Developing Response Surface Based Wing Weight Equations for Conceptual Morphing Aircraft Sizing

2005 ◽  
Author(s):  
Michael Skillen ◽  
William Crossley
Keyword(s):  
Response Surface ◽  
Morphing Aircraft ◽  
Aircraft Sizing

Morphing Aircraft Sizing Using Design Optimization

2011 ◽  
Vol 48 (2) ◽  
pp. 612-622 ◽  
Author(s):  
William A. Crossley ◽  
Michael D. Skillen ◽  
Joshua B. Frommer ◽  
Brian D. Roth
Keyword(s):  
Design Optimization ◽  
Morphing Aircraft ◽  
Aircraft Sizing

Elemental chlorine-free bleaching of North American hardwood kraft pulps: Evaluation of oxidant-reinforced extraction variables on overall bleaching optimization

TAPPI Journal ◽  
2013 ◽  
Vol 12 (10) ◽  
pp. 33-41 ◽  
Author(s):  
BRIAN N. BROGDON
Keyword(s):  
Response Surface ◽  
Chlorine Dioxide ◽  
North American ◽  
Yield Loss ◽  
Alkaline Extraction ◽  
Kraft Pulps ◽  
Pulp Bleaching ◽  
Response Surface Models ◽  
Elemental Chlorine ◽  
Surface Models

This investigation evaluates how higher reaction temperatures or oxidant reinforcement of caustic extraction affects chlorine dioxide consumption during elemental chlorine-free bleaching of North American hardwood pulps. Bleaching data from the published literature were used to develop statistical response surface models for chlorine dioxide delignification and brightening sequences for a variety of hardwood pulps. The effects of higher (EO) temperature and of peroxide reinforcement were estimated from observations reported in the literature. The addition of peroxide to an (EO) stage roughly displaces 0.6 to 1.2 kg chlorine dioxide per kilogram peroxide used in elemental chlorine-free (ECF) bleach sequences. Increasing the (EO) temperature by Δ20°C (e.g., 70°C to 90°C) lowers the overall chlorine dioxide demand by 0.4 to 1.5 kg. Unlike what is observed for ECF softwood bleaching, the presented findings suggest that hot oxidant-reinforced extraction stages result in somewhat higher bleaching costs when compared to milder alkaline extraction stages for hardwoods. The substitution of an (EOP) in place of (EO) resulted in small changes to the overall bleaching cost. The models employed in this study did not take into account pulp bleaching shrinkage (yield loss), to simplify the calculations.

Wear volume prediction of AISI H13 die steel using response surface methodology and artificial neural network

2020 ◽  
Vol 14 (2) ◽  
pp. 6789-6800
Author(s):  
Vishal Jagota ◽  
Rajesh Kumar Sharma
Keyword(s):  
Neural Network ◽  
Heat Treatment ◽  
Artificial Neural Network ◽  
Response Surface ◽  
Sliding Wear ◽  
Austenitizing Temperature ◽  
Tempering Temperature ◽  
Die Steel ◽  
Artificial Neural ◽  
Tempering Time

Resistance to wear of hot die steel is dependent on its mechanical properties governed by the microstructure. The required properties for given application of hot die steel can be obtained with control the microstructure by heat treatment parameters. In the present paper impact of different heat treatment parameters like austenitizing temperature, tempering time, tempering temperature is studied using response surface methodology (RSM) and artificial neural network (ANN) to predict sliding wear of H13 hot die steel. After heat treating samples at austenitizing temperature of 1020°C, 1040°C and 1060°C; tempering temperature 540°C, 560°C and 580°C; tempering time 1hour, 2hours and 3hours, experimentation on pin-on-disc tribo-tester is done to measure the sliding wear of H13 die steel. Box-Behnken design is used to develop a regression model and analysis of variance technique is used to verify the adequacy of developed model in case of RSM. Whereas, multi-layer feed-forward backpropagation architecture with input layer, single hidden layer and an output layer is used in ANN. It was found that ANN proves to be a better tool to predict sliding wear with more accuracy. Correlation coefficient R2 of the artificial neural network model is 0.986 compared to R2 of 0.957 for RSM. However, impact of input parameter interactions can only be analysed using response surface method. In addition, sensitivity analysis is done to determine the heat treatment parameter exerting most influence on the wear resistance of H13 hot die steel and it showed that tempering time has maximum influence on wear volume, followed by tempering temperature and austenitizing temperature. The prediction models will help to estimate the variation in die lifetime by finding the amount of wear that will occur during use of hot die steel, if the heat treatment parameters are varied to achieve different properties.

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