Thermal Conductivity of Porous Oxide Layer: A Numerical Model Based on CT Data

2021 ◽  
pp. 102705
Author(s):  
Krystof Mraz ◽  
Jan Bohacek ◽  
Ondrej Resl ◽  
Martin Chabicovsky ◽  
Ebrahim Karimi-Sibaki
Keyword(s):  
Thermal Conductivity ◽  
Numerical Model ◽  
Oxide Layer ◽  
Model Based ◽  
Porous Oxide ◽  
Ct Data

scholarly journals A Semi-Empirical Model for Predicting Frost Properties

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 412
Author(s):  
Shao-Ming Li ◽  
Kai-Shing Yang ◽  
Chi-Chuan Wang
Keyword(s):  
Thermal Conductivity ◽  
Linear Programming ◽  
Numerical Model ◽  
Empirical Model ◽  
Quantitative Method ◽  
Predictive Ability ◽  
Dimensionless Temperature ◽  
Crystal Shape ◽  
Proposed Model ◽  
Semi Empirical

In this study, a quantitative method for classifying the frost geometry is first proposed to substantiate a numerical model in predicting frost properties like density, thickness, and thermal conductivity. This method can recognize the crystal shape via linear programming of the existing map for frost morphology. By using this method, the frost conditions can be taken into account in a model to obtain the corresponding frost properties like thermal conductivity, frost thickness, and density for specific frost crystal. It is found that the developed model can predict the frost properties more accurately than the existing correlations. Specifically, the proposed model can identify the corresponding frost shape by a dimensionless temperature and the surface temperature. Moreover, by adopting the frost identification into the numerical model, the frost thickness can also be predicted satisfactorily. The proposed calculation method not only shows better predictive ability with thermal conductivities, but also gives good predictions for density and is especially accurate when the frost density is lower than 125 kg/m3. Yet, the predictive ability for frost density is improved by 24% when compared to the most accurate correlation available.

scholarly journals Impact of the non‐negativity constraint in model‐based iterative reconstruction from CT data

2019 ◽  
Vol 46 (12) ◽  
Author(s):  
Viktor Haase ◽  
Katharina Hahn ◽  
Harald Schöndube ◽  
Karl Stierstorfer ◽  
Andreas Maier ◽  
...  
Keyword(s):  
Iterative Reconstruction ◽  
Model Based ◽  
Ct Data

Forced Harmonic Response of Grouped Blade Systems: Part II—Application

1996 ◽  
Vol 118 (1) ◽  
pp. 137-145 ◽  
Author(s):  
L. F. Wagner ◽  
J. H. Griffin
Keyword(s):  
Numerical Model ◽  
Vibration Analysis ◽  
Natural Frequencies ◽  
Turbine Blades ◽  
Modal Identification ◽  
Harmonic Response ◽  
Model Based ◽  
Grouped Blades ◽  
Flexible Disk ◽  
Symmetric Structures

The vibration of grouped blades on a flexible disk should, for purposes of economy and clarity of modal identification, be analyzed using procedures developed for cyclically symmetric structures. In this paper, a numerical model, based on the theory of cyclically symmetric structures, is applied to the vibration analysis, and in particular, the harmonic response, of a flexible disk supporting a number of groups, or packets, of turbine blades. Results are presented to show variations in the modal participation factors as a function of such parameters as disk flexibility, blade density, and the total number of assembled groups. It is also shown that many characteristics of the system spectra of natural frequencies are strongly dependent on the number of blade groups.

scholarly journals Model-Based Optimization of Velocity Strategy for Lightweight Electric Racing Cars

2018 ◽  
Vol 2018 ◽  
pp. 1-20 ◽  
Author(s):  
Mirosław Targosz ◽  
Wojciech Skarka ◽  
Piotr Przystałka
Keyword(s):  
Numerical Model ◽  
Energy Efficient ◽  
Atmospheric Conditions ◽  
Vehicle Model ◽  
Model Based ◽  
Race Track ◽  
Functional Features ◽  
Race Conditions ◽  
Vehicle Dynamic Model ◽  
And Control

The article presents a method for optimizing driving strategies aimed at minimizing energy consumption while driving. The method was developed for the needs of an electric powered racing vehicle built for the purposes of the Shell Eco-marathon (SEM), the most famous and largest race of energy efficient vehicles. Model-based optimization was used to determine the driving strategy. The numerical model was elaborated in Simulink environment, which includes both the electric vehicle model and the environment, i.e., the race track as well as the vehicle environment and the atmospheric conditions. The vehicle model itself includes vehicle dynamic model, numerical model describing issues concerning resistance of rolling tire, resistance of the propulsion system, aerodynamic phenomena, model of the electric motor, and control system. For the purpose of identifying design and functional features of individual subassemblies and components, numerical and stand tests were carried out. The model itself was tested on the research tracks to tune the model and determine the calculation parameters. The evolutionary algorithms, which are available in the MATLAB Global Optimization Toolbox, were used for optimization. In the race conditions, the model was verified during SEM races in Rotterdam where the race vehicle scored the result consistent with the results of simulation calculations. In the following years, the experience gathered by the team gave us the vice Championship in the SEM 2016 in London.

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