scholarly journals An Interval-Arithmetic-Based Approach to the Parametric Identification of the Single-Diode Model of Photovoltaic Generators

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 932 ◽  
Author(s):  
Martha Lucia Orozco-Gutierrez
Keyword(s):  
Experimental Data ◽  
Interval Arithmetic ◽  
Crystalline Silicon ◽  
Parametric Identification ◽  
Search Space ◽  
Operating Conditions ◽  
Divide And Conquer ◽  
Divide And Conquer Algorithm ◽  
Voltage Curve ◽  
Photovoltaic Generators

Parametric identification of the single diode model of a photovoltaic generator is a key element in simulation and diagnosis. Parameters’ values are often determined by using experimental data the modules manufacturers provide in the data sheets. In outdoor applications, the parametric identification is instead performed by starting from the current vs. voltage curve acquired in non-standard operating conditions. This paper refers to this latter case and introduces an approach based on the use of interval arithmetic. Photovoltaic generators based on crystalline silicon cells are considered: they are modeled by using the single diode model, and a divide-and-conquer algorithm is used to contract the initial search space up to a small hyper-rectangle including the identified set of parameters. The proposed approach is validated by using experimental data measured in outdoor conditions. The information provided by the approach, in terms of parametric sensitivity and of correlation between current variations and drifts of the parameters values, is discussed. The results are analyzed in view of the on-site application of the proposed approach for diagnostic purposes.

scholarly journals Heuristic study of a full can implementation on a network of digital controllers based on experimental data

2020 ◽  
pp. 17-23
Author(s):  
Carlos Alberto Luján-Ramírez ◽  
Jesús Sandoval-Gío ◽  
Agustín Alfonso Flores-Novelo ◽  
Juan Alberto Ojeda-Arana
Keyword(s):  
Experimental Data ◽  
Parametric Identification ◽  
Operating Conditions ◽  
Access Time ◽  
Area Network ◽  
Digital Controllers ◽  
Electromagnetic Brake ◽  
Control Loops ◽  
Heuristic Study ◽  
Exchange Data

Over time, the CAN (Controller Area Network) communication bus has been implemented in different technological sectors, within which, depending on the application, the bus implementation may change. On the other hand, the design and implementation of digital controls based on experimental data is a well-known topic in the automation industry where the acquisition system is of great importance. In this document, a heuristic study of the behavior of a Full CAN network is reported to implement digital controllers in two interconnected control loops. This study takes into account the access time to the bus and the amount of data sent when observing the response to disturbances. The design of two digital controllers is presented based on the parametric identification of two plants: a DC motor with an electromagnetic brake and a pneumatic levitator. Using PSoC® microcontrollers, a Full CAN network is implemented, where the digital controllers exchange data by randomly accessing the bus. A specially designed interface allows visualizing the speed and amount of data transferred under different operating conditions of the control loops. At the document end, the experimental data obtained are discussed.

МАТЕМАТИЧЕСКОЕ МОДЕЛИРОВАНИЕ ДИНАМИКИ МИНЕРАЛЬНОГО ПИТАНИЯ РАСТЕНИЙ В СИСТЕМЕ «УДОБРЕНИЕ-ПОЧВА-РАСТЕНИЕ»

2020 ◽  
Vol 65 (6) ◽  
pp. 1219-1229
Author(s):  
В.А. Четырбоцкий ◽  
◽  
А.Н. Четырбоцкий ◽  
Б.В. Левин ◽  
◽  
...  
Keyword(s):  
Experimental Data ◽  
Temporal Dynamics ◽  
Plant Biomass ◽  
Environmental Parameters ◽  
Parametric Identification ◽  
Agricultural Crop ◽  
Parameter System ◽  
Plant Nitrogen ◽  
Rhizosphere Microorganisms ◽  
Nitrogen Phosphorus

A numerical simulation of the spatial-temporal dynamics of a multi-parameter system is developed. The components of this system are plant biomass, mobile and stationary forms of mineral nutrition elements, rhizosphere microorganisms and environmental parameters (temperature, humidity, acidity). Parametric identification and verification of the adequacy of the model were carried out based on the experimental data on the growth of spring wheat «Krasnoufimskaya-100» on peat lowland soil. The results are represented by temporal distributions of biomass from agricultural crop under study and the findings on the content of main nutrition elements within the plant (nitrogen, phosphorus, potassium). An agronomic assessment and interpretation of the obtained results are given.

scholarly journals A divide-and-conquer algorithm for quantum state preparation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Israel F. Araujo ◽  
Daniel K. Park ◽  
Francesco Petruccione ◽  
Adenilton J. da Silva
Keyword(s):  
Computational Cost ◽  
Quantum Circuit ◽  
Divide And Conquer ◽  
Computational Time ◽  
Quantum Computers ◽  
Dimensional Vector ◽  
Quantum Devices ◽  
Divide And Conquer Algorithm ◽  
Quantum State Preparation ◽  
Quantum Device

AbstractAdvantages in several fields of research and industry are expected with the rise of quantum computers. However, the computational cost to load classical data in quantum computers can impose restrictions on possible quantum speedups. Known algorithms to create arbitrary quantum states require quantum circuits with depth O(N) to load an N-dimensional vector. Here, we show that it is possible to load an N-dimensional vector with exponential time advantage using a quantum circuit with polylogarithmic depth and entangled information in ancillary qubits. Results show that we can efficiently load data in quantum devices using a divide-and-conquer strategy to exchange computational time for space. We demonstrate a proof of concept on a real quantum device and present two applications for quantum machine learning. We expect that this new loading strategy allows the quantum speedup of tasks that require to load a significant volume of information to quantum devices.

Modeling and Experimental Validation of the Effective Bulk Modulus of a Mixture of Hydraulic Oil and Air

2014 ◽  
Vol 136 (5) ◽  
Author(s):  
Hossein Gholizadeh ◽  
Doug Bitner ◽  
Richard Burton ◽  
Greg Schoenau
Keyword(s):  
Experimental Data ◽  
Bulk Modulus ◽  
Theoretical Models ◽  
Operating Conditions ◽  
Air Bubbles ◽  
Pressure Sensitivity ◽  
Hydraulic Oil ◽  
Effective Bulk Modulus ◽  
Experimental Apparatus ◽  
Major Factors

It is well known that the presence of entrained air bubbles in hydraulic oil can significantly reduce the effective bulk modulus of hydraulic oil. The effective bulk modulus of a mixture of oil and air as pressure changes is considerably different than when the oil and air are not mixed. Theoretical models have been proposed in the literature to simulate the pressure sensitivity of the effective bulk modulus of this mixture. However, limited amounts of experimental data are available to prove the validity of the models under various operating conditions. The major factors that affect pressure sensitivity of the effective bulk modulus of the mixture are the amount of air bubbles, their size and the distribution, and rate of compression of the mixture. An experimental apparatus was designed to investigate the effect of these variables on the effective bulk modulus of the mixture. The experimental results were compared with existing theoretical models, and it was found that the theoretical models only matched the experimental data under specific conditions. The purpose of this paper is to specify the conditions in which the current theoretical models can be used to represent the real behavior of the pressure sensitivity of the effective bulk modulus of the mixture. Additionally, a new theoretical model is proposed for situations where the current models fail to truly represent the experimental data.

Performance Characterization of a Twin Scroll Volute for Turbocharging Applications

2018 ◽  
Author(s):  
Carlo Cravero ◽  
Mario La Rocca ◽  
Andrea Ottonello
Keyword(s):  
Experimental Data ◽  
Scientific Literature ◽  
Aerodynamic Performance ◽  
Operating Conditions ◽  
Automatic Design ◽  
Cfd Model ◽  
Radial Turbine ◽  
Wide Range ◽  
Partial Admission

The use of twin scroll volutes in radial turbine for turbocharging applications has several advantages over single passage volute related to the engine matching and to the overall compactness. Twin scroll volutes are of increasing interest in power unit development but the open scientific literature on their performance and modelling is still quite limited. In the present work the performance of a twin scroll volute for a turbocharger radial turbine are investigated in some detail in a wide range of operating conditions at both full and partial admission. A CFD model for the volute have been developed and preliminary validated against experimental data available for the radial turbine. Then the numerical model has been used to generate the database of solutions that have been investigated and used to extract the performance. Different parameters and indices are introduced to describe the volute aerodynamic performance in the wide range of operating conditions chosen. The above parameters can be used for volute development or matching with a given rotor or efficiently implemented in automatic design optimization strategies.

NUMERICAL ESTIMATION OF MOISTURE CONTENT IN SPRAY DRIED JUICE POWDER

2016 ◽  
Vol 78 (8-3) ◽  
Author(s):  
Aliyu Bello A. ◽  
Arshad Ahmad ◽  
Adnan Ripin ◽  
Olagoke Oladokun
Keyword(s):  
Experimental Data ◽  
Moisture Content ◽  
Diffusion Model ◽  
Process Model ◽  
Small Error ◽  
Operating Conditions ◽  
Balance Model ◽  
Pineapple Juice ◽  
Droplet Drying ◽  
Spray Dried

The moisture contents of powders is an important parameter that affects the quality and commercial value of spray dried products. The utility of predicted moisture content values from two droplet drying models were compared with experimental data for spray dried pineapple juice, using the Ranz-Marshal and its modified variants for the heat and mass transfer correlations. The droplet Diffusion model, using the Zhifu correlation, gave estimates with errors of about 8% at 165 oC, 9% at 171 oC, 26% at 179 oC and 2% at 185 oC. The Ranz-Marshal correlation also gave comparable results with this model while results using the Downing and modified Ranz-Marshall correlations widely diverged. The Energy balance model predicted completely dried juice particles, and short drying times, in contrast to the experimental data. The small error sizes of the Diffusion model improves on the wide error sizes of an earlier process model, making is useful as a first approximation choice, for spray drier design and simulation, especially for juices under comparable operating conditions.

KNT-artificial neural network model for flux prediction of ultrafiltration membrane producing drinking water

2004 ◽  
Vol 50 (8) ◽  
pp. 103-110 ◽  
Author(s):  
H.K. Oh ◽  
M.J. Yu ◽  
E.M. Gwon ◽  
J.Y. Koo ◽  
S.G. Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Correlation Coefficient ◽  
Network Model ◽  
Operating Conditions ◽  
Transmembrane Pressure ◽  
Feed Water ◽  
Permeate Flux ◽  
Artificial Neural

This paper describes the prediction of flux behavior in an ultrafiltration (UF) membrane system using a Kalman neuro training (KNT) network model. The experimental data was obtained from operating a pilot plant of hollow fiber UF membrane with groundwater for 7 months. The network was trained using operating conditions such as inlet pressure, filtration duration, and feed water quality parameters including turbidity, temperature and UV254. Pre-processing of raw data allowed the normalized input data to be used in sigmoid activation functions. A neural network architecture was structured by modifying the number of hidden layers, neurons and learning iterations. The structure of KNT-neural network with 3 layers and 5 neurons allowed a good prediction of permeate flux by 0.997 of correlation coefficient during the learning phase. Also the validity of the designed model was evaluated with other experimental data not used during the training phase and nonlinear flux behavior was accurately estimated with 0.999 of correlation coefficient and a lower error of prediction in the testing phase. This good flux prediction can provide preliminary criteria in membrane design and set up the proper cleaning cycle in membrane operation. The KNT-artificial neural network is also expected to predict the variation of transmembrane pressure during filtration cycles and can be applied to automation and control of full scale treatment plants.

Influence and Information Flow in Online Social Networks

2017 ◽  
Vol 9 (4) ◽  
pp. 1-17
Author(s):  
Afrand Agah ◽  
Mehran Asadi
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Large Data ◽  
Marketing Strategies ◽  
Large Data Sets ◽  
Divide And Conquer ◽  
Data Sets ◽  
Divide And Conquer Algorithm ◽  
Flow Of Information

This article introduces a new method to discover the role of influential people in online social networks and presents an algorithm that recognizes influential users to reach a target in the network, in order to provide a strategic advantage for organizations to direct the scope of their digital marketing strategies. Social links among friends play an important role in dictating their behavior in online social networks, these social links determine the flow of information in form of wall posts via shares, likes, re-tweets, mentions, etc., which determines the influence of a node. This article initially identities the correlated nodes in large data sets using customized divide-and-conquer algorithm and then measures the influence of each of these nodes using a linear function. Furthermore, the empirical results show that users who have the highest influence are those whose total number of friends are closer to the total number of friends of each node divided by the total number of nodes in the network.

Experimental and Analytical Characterization of Alternative Aviation Fuel Sprays Under Realistic Operating Conditions

2018 ◽  
Author(s):  
Andrew Corber ◽  
Nader Rizk ◽  
Wajid Ali Chishty
Keyword(s):  
Experimental Data ◽  
Alternative Fuels ◽  
Laser Sheet ◽  
National Research Council ◽  
Diagnostic System ◽  
Operating Conditions ◽  
Aviation Fuel ◽  
Injection System ◽  
Test Time

The National Jet Fuel Combustion Program (NJFCP) is an initiative, currently being led by the Office of Environment & Energy at the FAA, to streamline the ASTM jet fuels certification process for alternative aviation fuels. In order to accomplish this objective, the program has identified specific applied research tasks in several areas. The National Research Council of Canada (NRC) is contributing to the NJFCP in the areas of sprays and atomization and high altitude engine performance. This paper describes work pertaining to atomization tests using a reference injection system. The work involves characterization of the injection nozzle, comparison of sprays and atomization quality of various conventional and alternative fuels, as well as use of the experimental data to validate spray correlations. The paper also briefly explores the application viability of a new spray diagnostic system that has potential to reduce test time in characterizing sprays. Measurements were made from ambient up to 10 bar pressures in NRC’s High Pressure Spray Facility using optical diagnostics including laser diffraction, phase Doppler anemometry (PDA), LIF/Mie Imaging and laser sheet imaging to assess differences in the atomization characteristics of the test fuels. A total of nine test fluids including six NJFCP fuels and three calibration fluids were used. The experimental data was then used to validate semi-empirical models, developed through years of experience by engine OEMs and modified under NJFCP, for predicting droplet size and distribution. The work offers effective tools for developing advanced fuel injectors, and generating data that can be used to significantly enhance multi-dimensional combustor simulation capabilities.

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