) has been recently proposed as a promising approach in learning interpretable deep latent representations for objects. In this work, we leverage its interpretable modelling architecture and propose a deep dynamic probabilistic framework -- the Recurrent Dirichlet Belief Network~(Recurrent-DBN) -- to study interpretable hidden structures from dynamic relational data. The proposed Recurrent-DBN has the following merits: (1) it infers interpretable and organised hierarchical latent structures for objects within and across time steps; (2) it enables recurrent long-term temporal dependence modelling, which outperforms the one-order Markov descriptions in most of the dynamic probabilistic frameworks; (3) the computational cost scales to the number of positive links only. In addition, we develop a new inference strategy, which first upward-and-backward propagates latent counts and then downward-and-forward samples variables, to enable efficient Gibbs sampling for the Recurrent-DBN. We apply the Recurrent-DBN to dynamic relational data problems. The extensive experiment results on real-world data validate the advantages of the Recurrent-DBN over the state-of-the-art models in interpretable latent structure discovery and improved link prediction performance.
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