convection model
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Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6218
Author(s):  
Pablo Nieto Muro ◽  
Florian M. Heckmeier ◽  
Sean Jenkins ◽  
Christian Breitsamter

Ice accretion or icing is a well-known phenomenon that entails a risk for the correct functioning of an aircraft. One of the areas more vulnerable to icing is the air data measuring system. This paper studies the icing protection offered by a heating system installed inside a multi-hole probe. The problem is initially solved analytically, creating a tool that can be used in order to predict the heating performance depending on the flying conditions. Later, the performance of the real system is investigated with a heated five-hole probe prototype in a wind tunnel experiment. The measured results are compared with the predictions made by the analytical model. Last, the icing protection provided by the system is estimated with respect to flying altitude and speed. As a result, a prediction tool that can be used in order to make quick icing risk predictions for straight cylindrical probes is delivered. Furthermore, the study provides some understanding about how parameters like altitude and air speed affect the occurrence of ice accretion.


2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Yongze Xu ◽  
Yu Liu ◽  
Lei Zhang

In this study, a heat convection model of the reflow oven and a heat conduction model of the soldering area are proposed based on heat transfer theory, and a dynamic Thomas algorithm is developed for solving linear equations with coefficient matrix evolving over time in the tridiagonal system, which is derived from a heat transfer problem with moving boundaries in the solder phase transition process. We have also carried out numerical simulations for investigating the accuracy of the mathematical model, in which the temperature profiles are calculated and compared for different cases with considering or ignoring phase transformations, respectively. Parameters of reflow soldering, such as the conveyor speed, the set temperature in each zone, and a part of the heating factor, are optimized by the use of the nondominated sorting genetic algorithm II. By comparing the temperature profile and optimal solutions in the two cases, numerical results show that phase transitions of the solder have great impacts on optimal parameters and the slope of temperature profiles. Moreover, the phenomenon that the heating factor varies with the maximum set temperature in a banded distribution is investigated and analyzed, which is an important part of this work.


Author(s):  
Shishir Priyadarshi ◽  
Jian Yang ◽  
Weiqin Sun

Interaction between Earth’s magnetotail and its inner magnetosphere plays an important role in the transport of mass and energy in the ionosphere–magnetosphere coupled system. A number of first-principles models are devoted to understanding the associated dynamics. However, running these models, including both magnetohydrodynamic models and kinetic drift models, can be computationally expensive when self-consistency and high spatial resolution are required. In this study, we exploit an approach of building a parallel statistical model, based on the long short-term memory (LSTM) type of recurrent neural network, to forecast the results of a first-principles model, called the Rice Convection Model (RCM). The RCM is used to simulate the transient injection events, in which the flux-tube entropy parameter, dawn-to-dusk electric field component, and cumulative magnetic flux transport are calculated in the central plasma sheet. These key parameters are then used as initial inputs for training the LSTM. Using the trained LSTM multivariate parameters, we are able to forecast the plasma sheet parameters beyond the training time for several tens of minutes that are found to be consistent with the subsequent RCM simulation results. Our tests indicate that the recurrent neural network technique can be efficiently used for forecasting numerical simulations of magnetospheric models. The potential to apply this approach to other models is also discussed.


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