scholarly journals Design of experiments and manufacturing design space for multi‐step processes

2021 ◽  
Author(s):  
Rosamarie Frieri ◽  
Marco Mariti ◽  
Marilena Paludi
Keyword(s):  
Design Of Experiments ◽  
Design Space ◽  
Manufacturing Design

Simple Recuperated s-CO2 Cycle Revisited: Optimization of Operating Parameters for Maximum Cycle Efficiency

2019 ◽  
Author(s):  
Anchit Dutta ◽  
Adhip Gupta ◽  
Sharath Sathish ◽  
Aman Bandooni ◽  
Pramod Kumar
Keyword(s):  
Design Of Experiments ◽  
Design Space ◽  
Pressure Ratio ◽  
Cycle Performance ◽  
Maximum Temperature ◽  
Brayton Cycle ◽  
High Side ◽  
Side Pressure ◽  
Cycle Efficiency ◽  
The Impact

Abstract The paper presents modeling and Design of Experiments (DOE) analysis for a simple recuperated s-CO2 closed loop Brayton cycle operating at a maximum temperature of 600°C and a compressor inlet temperature of 45°C. The analysis highlights the impact of isentropic efficiencies of the turbine and compressor, decoupled in this case, on other equipment such as recuperator, gas cooler and heater, all of which have a bearing on the overall performance of the s-CO2 Brayton cycle. A MATLAB program coupled with REFPROP is used to perform the thermodynamic analysis of the cycle. A design space exploration with a Design of Experiments (DOE) study is undertaken using I-sight™ (multi-objective optimization software), which is coupled with the MATLAB code. The outcome of the DOE study provides the optimal pressure ratios and high side pressures for maximum cycle efficiency in the design space. By varying pressure ratios along with a floating high side pressure, the analysis reveals that the cycle performance exhibits a peak around a pressure ratio of 2.5, with cycle efficiency being the objective function. A further interesting outcome of the DOE study reveals that the isentropic efficiencies of the compressor and turbine have a strong influence not only on the overall cycle efficiency, but also the optimum pressure ratio as well as the threshold pressures (low as well as high side pressure). An important outcome of this exercise shows that the isentropic efficiency of the turbine has a much greater impact on the overall cycle performance as compared to that of the compressor.

Quality by Design: Comparison of Design Space construction methods in the case of Design of Experiments

2020 ◽  
Vol 200 ◽  
pp. 104002
Author(s):  
Diane Manzon ◽  
Magalie Claeys-Bruno ◽  
Sophie Declomesnil ◽  
Christophe Carité ◽  
Michelle Sergent
Keyword(s):  
Design Of Experiments ◽  
Quality By Design ◽  
Design Space ◽  
Construction Methods ◽  
Space Construction ◽  
Design Comparison

scholarly journals Detailed Study on Stiffness and Load Characteristics of Film-Riding Groove Types Using Design of Experiments

2017 ◽  
Vol 139 (9) ◽  
Author(s):  
S. M. Tibos ◽  
C. Georgakis ◽  
K. Harvey ◽  
J. A. Teixeira
Keyword(s):  
Design Of Experiments ◽  
Reynolds Equation ◽  
Design Space ◽  
Hydrodynamic Load ◽  
Pressure Drops ◽  
Seal Design ◽  
Hydrostatic Load ◽  
Simple Linear Relationship ◽  
Load Characteristics ◽  
The Relationship

In the application of film-riding sealing technology, there are various groove features that can be used to induce hydrodynamic lift. However, there is little guidance in selecting the relative parameter settings in order to maximize hydrodynamic load and fluid stiffness. In this study, two groove types are investigated—Rayleigh step and inclined groove. The study uses a design of experiments approach and a Reynolds equation solver to explore the design space. Key parameters have been identified that can be used to optimize a seal design. The results indicate that the relationship between parameters is not a simple linear relationship. It was also found that higher pressure drops hinder the hydrodynamic load and stiffness of the seal suggesting an advantage for using hydrostatic load support in such conditions.

Design of experiments and design space approaches in the pharmaceutical bioprocess optimization

2021 ◽  
Author(s):  
Alice Kasemiire ◽  
Hermane T. Avohou ◽  
Charlotte De Bleye ◽  
Pierre-Yves Sacre ◽  
Elodie Dumont ◽  
...  
Keyword(s):  
Design Of Experiments ◽  
Design Space ◽  
Bioprocess Optimization

scholarly journals Model-Based Design of Experiments for High-Dimensional Inputs Supported by Machine-Learning Methods

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 508
Author(s):  
Philipp Seufert ◽  
Jan Schwientek ◽  
Michael Bortz
Keyword(s):  
Design Of Experiments ◽  
Chemical Engineering ◽  
Design Space ◽  
Directional Derivative ◽  
Gaussian Process Regression ◽  
Equivalence Theorem ◽  
Design Criterion ◽  
Linear Programs ◽  
Adaptive Selection ◽  
Novel Design

Algorithms that compute locally optimal continuous designs often rely on a finite design space or on the repeated solution of difficult non-linear programs. Both approaches require extensive evaluations of the Jacobian Df of the underlying model. These evaluations are a heavy computational burden. Based on the Kiefer-Wolfowitz Equivalence Theorem, we present a novel design of experiments algorithm that computes optimal designs in a continuous design space. For this iterative algorithm, we combine an adaptive Bayes-like sampling scheme with Gaussian process regression to approximate the directional derivative of the design criterion. The approximation allows us to adaptively select new design points on which to evaluate the model. The adaptive selection of the algorithm requires significantly less evaluations of Df and reduces the runtime of the computations. We show the viability of the new algorithm on two examples from chemical engineering.

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