Hydraulic Model Calibration of a Nuclear Plant Service Water System

2016 ◽  
Author(s):  
Erin A. Onat ◽  
Trey W. Walters ◽  
David M. Mobley ◽  
James J. Mead
Keyword(s):  
Water System ◽  
Complete Data ◽  
Second Phase ◽  
Data Set ◽  
Flow Rates ◽  
Pressure Drops ◽  
Calibration Process ◽  
Partial Data ◽  
Service Water ◽  
Data Calibration

As pipe networks age, build-up [scaling] and corrosion decrease pipe diameter and increase pipe roughness, leading to significant pressure drops and lower flow rates. When modeling the hydraulics of these systems, calibrating the pipes to account for additional scaling and/or fouling can be vital to accurately predicting the hydraulic behavior of the system. An automated, multi-variable goal-seeking software was used to calibrate the raw water system of the Duke McGuire Nuclear Station (MNS). This calibration process involved three phases. The first phase was the testing of the automated, multivariable goal-seeking software on a previously calibrated system. The second phase was the calibration of a partial data set. The third phase was the calibration of a complete data set. The automated goal-seeking software was found to have varying degrees of success in each phase. At the conclusion of the calibration process, the partial data calibration of two parallel systems at MNS yielded average overall calibration accuracies of 2.1% and 1% for flow rates, and 1.2 psig (8.4 kPa-g) and 1.7 psig (11.9 kPa-g) for pressures. The complete data calibration of one of these systems at MNS yielded an average overall calibration accuracy of 2.3% for flow rates, and 1.4 psig (9.5 kPa-g) for pressures.

Estimation of Gas and Liquid Flow Rates in Gas-Liquid Two-Phase System

2002 ◽  
Author(s):  
Koji Mori ◽  
Tetsumasa Ono ◽  
Masuo Kaji ◽  
Toru Sawai
Keyword(s):  
Gas Phase ◽  
Liquid Flow ◽  
Annular Flow ◽  
Water System ◽  
Phase System ◽  
Two Phase ◽  
Flow Rates ◽  
Mean Values ◽  
Pressure Drops ◽  
Flow Channels

A new method of estimating gas and liquid flow rates is proposed for a gas-liquid two-phase flow system. The method involves measurement of pressure drops in horizontal and vertical flow channels, and calculation of flow rates by Lockhart-Martinelli correlation. The method does not require the insertion of sensing device into the flow channel and does not rely on previously calibrated correlations. Experiments are performed in slug, froth and annular flow regimes for an air-water system, and the usefulness of the proposed method is examined. The results reveal that gas and liquid flow rates can be estimated with the accuracies of 45% for gas phase and 37% for liquid phase with respect to mean values.

Identification of Coronary Artery Disease using Artificial Neural Network and Case Based Reasoning

2020 ◽  
Vol 13 ◽  
Author(s):  
Varun Sapra ◽  
M.L Saini ◽  
Luxmi Verma
Keyword(s):  
Neural Network ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Early Stage ◽  
Second Phase ◽  
Case Based Reasoning ◽  
Data Set ◽  
Artery Disease ◽  
Severity Of The Disease ◽  
Case Based

Background: Cardiovascular diseases are increasing at an alarming rate with very high rate of mortality. Coronary artery disease is one of the type of cardiovascular disease, which is not easily diagnosed in its early stage. Prevention of Coronary Artery Disease is possible only if it is diagnosed, at early stage and proper medication is done. Objective: An effective diagnosis model is important not only for the early diagnosis but also to check the severity of the disease. Method: In this paper, a hybrid approach is followed, with the integration of deep learning (multi-layer perceptron) with Case based reasoning to design analytical framework. This paper suggests two phases of the study, one in which the patient is diagnosed for Coronary artery disease and in second phase, if the patient is suffering from the disease then employing Case based reasoning to diagnose the severity of the disease. In the first phase, multilayer perceptron is implemented on reduced dataset and with time-based learning for stochastic gradient descent respectively. Results: The classification accuracy is increase by 4.18 % with reduced data set using deep neural network with time based learning. In second phase, if the patient is diagnosed as positive for Coronary artery disease, then it triggers the Case based reasoning system to retrieve from the case base, the most similar case to predict the severity for that patient. The CBR model achieved 97.3% accuracy. Conclusion: The model can be very useful for medical practitioners as a supporting decision system and thus can save the patients from unnecessary medical expenses on costly tests and can improve the quality and effectiveness of medical treatment.

scholarly journals Genetic and Genome-Wide Association Analysis of Yearling Weight Gain in Israel Holstein Dairy Calves

Genes ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 708
Author(s):  
Moran Gershoni ◽  
Joel Ira Weller ◽  
Ephraim Ezra
Keyword(s):  
Weight Gain ◽  
Milk Fat ◽  
Variance Components ◽  
Complete Data ◽  
Variance Component Analysis ◽  
Coefficient Of Determination ◽  
Birth Date ◽  
Analysis Model ◽  
Data Set ◽  
Genetic Evaluations

Yearling weight gain in male and female Israeli Holstein calves, defined as 365 × ((weight − 35)/age at weight) + 35, was analyzed from 814,729 records on 368,255 animals from 740 herds recorded between 1994 and 2021. The variance components were calculated based on valid records from 2008 through 2017 for each sex separately and both sexes jointly by a single-trait individual animal model analysis, which accounted for repeat records on animals. The analysis model also included the square root, linear, and quadratic effects of age at weight. Heritability and repeatability were 0.35 and 0.71 in the analysis of both sexes and similar in the single sex analyses. The regression of yearling weight gain on birth date in the complete data set was −0.96 kg/year. The complete data set was also analyzed by the same model as the variance component analysis, including both sexes and accounting for differing variance components for each sex. The genetic trend for yearling weight gain, including both sexes, was 1.02 kg/year. Genetic evaluations for yearling weight gain was positively correlated with genetic evaluations for milk, fat, protein production, and cow survival but negatively correlated with female fertility. Yearling weight gain was also correlated with the direct effect on dystocia, and increased yearling weight gain resulted in greater frequency of dystocia. Of the 1749 Israeli Holstein bulls genotyped with reliabilities >50%, 1445 had genetic evaluations. As genotyping of these bulls was performed using several single nucleotide polymorhphism (SNP) chip platforms, we included only those markers that were genotyped in >90% of the tested cohort. A total of 40,498 SNPs were retained. More than 400 markers had significant effects after permutation and correction for multiple testing (pnominal < 1 × 10−8). Considering all SNPs simultaneously, 0.69 of variance among the sires’ transmitting ability was explained. There were 24 markers with coefficients of determination for yearling weight gain >0.04. One marker, BTA-75458-no-rs on chromosome 5, explained ≈6% of the variance among the estimated breeding values for yearling weight gain. ARS-BFGL-NGS-39379 had the fifth largest coefficient of determination in the current study and was also found to have a significant effect on weight at an age of 13–14 months in a previous study on Holsteins. Significant genomic effects on yearling weight gain were mainly associated with milk production quantitative trait loci, specifically with kappa casein metabolism.

scholarly journals Characterization of Transport Regimes and the Polar Dome during Arctic Spring and Summer using in-situ Aircraft Measurements

2019 ◽  
Author(s):  
Heiko Bozem ◽  
Peter Hoor ◽  
Daniel Kunkel ◽  
Franziska Köllner ◽  
Johannes Schneider ◽  
...  
Keyword(s):  
Lower Troposphere ◽  
High Arctic ◽  
The Arctic ◽  
Trace Gas ◽  
Second Phase ◽  
Aerosol Formation ◽  
Transport Barrier ◽  
Data Set ◽  
Air Masses ◽  
Mixing Ratios

Abstract. The springtime composition of the Arctic lower troposphere is to a large extent controlled by transport of mid-latitude air masses into the Arctic, whereas during the summer precipitation and natural sources play the most important role. Within the Arctic region, there exists a transport barrier, known as the polar dome, which results from sloping isentropes. The polar dome, which varies in space and time, exhibits a strong influence on the transport of air masses from mid-latitudes, enhancing it during winter and inhibiting it during summer. Furthermore, a definition for the location of the polar dome boundary itself is quite sparse in the literature. We analyzed aircraft based trace gas measurements in the Arctic during two NETCARE airborne field camapigns (July 2014 and April 2015) with the Polar 6 aircraft of Alfred Wegener Institute Helmholtz Center for Polar and Marine Research (AWI), Bremerhaven, Germany, covering an area from Spitsbergen to Alaska (134° W to 17° W and 68° N to 83° N). For the spring (April 2015) and summer (July 2014) season we analyzed transport regimes of mid-latitude air masses travelling to the high Arctic based on CO and CO2 measurements as well as kinematic 10-day back trajectories. The dynamical isolation of the high Arctic lower troposphere caused by the transport barrier leads to gradients of chemical tracers reflecting different local chemical life times and sources and sinks. Particularly gradients of CO and CO2 allowed for a trace gas based definition of the polar dome boundary for the two measurement periods with pronounced seasonal differences. For both campaigns a transition zone rather than a sharp boundary was derived. For July 2014 the polar dome boundary was determined to be 73.5° N latitude and 299–303.5 K potential temperature, respectively. During April 2015 the polar dome boundary was on average located at 66–68.5° N and 283.5–287.5 K. Tracer-tracer scatter plots and probability density functions confirm different air mass properties inside and outside of the polar dome for the July 2014 and April 2015 data set. Using the tracer derived polar dome boundaries the analysis of aerosol data indicates secondary aerosol formation events in the clean summertime polar dome. Synoptic-scale weather systems frequently disturb this transport barrier and foster exchange between air masses from midlatitudes and polar regions. During the second phase of the NETCARE 2014 measurements a pronounced low pressure system south of Resolute Bay brought inflow from southern latitudes that pushed the polar dome northward and significantly affected trace gas mixing ratios in the measurement region. Mean CO mixing ratios increased from 77.9 ± 2.5 ppbv to 84.9 ± 4.7 ppbv from the first period to the second period. At the same time CO2 mixing ratios significantly dropped from 398.16 ± 1.01 ppmv to 393.81 ± 2.25 ppmv. We further analysed processes controlling the recent transport history of air masses within and outside the polar dome. Air masses within the spring time polar dome mainly experienced diabatic cooling while travelling over cold surfaces. In contrast air masses in the summertime polar dome were diabatically heated due to insolation. During both seasons air masses outside the polar dome slowly descended into the Arctic lower troposphere from above caused by radiative cooling. The ascent to the middle and upper troposphere mainly took place outside the Arctic, followed by a northward motion. Our results demonstrate the successful application of a tracer based diagnostic to determine the location of the polar dome boundary.

scholarly journals Assessment of the Metabolic Pathways Associated With Glucose-Stimulated Biphasic Insulin Secretion

Endocrinology ◽  
2014 ◽  
Vol 155 (5) ◽  
pp. 1653-1666 ◽  
Author(s):  
Mei Huang ◽  
Jamie W. Joseph
Keyword(s):  
Insulin Secretion ◽  
Time Course ◽  
Tricarboxylic Acid Cycle ◽  
Global Analysis ◽  
Tricarboxylic Acid ◽  
Second Phase ◽  
Data Set ◽  
Negatively Associated ◽  
Biphasic Insulin ◽  
Biphasic Insulin Secretion

Biphasic glucose-stimulated insulin secretion involves a rapid first phase followed by a prolonged second phase of insulin secretion. The biochemical pathways that control these 2 phases of insulin secretion are poorly defined. In this study, we used a gas chromatography mass spectroscopy-based metabolomics approach to perform a global analysis of cellular metabolism during biphasic insulin secretion. A time course metabolomic analysis of the clonal β-cell line 832/13 cells showed that glycolytic, tricarboxylic acid, pentose phosphate pathway, and several amino acids were strongly correlated to biphasic insulin secretion. Interestingly, first-phase insulin secretion was negatively associated with l-valine, trans-4-hydroxy-l-proline, trans-3-hydroxy-l-proline, dl-3-aminoisobutyric acid, l-glutamine, sarcosine, l-lysine, and thymine and positively with l-glutamic acid, flavin adenine dinucleotide, caprylic acid, uridine 5′-monophosphate, phosphoglycerate, myristic acid, capric acid, oleic acid, linoleic acid, and palmitoleic acid. Tricarboxylic acid cycle intermediates pyruvate, α-ketoglutarate, and succinate were positively associated with second-phase insulin secretion. Other metabolites such as myo-inositol, cholesterol, dl-3-aminobutyric acid, and l-norleucine were negatively associated metabolites with the second-phase of insulin secretion. These studies provide a detailed analysis of key metabolites that are either negatively or positively associated with biphasic insulin secretion. The insights provided by these data set create a framework for planning future studies in the assessment of the metabolic regulation of biphasic insulin secretion.

scholarly journals Study of Transmissivity of a Fracture Under Shearing

2018 ◽  
Author(s):  
Amir A. Mofakham ◽  
Goodarz Ahmadi ◽  
Matthew Stadelman ◽  
Kevin Shanley ◽  
Dustin Crandall
Keyword(s):  
Surface Roughness ◽  
Rock Fracture ◽  
Marcellus Shale ◽  
Ct Scans ◽  
Navier Stokes ◽  
Fast Method ◽  
Flow Rates ◽  
Pressure Drops ◽  
Fracture Pressure ◽  
Cubic Law

A Marcellus shale rock fracture was subjected to four shearing steps and at the end of each shearing step CT (computed tomography) scans with resolution of 26.8 μm were obtained. The CT images were used to generate full aperture maps of the fracture configuration at the end of each shearing phase. The pressure drops along the fracture were also measured for different water flow rates through the fracture. The aperture map of the fracture was used to generate the geometry of the fracture for use in numerical simulations. The water flows and pressure drops in the fracture were simulated with different computational methods that included the full Navier-Stokes simulation, Modified Local Cubic Law (MLCL), and Improved Cubic Law (ICL) methods. Full 3-D Navier-Stokes simulation is the most accurate computational approach which was done with use of the ANSYS-Fluent software for each shear step and different flow rates. The MLCL is a 2-D relatively fast method which is commonly used for prediction of transmissivity of fractures. ICL is a 1-D method proposed in this study in which the effects of surface roughness and tortuosity were included in calculation of the effective aperture height of fractures. To provide an understanding of the accuracy of each of these models their predictions were compared with each other and with the experimental data. Also, to examine the effects of resolution of CT scans and the surface roughness on prediction of fractures transmissivity, similar simulations were performed on average aperture maps. Here the fracture of the full resolution data was averaged over 10 × 10 pixels. Comparing the results of the average aperture maps with those of the full maps showed that the lower resolution of CT scans led to underestimation of the fracture pressure drop due to missing the small features of the fracture surfaces and smoothing out their roughness.

scholarly journals COMPARISON OF DIFFERENT HYDRODYNAMIC CHARACTERISTICS OF AIR- WATER SYSTEM USING DISSIMILAR MOTIONLESS MIXERS

2021 ◽  
Author(s):  
Dr. Mazhar Hussain
Keyword(s):  
Mass Transfer ◽  
Power Consumption ◽  
Dissolved Oxygen ◽  
Bubble Diameter ◽  
Water System ◽  
Hydrodynamic Characteristics ◽  
Flow Rates ◽  
Static Mixers ◽  
Air Bubble ◽  
Transfer Study

The hydrodynamic characteristics of mixing fluids are always the points to consider in improvement of their mixing quality especially using motionless mixers normally stated as “Static Mixers”. Motionless mixing technique was adopted for Air-Water system with the advantage of negligible power consumption over dynamic mixers. Different hydrodynamic characteristics were experimented using “Baffle Type” static element and were compared to those of already used in recent studies. Dissolved oxygen content, Static mixer geometry (i.e. Baffle, Blade, Wheel, Plate and Needle), mixing fluids flow rates were chosen as variables and selected in this content as rate of mass transfer study which founds out to be significant using “Baffle Type” static element. Volumetric mass transfer was also achieved at higher scale which gives a clear indication of increase the mass transfer coefficient in between the comparison of “Baffle type” element and other mentioned elements. Pressure droplet and depletion in Air bubble size across static elements were visually perceived using Hg-Manometer and still photography respectively. A mathematical model was also developed portraying the Air bubble diameter at different flow rates for this system. Other hydrodynamics like higher Dissolved Oxygen (DO) Content, Less Power consumption were also found to be more advantageous for “Baffle Type” static element.

scholarly journals Climate change impact on streamflow in large-scale river basins: projections and their uncertainties sourced from GCMs and RCP scenarios

2018 ◽  
Vol 379 ◽  
pp. 139-144 ◽  
Author(s):  
Olga N. Nasonova ◽  
Yeugeniy M. Gusev ◽  
Evgeny E. Kovalev ◽  
Georgy V. Ayzel
Keyword(s):  
Climate Change ◽  
River Runoff ◽  
Climate Change Impact ◽  
Large Scale ◽  
River Basins ◽  
Surface Model ◽  
Second Phase ◽  
Rcp Scenarios ◽  
Data Set ◽  
Change Impact

Abstract. Climate change impact on river runoff was investigated within the framework of the second phase of the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP2) using a physically-based land surface model Soil Water – Atmosphere – Plants (SWAP) (developed in the Institute of Water Problems of the Russian Academy of Sciences) and meteorological projections (for 2006–2099) simulated by five General Circulation Models (GCMs) (including GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, and NorESM1-M) for each of four Representative Concentration Pathway (RCP) scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5). Eleven large-scale river basins were used in this study. First of all, SWAP was calibrated and validated against monthly values of measured river runoff with making use of forcing data from the WATCH data set and all GCMs' projections were bias-corrected to the WATCH. Then, for each basin, 20 projections of possible changes in river runoff during the 21st century were simulated by SWAP. Analysis of the obtained hydrological projections allowed us to estimate their uncertainties resulted from application of different GCMs and RCP scenarios. On the average, the contribution of different GCMs to the uncertainty of the projected river runoff is nearly twice larger than the contribution of RCP scenarios. At the same time the contribution of GCMs slightly decreases with time.

Real-Time Prediction of Choke Health Using Production Data Integrated with AI

2021 ◽  
Author(s):  
Ahmed Alghamdi ◽  
Olakunle Ayoola ◽  
Khalid Mulhem ◽  
Mutlaq Otaibi ◽  
Abdulazeez Abdulraheem
Keyword(s):  
High Pressure ◽  
Real Time ◽  
Large Scale ◽  
Production Systems ◽  
Rate Of Change ◽  
Production Data ◽  
Ann Model ◽  
Sand Production ◽  
Data Set ◽  
Pressure Drops

Abstract Chokes are an integral part of production systems and are crucial surface equipment that faces rough conditions such as high-pressure drops and erosion due to solids. Predicting choke health is usually achieved by analyzing the relationship of choke size, pressure, and flow rate. In large-scale fields, this process requires extensive-time and effort using the conventional techniques. This paper presents a real-time proactive approach to detect choke wear utilizing production data integrated with AI analytics. Flowing parameters data were collected for more than 30 gas wells. These wells are producing gas with slight solids production from a high-pressure high-temperature field. In addition, these wells are equipped with a multi-stage choke system. The approach of determining choke wear relies on training the AI model on a dataset constructed by comparison of the choke valve rate of change with respect to a smoother slope of the production rate. If the rate of change is not within a tolerated range of divergence, an abnormal choke behavior is detected. The data set was divided into 70% for training and 30% for testing. Artificial Neural Network (ANN) was trained on data that has the following inputs: gas specific gravity, upstream & downstream pressure and temperature, and choke size. This ANN model achieved a correlation coefficient above 0.9 with an excellent prediction on the data points exhibiting normal or abnormal choke behaviors. Piloting this application on large fields, where manual analysis is often impractical, saves a substantial man-hour and generates significant cost-avoidance. Areas for improvement in such an application depends on equipping the ANN network with long-term production profile prediction abilities, such as water production, and this analysis relies on having an accurate reading from the venturi meters, which is often the case in single-phase flow. The application of this AI-driven analytics provides tremendous improvement for remote offshore production operations surveillance. The novel approach presented in this paper capitalizes on the AI analytics for estimating proactively detecting choke health conditions. The advantages of such a model are that it harnesses AI analytics to help operators improve asset integrity and production monitoring compliance. In addition, this approach can be expanded to estimate sand production as choke wear is a strong function of sand production.

scholarly journals Dynamic behavior of coalbed methane flow along the annulus of single-phase production

2019 ◽  
Vol 6 (4) ◽  
pp. 547-555 ◽  
Author(s):  
Xinfu Liu ◽  
Chunhua Liu ◽  
Guoqiang Liu
Keyword(s):  
Water Column ◽  
Dynamic Behavior ◽  
Coalbed Methane ◽  
Single Phase ◽  
Compressibility Factor ◽  
Production Performance ◽  
Flow Rates ◽  
Pressure Drops ◽  
Bottom Hole ◽  
Gravitational Gradients

Abstract Dynamic behavior of coalbed methane (CBM) flow will provide the theoretical basis to optimize production performance for a given well. A mathematical model is developed to simulate flowing pressures and pressure drops of CBM column from well head to bottom hole. The measured parameters and independent variables of flow rates, flowing pressures and temperatures are involved in CBM producing process along the annulus. The developed relationships are validated against full-scale measured data in single-phase CBM wellbores. The proposed methodology can analyze the dynamic behavior in CBM reservoir and process of CBM flow with an overall accuracy of 2%. The calculating process of flowing pressures involves friction factor with variable Reynolds number and CBM temperature and compressibility factor with gravitational gradients. The results showed that the effect of flowing pressure on CBM column was more obvious than that on CBM and water column accompanied by an increase of dynamic water level. The ratios of flowing pressure on increment of CBM column to the whole column increased with the declined flow rates of water column. Bottom-hole pressure declined with the decreased flowing pressure of CBM column along the annulus. It will lead to the results of the increased pressure drop of CBM column and CBM flow rate in single-phase CBM wellbores.

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