Prediction of Premixed Flame Dynamics Using LES With Tabulated Chemistry and Eulerian Stochastic Fields

2019 ◽  
Author(s):  
Alexander Avdonin ◽  
Alireza Javareshkian ◽  
Wolfgang Polifke
Keyword(s):  
Time Series Data ◽  
Reaction Scheme ◽  
Series Data ◽  
Combustion Model ◽  
Data Simulation ◽  
Combustion Chemistry ◽  
Stochastic Fields ◽  
Progress Variable ◽  
Flame Dynamics ◽  
Tabulated Chemistry

Abstract This paper demonstrates that a Large Eddy Simulation (LES) combustion model based on tabulated chemistry and Eulerian stochastic fields can successfully describe the flame dynamics of a premixed turbulent swirl flame. The combustion chemistry is tabulated from one-dimensional burner-stabilized flamelet computations in dependence of progress variable and enthalpy. The progress variable allows to efficiently include a detailed reaction scheme, while the dependence on enthalpy describes the effect of heat losses on the reaction rate. The turbulence-chemistry interaction is modeled by eight Eulerian stochastic fields. A LES of a premixed swirl burner with a broadband velocity excitation is performed to investigate the flame dynamics, i.e. the response of heat release rate to upstream velocity perturbations. In particular, the flame impulse response and flame transfer function are identified from LES time series data. Simulation results for a range of power ratings are in good agreement with experimental data.

Prediction of Premixed Flame Dynamics Using Large Eddy Simulation With Tabulated Chemistry and Eulerian Stochastic Fields

2019 ◽  
Vol 141 (11) ◽  
Author(s):  
Alexander Avdonin ◽  
Alireza Javareshkian ◽  
Wolfgang Polifke
Keyword(s):  
Large Eddy Simulation ◽  
Time Series Data ◽  
Series Data ◽  
Combustion Model ◽  
Stochastic Fields ◽  
Progress Variable ◽  
Eddy Simulation ◽  
Flame Dynamics ◽  
Tabulated Chemistry ◽  
Large Eddy

Abstract This paper demonstrates that a large Eddy simulation (LES) combustion model based on tabulated chemistry and Eulerian stochastic fields can successfully describe the flame dynamics of a premixed turbulent swirl flame. The combustion chemistry is tabulated from one-dimensional burner-stabilized flamelet computations in dependence on progress variable and enthalpy. The progress variable allows to efficiently include a detailed reaction scheme, while the dependence on enthalpy describes the effect of heat losses on the reaction rate. The turbulence-chemistry interaction is modeled by eight Eulerian stochastic fields. An LES of a premixed swirl burner with a broadband velocity excitation is performed to investigate the flame dynamics, i.e., the response of heat release rate to upstream velocity perturbations. In particular, the flame impulse response and the flame transfer function (FTF) are identified from LES time series data. Simulation results for a range of power ratings are in good agreement with the experimental data.

Data Simulations Using Cosine and Sigmoid Higher Order Neural Networks

2021 ◽  
pp. 346-374
Keyword(s):  
Neural Network ◽  
Time Series Data ◽  
Learning Algorithm ◽  
Higher Order ◽  
Series Data ◽  
Average Error ◽  
Data Simulation ◽  
New Learning ◽  
Higher Order Neural Networks ◽  
Analysis System

A new open box and nonlinear model of cosine and sigmoid higher order neural network (CS-HONN) is presented in this chapter. A new learning algorithm for CS-HONN is also developed in this chapter. In addition, a time series data simulation and analysis system, CS-HONN simulator, is built based on the CS-HONN models. Test results show that the average error of CS-HONN models are from 2.3436% to 4.6857%, and the average error of polynomial higher order neural network (PHONN), trigonometric higher order neural network (THONN), and sigmoid polynomial higher order neural network (SPHONN) models range from 2.8128% to 4.9077%. This suggests that CS-HONN models are 0.1174% to 0.4917% better than PHONN, THONN, and SPHONN models.

Cosine and Sigmoid Higher Order Neural Networks for Data Simulations

2016 ◽  
pp. 237-252
Author(s):  
Ming Zhang
Keyword(s):  
Neural Network ◽  
Time Series Data ◽  
Learning Algorithm ◽  
Higher Order ◽  
Series Data ◽  
Average Error ◽  
Data Simulation ◽  
New Learning ◽  
Higher Order Neural Networks ◽  
Analysis System

New open box and nonlinear model of Cosine and Sigmoid Higher Order Neural Network (CS-HONN) is presented in this paper. A new learning algorithm for CS-HONN is also developed from this study. A time series data simulation and analysis system, CS-HONN Simulator, is built based on the CS-HONN models too. Test results show that average error of CS-HONN models are from 2.3436% to 4.6857%, and the average error of Polynomial Higher Order Neural Network (PHONN), Trigonometric Higher Order Neural Network (THONN), and Sigmoid polynomial Higher Order Neural Network (SPHONN) models are from 2.8128% to 4.9077%. It means that CS-HONN models are 0.1174% to 0.4917% better than PHONN, THONN, and SPHONN models.

Cosine and Sigmoid Higher Order Neural Networks for Data Simulations

2016 ◽  
pp. 745-760
Author(s):  
Ming Zhang
Keyword(s):  
Neural Network ◽  
Time Series Data ◽  
Learning Algorithm ◽  
Higher Order ◽  
Series Data ◽  
Average Error ◽  
Data Simulation ◽  
New Learning ◽  
Higher Order Neural Networks ◽  
Analysis System

New open box and nonlinear model of Cosine and Sigmoid Higher Order Neural Network (CS-HONN) is presented in this paper. A new learning algorithm for CS-HONN is also developed from this study. A time series data simulation and analysis system, CS-HONN Simulator, is built based on the CS-HONN models too. Test results show that average error of CS-HONN models are from 2.3436% to 4.6857%, and the average error of Polynomial Higher Order Neural Network (PHONN), Trigonometric Higher Order Neural Network (THONN), and Sigmoid polynomial Higher Order Neural Network (SPHONN) models are from 2.8128% to 4.9077%. It means that CS-HONN models are 0.1174% to 0.4917% better than PHONN, THONN, and SPHONN models.

Large Eddy Simulation of ALSTOM’s Reheat Combustor Using Tabulated Chemistry and Stochastic Fields-Combustion Model

2014 ◽  
Author(s):  
Rohit Kulkarni ◽  
Birute Bunkute ◽  
Fernando Biagioli ◽  
Michael Duesing ◽  
Wolfgang Polifke
Keyword(s):  
Turbulent Flows ◽  
Mixture Fraction ◽  
Turbulent Flame ◽  
Computational Cost ◽  
Chemical Mechanism ◽  
Combustion Model ◽  
Atmospheric Conditions ◽  
Stochastic Fields ◽  
Tabulated Chemistry ◽  
Large Eddy

Large Eddy Simulations (LES) of natural gas ignition and combustion in turbulent flows are performed using a novel combustion model based on a composite progress variable, a tabulated chemistry ansatz and the stochastic-fields turbulence-chemistry interaction model. It is a significant advantage of this approach that it can be applied to industrial configurations with multi-stream mixing at relatively low computational cost and modeling complexity. The computational cost is independent of the chemical mechanism or the type of fuel, but increases linearly with the number of streams. The model is validated successfully against the Cabra methane flame and Delft Jet in Hot Coflow (DJFC) flame. Both cases constitute fuel jets in a vitiated coflow. The DJFC flame coflow has a non-uniform mixture of air and hot gases. The model considers this non-uniformity by an additional mixture fraction dimension, emulating a ternary mixing case. The model not only predicts flame location, but also the temperature distribution quantitatively. The LES combustion model is further extended to consider four stream mixing. It has been successfully validated for ALSTOM’s reheat combustor at atmospheric conditions. Compared to the past steady-state RANS (Reynolds Averaged Navier-Stokes) simulations [1], the LES simulations provide an even better understanding of the turbulent flame characteristics, which helps in the burner optimization.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

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.

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

Economic Turmoil in Islamic Banking Investment

ETIKONOMI ◽  
2020 ◽  
Vol 19 (2) ◽  
Author(s):  
Budiandru Budiandru ◽  
Sari Yuniarti
Keyword(s):  
Interest Rates ◽  
Stock Price ◽  
Time Series Data ◽  
Series Data ◽  
Islamic Banking ◽  
Vector Error Correction ◽  
Investment Financing ◽  
Vector Error ◽  
Operational Activities

Investment financing is one of the operational activities of Islamic banking to encourage the real sector. This study aims to analyze the effect of economic turmoil on investment financing, analyze the response to investment financing, and analyze each variable's contribution in explaining the diversity of investment financing. This study uses monthly time series data from 2009 to 2020 using the Vector Error Correction Model (VECM) analysis. The results show that the exchange rate, inflation, and interest rates significantly affect Islamic banking investment financing in the long term. The response to investment financing is the fastest to achieve stability when it responds to shocks to the composite stock price index. Inflation is the most significant contribution in explaining diversity in investment financing. Islamic banking should increase the proportion of funding for investment. Customers can have a larger business scale to encourage economic growth, with investment financing increasing.JEL Classification: E22, G11, G24How to Cite:Budiandru., & Yuniarti, S. (2020). Economic Turmoil in Islamic Banking Investment. Etikonomi: Jurnal Ekonomi, 19(2), xx – xx. https://doi.org/10.15408/etk.v19i2.17206.

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