scholarly journals A Heterothermic Kinetic Model of Hydrogen Absorption in Metals with Subsurface Transport

Metals ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 1131 ◽  
Author(s):  
Ono ◽  
Uchikoshi ◽  
Hayashi ◽  
Kitagawa ◽  
Yeh ◽  
...  
Keyword(s):  
Experimental Data ◽  
Kinetic Parameters ◽  
Hydrogen Absorption ◽  
Hydrogen Adsorption ◽  
Bulk Diffusion ◽  
Operating Conditions ◽  
Calculation Scheme ◽  
Kinetic Constants ◽  
Absorption Process ◽  
Subsurface Transport

A versatile numerical model for hydrogen absorption into metals was developed. Our model addresses the kinetics of surface adsorption, subsurface transport (which plays an important role for metals with active surfaces), and bulk diffusion processes. This model can allow researchers to perform simulations for various conditions, such as different material species, dimensions, structures, and operating conditions. Furthermore, our calculation scheme reflects the relationship between the temperature changes in metals caused by the heat of adsorption and absorption and the temperature-dependent kinetic parameters for simulation precision purposes. We demonstrated the numerical fitting of the experimental data for various Pd temperatures and sizes, with a single set of kinetic parameters, to determine the unknown kinetic constants. Using the developed model and determined kinetic constants, the transitions of the rate-determining steps on the conditions of metal-hydrogen systems are systematically analyzed. Conventionally, the temperature change of metals during hydrogen adsorption and absorption has not been a favorable phenomenon because it can cause errors when numerically estimating the hydrogen absorption rates. However, by our calculation scheme, the experimental data obtained under temperature changing conditions can be positively used for parameter fitting to efficiently and accurately determine the kinetic constants of the absorption process, even from a small number of experimental runs. In addition, we defined an effectiveness factor as the ratio between the actual absorption rate and the virtually calculated non-bulk-diffusion-controlled rate, to evaluate the quantitative influence of each individual transport process on the overall absorption process. Our model and calculation scheme may be a useful tool for designing high-performance hydrogen storage systems.

A Biomass Combustion-Gasification Model: Validation and Sensitivity Analysis

1995 ◽  
Vol 117 (4) ◽  
pp. 329-336 ◽  
Author(s):  
N. Bettagli ◽  
U. Desideri ◽  
D. Fiaschi
Keyword(s):  
Experimental Data ◽  
Sensitivity Analysis ◽  
Kinetic Parameters ◽  
Chemical Kinetics ◽  
Operating Conditions ◽  
Waste Materials ◽  
Biomass Combustion ◽  
Gas Composition ◽  
Combustion Processes ◽  
Kinetics Of

The aim of the present paper is to study the gasification and combustion of biomass and waste materials. A model for the analysis of the chemical kinetics of gasification and combustion processes was developed with the main objective of calculating the gas composition at different operating conditions. The model was validated with experimental data for sawdust gasification. After having set the main kinetic parameters, the model was tested with other types of biomass, whose syngas composition is known. A sensitivity analysis was also performed to evaluate the influence of the main parameters, such as temperature, pressure, and air-fuel ratio on the composition of the exit gas. Both oxygen and air (i.e., a mixture of oxygen and nitrogen) gasification processes were simulated.

CFD Simulation of Hydrogen Separation in Pd Hollow Fiber Membrane

2016 ◽  
Vol 11 (1) ◽  
pp. 83-88 ◽  
Author(s):  
Zahra Mansourpour ◽  
Hooman Ziaei-Halimejani ◽  
Seyed Morteza Sadeghnejad ◽  
Mohammadreza Boskabadi
Keyword(s):  
Experimental Data ◽  
Hollow Fiber ◽  
Cfd Simulation ◽  
Hydrogen Adsorption ◽  
Separation Efficiency ◽  
Hollow Fiber Membrane ◽  
Hydrogen Separation ◽  
Operating Conditions ◽  
Fiber Membrane ◽  
Membrane Modeling

Abstract In this article, hydrogen adsorption from gas mixture involve nitrogen and hydrogen by palladium hollow fiber membrane investigated and two dimensional model proposed for hydrogen adsorption in this model. This model has been evaluated based on equations (momentum and mass transfer) in all three parts (shell, membrane and tube) for existing gaseous compounds with using finite element method. The results of simulation validated by experimental data of hydrogen adsorption by palladium hollow fiber membrane. Modeling predictions shows good agreement with experimental data at different operating conditions such as different gas flows, temperature, pressure and etc. the result of simulation shows hydrogen separation efficiency increases with increasing temperature and pressure and decreases with increasing inlet rate. Also with using this model better performance of hollow fiber membranes can be obtained. In fact, hollow fiber membrane can be designed at different conditions and for different rates that it can save cost of various tests.

Control of hydrogen absorption by nickel films obtained upon magnetic spraying of zirconium alloy using the thermoEMF method

2020 ◽  
Vol 86 (8) ◽  
pp. 32-37
Author(s):  
V. V. Larionov ◽  
Xu Shupeng ◽  
V. N. Kudiyarov
Keyword(s):  
Magnetron Sputtering ◽  
Hydrogen Concentration ◽  
Hydrogen Absorption ◽  
Zirconium Alloy ◽  
Hydrogen Adsorption ◽  
Zirconium Alloys ◽  
Nickel Film ◽  
Protective Film ◽  
Nickel Films ◽  
Hydrogen Penetration

Nickel films formed on the surface of zirconium alloys are often used to protect materials against hydrogen penetration. Hydrogen adsorption on nickel is faster since the latter actively interacts with hydrogen, oxidizes and forms a protective film. The goal of the study is to develop a method providing control of hydrogen absorption by nickel films during vacuum-magnetron sputtering and hydrogenation via measuring thermoEMF. Zirconium alloy E110 was saturated from the gas phase with hydrogen at a temperature of 350°C and a pressure of 2 atm. A specialized Rainbow Spectrum unit was used for coating. It is shown that a nickel film present on the surface significantly affects the hydrogen penetration into the alloy. A coating with a thickness of more than 2 μm deposited by magnetron sputtering on the surface of a zirconium alloy with 1% Nb, almost completely protects the alloy against hydrogen penetration. The magnitude of thermoemf depends on the hydrogen concentration in the zirconium alloy and film thickness. An analysis of the hysteresis width of the thermoEMF temperature loop and a method for determining the effective activation energy of the conductivity of a hydrogenated material coated with a nickel film are presented. The results of the study can be used in assessing the hydrogen concentration and, hence, corrosion protection of the material.

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.

Kinetic parameters estimation in an anaerobic digestion process using successive quadratic programming

2005 ◽  
Vol 52 (1-2) ◽  
pp. 419-426 ◽  
Author(s):  
C.A. Aceves-Lara ◽  
E. Aguilar-Garnica ◽  
V. Alcaraz-González ◽  
O. González-Reynoso ◽  
J.P. Steyer ◽  
...  
Keyword(s):  
Experimental Data ◽  
Anaerobic Digestion ◽  
Quadratic Programming ◽  
Kinetic Parameters ◽  
Parameters Estimation ◽  
Fixed Bed Reactor ◽  
Accurate Estimation ◽  
Successive Quadratic Programming ◽  
Anaerobic Digestion Process ◽  
Digestion Process

In this work, an optimization method is implemented in an anaerobic digestion model to estimate its kinetic parameters and yield coefficients. This method combines the use of advanced state estimation schemes and powerful nonlinear programming techniques to yield fast and accurate estimates of the aforementioned parameters. In this method, we first implement an asymptotic observer to provide estimates of the non-measured variables (such as biomass concentration) and good guesses for the initial conditions of the parameter estimation algorithm. These results are then used by the successive quadratic programming (SQP) technique to calculate the kinetic parameters and yield coefficients of the anaerobic digestion process. The model, provided with the estimated parameters, is tested with experimental data from a pilot-scale fixed bed reactor treating raw industrial wine distillery wastewater. It is shown that SQP reaches a fast and accurate estimation of the kinetic parameters despite highly noise corrupted experimental data and time varying inputs variables. A statistical analysis is also performed to validate the combined estimation method. Finally, a comparison between the proposed method and the traditional Marquardt technique shows that both yield similar results; however, the calculation time of the traditional technique is considerable higher than that of the proposed method.

The Kinetics of Austenization Process of T91 Ferritic Heat-Resistant Steel

2011 ◽  
Vol 317-319 ◽  
pp. 42-47
Author(s):  
Li Fang Zhang ◽  
Yong Chang Liu
Keyword(s):  
Experimental Data ◽  
Phase Transformation ◽  
Kinetic Parameters ◽  
Nucleation And Growth ◽  
Transformation Model ◽  
Resistant Steel ◽  
Kinetic Characteristics ◽  
Heat Resistant Steel ◽  
Austenite Grain ◽  
Kinetics Of

By fitting the calculated transformed fraction according to developed phase-transformation model to the experimental data obtained by differential dilatometry, the kinetic characteristics of the austenitization process in T91 steels have been investigated. According to the kinetic parameters fitted, we recognize that the nucleation and growth of austenite grain are mainly controlled by the diffusion of carbon in ferritic and austenite respectively. In addition, by increasing the diffusion active energy of carbon in austenite, carbides hinder the motion of interface and thus refine austenite grain.

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.

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