An improved model for predicting the efficiency of hydraulic propeller turbines

2005 ◽  
Vol 32 (5) ◽  
pp. 789-795 ◽  
Author(s):  
Jessica Manness ◽  
Jay Doering
Keyword(s):  
Performance Test ◽  
Performance Testing ◽  
Field Performance ◽  
Large Data ◽  
Hydraulic Turbine ◽  
Original Method ◽  
Large Network ◽  
Periodic Field ◽  
Data Set ◽  
Efficient Operation

Field performance testing of hydraulic turbines is undertaken to define the head-power-discharge relationship that identifies the peak operating point of the turbine. This relationship is essential for the efficient operation of a hydraulic turbine. Unfortunately, in some cases it is not feasible to field test turbines because of time, budgetary, or other constraints. Gordon (2001) proposed a method of predicting and (or) simulating the performance curve for several types of turbines. However, a limited data set was available for the development of his model for certain types of turbines. Moreover, his model did not include a precise method of developing performance curves for rerunnered turbines. Manitoba Hydro operates a large network of hydroelectric turbines, which are subject to periodic field performance testing. This provided a large data set with which to refine the model proposed by Gordon (2001). Furthermore, since these data include rerunnered units, this provides an opportunity to refine the effects of rerunnering. Analysis shows that the accuracy of the refined model is within 2% of the performance test results for an "old" turbine, while for a newer turbine or a rerunnered turbine the error is within 1%. For both an old turbine and a rerunnered turbine, this indicates an accuracy improvement of 3% over the original method proposed by Gordon (2001).Key words: hydraulic turbine, efficiency, simulation modeling

Evaluation of Asphalt Mixture Performance Using Cracking and Durability Tests at a Full-Scale Pavement Facility

2021 ◽  
pp. 036119812110218
Author(s):  
Amir Golalipour ◽  
Varun Veginati ◽  
David J. Mensching
Keyword(s):  
Performance Test ◽  
Asphalt Mixture ◽  
Performance Testing ◽  
Field Performance ◽  
Optimization Procedure ◽  
Cyclic Fatigue ◽  
Intermediate Temperature ◽  
Index Test ◽  
Reclaimed Asphalt ◽  
Indirect Tensile

In the asphalt materials community, the most critical research need is centered around a paradigm shift in mixture design from the volumetric process of the previous 20-plus years to an optimization procedure based on laboratory-measured mechanical properties that should lead to an increase in long-term pavement performance. This study is focused on advancing the state of understanding with respect to the value of intermediate temperature cracking tests, which may be included in a balanced mix design. The materials included are plant-mixed, laboratory-compacted specimens reheated from the 2013 Federal Highway Administration’s (FHWA’s) Accelerated Loading Facility (ALF) study on reclaimed asphalt pavement/reclaimed asphalt shingle (RAP/RAS) materials. Six commonly discussed intermediate temperature (cracking and durability) performance testing (i.e., Asphalt Mixture Performance Tester [AMPT] Cyclic Fatigue, Cantabro, Illinois Flexibility Index Test [I-FIT], Indirect Tensile Cracking [ITC, also known as IDEAL-CT], Indirect Tensile Nflex, and Texas Overlay Test) were selected for use in this study based on input from stakeholders. Test results were analyzed to compare differences between the cracking tests. In addition, statistical analyses were conducted to assess the separation among materials (lanes) for each performance test. Cyclic fatigue and IDEAL-CT tests showed the most promising results. The ranking from these two tests’ index parameters matched closely with ALF field performance. Furthermore, both showed reasonable variability of test data and they were successful in differentiating between different materials.

A Comparative Study of Genetic Algorithms and Gradient Methods for RM12 Turbofan Engine Diagnostics and Performance Estimation

2004 ◽  
Author(s):  
Tomas Gro¨nstedt ◽  
Markus Wallin
Keyword(s):  
Gas Turbine ◽  
Performance Test ◽  
Gradient Methods ◽  
Performance Testing ◽  
Performance Estimation ◽  
Data Sets ◽  
Data Set ◽  
Turbofan Engine ◽  
Local Optima ◽  
Bypass Ratio

Recent work on gas turbine diagnostics based on optimisation techniques advocates two different approaches: 1) Stochastic optimisation, including Genetic Algorithm techniques, for its robustness when optimising objective functions with many local optima and 2) Gradient based methods mainly for their computational efficiency. For smooth and single optimum functions, gradient methods are known to provide superior numerical performance. This paper addresses the key issue for method selection, i.e. whether multiple local optima may occur when the optimisation approach is applied to real engine testing. Two performance test data sets for the RM12 low bypass ratio turbofan engine, powering the Swedish Fighter Gripen, have been analysed. One set of data was recorded during performance testing of a highly degraded engine. This engine has been subjected to Accelerated Mission Testing (AMT) cycles corresponding to more than 4000 hours of run time. The other data set was recorded for a development engine with less than 200 hours of operation. The search for multiple optima was performed starting from more than 100 extreme points. Not a single case of multi-modality was encountered, i.e. one unique solution for each of the two data sets was consistently obtained. The RM12 engine cycle is typical for a modern fighter engine, implying that the obtained results can be transferred to, at least, most low bypass ratio turbofan engines. The paper goes on to describe the numerical difficulties that had to be resolved to obtain efficient and robust performance by the gradient solvers. Ill conditioning and noise may, as illustrated on a model problem, introduce local optima without a correspondence in the gas turbine physics. Numerical methods exploiting the special problem structure represented by a non-linear least squares formulation is given special attention. Finally, a mixed norm allowing for both robustness and numerical efficiency is suggested.

scholarly journals What Is Your Actual Pump Flow Rate?

2017 ◽  
Author(s):  
Norman F. Perkins ◽  
Philip S. Stacy
Keyword(s):  
Power Plant ◽  
Performance Test ◽  
Performance Testing ◽  
Field Performance ◽  
Thermal Plant ◽  
Power Plant Equipment ◽  
Uncertainty Estimates ◽  
Pump Flow Rate ◽  
Aging Infrastructure ◽  
Simple Question

What appears to be a simple question is often quite difficult to answer depending on the quantity of flow; and size, type, and location of piping. Even the reason for asking the question can be varied and complex — ranging from environmental regulation, investment decisions, aging infrastructure improvement planning, and new equipment evaluation. Absolute field performance testing of power plant equipment yields valuable data that can be used in a variety of ways. National and International codes list several methods to measure water flow in a performance application and provide realistic uncertainty estimates. Codes and standards exist for equipment evaluation and contractual performance tests. These Codes, though, are sometimes viewed as costly or perceived to impose additional risk on suppliers. Herein, we will present how to obtain performance test data and how that data can be used. In many rehabilitation or regulation driven projects, an accurate representation of the state of the existing power plant is desired. Pump curves typically do not represent an accurate depiction of flow due to equipment degradation, changes in system components/geometry, and/or bio-fouling. While the testing may be considered costly, it can often be justified as part of a rehabilitation project. Absolute testing provides a lower uncertainty that can yield more definitive estimates of return on investment to justify projects that might be otherwise considered marginal. Case studies will be discussed that illustrate these points, including: • Flow measurement feasibility and site testing at a nuclear thermal plant • In-situ flow testing to calibrate existing ultrasonic flow meters at a biomass thermal plant • Condenser performance testing at a nuclear thermal plant Paper published with permission.

Some statistical and CI models to predict chaotic high-frequency financial data

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Methodological Approach ◽  
Back Propagation ◽  
Large Data ◽  
Series Data ◽  
Data Set ◽  
Training Time ◽  
Optimal Population ◽  
Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning

2019 ◽  
Vol 21 (9) ◽  
pp. 662-669 ◽  
Author(s):  
Junnan Zhao ◽  
Lu Zhu ◽  
Weineng Zhou ◽  
Lingfeng Yin ◽  
Yuchen Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Regression Tree ◽  
Large Data ◽  
Thrombin Inhibitors ◽  
Coagulation Cascade ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Set ◽  
Descriptor Selection

Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.

scholarly journals Correlation between the structure and skin permeability of compounds

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ruolan Zeng ◽  
Jiyong Deng ◽  
Limin Dang ◽  
Xinliang Yu
Keyword(s):  
Large Data ◽  
Qsar Model ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Skin Permeability ◽  
Data Set ◽  
Test Set ◽  
Svm Algorithm ◽  
Svm Model ◽  
Toxicity Relationship

AbstractA three-descriptor quantitative structure–activity/toxicity relationship (QSAR/QSTR) model was developed for the skin permeability of a sufficiently large data set consisting of 274 compounds, by applying support vector machine (SVM) together with genetic algorithm. The optimal SVM model possesses the coefficient of determination R2 of 0.946 and root mean square (rms) error of 0.253 for the training set of 139 compounds; and a R2 of 0.872 and rms of 0.302 for the test set of 135 compounds. Compared with other models reported in the literature, our SVM model shows better statistical performance in a model that deals with more samples in the test set. Therefore, applying a SVM algorithm to develop a nonlinear QSAR model for skin permeability was achieved.

scholarly journals Galaxy spin direction distribution in HST and SDSS show similar large-scale asymmetry

2020 ◽  
Vol 37 ◽  
Author(s):  
Lior Shamir
Keyword(s):  
Large Scale ◽  
Spiral Galaxies ◽  
Hubble Space Telescope ◽  
Gravitational Interaction ◽  
Large Data ◽  
Sloan Digital Sky Survey ◽  
Data Sets ◽  
Dipole Axis ◽  
Data Set ◽  
The Asymmetry

Abstract Several recent observations using large data sets of galaxies showed non-random distribution of the spin directions of spiral galaxies, even when the galaxies are too far from each other to have gravitational interaction. Here, a data set of $\sim8.7\cdot10^3$ spiral galaxies imaged by Hubble Space Telescope (HST) is used to test and profile a possible asymmetry between galaxy spin directions. The asymmetry between galaxies with opposite spin directions is compared to the asymmetry of galaxies from the Sloan Digital Sky Survey. The two data sets contain different galaxies at different redshift ranges, and each data set was annotated using a different annotation method. The results show that both data sets show a similar asymmetry in the COSMOS field, which is covered by both telescopes. Fitting the asymmetry of the galaxies to cosine dependence shows a dipole axis with probabilities of $\sim2.8\sigma$ and $\sim7.38\sigma$ in HST and SDSS, respectively. The most likely dipole axis identified in the HST galaxies is at $(\alpha=78^{\rm o},\delta=47^{\rm o})$ and is well within the $1\sigma$ error range compared to the location of the most likely dipole axis in the SDSS galaxies with $z>0.15$ , identified at $(\alpha=71^{\rm o},\delta=61^{\rm o})$ .

scholarly journals The Complete DNA Sequence of the Mitochondrial Genome of a “Living Fossil,” the Coelacanth (Latimeria chalumnae)

Genetics ◽  
1997 ◽  
Vol 146 (3) ◽  
pp. 995-1010 ◽  
Author(s):  
Rafael Zardoya ◽  
Axel Meyer
Keyword(s):  
Mitochondrial Genome ◽  
Tandem Repeats ◽  
Phylogenetic Analyses ◽  
Large Data ◽  
Molecular Data ◽  
Phylogenetic Position ◽  
Data Set ◽  
Living Fossil ◽  
Latimeria Chalumnae ◽  
Relationship Of

The complete nucleotide sequence of the 16,407-bp mitochondrial genome of the coelacanth (Latimeria chalumnae) was determined. The coelacanth mitochondrial genome order is identical to the consensus vertebrate gene order which is also found in all ray-finned fishes, the lungfish, and most tetrapods. Base composition and codon usage also conform to typical vertebrate patterns. The entire mitochondrial genome was PCR-amplified with 24 sets of primers that are expected to amplify homologous regions in other related vertebrate species. Analyses of the control region of the coelacanth mitochondrial genome revealed the existence of four 22-bp tandem repeats close to its 3′ end. The phylogenetic analyses of a large data set combining genes coding for rRNAs, tRNA, and proteins (16,140 characters) confirmed the phylogenetic position of the coelacanth as a lobe-finned fish; it is more closely related to tetrapods than to ray-finned fishes. However, different phylogenetic methods applied to this largest available molecular data set were unable to resolve unambiguously the relationship of the coelacanth to the two other groups of extant lobe-finned fishes, the lungfishes and the tetrapods. Maximum parsimony favored a lungfish/coelacanth or a lungfish/tetrapod sistergroup relationship depending on which transversion:transition weighting is assumed. Neighbor-joining and maximum likelihood supported a lungfish/tetrapod sistergroup relationship.

Climatology of nutrient distributions in the South China Sea based on a large data set derived from a new algorithm

2021 ◽  
pp. 102586
Author(s):  
Chuanjun Du ◽  
Ruoying He ◽  
Zhiyu Liu ◽  
Tao Huang ◽  
Lifang Wang ◽  
...  
Keyword(s):  
South China Sea ◽  
South China ◽  
Large Data ◽  
The South China Sea ◽  
The South ◽  
Data Set ◽  
China Sea ◽  
Large Data Set

scholarly journals Spike detection: Inter-reader agreement and a statistical Turing test on a large data set

2017 ◽  
Vol 128 (1) ◽  
pp. 243-250 ◽  
Author(s):  
Mark L. Scheuer ◽  
Anto Bagic ◽  
Scott B. Wilson
Keyword(s):  
Large Data ◽  
Turing Test ◽  
Spike Detection ◽  
Data Set ◽  
Large Data Set
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