scholarly journals Mathematical modelling and experimentation of soy wax PCM solar tank using response surface method

2020 ◽  
Vol 14 (2) ◽  
pp. 35-42
Author(s):  
Rajab Ghabour ◽  
Péter Korzenszky
Keyword(s):  
Response Surface ◽  
Response Surface Method ◽  
Energy System ◽  
Hot Water ◽  
Water Tank ◽  
Magnitude Effect ◽  
Surface Method ◽  
Two Factors ◽  
Contour Plots ◽  
Temperature Levels

Worldwide, governments tend to reduce the CO2 emissions, and the storage of the solar energy system is still considered the most challenging problem to solve under the current state. Mainly, in relatively cold countries, as domestic hot water or for heat process services, where the loss in the tank is huge. Any improvement in the design can achieve a higher solar yield. Since water is the usual medium for heat storage, the integration with phase change material (PCM) can store energy when there is abundant energy and release it when it is needed. In this study, we conducted a capsulated PCM soy wax 52⁰C in an insulated water tank filled with 5 litres of water. To estimate the appropriate number of samples and the quantity of the PCM at two temperature levels using the response surface method with non-linear correlation for the charging phase. The results show 3.16, 0.95, 0.38 first degree magnitude effect for temperature, sample numbers, and wax quantity respectively and 0.29, -0.38 second-degree magnitude effect for quantity and temperature. In addition, an illustration of each two-factors interaction contour plots.  

A Proposal of PSO with Presumption Function of the Solution Landscape by Response Surface Method

2014 ◽  
Vol 134 (9) ◽  
pp. 1293-1298
Author(s):  
Toshiya Kaihara ◽  
Nobutada Fuji ◽  
Tomomi Nonaka ◽  
Yuma Tomoi
Keyword(s):  
Response Surface ◽  
Response Surface Method ◽  
Surface Method

scholarly journals Creep-Based Reliability Evaluation of Turbine Blade-Tip Clearance with Novel Neural Network Regression

Materials ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 3552 ◽  
Author(s):  
Chun-Yi Zhang ◽  
Jing-Shan Wei ◽  
Ze Wang ◽  
Zhe-Shan Yuan ◽  
Cheng-Wei Fei ◽  
...  
Keyword(s):  
Neural Network ◽  
High Pressure ◽  
Reliability Analysis ◽  
Response Surface ◽  
Response Surface Method ◽  
Tip Clearance ◽  
Strong Nonlinearity ◽  
Surface Method ◽  
Blade Tip ◽  
Blade Tip Clearance

To reveal the effect of high-temperature creep on the blade-tip radial running clearance of aeroengine high-pressure turbines, a distributed collaborative generalized regression extremum neural network is proposed by absorbing the heuristic thoughts of distributed collaborative response surface method and the generalized extremum neural network, in order to improve the reliability analysis of blade-tip clearance with creep behavior in terms of modeling precision and simulation efficiency. In this method, the generalized extremum neural network was used to handle the transients by simplifying the response process as one extremum and to address the strong nonlinearity by means of its nonlinear mapping ability. The distributed collaborative response surface method was applied to handle multi-object multi-discipline analysis, by decomposing one “big” model with hyperparameters and high nonlinearity into a series of “small” sub-models with few parameters and low nonlinearity. Based on the developed method, the blade-tip clearance reliability analysis of an aeroengine high-pressure turbine was performed subject to the creep behaviors of structural materials, by considering the randomness of influencing parameters such as gas temperature, rotational speed, material parameters, convective heat transfer coefficient, and so forth. It was found that the reliability degree of the clearance is 0.9909 when the allowable value is 2.2 mm, and the creep deformation of the clearance presents a normal distribution with a mean of 1.9829 mm and a standard deviation of 0.07539 mm. Based on a comparison of the methods, it is demonstrated that the proposed method requires a computing time of 1.201 s and has a computational accuracy of 99.929% over 104 simulations, which are improvements of 70.5% and 1.23%, respectively, relative to the distributed collaborative response surface method. Meanwhile, the high efficiency and high precision of the presented approach become more obvious with the increasing simulations. The efforts of this study provide a promising approach to improve the dynamic reliability analysis of complex structures.

scholarly journals The optimization of Sumbawa manganese ore beneficiation using response surface method (RSM)

2021 ◽  
Author(s):  
Alfikri Khair ◽  
Haryudini A. Putri ◽  
Suprapto Suprapto ◽  
Yatim L. Ni’mah
Keyword(s):  
Response Surface ◽  
Response Surface Method ◽  
Manganese Ore ◽  
Surface Method
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